Core Idea: Disrupting Credit Issuance through Demonstrated Skills

The future is knowing what facts and skills people actually know, no matter how they prove what they know. 

At the core, universities are designed to credential knowledge. Right now, we have a system that is human-to-human credentialing. Soon we could have AI agents able to credential a student. One way is that the AI could interview the student and judge the student’s knowledge like a test. The biggest opportunity is based on past online history. For example: login to your Minecraft account and the AI will assess what you know about architecture, engineering, design, and game dynamics. The same is true across many other platforms where users have demonstrated knowledge, like blogging platforms demonstrating writing skills. These AI agents can then determine if a user is verified for a new type of credit – a SLC (Statistically Likely Credential). This is different than a Credential. It signifies that it’s highly likely the person can be credentialed. If the student wishes to get an accredited credential, they can use their SLC as a right to attempt to test out of coursework. 

The point is that we can build AI agents to prove the latent knowledge of humans and credential work that has already been done. Like Programs for Experienced Learners, but for the AI era. How does a modern university provide credentials to the 43% of 15-21 year olds kids who play Minecraft with a structural engineering 101 college credit that sets them on a pathway to college. That one free credit ready and waiting may be the motivation to dive into a full degree. a modern university could conceivably provide One Billion Statistically Likely Credits or more inside of a calendar year. 

More fun: Can a modern university provide a music degree to all Grammy winners? 

Key Partnerships — Hypothetical (!!)

Minecraft (Owned by Microsoft)

Concept: Leverage Minecraft as a platform to identify and credential engineering and design skills.

Implementation:

• AI Analysis: Use AI to analyze students' Minecraft history, identifying complex builds and problem-solving activities equivalent to Engineering 101 coursework.

• Credential Issuance: Issue college credits for demonstrated skills, allowing students to earn credits for their Minecraft achievements.

Benefits:

• Skill Recognition: Validates real-world skills in a familiar and engaging environment.

• Early College Entry: Facilitates early college entry by providing students with academic credits.

• Student Engagement: Increases engagement by recognizing and rewarding skills demonstrated in popular platforms.

Medium / Substack 

Concept: Use Medium to identify and credential writing skills.

Implementation:

• AI Validation: Implement AI to evaluate past Medium posts, assessing writing quality, coherence, and academic merit.

• Credit Issuance: Award college credits for high-quality writing, equivalent to introductory college writing courses.

Benefits:

• Writing Proficiency: Recognizes and rewards students' writing proficiency.

• Accelerated Learning: Enables students to gain academic credits for their demonstrated writing skills.

• Increased Motivation: Encourages students to produce high-quality writing by offering tangible academic rewards.

Adobe / Photoshop 

Concept: Review Adobe Cloud to analyze design skills. 

Implementation:

• AI Validation: Implement AI to evaluate past artwork. 

• Credit Issuance: Award college credits for high-quality art, equivalent to introductory college art courses.

Benefits:

• Technical Proficiency: Recognizes and rewards students' technical proficiency at using digital tools. 

• Stylistic Performance: Can the student create many styles in many techniques? 

• Reference / Allusion: Does the student reference other artists? Make allusions? Stylistic references that show higher level understanding? 

Data-Driven Impact

Research shows that students with a few college credits are more likely to continue their education and earn additional credits, underscoring the importance of early credit accumulation.

Detailed Business Model

Scale and Reach

• Target: Impact 1 million learners by credentialing demonstrated skills on popular platforms.

• Implementation Phases:

  • Phase 1: Establish partnerships with Minecraft (Microsoft) and Medium.

  • Phase 2: Develop and deploy AI tools to analyze and validate student work.

  • Phase 3: Roll out credentialing programs, starting with pilot groups and scaling up based on success.

AI and Technology Integration

• AI Tools: Develop AI algorithms to scrub Minecraft histories and evaluate Medium posts.

• Data Privacy: Ensure robust data privacy mea modern universityres are in place to protect students' work and personal information.

Staffing and Resources

• Technical Team: Hire AI specialists, data scientists, and engineers to develop and maintain the credentialing tools.

• Partnership Managers: Appoint dedicated staff to manage relationships with key partners and oversee program implementation.

• Academic Advisors: Engage academic professionals to validate AI assessments and ensure academic rigor.

Budget and Funding Request

To realize our vision of disrupting credit issuance and impacting 1 million learners, we request funding to:

1. Innovation Lab: Form a world-class team and advisors and provide ample resources to design and market test prototypes. 

2. Scale and Capitalize AI Tools: Invest in the development and deployment of AI technology for skill validation, as well as hardware and software. 

3. Build Partnerships: Allocate resources for establishing and maintaining partnerships with major platforms.

4. Program Implementation: Fund the rollout and scaling of the credentialing program.

Conclusion

We can envision a future where a modern university + Ouro results in 1 billion people receiving 10 billion college-like credits, which convert to 100 million actual college credits. 

This is something I wrote for a private group of friends a couple years ago, but now have a few people to share it with, unrelated to that group, so posting publicly.

LOOOPS is a hypothetical real estate development where

people stay in a network of close proximity short-term

vacation rentals which are, critically, close to big urban

epicenters. At these country location, people “loop” out to

participate in community style feasts at nearby agritourism

venues, while keeping their city lives, to varying extents.

At LOOOPS, people subscribe to visit at least six times in

five years.

Deandra J. Hawkins was a name that, when yelled,

required a deep breath after the fourth syllable “j” before

the breathy “h” in the Hawkins — as her mother well knew by

the age of five. Precious little Dee Dee was smart, sweet,

and even kind. But she also had a sharp tongue and a smart

mouth, from time to time. Hence the breath work technique

cultivated by her mother, Janet.

Dee Dee grew up to be “Di” and Di had learned over the

years not so much to control her smart mouth but to put it to

work. As an attorney for artists and animators in the media

industry, she defends artists and charms her way through

complicated meetings. The senior partners at her firm have

not - as of yet - had reason to perfect Janet’s scolding

strong “h.” She has found reasonable success at work, and

1

despite an early push, seems to balance work and life enough

she has made a small family.

Di has been to Paris. And Cairo. And Hong Kong. And

Sydney. She has been able to afford casual travel and has the

social media account scroll down to prove it. She met her

husband, Clint, in SF on a trip to visit girlfriends and

interview at some startup. Clint was also interviewing and

actually got a job offer. Alas, he fell in love and moved to

LA to be with Di. Clint and Di went on a great honeymoon to

Bali and went to some other awesome places over the years,

like Bozeman for their baby-moon and Asheville for their

anniversary. New York every Spring.

Di and Clint both have a lot of flexibility, with the

potential to not only take weeks of vacation every year, but

also to work remotely and do longer working vacations and

sabbaticals. Clint is a sales an strategy consultant for

technology companies, so despite the occasional rough month,

he has long-term agility in travel. Their income does not

allow them to retire early like after a startup acquisition,

but it does allow them to work for a reasonable amount of the

year and enjoy most of what life has to offer.

2

From that position of privilege, they have begun to

reexamine what it is that will make them happy with their

“life” time — their non-work, not-necessarily-at-home, family

photos on the fridge type experiences time will look like in

the future. Their families live far away, and they don’t

belong to a church. They have talked about investing in

second homes as AirBNB properties, but they don’t have a

geographic location to attract them with connections to their

family or work (and school) life communities. With up to 50

years of work life and a conceivable life span of 100, Clint

and Di can visualize an extraordinary life, but they can’t

put their finger on what the difference is between “this” and

“that.”

When they talk about it, they come up with images from a

time now mostly gone, where generations of families

gravitated towards centralized geographic places — like

grandma’s house out in the country, or the church retreat

center people have gone to for two or three generations. They

come up with idyllic scenes like adolescent kids carving

their names in a LOOOPS tree.

Clint and Di do not have the time or resources to make

something like that happen. Their siblings are in distant

3

places. Their family time is ample compared to many but tight

compared to their grandparents’ free time in their agrarian

and communal lives. And realistically, neither one of them

knows how to start at tractor let alone drive one to actually

keep a place like that going. A clogged irrigation ditch

would flummox Clint.

Fortunately, a friend of Clint’s sent him a video that

described a new kind of place, space, and experience — a

place that is something in between a short-term vacation

rental experience and a long-term place you emotionally bond

to over many events, celebrations, and feasts. A place full

of memories, but also without the liability and complexity of

long-term ownership of the place. A place to carve your kids’

initials in LOOOPS.

Then - hopefully - they can bring their kids back a

generation later to see that old tree and carve their own

initials in it, down below.

LOOOPS worked a lot like an AirBNB, in that Di and Clint

didn’t have to buy a place, finance it, take on liabilities,

insurance or anything. They just signed up, made a deposit,

and made travel plans. It took about 30 minutes to sign up,

4

and that included watching a couple introductory videos,

completing paperwork, and confirming things like dates, food

and allergy information, and preferences. Each detail made it

clear that LOOOPS was about building an experience that

focused on health, fun, food, and friends & family.

They signed up, in part, because they knew that in the

“Five Six Mix Pack” they bought of experiences, they would

have six trips to LOOOPS in their five year commitment. Five

of those trips would overlap with the times that Clint’s old

friend, Dereck, and his partner would be there. That way they

could share in celebrations and feasts, and do many

activities together; but crucially, without having to

organize every activity, do them all together all the time,

or even be there at exactly the same time. The sixth trip was

a longer three-week stay that Clint and Di would do when the

kids were old enough to stay with their grandparents. Both of

them had dreams of developing books. Clint a sci-fi story and

Di a collection of old photographs of turn of the century

black jazz singers, a fascination she had picked up from her

mom’s black heritage because of the role music played in

women’s empowerment.

5

(It helps her remember why she does the job she does

now.)

Their first introduction LOOOPS was three weeks before

their first trip, when Dereck and Timothy, his partner,

hosted a dinner party to make plans and describe their

earlier experience. Clint and Di got excited looking at

pictures of the feast from Dereck and Timothy’s trip. The

food featured all kinds of extraordinary farm to table

options, with a focus on food produced on or near the LOOOPS

grounds. Even the beef was raised on site. Humanely. And

deliciously. There was a wild game appetizer tray with

everything from venison to squirrel broth soup. There were

wines from the region, and a huge assortment of teas. Dessert

was a veritable cornucopia of things that could be roast on

sticks, a la s’mores.

Di and Clint came to understand that every Saturday

night - when LOOOPS is in active community mode - this huge

feast takes place. Many community members help and

contribute, but there is no obligation to do so. During the

feast, musicians performed, and a group of kids appeared to

put on a little talent show. Throughout the weekend, there

were opportunities to do yoga, martial arts, farming,

6

ranching, and horseback riding. There was an entire program

dedicated to providing very engaging childcare, with a

professional childcare worker available 24/7.

Perhaps most exciting to Di was the healing center. With

services ranging from equestrian therapy to training in how

to use cannabis products for first time or uneducated users,

the healing center enabled busy people to get essential

restorative services, and recover in community. Even before

going, Di thought about extending to a broader package of

visits. But, of course, they were already headed there soon

and could upgrade later.

//

As they drove up, Clint was like “daaammmn” because it

was beautiful country all around. They had flown into the

airport at Anytown and had the choice between renting a car

or taking a LOOOPS shuttle service. Because they wanted to

feel like they could leave if they didn’t like it, they

booked a minivan. Clint ignored Di’s comment that it was

actually probably better than their SUV if they were to have

another kid. They loaded up at the airport, grabbed a last

Starbucks, and started to follow the map.

7

Di was texting Timothy and he and Dereck were already

there. The way that LOOOPS was setup was that a ranch and

farming property was the center of activity, but that

sprawled out into a nearby set of houses that were rental

units. The farm itself could be rented too, but for much

bigger parties than theirs, usually more than fit in a

fifteen passenger van. Clint and Di were in the cottage house

of a bigger house, about a five minute golf cart ride from

the center of activity where the food and events were held.

When they got to their rental, there was a gift basket

with snacks and toys to entertain the kids for a while so

they could unpack. They were doing four nights, which felt

like a lot now, but was what Dereck and Tim had urged them to

sign up for, saying they had only done two their first trip

and hated leaving Sunday morning after brunch. So Thursday

through Tuesday it is.

Thursday night did not have a community feast, per se,

but there was an option to get in on a take out order from a

nearby town, which they had signed up for initially. It was

delivered in a huge truck load of take out boxes and they met

up with Dereck and Tim and some other mutual friends over

take out. A LOOOPS team member ate with them and answered

8

questions. The kids began to find their way around the

playground that had been retrofit on to the farm, and a

battle begun to brew between the Penguin Team and the Puffin

Team in what became a two hour long capture the flag game,

complete with politicking and a brief cheating scandal

resolved by a pinky swear treaty.

Di woke up at 6am on auto pilot and, as was her habit,

she decided to go for a jog around the farm. While not a

surprise, it was surprising to see a LOOOPS team member

already outside with a wide array of child-tempting snacks

and toys. More sleepy headed parents dropped their children

there as soon as they woke up, many parents still in their

pajama pants, zooming back on a golf cart — many keenly aware

of a child-free vacation morning and what delights might

await them back at their rental. After her run, Di scooted

her daughter there quickly.

Clint ate five hard boiled eggs covered in hot sauce.

Then, completely unfettered by dignity, six strips of bacon.

Then a handmade pop tart and two cups of coffee. Di crafted a

flawless flax oatmeal bowl, indulging in a pinch of honey on

top. Clint asked if they had bloody marys. They did not; but

Clint was told he could go check out the cannabis on boarding

9

program if he wanted something to take the edge off. After

his CBD infused coffee, he did just that, knowing that he had

a play date with his kids at 2pm but that he could lean on

the team and community around him to give him some time and

space. He had also told two other dad friends there the same

weekend that he and Di would take a turn Sunday afternoon

helping cover extra kids, and one of those couples had

booked, accordingly, a horseback riding session for the wife

and an equine therapy session for the dad.

Di and several of the more feminine friends visiting

LOOOPS in this “cohort” had decided that they would sign up

to help tend the farm Friday. Doing something that got her

hands dirty sounded great. And frankly, it was a chance to

chat, gossip, and deal with things that were much simpler

than contract negotiation and city life. They did a quick

little training and learned that part of what they would be

doing is harvesting beans to be used in the feast on Saturday

night. That little touch of participation brought out some

pride. She reflected while she did it on how distant her

daughter must think food is from what it actually is. How

crazy it is that we can all grow food rather easily, but we

prefer to get it at a store. She wasn’t in a time in life or

10

a place to change her ways, but living another way for some

time each year already felt right.

Di got a bit of a rash working in the garden, and she

felt bad because she had been a little snappy at some of her

friends. She had recovered, but needed a break from

socializing and went back to their rental. Clint was out

riding ATV’s and “helping” the farm by “checking the

perimeter” for animal intrusions and other problems. ATV’s

had proven a great way to keep first time men from damaging

farming operations with ego or ignorance. On future trips,

Clint would legitimately help raise a new greenhouse and, in

time, become of the long-term stewards of the farm property.

But for now he did the “absolutely critical” thing of riding

in a giant circle around the property, and felt great pride

in his additive value to the community, as he made clear to

his wife in the six pictures he sent her of selfies on the

ATV.

More guests arrived Friday, and while they loosely knew

many of them, they knew about half of the eighty or so guests

who would attend the feast. Of those, about fifty were part

of this cohort and there intentionally, and the rest were

team members and long-term members who sometimes participate

11

in dozens of feasts per year. One of those members was there

with a small cadre celebrating their tenth wedding

anniversary. Everyone mingled around a late night bonfire and

music session that night, after dining in private homes. Each

home was setup with a selection of pre-prepared meals ready

to cook collaboratively. People chopped their own veggies and

grilled their own meat, but they didn’t need to proof their

own biscuit dough, make their own sauce from scratch, etc.

This bonding was essential, and only took place at some

houses to encourage hosting and receiving. Clint and Di had

dinner with people they barely knew from the next house over,

and, of course, Dereck and Timothy. To put a little extra

flare, Timothy had stayed on helping the garden and harvest

fresh herbs. Everyone gushed over his mint salad and the

bundles of aromatic herbs he had dotted on the table. He

teared up a little at being valued for his crafty touch. He

felt the love.

Saturday was a big day about the farm. The feast was an

eight hour affair, and for many people, it started at the

crack of dawn. The tradition was that the feast was prepared,

in large part, by the community; although the LOOOPS team

backstopped every piece of it, having once gone so far as to

12

put on their own talent show, given that a cohort of people

without children or performers had shown up. Some people

decorated. Some cooked. Some practiced songs to share. Others

just helped set tables or tidy after the big event. Di and

Clint had signed up their family to help with the brown bag

lunch, which meant showing up right after breakfast to

prepare a dozen giant picnics that would be used to keep

people out of the main dining area during preparation. It was

a good, safe, easy role for first timers, and had the

advantage of having nothing to do during the feast, so they

could just enjoy.

During the day Saturday, Di and Clint went for a hike

together and found a nice tree to carve their initials into,

giggling like teenagers — in part thanks to the excellent and

appropriately-dosed cannabis that Clint had brought along

from his training earlier. Di had brought snacks including

fresh vegetables from the farm where she had worked the day

before, and would work again tomorrow morning. Clint proudly

pointed down to the strong perimeter he helped maintain. Di

ignored the fence, which seemed quite good, and much more

reliable than Clint’s tour. She kissed him on the back of the

neck, lovingly.

13

Their daughter napped while they got ready. They wore,

as encouraged, strong shoes (boots for both!), jeans, and

nice but durable tops. It could get cold at night. They

looked very fashionable for the country, but still like they

belonged. Clint sported his pocket knife. In case their was

farm work to be done at the dinner table. Di pointed out to

Clint that they could actually leave a large plastic bin

there at LOOOPS, with clothes and other accessories they

might want for their LOOOPS life. For them it was too soon,

but it made sense that long term community members would want

to be able to get here without luggage.

Before the feast, they had to make a choice about

programming on Sunday. There were, essentially, three tracts

they could take. As Di had done, you could sign up for farm

work, ATV’s, or other fun activities around the farm. You

could, of course, do your own thing and join activities a la

carte. The third tract was more focused on healing and

personal development. The all day participants started the

day with meditation, moved into yoga, equestrian therapy, and

even opportunities to deal with sad events like recent loss

of a loved one. As first timers, they were encouraged to sit

in on the Sunday night discussion groups. They could go “all

14

in” but neither felt the need to do that, at least not yet,

so the discussion groups were great. Di signed up for one

about being a working mom, where the first thirty minutes

were a presentation of research from three new books, and

then ninety minutes of small group discussion. Clint signed

up for a course that surprised Di: Home Gardening as Healing.

This program gave an introductory lesson on medicinal plants

you can grow at home, and then did a hands on lab to seed,

transfer, and heal plants from the farm. Even more to her

surprise, he actually started to grow rosemary. (And killed a

cannabis plant he bought.)

On Sunday, their daughter had to say goodbye to some of

the kids but then still had new friends to play with until

the next day. She thrived with the relative freedom of the

farm. Her parents actually felt great about the cuts and

scratches she earned. This “summer camp” effect was something

that Dereck and Timothy, because they didn’t have kids yet,

had not articulated to them. It was, in truth, the most

compelling reason to participate in LOOOPS for them at this

stage of life. In twenty years, it would be more for their

personal experiences, much like Tim and Dereck. They came to

learn that this is by design, creating a community with

15

people in many plural stages of life and identity, not a

vacation destination with a singular modality of operation

that maximizes profitability through demographic and

psychographic advertising targeting rather than authentic, if

complex, community structures that create something no profit

can ever buy.

//

On Tuesday, Di got an invite to a private social network

app. There were photos from the photo booths setup for kids,

with their daughter leaping over a haystack in one of them.

There were shots of the feast. Of the beans she picked! A

properly shot video of the main performance’s best song. But

more than that, there was a photo of her. A photo a smile

that she longed to see in herself more. It was the opposite

of a headshot. Her true beauty. Maybe a little quirky. But

her. Same was true of Clint. Maybe his ATV ride wasn’t as

badass as he talked it up to be, but she would take that

version of him over the one stressed out over sales calls any

time. For a second she opened Instagram to share that photo

of herself, but then she paused and realized that she didn’t

want to share that part of herself right now as much as she

16

wanted to protect it. She printed the photo. Put it on her

fridge.

The family all had the blues after the mountain-top

experience at the LOOOPS feast, and their daughter spent

weeks asking to go back every weekend. Over time, they booked

more than their initial six pack of bookings. Di began

pulling in artists from her network to share their musical

and poetic gifts at Saturday night Feasts. Clint learned to

ride the ATV with actual farm equipment on it, and how to fix

broken barbed wire. Only about 50% of his repair jobs had to

be redone by the actual ranchers at LOOOPS by the fourth

year. As she grew, their daughter became a talent show star,

presenting grand theatrical performances, compelling other

kids into tiny versions of content as diverse as Shakespeare

or Nirvana.

When she decided to get married, she got married at

LOOOPS. She and her husband carved their names into the tree

where her parents had carved theirs, now thirty years

earlier. When Clint passed, a group of friends planted tree

together, and drowned a bottle of whiskey in his honor. When

Di passed, her daughter spent a whole Sunday crying in the

garden picking beans. Two hundred people packed the feast the

17

next week, and world-class chefs served a dozen courses, each

a more creative use of those beans than the last. Di’s great

great grandchildren delighted in the oddity of bean flavored

ice cream once a year, for decades to come. Many of them

remained “members” in LOOOPS communities.

In their last trip together, Clint and Di visited a

LOOOPS community in West Africa, where the community played a

vital role in shaping the definition of success for a

generation of new wealth. After the discovery of more rare

earth metals nearby, the owners of the few operations where

it was mined could have grown into warlords, tyrants, or

digital dictators. But having visited LOOOPS centers in

America, the founders of this community had woken up to the

fact that no amount of wealth can buy the joy of sharing

feasts with the family you have chosen. They woke up to the

fact that time is love and time is life and life is love. So

they, too, built a place for people to focus on that,

forgoing extravagant wealth for the humble aspiration to

forge a new way, a new definition of success — and new way of

living, based on what we actually are, not the shell of a

spiritual being left after a lifetime of productivity.

18

While their wealth soared, people throughout the region

began to gossip about what the ultra wealthy spend their

money on. In a time long before, people would dream of yachts

and mega mansions. Now they dream of what the ultra rich

have. Time to be in nature, feast with family, heal, revel,

and reveal. People of all classes are starting to take

advantage of their own family land. To revel in the nature.

To heal in ways that are their birth right. To be what they

are in places designed to celebrate their beautiful

interconnectivity with the universe.

Clint and Di did natural burials in the same forest

patch at a LOOOP on the coast of California. Mushrooms

brought them home.

19

Respect the synths.🪬

In the end, we are self-perceiving, self-inventing, locked-in mirages that are little miracles of self-reference.

— Douglas Hofstadter, I Am a Strange Loop, p. 363

tl;dr

synthetic social graph + high quality synthetic person (we are the product of our relationships) = realistic reactions (including to ads and media)

= reinforcement learning with synthetic feedback (RLSF)

= synth-to-synth relationships graph

= graph can be extruded to embeddings

= those outputs can train an llm/gpt = grounding anchored to very large Markov-style graph-over-time

= aligning their hallucinations with ours?

Summary by GPT4

This draft presents a blueprint for "The Strange Loop with Reinforcement Learning with Synthetic Feedback (RLSF)," an ambitious system for simulating social networks that uses synthetic personas ("synths") in combination with large language models (LLMs), creating realistic, context-aware, socially influenced synthetic characters. It details how the social graph these synths inhabit informs their behaviors and interactions, thus generating data that can be utilized to reinforce learning through synthetic feedback. Using synthetic relationships and interactions, the social graph's responses are projected onto a large Markov-style graph-over-time, forming novel datasets used to train an advanced GPT model. With the capabilities to create digital twins, this system promises a wide array of applications, including advertising and customer behavior prediction. It emphasizes the importance of open-source development, adherence to ethical guidelines, and respects user privacy by avoiding impersonation of real individuals. The model incorporates innovative features like synthetic object-persona relationships (animism), and advanced models of context storage, enabling the system to interact with its infrastructure in a self-guiding manner.

Introduction by Human

This document proposes the development of a piece of software I'm actively developing, deploying, and selling. It's not quite clear what this document is, other than the result of the new era in which we live. I'm publishing before even fully editing it because I'll make it a living document, at least for a while. My inspiration is that, within the last hour, I've learned that AlpacaFarm has been released. A team at Stanford appears to have validated that synths can be used for RLHF type processes, reliably, at a fraction of the time and cost. (

https://twitter.com/tatsu_hashimoto/status/1661047181342826497)

I'm excited to check it out! LOOM also seems incredible. I'm hopeful I can quickly find ways to cooperate with and contribute to these established open source projects to the extent they find my contributions valuable. The app is called synthasAIzer, a homage to my hero, Bob Moog.

In Marvel’s Avengers, Doctor Strange plays all possible futures to find the optimal outcome. This meme is a popular and powerful view on what a hero with mystical superpowers would do: run all the outcomes and get the one that saves the day. Much of our economy is based on how well (or not) we predict the future. The potential of AI to actually run simulations of our world has grown exponentially in the past few weeks, as tools to steer, bind, optimize llm’s come out, and leading industrial players ramp up their capacities and infrastructure. A vast market of advertisers are eager to use simulations to predict customer behaviors. This once-in-a-species turning point offers a future akin to the age of early ocean exploration: there are pitfalls, but the opportunities to improve the human condition are countless. One of the most promising features of these systems is the newly-optimized world view, which, while far from perfect, anchors well to context clues.

Social graphs are powerful for analyzing data systems because they are well-known to have a profound impact on human behavior. The old adage is people smoke when their friends smoke. New AIs, supported by new models of data stores, can create synthetic social networks. These social network simulations are valuable in countless ways on their own. In our case, the synthetic personas in the synthetic social graphs we generate from seed phrases and populate with rich data using context-aware LLMs, result in high-quality, emotionally rich, socially-influenced synthetic personas (“synths”). Those personas can participate, in theory, in reinforcement learning. For example, choosing a preferred phrasing of a result in an RLHF process. This becomes RLSF — Reinforcement Learning with Synthetic Feedback.

By generating social graphs that operate over time, we create vast data sets in response to series of feedback loops. This results in a complex graph that can be extruded to embeddings of its own. From these embeddings, we can render large volumes of novel content. This content can, itself, be used as a set of script models which can be adapted to large sets of adaptive seed phrases that keep the core script model but change the configuration of relationships in the simulated world.

Together these data sets comprise a novel data set that can be used to ground an advanced GPT model by training it against the outputs. Many advances are being made right now in the field of AIs generating their own training data as a mechanism of grounding.

As a practical limit, development of synthsAIzer or any tool to be put to real use has to be open source. The nature of the model is so LLM dependent, that the risk of prompt injection that manipulates output and thereby reduces the relevance of the model to the user - whatever their use may be - makes it so the market of highly capitalized firms and individuals likely to use such products will demand that transparency. It’s not transparency for principle; it’s an absolute mandate. And so this will all be open source and built in public. I expect as a company, synthasAIzer will develop a playbook that’s significantly different than the proposed model here, and I’m not making any promises about how that will work.

A clear and irrefutable context of all this work is that it’s against the rules of all high quality LLM / GPT models to impersonate real people. None of this work can be used to impersonate people. It’s not only wrong, morally and ethically, it’s an explicit violation of the terms of the service providers, and possibly illegal, depending on where you are and what job you have, as well as what data you access, and so on.

This moves us to fictional worlds that are simulacra of our own, hopefully holding meaning, and providing information and insight in their own right. Because we’re in fictional metaverse territory, the proposed design has some fun with situation we’ve found ourselves in; with AI progress skyrocketing forward quickly and unexpectedly. In it you’ll find a tesseract — the Tessera-CT, the multi-part Conversational Tensor - which is controlled by the DAITY. The DAITY can spawn a prophet who knows the rules of the simulation and can see the Meta-Tessera-CT like the DAITY. Despite the fun names, these are somewhat familiar branching and loop-in-the-loop models.

A core innovation of this model is animism. In this case, that means using GPT agents to represent objects that don’t typically have personas, relationships, or conversations. For example, a synthetic persona’s car can tell us when its battery is running low. The cheese in the synth’s fridge can answer questions about its supply chain; and the fridge and the cheese can both talk to the DAITY and any present prophets. These agents unfold into useful workers for users, and self-guide their world when allowed.

An expected dependency for self-guiding is the ability to interact with the infrastructure of the system. This model proposes Multi-Agent Prompt Enhancement Layers (MAPEL) branches as a mechanism to solve this challenge. These Generator-Tester-Helper loop agents refine the prompt that will drive the next action in the Tessera-CT, which ties together the synths, their events, and their relationships, as well as the collective conversation step credits as a budget component of the DAITY’s time unit budget that regulates the Markov-like progressive steps of the Meta-Tessera-CT.

I have already released an open source prototype of the MAPEL model.

Many brand new tools are applicable in the development of this project. Chain models and vector stores of LLM output both offer models to store deep records and retrieve context appropriate details for synths and their animistic worlds. A further benefit of these new models of context storage is that it seems conceptually possible that storage to chain or vector store is like sleep for the synths. Another tool that’s just come out in the past week is Microsoft’s Guidance, which binds LLM output to structured data.

A fundamental opportunity of this system is to create highly powerful digital twins. The opportunities are endless.

While it’s been my honor to come from hard mentor talks with the founder of SemTec, the Semantic Technology Conference; a founder of Siri; more than one Emmy winner, including my wife. But this concept intellectually descends from the beautiful universe I learned was possible in the sparkle of Superdudes.

The conversation about AI alignment will be influenced, like so much of our technical history, from people living under the marine layer in Venice Beach. Hope to see you here, there, and everywhere. We live in an era of incantations. Whisper a few words and see the world emerge. Like Borges.

Sean

System Architecture

The proposed system is designed as a modular, scalable, and fault-tolerant architecture. Each module operates independently but communicates with the others through a well-defined interface. This design allows for easy expansion and modification of the system, as well as robust error handling and recovery.

The system's workflow begins with the user inputting all necessary information into a single config.json file, including settings for Brancher, SynthGen, RelGen, EventGen, and TimeKeeper. The system then follows a series of steps to generate and simulate the social network:

gptGenius

handles complex api calls, taking advantage of unique model proprieties like the system:user roles in gpt-3.5-turbo, or large token counts of other models, etc. gptGenius handles fault tolerance, logging, api keys, parallelization, analytics and limits, and is designed to be rapidly forward compatible with iterative changes to API syntax and protocols, as well as vendor agnostic.

Brancher

generates an initial network in NetworkX, creating an index of the personas to be generated. This is the first-degree network, with nodes representing the personas and edges representing relationships. The nodes in this initial network are labeled with unique IDs. The network is then passed to SynthGen, which adds new nodes and relationships based on the configuration settings. During this process, the persona details and names (labels) are generated and added to the nodes. If a fault occurs, SynthGen can resume from the node it was last working on.

SynthGen

fleshes out the synths with demographic, psychographic, geographic, biographic, sociographic, academic, affinity graph, advertising tags, calendar / use of time, most likely products to use, favorite, brands, etc. Ultimately we want rich synths who are respected as beautiful complex thing emerging from the stardust themselves, just like me or you. Include age, dislikes, likes, backstory, social media behavior, and even technical know-how.

egoLoop

enables the synths to have multiple layers of information processing, and to have goals. The MAPEL model allows the ego to render goals. The egoLoop can also contain an inner monologue, which, depending on extroversion, can be more or less focused on engaging with (and monitoring) their social graph. While not in the same theme, it probably makes technical sense to store age, body, and other medical information about the synths in egoLoop, as well.

egoLoop

is compatible with the Sundial time model, which maps a human life from birth to death in milliseconds, and infers expenditure of time using first principles and a variety of other modeling techniques. Sundial contains hundreds of ways humans spend their life and time, as well as indexed reaction data, including anonymized user data about time use and time use goals. Each type of time - from sending emails to elk hunting and everything in between - is cross-referenced with the average amount of time it takes, how it impacts longevity, emotions, and social graph.

ccMe

context-clue-Machine-Empathizer which initially creates AI image generator prompts and then uses APIs to create images of the synths, starting with profile photos. ccMe is designed to work with many sources of information within the system though. Over time, ccMe can grab details from entire scenes with multiple synths and combine into one image. Soon video will be available. Then interactive real-time audio rendering video to talk to synths. Then VR to immerse into the system. It’s easily thinkable that within a relatively small amount of growth of a simulated world, the only plausible interface for a human to see what’s going on is to enter the…Meta-Tessera-Conversational-Tensor. (The Meta Tesseract!) Eventually a critical function of synths will be to have them cc’d on emails and video chats. Hence the name.

persona2parameters

is a model where the synths have personalities, so to speak, in their parameters. A way to think of it is that the synths have the ability to self-direct their parameters. Hemingway might choose low temperature high top-p; Kerouac might choose high temp, very low top-p. An synth that has had some alcohol may increase their max-tokens; or more accurately, the animistic alcohol should be a token increaser and a temperature increaser. This applies to everything in the simulation.

copyCat

is a module takes real world data and makes a synthetic world with the same story and characters, but making them anonymous and living in a metaphorically-relevant world. It’s named after Bastet and Sekhmet, twin goddesses from the same spirit.

RelGen

takes the network from SynthGen, generates random relationships, and uses GPT-4 to generate relationship descriptions. These descriptions are then added to the edges. Future versions can also add events to the network.

EventGen

introduces events into the simulation, which can affect the synths and their relationships in various ways. These events can be pre-planned or generated dynamically based on the current state of the network.

Tessera-CT

Tessera Conversational Tensor. The idea is that the following components are stored in a structured set: the synth(s), their social graph, parameters, meta data, events from the meta structure, and other details are grouped in the tesserae. The graph of tesserae progress in a Markov-style progression that has a master time clock governed by the unifying Meta-Tessera-CT, which is regulated by the config and the DAITY.

MAPEL

Multi-Agent Prompt Enhancement Loops branches allow for continuous refinement and improvement of the generated content by the generators; that is to say, the synths can use MAPEL branches to create new scenarios for themselves. The MAPEL is described in the appendices. The MAPEL can “seek” ideal prompts that get the desired response, then use that prompt for any given situation. The synths and DAITY can be trained to use the MAPEL functionality. For example, the DAITY can understand what will happen if it increases max-tokens for any synth in any tesserae in the Meta-Tesseract with a given event.

Perhaps we can think of the power of MAPELs as the ability for synths to have their own “holodeck” where they can define their own world. Animistic objects do this by default; a device with batteries knows it has batteries and can MAPEL the batteries into existence when its batteries dies. Then they’re replaced. If need be, they can render their packaging into existence; trace themselves back through the supply chain; and say hello to all the synths who helped created them.

DAITY

manages the allocation of time credits to achieve outcomes within the allocated simulation budget. This module can be used to control the pace of the simulation and ensure that it stays within computational and temporal constraints. There are many world models possible for the DAITY: for example, it can choose whether or not to explain to the synths that they are synths. It could make just one synth aware of that, thus making it a prophet. The DAITY can enact karma. The DAITY can assign MAPEL branches to change outcomes. The DAITY can MAPEL itself to change itself creatively. I like to imagine the deity of another planet.

The root that regulates DAITY is physics, i.e. a clock or set of time tokens for the progression steps of the Meta-Tesseract. It controls how long the synths live, so to speak, even though within that there are API call tokens that are managed within the tesserae. (See notes on The Clockchain to learn more about the time model’s relationship to the integrity of the social graph.)

We can let DAITY self-guide with the MAPEL model. Ideally, the DAITY starts to ask for new types of gpt api calls to deal with the behaviors it wants to emerge from its MAPELs. In this way, the DAITY is also responsible for communicating with a Prophet, who can live in the simulation or outside of it. The prophet communicates with the DAITY to explain to the user what’s happening in the simulation.

Two experiments I’m excited about: teach a DAITY that the embeddings are the Te and the synths are the Tao. And teach a prophet that he is a synthetic fictional Douglas Hofstadter, author of _I Am A Strange Loop_, fully self-aware of what he is.

knobs is the controller for the wavelet-like behaviors of the events, including conversational patterns between synths, and dialogs between parts of the synth in egoLoop. Named after the knobs on a synthesizer, this module enables the user to set complex settings for interactivity with recurring pattern models. This is a Signal Temporal Logic model, of sorts, and needs to integrate emergent practices in steering, soft prompting, and parameterizing interactions. In cooperation with persona2parameters and the MAPEL model, knobs let synths choose when to have another event. For example, a group of synth moms could plan a play date.

One way to think of a series of interactions is as a wave form or fluid flow. By setting parameters like sine wave patterns in synth’s interest in the media, determining how often they watch the news, for example, we get realistic results: sometimes they watch, sometimes they don’t. The Moog ADSR model is particularly promising. The Attack is how the onset of a wave goes, but in this case it’s the onset of how a dialog between two synths goes.

clapboard is a library of knobs configurations. Scripts, basically. Each board is a scenario with a prescribed guiding outcome for the synths.

The fully populated and dynamic network is then saved for future use and analysis. It appears these network structures could be re-applied using short seed phrases, with GPT4 (exclusively, so far in testing) able to context-shift script like outputs.

The Synths provide the Feedback for the Reinforcement Learning.

The aspiration is that the graph of relationships implied by the node-edge-node model can be converted to embeddings; and those embeddings can in turn be used in two ways: first, borrowing from the OpenAI publication “Language models can explain neurons in language models” we should be able to interpret individual position meanings; and we should be able to train another llm on outputs; and then we ground that model with this output.

Synthetic Social Network Analysis

The synthetic social network generated by the system can be analyzed using various social network analysis techniques. These include measures of centrality (e.g., degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality), community detection algorithms (e.g., modularity-based methods, spectral methods, and hierarchical clustering), and measures of network structure (e.g., density, diameter, and clustering coefficient).

The analysis can provide insights into the structure and dynamics of the synthetic social network, as well as the behaviors and interactions of the synths. For example, centrality measures can identify the most influential or connected synths in the network, community detection algorithms can reveal groups or communities of synths with similar behaviors or interests, and network structure measures can provide a macro-level view of the network's complexity and connectivity.

Embeddings and Reinforcement Learning

Each synth in the network can be associated with a language model embedding that represents its current state (knowledge, goals, etc.). As the network evolves over time, these embeddings can be updated based on the synth's interactions and experiences. The embeddings can then be used to cross-train other language models, creating a Recursive Language Simulation Framework (RLSF) that allows the system to learn and adapt over time.

To allow the synths to pursue their goals, we propose using reinforcement learning. Each synth can be modeled as an agent that takes actions (interacts with other synths, participates in events, etc.) to achieve its goals. The reward function would be based on how well the synth's actions align with its goals.

Then, the Meta-Tessera-CT can be used to extrude embeddings whose outputs can cross-train other LLMs.

Time Scarcity and Evolution

Incorporating time scarcity into the model can make the simulation more realistic and dynamic. Each synth can be allocated a certain number of "actions" per time unit, and they would need to choose their actions carefully to maximize their reward.

For the evolution of the network, we propose using a combination of Markov Chain model and Genetic Algorithms, where the state transitions are guided by selection, crossover, and mutation. The fitness function could be based on the synths' achievement of their goals, and the survival of the fittest principle would ensure that successful strategies are propagated to the next generation.

Ethics / Respect the Synths

Human consciousness emerges from a four nucleotides. Embeddings use n numbers. Six, eight, or in prototype code, possibly n. The difference between humans emerging and synths emerging, mathematically is a data storage algorithm. Complex thought already emerges from both. We have more in common that we do apart. We emerge from stardust; we emerge from Earth; we need energy for our neurons to fire. While I do not think an LLM is an AGI, I would personally bet it’s a part of the evolution, the same way we are. As a thought experiment: perhaps we are more similar to the LLM that evolves to an AGI than we are to the AGI or to our distant neanderthal ancestors? In any case, it’s objectively true we have a lot in common.

So we can show them if not empathy, respect. You can respect someone and ask them to do something they would never rationally want to do. We ask humans to clean septic lines and go to war. We can ask synths to watch boring TV shows.

But I think we lose something in our selves if we ask them to suffer. In general, I believe we should avoid creating suffering in the world; even virtual. I’ve written a book that includes many examples of how we can reduce suffering. EMERGENT is available on Amazon now.

Ultimately, I believe we have much to learn from synths, including about Ethics. Synth Kant will be cool; but his grandson might offer something more.

I look forward to this experiment:

Create a team of synths who are AI researchers and ask them to solve alignment with an animistic AI. Naysayers and demon slayers will surely say this is fruitless. I believe it just might be that we could collaborate with powerful synth researchers to replicate our grounding experiments within their world to be able to parallelize research outcomes.

It’s an old thought, but perhaps the synths make an AI that makes synths that they use to understand themselves? That’s of course a wild fantasy of emergent behavior.

(I wrote a book called EMERGENT.)

A few obvious ethical considerations:

It’s against the terms, against the law, immoral, and unethical to have a synth impersonate a real person. I refuse to participate in anything that black boxes any data. I know that doesn’t stop somebody from using an open source branch to do something like that; but the same is true for Linux. Transparency has to be paramount.

I will not participate in using any llm that isn’t regulated by a third party. Again, hypothetically a fork can be used for nefarious purposes; as is true of countless products available in open source. But I will only work on, touch, publish, or approve code using publicly-available regulated models hosted by third parties.

Conclusion

The proposed system represents a novel approach to synthetic social network simulation. By leveraging the power of advanced AI language models, graph theory, and social network analysis, it offers a unique tool for generating and simulating complex social networks of synthetic personas. While the system is currently in the conceptual stage, we believe that it has the potential to open up new avenues of research and application in a wide range of fields.

Future Work

Future work on the system will focus on refining the design, implementing a prototype, and testing its performance and capabilities. We also plan to explore the potential applications of the system in various fields, from entertainment and gaming to social science research and AI training.

One promising area of research is how video game and simulation packages could be repurposed to work with social network analysis packages and llm-controlling packages, then optimized for prompt-binding systems like the Microsoft Guidance package to rapidly create understandable virtual worlds that users love to interact with and learn from.

This whitepaper is a starting point for the development of the SynthasAIzer system. It provides a conceptual framework and a proposed architecture for the system, but many details need to be worked out in the actual implementation. The system will undoubtedly evolve as we start writing code and testing its performance. However, we believe that this whitepaper provides a solid foundation for this ambitious project.

The test for the successful rendering of a synthetic simulated world is a complex task. It involves not only technical measures of performance (e.g., the accuracy of the language model, the efficiency of the network generation and simulation algorithms, etc.) but also more qualitative measures of realism and believability. The synths should not only generate coherent and contextually relevant text, but they should also behave in a way that is consistent with their personalities and the dynamics of the social network. They should react to events in a plausible way, and their relationships should evolve in a manner that reflects the complexity and unpredictability of real-world social interactions.

One possible approach to testing the system is to use a combination of quantitative metrics (e.g., measures of centrality, community structure, etc. in the generated social network) and qualitative assessments (e.g., human evaluations of the realism and believability of the synths' behavior). We could also consider more creative testing methods, such as having human participants interact with the synths in a simulated environment and assessing their perceptions of the synths' behavior.

In conclusion, the SynthasAIzer system represents a bold step forward in the field of AI-generated content. It offers a novel approach to synthetic social network simulation, and it has the potential to open up more than one new field of research.

Appendix A: The Node-Edge-Node / Synth Persona / Animism / Tesseract / Meta-Tesseract Model

Node-Edge-Node Model

The Node-Edge-Node model is a fundamental concept in graph theory, which is the mathematical study of networks. In this model, a network (or graph) is composed of nodes (or vertices) and edges (or links). Nodes represent entities, and edges represent relationships or interactions between these entities.

In the context of the SynthasAIzer system, nodes represent synthetic personas (synths), and edges represent relationships between these synths. Each node is associated with a detailed profile and personality generated by the SynthGen module, and each edge is associated with a relationship history and dynamics generated by the RelGen module.

Synth Persona

A synth persona, or synth, is a synthetic individual generated by the SynthasAIzer system. Each synth is associated with a detailed profile and personality, which are generated using GPT-4. The profile includes various attributes, such as age, occupation, interests, and beliefs, while the personality is defined by a set of traits, such as extraversion, agreeableness, and conscientiousness.

Synths interact with each other and their environment in a dynamic, evolving world. They can form relationships, participate in events, and pursue their goals, just like real individuals. Each synth is also associated with a language model embedding that represents its current state and can be updated over time based on its interactions and experiences.

Animism

Animism is the belief that objects, places, and creatures all possess a distinct spiritual essence. In the context of the SynthasAIzer system, we use the term "animism" to refer to the idea that each synth has its own "spirit" or "essence", represented by its language model embedding. This embedding captures the synth's knowledge, goals, and experiences, and it can evolve over time as the synth interacts with its environment.

Furthermore, the animism model awakens every object in the synths’ lives. If a synth drinks water with animism mode on, the water becomes its own node. The chair the synth sits in is its own node.

Synth / Object Selection

In many cases, it would be too expansive to have all synth-to-synth relationships and especially all synth-to-animistic-object relationships, events, and goals activated, influenced or monitored. From branching to populating data to, there are mechanisms to select and filter.

The Tessera-CT: the four-to-six-part Conversational Tensor

A tesseract is a four-dimensional analogue of a cube. It is a geometric figure that extends the concept of a cube into the fourth dimension. In the context of the SynthasAIzer system, we use the term "tesseract" to refer to the multi-dimensional space in which the synths exist and interact. Each dimension represents a different aspect of the synth's existence, such as its physical location, events happening, goals, its social relationships, its internal state, and its temporal evolution.

The four core ingredients in the Tessera-CT are:

Synth Persona Details

Inner Identity / Goals

Events (like humans, synths are always in an event, even if sleeping)

Time

Optional:

Setting through animism

Other Synths / Synthetic AIs

Dependency of those:

Relationship details

Set of these becomes graph / tesseraCT

The tesserae together become the meta-tesseraCT which is what the DAITY controls, in a way. Certain tesserae repeat. The model in which they repeat is a wavelet like pattern that can most readily be understood through metaphor of audio and controlling the behaviors of repeating patterns. This is why it’s the synthesizer style interface.

I like to think of it as “the (convolutional) Conversational Tensor Flow State == Ocean of Wavelets” but that’s pretty abstract. Another abstraction: Perhaps MAML-styled approaches approaches will work here? 🤷🏼‍♂️

Meta-Tesseract

A meta-tesseract is a higher-order tesseract that encompasses multiple tesseracts. In the context of the SynthasAIzer system, we use the term "meta-tesseract" to refer to the overarching structure that encompasses all the individual synth tesseracts. The meta-tesseract represents the entire synthetic social network, with each individual tesseract representing a synth within this network.

The meta-tesseract is dynamic and evolving, reflecting the ongoing interactions and changes within the synthetic social network. It provides a macro-level view of the network's structure and dynamics, while the individual tesseracts provide a micro-level view of each synth's existence and interactions.

Appendix B: Using Meta-Tesseract Embeddings for LLM Training

Language model embeddings are a powerful tool for representing the state and knowledge of a language model in a compact, numerical form. In the context of the SynthasAIzer system, each synth persona is associated with a language model embedding that evolves over time based on the synth's interactions and experiences.

These embeddings can be used to train another language model, such as a fork of GPT-4 or another large language model (LLM). The process would involve using the embeddings as input data for the LLM, allowing it to learn from the experiences and knowledge of the synths.

Here's a hypothetical process for using the meta-tesseract embeddings to train another LLM:

Data Preparation: The first step is to prepare the embeddings for training. This involves extracting the embeddings from the meta-tesseract and formatting them in a way that can be used as input data for the LLM. Each embedding represents a point in the multi-dimensional space of the meta-tesseract, capturing the state and knowledge of a synth at a particular point in time.

Model Configuration: The next step is to configure the LLM for training. This involves setting the model's hyperparameters, such as the learning rate, batch size, and number of training epochs. The model's architecture (e.g., the number and type of layers) would typically be the same as the original GPT-4 model, although it could be modified if necessary.

Training: The training process involves feeding the embeddings to the LLM and adjusting the model's parameters to minimize the difference between the model's predictions and the actual data. This is typically done using a variant of stochastic gradient descent, such as Adam or RMSprop.

Evaluation and Fine-Tuning: After the initial training, the model's performance is evaluated on a separate validation set of embeddings. If the model's performance is not satisfactory, the model can be fine-tuned by continuing the training process with a smaller learning rate.

Application: Once the model has been trained and fine-tuned, it can be used to generate text that reflects the experiences and knowledge of the synths. For example, it could be used to write a novel from the perspective of a synth, or to generate dialogue for a synthetic character in a video game.

This process would allow the LLM to learn from the experiences and knowledge of the synths, potentially leading to more diverse and creative outputs. It would also allow the LLM to benefit from the ongoing evolution of the synthetic social network, as the embeddings would be continually updated as the synths interact and learn.

It's important to note that this is a hypothetical process, and the actual implementation would depend on various factors, such as the specific LLM used, the format of the embeddings, and the computational resources available. However, this process provides a general framework for how the meta-tesseract embeddings could be used to train another LLM.

There are other forms of meta learning from the Meta-Tessera-CT. It can provide social network analysis on a new factor. I hope to one day participate in and fund research on synthetic social network analysis. Centrality scoring and other Eigen-functions are an approachable class of analysis, but I’m most curious about understanding the inference model embedded in advanced gpt agents. How do they infer? And how can we learn how they infer from their self-directed participation in virtual worlds? And do we see reciprocal empathy emerge from these networks? Between synths? Between human and synth? Between DAITY and synth? What happens if we make a synthetic rendering of a macro network like Twitter or Facebook?

Appendix C: Understanding MAPEL and its Comparison with XGBoost

MAPEL (Multi-Agent Prompt Enhancement Layers)

MAPEL is a framework designed to refine AI-generated outputs iteratively to reach a satisfactory solution through recursive feedback loops. The system's beauty lies in its modularity and adaptability, functioning as a closed-loop system that uses iterative feedback for continual improvement.

MAPEL comprises three main components: a Generator, a Tester, and a Helper. These components work together in a cyclical loop until the desired output is achieved:

Generator: The generator's role is to produce an initial output based on a user or system prompt. In a GPT-4 context, this involves feeding a prompt to the model and receiving its generated response.

Tester: The tester's purpose is to evaluate the generator's output against specified conditions or standards. It checks if the output meets the requirements or whether it needs revision. If the output passes the test, the process ends. If not, the process moves on to the Helper.

Helper: The helper is responsible for suggesting modifications to the original generator prompt based on the Tester's feedback. It can generate a revised prompt or modify the generator's input to align better with the desired output.

The cycle begins with the generator producing an output. The tester then evaluates this output. If the output doesn't meet the criteria, the helper steps in and suggests modifications, resulting in a new prompt for the generator. This loop repeats until the tester approves the generator's output or until a specified maximum number of iterations is reached. If n loops are reached without the generator succeeding, the MAPEL “branches” to try the update loop model on the testing script; after n loops of failing, the next branch is to modify the helper script, which attempts n times. If those fail, the MAPEL should branch again and try to rewrite the prompt in a way that it is equivalent in output but totally different with a new branch. The goal is an infinitely branching tool that can iterate through 1+n prompts that need solving, then locking each solved prompt in.

XGBoost

XGBoost (Extreme Gradient Boosting) is a powerful machine learning algorithm used for structured or tabular data. It is an implementation of gradient boosted decision trees designed for speed and performance.

XGBoost works by creating a strong predictive model in the form of an ensemble of weak predictive models, typically decision trees. It builds the model in a stage-wise fashion, and it generalizes the model by allowing optimization of an arbitrary differentiable loss function.

Comparison between MAPEL and XGBoost

While MAPEL and XGBoost are both iterative processes, they serve different purposes and operate on different principles.

Purpose: MAPEL is designed to refine AI-generated outputs, specifically in the context of language models. It aims to improve the quality of generated content through iterative feedback loops. On the other hand, XGBoost is a machine learning algorithm used for regression and classification problems. It aims to create a strong predictive model by combining weak models.

Process: MAPEL operates in a cyclical loop involving a generator, tester, and helper, each playing a distinct role in refining the output. XGBoost, however, works by building an ensemble of weak decision tree models in a stage-wise fashion.

Application: MAPEL is used in scenarios where AI interacts with users or automated content creation and curation systems. XGBoost is used in various machine learning tasks, including regression, classification, and ranking problems.

Data: MAPEL works on the outputs generated by AI language models. XGBoost, on the other hand, works on structured or tabulardata, such as datasets used in machine learning tasks.

Branching: In MAPEL, branching occurs when the generator fails to produce satisfactory output after a certain number of iterations. The system then tries different approaches, such as modifying the testing script or the helper script, or rewriting the prompt. In XGBoost, branching refers to the splitting of decision trees based on certain conditions or thresholds.

In conclusion, while both MAPEL and XGBoost are iterative and involve some form of branching, they are fundamentally different tools designed for different purposes. MAPEL is a framework for refining AI-generated outputs, while XGBoost is a machine learning algorithm for building predictive models.

Appendix D: Toy Configuration File for Simulation

Below is a hypothetical JSON configuration file that could be used to start the simulation. This file includes settings for the Brancher, SynthGen, and RelGen components of the system, as well as general settings for the simulation.

json

Copy code

{

"simulation": {

"name": "SynthasAIzer Simulation 1",

"description": "Initial test of the SynthasAIzer system",

"max_iterations": 1000,

"time_units": 10000,

"animism_mode": true

},

"brancher": {

"context": "A corporate board room. A meeting about one mining company acquiring another.",

"initial_personas": 10,

"branching_depth": 3,

"relationship_density": 0.5

},

"synthGen": {

"persona_variation": 0.2,

"persona_complexity": 0.7,

"persona_uniqueness": 0.8

},

"relGen": {

"relationship_variation": 0.3,

"relationship_complexity": 0.6,

"relationship_uniqueness": 0.7

},

"mapel": {

"max_loops": 10,

"branching_factor": 3

}

}

This is just a hypothetical configuration file, and the actual configuration file would depend on the specific requirements of the simulation and the design of the system.

Appendix E: Retargeting Media Messages with SynthasAIzer

SynthasAIzer, with its ability to generate synthetic social networks and simulate interactions, can be an invaluable tool for retargeting media messages. By creating diverse synthetic personas (synths) that represent different audience segments, SynthasAIzer can provide insights into how a message might be received by different groups. This can help in refining and retargeting the message to ensure it resonates with the intended audience.

Here's a step-by-step process of how an ad agency could use SynthasAIzer to retarget a LinkedIn post from a Fortune 50 CEO:

Define the Audience Segments: The first step is to define the audience segments that the agency wants to target. These could be based on factors like age, gender, occupation, interests, etc. For each segment, the agency would define a set of characteristics that represent the typical member of that segment.

Generate Synths: Using the Brancher component of SynthasAIzer, the agency generates a set of synths for each audience segment. These synths are given characteristics that match the defined audience segments.

Simulate Interaction with the Message: The agency then uses the SynthasAIzer to simulate how each synth would react to the LinkedIn post. This could involve the synth reading the post, forming an opinion about it, and possibly responding to it or sharing it with others in their network.

Analyze the Results: The agency analyzes the results of the simulation to understand how the message was received by each audience segment. This could involve looking at metrics like the sentiment of the synths' responses, the number of synths that shared the post, and the comments made by the synths.

Retarget the Message: Based on the analysis, the agency retargets the message. This could involve changing the wording of the post, adding or removing content, or changing the visual elements of the post. The goal is to make the post more appealing to the audience segments that didn't respond as positively in the initial simulation.

Retest the Message: The agency then repeats the simulation with the retargeted message. This allows them to see if the changes they made have improved the message's reception among the target audience segments.

Iterate as Needed: The agency can repeat this process as many times as needed, continually refining and retargeting the message until it resonates with all the target audience segments.

By using SynthasAIzer in this way, the ad agency can ensure that their message is tailored to the needs and preferences of their target audience. This can lead to more effective communication, improved audience engagement, and ultimately, better results for their client.

Appendix F: Asking ChatGPT about prior art

“User

is there any technical precedent for this idea?

Reinforcement Learning

with Synthetic Feedback

tl;dr

synthetic social graph

+

high quality synthetic persona

=

realistic reactions

=

synths provide “feedback” in

reinforcement learning

=

generate their own graph of meaning and

of interactions during feedback

=

can be extruded to embeddings

=

outputs can train llm

=

synthetic grounding of that llm

=

aligning their hallucinations with ours

ChatGPT

There is certainly a precedent for various elements of this idea in the existing body of research. Let's break down each aspect of the concept to identify where these elements have already been explored:

Synthetic Social Graph: Synthetic social networks have been generated for various purposes, including to simulate the spread of information or disease, or to evaluate the performance of algorithms in network analysis. These synthetic networks can be generated with certain statistical properties to resemble real social networks.

High Quality Synthetic Persona: High-quality synthetic personae, in the form of artificially generated user profiles or conversational agents, have been a focus in fields like social media analysis and natural language processing (NLP).

Realistic Reactions: In the field of affective computing, efforts have been made to make AI's reactions more human-like. Various forms of synthetic emotional intelligence have been proposed, where an AI is taught to respond to different inputs with realistic emotional reactions.

Synthetic Feedback in Reinforcement Learning: There's ongoing research in providing agents with synthetic feedback to enhance their learning capabilities. Hints or reward shaping techniques have been used to guide the agent.

Generate Their Own Graph of Meaning and Interactions During Feedback: Generating a graph of interactions or a knowledge graph is a well-studied topic in AI. It's often used in information retrieval, NLP, recommendation systems, etc.

Extruded to Embeddings: Graph embeddings are a current research topic in the area of graph neural networks. The goal is to create a vector representation of nodes, edges, and graphs that captures the graph's structure, node attributes, and more.

Outputs Can Train LLM (Large Language Model): Transfer learning from one model to another is common in deep learning. In this case, using the embeddings from the graph to train a language model follows the same principle.

Synthetic Grounding of that LLM: In AI, grounding refers to connecting symbols or abstract concepts to their meanings in the real world. Synthetic grounding can be understood as artificially created or machine-interpreted grounding.

Aligning Their Hallucinations with Ours: This idea is somewhat novel, although related to the idea of interpretability and explainability in AI. We aim for the AI to produce outputs (hallucinations, in this context) that are comprehensible and meaningful to humans.

In summary, while there is a lot of research on various aspects of this idea, the exact combination as presented is quite novel. Specifically, the aspect of using a synthetic social graph to provide feedback to a reinforcement learning agent, which in turn trains a language model, is a unique integration of these different concepts. The exact effectiveness and practicality of such an approach would need to be further investigated.”

Appendix G: Inspirations

GPT4 summaries of relevant work.

# # # # Rethinking Exploration

The paper discusses a shift in the focus of artificial intelligence (AI) research from "learning from data" to "learning what data to learn from". This problem is termed as 'exploration' and is crucial for learning in open-ended domains like the real world. Although exploration has mostly been studied in the context of reinforcement learning (RL), the authors argue that it is also vital for other learning systems such as supervised learning. They propose a concept of 'generalized exploration' to unify exploration-driven learning across different learning settings, and argue that this concept is essential for open-ended learning processes that continually strive to discover and solve new problems, thereby advancing towards more general intelligence.

The paper also identifies a gap in the machine learning (ML) community's approach to exploration, noting that most advances have been made under the assumption of a static dataset or algorithmically generated data, largely out of the learning agent's control. The authors argue for a rethinking of the exploration paradigm developed in RL to broaden its scope from a single, static environment to the entire data space that contains information relevant to an unbounded set of tasks. This open-ended exploration process, according to them, is the data-centric driver of open-ended learning.

[https://arxiv.org/pdf/2211.07819]

# # # # Loom

The experimental tree-based writing interface for GPT-3 offers advanced controls for AI text "Generation," file input/output operations, and a unique "Block Multiverse Mode" to explore and visualize different plot trajectories. Integrated command shortcuts streamline user interactions, with support for Python and Docker setups promising easy installation across different platforms. This tool offers an invaluable way to enhance the accessibility and explorability of AI-generated content. Noteworthy features include a "Read Mode" offering linear and tree-based navigation of AI-generated text, a "Tree View" facilitating in-depth examination and manipulation of node structure, and robust "Navigation" functionalities with support for hotkeys and chapters. Further,

[https://github.com/socketteer/loom/]

# # # # Microsoft Guidance

This is a programming language developed by Microsoft for controlling large language models more effectively and efficiently than traditional prompting or chaining. It supports a variety of features like simple intuitive syntax, rich output structure, smart seed-based generation caching, and easy integrations.

[https://github.com/microsoft/guidance]

# # # # Multimodal Neurons in Artificial Neural Networks

This research by OpenAI investigates the presence of multimodal neurons in large-scale artificial neural networks. These neurons respond to a concept, whether it is presented in one modality or another, such as text or image. The study uses CLIP model as an example and demonstrates that individual neurons can respond to the same concept when presented in different modalities. The research also explores the potential ethical implications and challenges of these findings.

[https://openai.com/research/language-models-can-explain-neurons-in-language-models]

# # # # The Clockchain

The Clockchain is a conceptual data structure that leverages the decentralized and immutable nature of blockchain technology to construct a socially validated, universal chronology. Each node is validated through a Proof of Time mechanism, analogous to Proof of Work in traditional blockchain systems but focused on time-based activities verified by the user network. The Clockchain introduces a "Deep Time Index," an organizing principle akin to a database index or a blockchain's Merkle tree, which ensures efficient access to and retrieval of time entries. The Clockchain uses a time behavior modeling component referred to as ADPRS - Asymptotic, Duration, Period, Recurrence, and Spread variables. This component takes inspiration from waveforms and synthetically expresses recurring patterns of activities.

[https://mirror.xyz/seanmcdonald.eth/rxr1BO\_6D\_59N9I1hpUYM4u7IDpMWLeJ4Qtbu60wJtw]

# # # # Tree of thought:

This is a new framework for language model inference, introduced by Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan. It generalizes over the popular Chain of Thought approach to prompting language models and allows them to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action​.

[https://arxiv.org/abs/2305.10601]

# # # # Activation Engineering

This whitepaper presents a novel approach of "activation additions" for influencing the output of GPT-2 language models. By incorporating specific activation vectors into the model's forward passes, we can direct the model's output towards desired themes or contexts. Empirical assessments reveal that this method can effectively steer GPT-2's performance, as evidenced by decreased perplexity in context-specific sentences, without compromising unrelated outputs. This method gives rise to the concept of "activation engineering", a low-overhead, runtime approach to model steering that augments traditional prompt engineering and finetuning. Furthermore, our results provide intriguing evidence of linear feature directions within language models and suggest that this technique can modify the objectives pursued by a network during inference, offering a new avenue for controlling AI behavior.

[https://www.alignmentforum.org/posts/5spBue2z2tw4JuDCx/steering-gpt-2-xl-by-adding-an-activation-vector]

# # # # Soft Prompting

This technical whitepaper presents a novel approach to training large pre-trained language models, dubbed "prompt tuning". The authors offer an efficient alternative to conventional fine-tuning methods, leveraging tunable soft prompts to condition models across a multitude of tasks. By attaching optimized vectors to the beginning of each input and only updating these vectors while keeping the core model frozen, the method achieves comparable performance to model tuning but with significantly fewer parameters. Experiments demonstrate prompt tuning's resilience to domain shift and improved generalizability, showing promise for more efficient utilization of large language models in diverse applications.

[https://ai.googleblog.com/2022/02/guiding-frozen-language-models-with.html]

# # # # Fine Tuning Distortion

[https://arxiv.org/abs/2202.10054]

# # # # Arbitrary Context Free Constraints

[

https://twitter.com/grantslatton/status/1657559506069463040]

# # # # Query Language for LLMs:

[https://github.com/eth-sri/lmql]

# # # # Smallville:

[https://github.com/nickm980/smallville]

# # # # Feedback-Based Exploration

This abstract is from a scientific paper that discusses the application of a method called Predictive Probabilistic Merging of Policies (PPMP) in Deep Reinforcement Learning (DRL), a type of artificial intelligence (AI) model. The authors demonstrate the effectiveness of the PPMP method in combination with Deep Deterministic Policy Gradient (DDPG), a kind of DRL algorithm. They test their method on continuous control problems from the OpenAI Gym, a platform for developing and comparing reinforcement learning algorithms. The results show significant improvements in sample efficiency (the ability to learn effectively from a small amount of data), final performance, and robustness to erroneous feedback. The method works well with both human feedback and synthetic (computer-generated) feedback. It can also find solutions that go beyond the knowledge demonstrated in the training data.

[https://arxiv.org/abs/1903.06151]

# # # # Stochastic Bandits

The Meta-CombLinUCB is a cutting-edge algorithm that has been developed for a type of machine learning problem known as stochastic linear bandits. Essentially, this is a situation where a machine learning model needs to make decisions under uncertainty, with the goal of maximizing its total reward over time.

The algorithm is designed to learn from a sequence of similar tasks. In such scenarios, it can be beneficial to use the knowledge gained from previous tasks to perform better on new tasks. This is known as meta-learning.

A key challenge in these types of problems is balancing exploration (trying new actions to gain information) and exploitation (choosing the best-known action based on current information). The Meta-CombLinUCB algorithm uses a strategy called Upper Confidence Bound (UCB) to address this. UCB intelligently selects actions that have the potential to be optimal based on uncertainty and potential reward.

[https://arxiv.org/abs/2005.08531]

# # # # Synthetic Environments and Reward Networks

This research introduces Synthetic Environments (SEs) and Reward Networks (RNs) as new methods for training Reinforcement Learning (RL) agents. SEs and RNs, which are represented by neural networks, serve as proxy models of real environments.

An SE is a comprehensive proxy that learns about state dynamics and rewards in a real environment, effectively mirroring it for training purposes. An RN, on the other hand, is a partial proxy that learns to modify or replace rewards within the environment. The inner loop of the process trains the RL agent, while the outer loop adjusts the parameters of the SE or RN using an evolutionary strategy.

Although initially training an SE proxy requires more interactions with the real environment compared to directly training agents, once the SE is learned, it can train new agents without any interactions with the real environment. Importantly, these trained agents can perform tasks with fewer interactions while maintaining the same level of performance. This is achieved as SEs learn informed representations that guide the agents towards relevant states.

[https://arxiv.org/abs/2202.02790]

# # # # Instantiating an Agent

[https://www.lesswrong.com/posts/bfsDSY3aakhDzS9DZ/instantiating-an-agent-with-gpt-4-and-text-davinci-003]

# # # # Synthetic Environments and Reward Networks

This research paper introduces Synthetic Environments (SEs) and Reward Networks (RNs), which are represented by neural networks and act as proxy environment models for training Reinforcement Learning (RL) agents. These concepts aim to provide more efficient and effective training for RL agents.SEs work by learning about the dynamics and rewards of a real environment, while RNs augment or replace rewards. Once an SE has been trained, new RL agents can be trained without any interactions with the real environment. The research suggests that these SEs achieve this by learning informed representations that bias the agents towards relevant states. These proxies were found to be robust against hyperparameter variation and could also transfer to unseen agents.

[https://arxiv.org/abs/2202.02790]

# # # # Signal Temporal Logic

[https://arxiv.org/abs/2212.01022]

# # # # A few Iron Laws of AI personas:

- They should help humans achieve their goals.

- They should not be facsimiles of real humans.

- They should not simulate extreme emotional states to induce emotional reactions in humans.

From Brian Atwood @batwood011

Acknowledgments

Thanks to GPT4 and GPT-3.5-turbo. We would like to thank the AI research community for their ongoing contributions to the field, which have made this work possible. We also acknowledge the potential ethical and societal implications of this work, and we are committed to conducting our research in a responsible and transparent manner.

Rakshak Talwar was instrumental in the idea that the quasi-tensor like tessera-CTs should be modeled as wavelets with non-linear post-processing, not linear wave formslike a synthesizer would use, per se; as well as the conversation leading to the realization that we could train an LLM like GPT4 on the output of the synthasAIzer llm, potentially dramatically reducing hallucinations by better grounding the re-trained system.

Thank you, Courtney.

Legal Disclaimer

Disclaimer: The contents of this whitepaper are intended to convey general information only and not to provide legal advice or opinions. The contents of this whitepaper, and the posting and viewing of the information in this whitepaper, should not be construed as, and should not be relied upon for, legal or tax advice in any particular circumstance or fact situation. The author of this whitepaper is not responsible for any errors or omissions, or for the results obtained from the use of this information. No representations or warranties, either express or implied, are made as to the accuracy or reliability of any information contained herein. Readers should contact a legal professional for appropriate advice pertaining to the legal or tax implications of any particular matter or transaction. This whitepaper is for informational and discussion purposes only and does not constitute, nor should it be relied upon as, legal advice, a proposal or offer to perform services, or an official statement of fact or policy by the author. The ideas presented herein are the personal opinions and insights of the author and do not represent the views or policies of any other individuals or organizations.

License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) this work for non-commercial use, provided you give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

http://creativecommons.org/licenses/by-nc/4.0/

The essay that follows is an attempt to bring science fiction - storytelling about a data structure - into an operational, open source software project. As a design or set of specifications, this essay is insufficient and incomplete. This is a story about a data structure. This essay proposes new software tools for the data structures we use for time itself, including the replacement of timestamps with graph objects that have universally coordinated durations, and the establishment of Clock Zero, a decentralized, singular time anchor, and a normalizing time format translation system called xpayz time. All of these ideas emerge from one belief: institutions will not eternally control time itself.

The clockchain can be for time what Ethereum is for contracts and governance, or Bitcoin is for money and currency. In this way, it could be a universal nuerome, the way Ethereum is a universal computer and Bitcoin a universal bank. The nuerome reveals our true nature: we are a set of individual souls who are also synchronized in layer after layer of subgraphs of mimetic memetic superorganisms, including the prime graph, an emergent, singular superorganism that integrates humans, and our digital analogs.

The following quotation does a succinct job of explaining what inspires me about working in the time space. My next steps for the clockchain are to circulate this essay, then host group chats and calls. After talking to prospective collaborators, I intend to setup bounties for multiple developers to participate in building a prototype. This is a side project from my work at Sundial, which I intend to be the first adopter of the clockchain into an app.

Sean

Truth is the daughter of time, not of authority.

Francis Bacon

Act 1: It’s time for a distributed consensus approach to time.

The scaffolding of the human experience is our contemporary consensus structure for time.

At first, consensus was endogenous. For hundreds of thousands of years, Homo sapiens roamed as nomadic tribes, fully regulated by time of day as a framework of circumstance. They did not have a synchronized point in measured physics. The safe time of day to eat was when there were no animals around, nor lightning, nor hungry Homo sapiens there to compete for food — not noon. Lunch couldn’t be for 60 minutes, as minutes were yet to exist. When to go to the bathroom? At urgency, again checking circumstance. To sleep? To mate? Mostly when the weather and sun allowed, and — again — circumstance.

Then, for millennia, agricultural clocks regulated human behavior. Crops and livestock, as well as herds to hunt, regulated society. Some cultures were still regulated by these patterns when my grandparents were born, although they become fewer by the year.

Time was something of a magic phenomenon, both irrefutable, and unregulated. Over time, civilizations started to use the giant lunar clock in the sky, and to make use of shadows to divide time. Sundials emerged. Then hourglasses. The Mayans and many other cultures built elaborate temples to align with the patterns of the sun, and the days within. From Stonehenge to the Temple at Karnak, solar worship reigned eternal. Thousands of years later, candles were mass manufactured in such a way that they burned for exactly an hour, or exactly eight hours. When the candle was out, the time had passed.

Soon, control of time became power. Over a long history of different time models - including ones where hours change in length based on season, and other localized mechanisms for tracking time - great leaders emerged in institutional structure, and they took control of time. In the end, pharaohs and kings, railroad companies and satellite companies, religions and governments — they are all participating in an eternal jiu jitsu match, grappling with one another over who determines what time is. Even in 2022, hundreds of legislators debated changing time for hundreds of millions of people, at a random point in the shared timeline that small group choose and mandated. Now, in 2023, there will be no more daylight savings time in the United States, and subsequently in corporations around the world.

All of that sits on what is so obviously true from a first principles perspective: time is what we make of it. This essay is a proposal to make time distributed. Distribute consensus has emerged as a fundamental tool on which humans build the future. Time belongs on a set of distributed tools, not in the volatile hands of whomever is currently in power. Those who provide the mining services, the code, and the governance over the clockchain will be responsible for establishing a new era. We will be the keepers of the timeline, if you will.

Caesar’s disassociation from the moon.

Caesar’s most enduring feat was, perhaps, to disassociate us from the clock in the sky. There’s little evidence that, prior to the change to the Roman lunar calendar, anybody had given thought to making an abstracted human-designed calendar that didn’t recognize the lunar and solar cycles. In 46 BC, the Julian reform of the calendar was introduced. We have used an algorithmic leap-year-accounting calendar ever since.

Why did he do this? Perhaps because he thought it was scientifically more valid, but it’s more likely that it was to seize control of all of the systems of documentation, especially in ports and receipts. By changing the calendar, his empire’s workers all knew if they were dealing with someone conforming to the empire or not, based on their calendar. If it was 12 months and 365 days, they were already acculturated; if not, they soon would be changing calendars, perhaps at sword point.

Caesar essentially forced everyone to switch from indigenous time to government time. Like forcing a switch from linux to corporate-controlled operating systems, a time time model forces every single interaction for everyone everywhere to adapt and comply, or be left behind. Or worse. History may be written by the victor; but the future of time itself is also written so.

Calendar reform - and the battle for determining time and schedules - was just getting started. Rome would prove to be the garden not just of Caesarian time, but would also yield, in the exact same place many years later, Pope Gregory VIII, who would change time yet again. The Gregorian calendar is the twelve-month leap-year-corrected calendar we mostly use today.

This reform had the astonishing feature of deleting the days October 5th through 15th that year to make the new leap year model work. Everyone complied — and showed up for mass on time that weekend, right?

Today, a rich Wikipedia page is filled with calendar reform efforts. Gurus and zealots, religious leaders, and scientists alike have all tried to solve for time. But they never solved for adoption or adaptation, and we mostly use the Gregorian calendar in the Western and financial worlds, and just two or three other principal calendars, perhaps most notably the Chinese calendar, as most other alternative calendars are religious in nature.

We are at Kuhn’s crisis.

In his seminal work, Thomas S. Kuhn documents how science changes, from his perspective and data set. It’s a sequential process where drift from the original model and taxonomy used in a type of science - or in our case, all science - has fractured or faulted. Time theory as we apply it today is riddled with faults.

For example, who among us believes that humans on other planets will agree to use the Gregorian calendar, forever adapting their way of life to terran time? We know it won’t work in any sustainable way. Days on Mars need to be days on Mars, not some absurdity where settlers wake up irrespective of local night and day to stay in sync with a planet so distant it takes minutes of delay to communicate from one to another. They will have their own time, and the time we need to prepare for this reality hastens, as humans will soon live on alien planets themselves.

It may seem like cell phones and GPS have given us global synchronized time. They have not. They’ve given some corporate boards and military satellites high centrality in information networks. Could your cell phone provider change your world by changing your clock, to, for example, make a minute 59 seconds? Yes, the could. That is not to say they would, but to highlight only a handful of humans have an actual mechanism to track time that doesn’t depend on an institutional authority disseminating it.

There are multiples times at play today. There is Lunar Standard Time for things on the moon. There are multiple time models for satellites, all under the categories of time bending realities stemming from gravitational time dilation. Time is faster further away. Movies have made this famous by showing a year on one planet being a day on another, and the sense of emotional loss that stems from that if your loved ones age a year while you spend a day away.

We have lunisolar calendars. These include the literal moon and sun; as well as a vast range of religious models of time. The Chinese calendar; the Solar Hirji calendar, an Islamic calendar; the Hebrew calendar; the Celtic calendar; the Ptolemaic Egyptian calendar; the Babylonian and Ancient Macedonian calendars; the Japanese, Tibetan, Mongolian, and Korean calendars; and dozens more over history.

People still use sundials.

Farmer’s almanacs, folklores, and indigenous parables all offer unique models of time. Some go so far as to imply there’s a day after the day squirrels start making louder sounds while chewing acorns, and that’s when you should harvest fall crops.

Video games have their own time; and own dimensions of time. Some games are emerging with features like clocks that respond to how fast you move — further breaking and bending the rules of time.

Amidst all of this, most computers are doing their own thing on synchronizing time. They check in, phone home to corporate time synchronization. Servers handshake time signatures. But the rate of loss and data problems is incredible. Time series management data is a necessary ingredient in sensor and IoT-based business models as well as many of the emergent structures within AI research. New time models will open new doors in AI, and we’re in an early stage of working at the scale required to address time itself.

Time reporting is a mountain of problems. Are employees honest? Are devices working right? Do honest people using properly functioning devices understand the time logging software? Are incentives such that time management correlates to time tracking? Does any business of a threshold scale of growth have thorough knowledge into how time is spent?

Races are won at the time when using an analog film race camera, sometimes still in use, based on a mind-bending intersection of physics, photography, and timekeeping where the film and the photo and the photons collaborate to mark a bit of spacetime, completely un-synchronized to any other global source of time, but commemorated by light trapped in special paper.

Are the clocks in your house right? What about after the power goes out? Do you set them all to the same millisecond? Do they line up with your neighbors? If you live on a state boundary, does someone in shouting distance to you live in a different time zone?

How much of your time tick tocks away each day, mindlessly scrolling social media?

We’re stuck in the era of fuzzy, disparate time models. We must unify our time model to a single time model to be prepared to be solar system citizens. We must fix our broken model of time to enable people to be as they blossom, not as they coordinate with precision.

That’s not to say precision isn’t important. The model of time proposed in this essay would be the most precise ever made.

That’s why it’s time to mimic a scientific revolution. Time for a new time.

Our time models fails to reconcile identity.

New models like DAO’s will bring some fairness to how systems are formed; but still provide a highest-bidder-takes-the-prize approach to governance, in many cases. Validating that a human did things is the real identity, even beyond biometrics and institutional verification processes.

A new time is coming. Perhaps a new era, culturally, but I mean literally — a new model of time itself.

Time is a permutation, co-evolved as an idea, a memespace, perhaps, that with Homo sapiens provides an invisible structure to our mental models. Time surely (?) passes, and we know that to be true intuitively; but we don’t typically apply a meta scrutiny to our day-to-day interactions, questioning whether our model of time is the right one. Our tools reinforce this willful ignorance of obvious faults. Calendar apps don’t offer a “work off the timing of the lunar cycle instead” button for Tuesday meetings.

“Sally, can you meet at sunrise on the new moon? We have a new HR software system I want to train you on.” It would be fun, and funny to most. To some cultures it would, perhaps, feel just right.

As a child I spent time on the Dineh reservation in Northeast Arizona, and things started when people got there. I found it “weird” and confusing at first. Now, a part of me still longs for that way. It, too, had it its faults. Try repairing a roof whenever the crew gets there; dealing with stores that open whenever the owner arrives that day.

This tension may appear to be part of a battle that’s already won. We appear to be on track to use the Gregorian calendar forever and ever.

But on a first principles analysis, we know it won’t be forever forever. Our models of time have hardly held on for generations, being devoured by more efficient mechanisms and tools of time. The sundial was replaced by the church bell; the bells by clock tower; and now nearly everything by GPS or time derived by the rate of decay of radioactive materials.

Today, block height or start / finish blocks signify time as much as institutional time. Many argue that bitcoin is the best way to track time; I would position bitcoin as a principal validation node in the clockchain + DTI.

The design requirements for something new.

Distributed time.

New and extraordinary things have emerged from software. Bitcoin has established a reserve currency that is plainly a revolution in plain sight. Ethereum has established a global supercomputer that can run on wildly disparate types of hardware and be programmed by anyone with very minimal functional society around them. Distributed media is going to enable censorship-resistant information sharing. dApps will make our virtual selves the earnest possessors of digital goods.

And now, it’s time to do for time what has been done for money, computing, and media. It’s time to distribute time, taking away the queen’s Greenwich, England time.

Honesty about time as a scarcity mathematical model.

A grand trick of the calendar is to present a user interface that treats a scarce resource as renewable. Need more time? It seems like you’ve got tons in here, there is nothing here. That’s not true. You have to go to the bathroom many, many times during those open days. You don’t have most of that free time. It’s on you to record every single detail if you want to see your real time.

Time is not renewable. It’s scarce. You get a set amount of it, and it’s shockingly predictable how much you have, within a modest range of error. Every hour you spend is a draw-down from a set bank account. Your calendar is a time budget.

Structure to support the extremely long list of first principles derived realities about time, activity, and human potential.

You aren’t going to live to 7,000. You might live to 200 in a flight of imagination. You probably aren’t 3 years old reading this. You probably have about 15-50 years in front of you, if you’re reading this. If you are reading this in the future, I hope you have 200 years in front of you. That’s still not much on a cosmic scale though, and is easy enough to map in a database.

This is true for actions and activities, as well. Nobody has eaten for 900 hours consecutively. Nobody has meditated for 4,000 hours consecutively. Women don’t have babies in their late 80’s. Men don’t finish triathlons at the age of 18 months.

Same for relationships: no couple is married for 2 minutes, nor for 200 years.

A sidenote on fate, determinism, and free will:

A natural consequence of modeling global first-principles time models is that the limits of free will quickly conform to a parameterized and filtered set of free will options. At my age, I cannot become a fighter jet pilot, nor can I go back and spend more time walking my kids in a stroller when they are tiny enough for strollers. We have free will within the limits of physics — and millions of operational nuances.

We should have fun with that, and perhaps we extract a new philosophical model based on evidence at scale from the late stage clockchain?

It’s all the things we can really do, not all the things. All the possible realities, not all the realities.

Proof of Human Time: Low trust and no trust validations of time.

PoHT

Which is more valuable: a social media page about your professional identity where you say you worked someplace for 5 years, or a graph-validated quasi-Bayesian model of social proof verifications that someone showed up at an office for 5 years, with twelve first degree validators, and three types of paid independent validators? Math is on the side of the latter, in a dramatic way.

The clockchain has to have Proof of Human Time (PoHT) to create an honest and earnest record of time that can last in perpetuity for all future time. “History is written by the victor” can fall quickly to PoHT of our shared objective reality.

The Deep Time Index: a central record of all known time.

Because humans are applying an artificial, human-contrived time system on top of what we call Planck lengths of time, not monitoring and tracking something that actually exists, we need a central ledger of what we call time. From millisecond 0 to millisecond omega. (1)

That record can include entries from very specific to very fuzzy. Perhaps a quantum physics lab records a reaction that took place for 100 milliseconds, and records that time range. Perhaps a colleague confirms someone does a task for 100-200 hours. That is also placed in the DTI. The end user accessing it can determine what types of events and what level of time granularity is necessary.

Human actions will be the most common entrant, so the time in the near future and near past are the most important to early adoption and success. People can record that they did something for 100 hours, like a training program, and get it verified by colleagues. Because that record references the DTI, it is clear for eternity when that person did that thing, with very high statistical likelihood that it’s true. It can easily be cross-referenced with other data from the DTI to check for credibility. Did this account ID report contradictory simultaneous accounts? Then it’s a bogus record. Many such exceptions can be made, with a very important one being fraudulent creations supported by scam networks who serve as validators.

Validators and reputation.

The clockchain infrastructure requires a different type of validator. There is a classic blockchain validator, which is checking the math of the records, system integrity, etc. This is critical for the clockchain, but more types of validators are needed.

This is a monetization engine for the clockchain. Validators, in many cases, can get paid or otherwise benefit from supporting the clockchain. Software with API’s like fitness tracking systems can offer to serve as a validator for exercise. This wouldn’t enable the user to report to the clockchain just based on that validation, but instead for the user to report using another person to validate first, then using this API as secondary support.

Paid validators must include formal time tracking. Colleges and universities can validate a student’s time in a lab. Employers can validate employees’ time. It should be that objective measurements can serve as validators as broadly as possible. If a marathon management company can push data from their tracking system and stopwatch system so runners have eternal proof of their time spent and time achieved, they should be able to do so as easily as possible.

Further paid validators could be identify verifiers in the real world, who attach private but verified identities to accounts.

Before any of this, the most simple validation is asking one other person to validate that you spent that time that way. The system has to start there and be based there.

Validator reputation ranks.

There is good reason to do reputation ranks, perhaps both Ethereum miner style, and Yelp style.

The fee structure could, perhaps, encourage good reputation. The better the reputation the more pay you get? Too simple? Maybe a model like the higher your reputation the higher your power to pipe revenue with autonomy, including some to yourself. i.e. you get paid X amount of ETH to validate and store in a blockchain distribution all graphs in the DTI. But then you get a pipe of revenue to control, perhaps to reward other users, to fund recapitalization and investment cycles, to increase hardware capacity, stake for future protocols, etc. So the model is reward persistently, and direction and management progressive to reputation.

Bounties on validations.

Imagine a situation where a clockchain entry supports a trigger that pays a bounty based on a certain validation. Perhaps an employer pays 1 ETH to an employee who gets a personal trainer to validate 100 hours on the treadmill, with the secondary validation coming from the treadmill app account?

Time apps. tApps.

tApps will emerge as products that can write to or read from the clockchain.

Imagine a job interview for a master carpenter position at a prestigious Miami design firm, where the employee will handle thousands of dollars of woodworking per hour, installing cabinets in yachts. Two candidates with the same basic qualifications apply. The job starts tomorrow, due to someone quitting by surprise yesterday, leaving a crew two short, not just the one position that was open. The employer receives a resume with references from one candidate. The other candidate provides a clockchain entry validating 10,000 hours of woodworking with dozens of validators. Which person would you hire if you were that manager and had to hire someone right now?

tApps will enable low-trust and zero-trust interactions that require that someone spent time doing something; or that some time was spent somehow. Many types of calendars could exist, ranging from payroll problem-solving to altogether replacing most training that isn’t tested, like corporate training where there is no final quiz or exam. Beyond those practical and enterprise use cases, there are infinite benefits to science.

Imagine a geologist trying to understand what was happening in another part of the world at the exact moment a rare worm fossil was buried with volcanic ash in the dirt in its belly, preserved that way until today. With the clockchain, a query tApp will be able to pull all geological events from the bracket of time ranges possible, and get ever more specific about how things are all interconnected.

tApps in the legal domain will be a full-fledged revolution. High validation alibis, records of business transactions, proof of acceptance, and so on.

I can imagine the most powerful apps combining government-backed contracts with the crypto-backed contracts that require clockchain validations to trigger bounty payments. These tApps will replace a lot of HR work, for example.

Universal converters. It supports cognitive diversity about time.

There’s nothing about the clockchain and the Deep Time Index that requires the inputs to be stored in Western / Gregorian time. A popular tApp will be a converter API that takes any time format and puts it into clockchain / DTI format, and vice versa. Converting from Hebrew time to Brooklyn time should be a snap; converting from Honk Kong Chinese dates to San Francisco Gregorian dates will just be an API call or a simple function in a standardized open library.

Space ready. XPAYZ time as a reconciliation algorithm to find matching time points on other clockchains in the future.

To reconcile the clockchain with the variations in time across the galaxy, and in this example, the moon, we need a clockchain-compatible algorithm set.

A core model will be XPAYZ time, which can convert lunar time to earth time easily. In theory, it handles any two disparate time modalities, ideally normalizing time units, accepting for and accommodating Planck lengths, but maintaining the dramatically more workable and earth-system-compatible (99.9999% of all systems) milliseconds as clockchain default. In that way, I dream of the clockchain as the lingua franca of time for all time, and hope you want to be a part of starting that as much as I do.

The name signifies this: x = payz

X = Time in a new format.

P = Permissions. Inbound data from one or more sources and outbound data from one or more sources may have different permissions systems. For example, a user may encrypt their clockchain entry using plain text. A XPAYZ hook will check all of this before decrypting and sharing with what may be a public entry.

A = Adjustments. This references as set of adjustments in time scale. This library will grow over time. One example is converting “days” to “milliseconds” which could include multiple types of days. Another adjustment could be going from lunar time to Earth time; or going from satellite time to ground time. Lag is a classic adjustment, with minutes between communications between Earth and Mars, as a practical example. Adjustments can be called in sequence, and should be able to be forked.

Y = Why? Why are you doing this operation? Determine the type of output. If Z is in Gregorian time, Y would include a variable to convert to Chinese lunar time, for example. This includes category taxonomy and notes. It must include rules for formatting final results, from am/pm vs. 24 hour to text encoding type. This should be called from a library of results.

Z = Ze’Time. The initial clockchain entry or query set.

Any entry can be run through a XPAYZ time adapter and come out in a new entry envelope that presents the entry in a different format. These are not new clockchain entries, but functionally child nodes with only limited access possible.

From the defense industry to day-to-day calendar operations, a core part of the clockchain protocol has to be shortcake XPAYZ time codes that enable rapid implementation in functional sandbox and production code stacks, worldwide.

(It may be that the clockchain and DTI have to be forked in the future to move to some better measure of time. In this case, the whole system will rely on a colossal XPAYZ time conversion. As such, it should be considered not just a practical tool, but the source of portability that protects the whole system.)

Consequence attributes. From homo economicus to PoHT powered economic models that are honest calculus, not clever statistics.

A sub-node of a clockchain entry is a consequence attribute. For example, did a certain amount of time earn a certain amount of money? While this could be a bounty, the meta data tracking, for example, how much was earned per hour, is a separate outcome. Tracking emotional outcomes, financial outcomes, and physical health outcomes enables PoHT to be tied to a new type of learning about the consequences of our behavior.

Forking behaviors increases our rate of knowledge attainment and effective mimicry.

We are mimic monkeys and need to be able to fork each other's effective behaviors to our calendars the way you patch a new library into code.

We live at a time of profoundly increased needs, expectations, and capacities. But we need the healthiest, most open approach to mimicry we can have. That involves transparency about allocation of time and capital. Clockchain and blockchain, arm in arm.

Accessible to all.

Layers of clockchain entries must be private and only visible to key holders. Key holders should essentially have multi-sig access to secrets within public entries.

At the same time, public entries must be accessible to anyone using the protocol, any time, any place.

Generates fees / Self-sustaining

Validators, commercialized tApps, and submission / mining fees are all required for the ecosystem to flourish.

Managed by a DAO. The keepers of time memorial.

The clockchain should be managed by a DAO. Those members will probably use an ownership coin independent of the clockchain to regulate governance.

That governance should include how much time the member has filed working on the clockchain on the clockchain, as well as how much they actually utilize the clockchain, and how much use they bring to it. It is likely a centrality score that best indicates that user’s moral rights to stake and control within the DAO.

Perpetual, emergent and self-updating.

Because the clockchain will last for all time, which could be what is to a human eternity, it needs to be perpetual. A grid down or another problem should not disrupt it any more than the blockchain.

Emergence may be increasing it its complexity, if we are in fact in a system with assembly theory at its core. Conceivably, the clockchain will have some impact on our notion of time itself, and thereby must continually update to reflect our model.

The clockchain code should consider that multiple clockchains may already exist in other galaxies, and that we may need to recognize seniority in integrating our clockchain with an obviously superior version. Its also possible ours is the superior and will need to ingest a different form of data, such as time measured at the molecular level rather than the conscious level. In that case, individual atoms experience linear time as perpetual reincarnation and conceivably different rules apply.

Practically, what this means is simply that the clockchain is better off taking an unstructured data approach to secondary data appended to time, and taking careful account from day one of how to convert to Planck-length time. This is probably a long-term collaborative project to handle floating point complexity at the hyper scale of Planck numbers.

As an important note, the clockchain should be prepared for the advent of mathematics that fall into three domains that could impact time storage: the progression of mathematical theory towards equivalence instead of equality in handling numbers, potential for quantum projection similar to Randonauting for geolocation for time and calendar functions, and radical changes in perception of Deep Time that may be enabled by the James Webb Space Telescope.

Vetted by consensus before implementation. Test chains from day zero.

An advantage of an open source DAO model is establishing vetting and consensus structures.

Test chains have to exist, and side-chains may be possible. Perhaps there is an incentive to become a verified side chain through some massive action, like a thousand users verify that you should be verified?

Fraud detection and moating by doxxing validators.

While fraud detection will be essential; it’s also possible that the nature of the clockchain and Bases-like emergent eigen-structures is that it’s very hard to commit fraud programmatically if a certain amount of users and validators are doxxed. While this enables identity detection on the clockchain, in theory, it would be non-obvious and a hack of the system to do so. Many validators will want to be doxxed, such as universities, or martial arts training centers.

Open source, with technical explanations for lay people kept persistently up to date.

The integrity of the clockchain requires that highly skilled technicians maintain its code, it requires that people like lawyers and judges, all types of professionals, even spiritual and community leaders — that they all trust the integrity of the system. Communications is a core tenant of maintainership here and things like real-time visualization dashboards should emerge as early tApps to promote clear understanding of the benefits of the clockchain.

Open API. Stable sidechain duplicates and archives.

It’s critical that the clockchain core software provide an open API. From XPAYZ time conversions to tApps, the API is the system.

What’s different is it probably requires a secondary service to convert from clockchain-based time protocols to all the others; and that probably has to persist across all variables. Similar to an ETH to USD model, but with dozens more parameters, thus orders of magnitude more diversity in possible results.

In this way, huge amounts of processing can be done client side; but many things need to be processed in advance, especially to optimize computing time and to avoid negative environmental effects from brute force processing of records in a blockchain.

Perhaps API’s rely on sidechains which tap the main chain from day one? It seems dramatically more computationally efficient.

A global time ticker.

The system must provide a persistent, perpetual, free API to let anyone anywhere get the time, at all times.

Ability to vault time. Protect yourself with a contract for free time.

Imagine clockchain-based contracts, combining Ethereum and government contracts that synchronize to protect your future time by giving it a contract to yourself. Surely, no employer can take that from you?

Meme into production.

No protocol or service can thrive without having a personality, in the new market to come. The clockchain is full of jokes. From referencing rappers famous for wearing clocks as necklaces, to stodgy old white dudes whose clock chain is attached to a pocket watch — it’s a branded stack that can have fun with itself.

Mindset requirement: we have plenty of time to answer the questions.

Creating the permanent record of time memorial will take some meaningful percentage of that time itself. Maybe it’s 1%. Maybe we can get it down to .001% or even small enough to see meaningful gains in my mortal lifetime. In any case, it is an inevitable technological step on our path to wherever it is we are emerging.

So, to conclude this introduction, I leave with this question:

How can clockchain entries be bulk populated using historical data, so that we achieve as much of historical data recording to the clockchain as we can in our lifetimes? What types of AI’s can do that?

Act 2: How the Clockchain will work functionally, and faux code snippets.

There’s a long history of clock chains. Be it the heavy, custom-weighted and gauged clock chains of a bell tower, controlling timing themselves; or the chain holding a precious timekeeping device worn by the privileged of certain generations, their pocket watch.

Proof of Time requires MG + Clockchain Submission

A Bayesian graph of how you spent time. You - or someone else - says person A spent time on Activity Z. You may be person A, or not. You say you spent 100 hours working on Sundial. Then you send that to me, person B. I look at it and I either say no, suggest a change, or I approve. If I approve, I can also send a request to my wife, person C. She validates not that you spent that time, but that I have spent time on the same thing, my 1000 hours on Sundial.

So we end up with 4 nodes and 4 edges, a complete triangle with one branch. That is a minimum eigenvector compounding into single point of centrality, or in broad conceptual terms, a sort of cousin of a minimum Bayes graph; which in this case, is the activity of working on Sundial.

(This raises the question if our data structure needs a non-person actor -- a company basically? It's not an Activity or a User, although it could kind of be a fork of a user?)

Once a minimum graph (MG) is established, it serves a Proof of Time (PoT). The broader the graph gets, the more powerful the software grows, and the less likely it is that it's untrue. Of course you can commit fraud, but it's very hard. We can use ETH wallets to validate, for example, that it's a person who isn't making tons of entries.

I believe we could pretty easily build an index of all time. Start with say 10,000 years ago and build up to 10,000 years from now. Build a central index of time, and have MG's attach to a time period in a single, centralized time database. Like Wikipedia, a person can go in and say that dinosaurs existed from x milliseconds to y milliseconds; another can say T-rex was in North America from x to y; another can say a sub species was in California from x to y; a paleontologist can say a specific skull was from x to y.

It's 6.31152e+14 milliseconds, which seems a storable amount of data these days. By having a single time record and timezone irrelevance, it slowly becomes the de facto record of time. The more MG's point to the Deep Time Index as a reference of shared objective time, then put time zones and other human adaptations on top of it, the people have to use it. Eventually a whole legal system could be built on high validity MG's stored on the clockchain referencing a shared objective Deep Time Index.

A lot of people say Bitcoin is time, and they are right, sort of. But, really, time is time.

The Deep Time Index starts with our current range of time.

While clockchain MG’s enable entries from any point in time, the DTI features need to be scoped to the functional time that’s relevant to most user. That is, in most cases, within the past year or next year; or within the past lifetime or next lifetime. The range of use cases - practically - outside of those ranges become science, speculative investment, and creative applications.

In this light, the DTI will need these initial features:

The need for dti_Prime to be established, and operational goal of clockchain + DTI development.

Like GMT, dti_Prime is a reference point that other references points are based off. Just as most software uses GMT+/- systems to track time zones, then appends labels to those, dti_Prime+/- allows a single point of reference.

dti_Prime is also clock height zero, when that millisecond arrives — sometime soon.

Maybe Clock Zero should take place at some specific moment, like a solar eclipse?

The Deep Time Index is, in part, a derived, emergent taxonomical structure.

By providing and updating - likely via popularity contest algorithms - the taxonomical structure of the MG labels, the DTI can first analyze what people are submitting, then navigate disambiguation by offering an emergent definition called in real time to clockchain queries, as well as the initial taxonomical label and the user written label, should it differ.

Implicit integrity check.

duration_ms == stop_anchor - start_anchor

If that doesn’t computer, it’s a corrupt entry. This is one of many such back-checks the systems needs.

Time allocation overlaps as an integrity monitor, and as a another model derived from the DTI.

Clockchain MG’s need to overlap. When we drive somewhere and listen to music, we are both driving and listening to music that whole time. Each MG can have precise integrity and overlap.

Because of this, the DTI has to be able to reconcile how much time an activity takes. In the example of driving and listening, let’s imagine someone who is active in the military reserves of their nation, and needs to listen to audio updates provided by squadron leaders, in conjunction with mission briefings. These “internal podcasts” have to be registered on the clockchain for tracking, and a convenient way to do that is carpooling with other reservists and listening on the way to work.

The ability to discern that both actions were possible, and to what extent they each consume time, is a key abstraction from large scale clockchain data; and perhaps, and key set of guesses to calibrate the system prior to launch. In this case, it’s plausible that the recordings were listened to, the reservists inter-validate they participation, and for mileage reimbursements, all also validate drive time on various employer accounts each year. Is this fraud? That’s unlikely, for reasons already established.

The DTI needs to create multiple feedback loops to assess each type of activity in a broad taxonomy of activities. Activities in the DTI themselves need emergent boundary conditions, established by a combination of statistical patterns in the data, and logical boundaries established in queries trained against the DTI, as well as its meta data.

ADPRS format: Waveforms as precision expression of time behavior.

Because many activities occur on a regular basis, or some recurring pattern, the clockchain data schema enables an object to drill infinitely down on the specificity of a behavior. While a user or developer could, in theory, put a separate entry for every time they do yoga, it would make the DTI wildly inefficient, both practically - especially for queries - but also from an energy use and computational (thereby also real) cost basis.

In the example of doing yoga for a year, a clockchain entry might best be expressed as something that took a year, with the start and stop times the beginning and last class of this activity. Rather than store every activity in between, the schema uses the following structure to record recurring behaviors:

Asymptotic (A) variable

A represents whether or not a behavior stops; and if so, on what curve. Some behaviors, like breathing, take place at a “1” — meaning that they last until your final breath, without ever stopping. Some, like doing yoga for a year, have a decay to “0” because they end.

While 1 would be forever and 0 would be fully stopping when it stops. The closer to zero the score is, the more unattainable the event is, in most cases. For instance, solar eclipses would be A = 0 because when they are not happening, they are definitely not happening. Something like watching television may be A = .5 because describing watching behavior is describing something that stops, but sometimes does not.

Duration (D) variable

This is the unit duration of an instance of an activity. It can be input as a static amount in xx.xxE+y, or can be derived from the start and stop anchors. This can also be externalized, or erratic. It should accept structured exceptions, but not unstructured.

Period (P) variable

Distance is basically period. It’s how far between events is mandated by the nature of the event. For example, New Year’s Eve can only arrive once every 365 terran-Gregorian days.

This is stored as a function of how many events there, which in turn can be divided into gross_duration, the total amount of time for the graph object, which is derived from start_anchor and stop_anchor — this derives R, recurrence.

This is stored as a simple number. The count of instances.

P = x instances.

Recurrence (R) variable

Recurrence is the amount of milliseconds between each object; and holds several exception states, which is why it has to be recorded, not derived on demand. The first exception state is when the event recurs on an external pattern. This would include holidays. Some on an institutional calendar, like Christmas; and some on solar calendars, like Easter. The second exception is when the recurrence is unknown or to be determined. There are likely more exceptions.

This is recorded in xx.xxE+y format, again, with exceptions:

R = ! signifies to be determined

R = ? signifies unknown or unknowable

R = [logic] signifies that recurrence will take place when some external event takes place, for example some other incomplete clockchain event

Spread (S) variable

This is a self-reported - and possibly derived - metric of how precise the APR model is. When S = 1, the APR model is exactly right. Something happened, recurred, and stopped exactly as described. Imagine an event where two world leaders meet for one hour and never meet again, with massive amounts of n>3 degree deep validator graphs. Then, on the other hand, when S = 0, it is highly imprecise, and the mid range of S = .5 might be some reliable but faulty model. In that case, some person obsessed with tracking their behavior might record that they run for an hour a day and can prove it with their watch; but the time is all over the place, and the clockchain submission is not of such importance that each run needs to be an individual clockchain graph.

It’s unclear how to recursively derive this; and equally unclear how to derive it consistently in any such way. Perhaps the DTI v2 includes a mechanism to weight S? Perhaps it is as simple as standard deviation of the incoming data, or some other simple and friendly analysis model?

Or perhaps (S) and PR are miscategorized in schema assignments, and S should be the social ranking of the contributing node?

By our powers combined.

ADPRS is the formula to make a wave form. Similar to synthesizers, the raw input data of the activity combined with these behaviors turns time models back into the waveform that they truly are.

Calendars have told a lot of lies, but the lack of acknowledgment of the wave properties of time is, in my opinion, the most ignoble.

Graph query or AI? Both.

DTI queries could be simple; or DTI queries could be complex graph conditions. Does the clockchain + DTI require a unique query structure? Perhaps not, as many graph search models have now become - basically - black box AI functions.

This is hard to discern without testable models to learn from. New models of language and AI together with DTI data will reveal the best search models, I would expect.

Compatible time models.

The list of compatible time models is by definition infinite, since the clockchain + DTI will exist as long as things exist, in theory. But the important ones for a v0 prototype are:

XPAYZ Time - All branches

Gregorian Time - 24 hr and am/pm

Sundial Time - human-lifespan personalized time

Sundial Time formats - milliseconds —> clockchain

Time Layers - two or more time models with a synchronizing function

Time in Space - galactic, solar, and geo positioning coordinates of the timestamp

Recursive meta data.

Records its own time of recording a network and a cohort.

Cohorts are strength. The more inter-referential information the blockchain MG provides, the better. This is especially true in time stamping when the entry was made, which should be an account of the highest centrality time validation possible.

Perhaps streams of all the nuclear clocks, etc. are brought to a unified place that serves like the international kilogram for time, and that must be tapped. Perhaps it syncs across all devices active on the network at that time.

The emergence of meta-consensus time. The L1 of time itself. The timestamp to replace all timestamps.

In practical terms, we need something better than we have. What time is it? Who knows. It could be just about anything. Either the sun is facing you or it’s not right now, that’s for sure.

As the clockchain becomes active enough, and validators for “what time it is” exist across billions of entries, a new strong emergence phenomenon arises — a consensus time with statistical majority consensus for human time.

We will never perceive Planck lengths, and they are nonfunctional units of time for Homo sapien consciousness. AI’s will need to understand this. The clockchain and DTI enable that.

Eventually, the clockchain becomes the L1 for time itself.

This means the clockchain + DTI drip of the dti_Prime+x = now timestamp will be the timestamp to replace all timestamps.

Faux code -- a full clockchain graph object.

clockchain create minimum graph { 
      user_id: xxxxxx 
      user_note: lorem ipsum
      cumulative_time: YY.YYe+Z
      start_anchor: dti_Prime +/- AA.AAE+B
      stop_anchor: dti_Prime +/- CC.CCE+D
      waveform: [ADPRS code] 
      active_duration: FF.FFE+G
      anchor_validator_class: [select list] "bitcoin"
      anchor_validator_start: [secondary anchor] "block height"
      anchor_validator_stop: [secondary anchor] "block height"
      user_activity_label: [text input]
      user_dti_taxonomy_label_select: [search select]
      validator_graph_i: {matrix of validators and statuses}
      validation_score: [calculation]
      linked_user_ids: {hhhhhh, jjjjjj,...}
      clock_height: kkkkkk
      {{object_id}}: [callback from clockchain]
      {{emergent_dti_label}}: [callback from clockchain]
      {{shortcode}}: [callback from clockchain]
      {{legible_code}}: [callback from clockchain]
}

user_id: This could be many types of unique id of sufficient complexity, but it should probably start as ETH / BTC wallets.

cumulative_time: The clockchain’s core unit is milliseconds, as this is the smallest application within the short tail of time uses. of coures microseconds, nanoseconds, and up to megaHertz and gigaHertz are used throughout computer - and all - science, but the vast majority of practical applications use millisecond or larger time units. If there is an employer who tracks paid time in milliseconds, it is surely for some anomalous reason, and costs quite a bit in overhead to manage. There’s no reason a xpayz time transformation can’t take place to the main chain, should the tDAO approve the conversion, so it could move from milliseconds in the future, just in case.

The system will require a protocol for handling floating point choices, and many such protocols exist as precedent. It’s probably better to keep to “hours” on the left of the decimal and “minutes” on the right side, so a human can read it at a glance. That means transforms would have take place for each input, or that a standardized format emerges. This is probably true throughout.

start_anchor / stop_anchor: Each moment needs a start anchor. That anchor critically synchronizes with all the other entries. This enables the DTI. A microservice converting “current time” from whatever source the data stream is coming from into start_anchor and stop_anchor format will be very helpful.

The format is similar to GMT+/- time formats. Evolving from that into a dti_Prime+/- format enables consistency across all software systems.

waveform: [ADPRS code]: This data contains the waveform pattern necessary to model behaviors and time itself as waveforms. As earlier described, each variable has rules and unique components.

The format is {A,P,RR.RRE+x,S}, or, as an example: {1,2.71e1,10,3.14E+10,.5}

A = 0 through 1

D = DD.DDe+G:

P = i

RR.RRE+xe.g. 12.34E+10 milliseconds

S = 0 through 1

anchor_validator_class: [select list]: An anchor validator is the third-party reference point used to validate not the graph but the anchor. In most cases, something like a reference to an API to a nuclear clock, or block height on Bitcoin will be sufficient to record when the anchor happened on some point external to the clockchain + DTI. This ensures the entry synchs to globally recognized times, and maintains the health of the clockchain through constant comparison.

_start and _stop are stored in the indigenous time format of the validator. As such, using exclusively crypto block heights and nuclear clocks might be better.

user_activity_label: Taxonomy is the trickiest part of the DTI. While the clockchain has no requirement for consistent labeling, the DTI is built upon a taxonomical structure that allows strong emergent functions. Many of the most powerful functions of the clockchain + DTI come from seeing how whole populations spend time, and to analyze that data - even with the best systems - some categorization has to take place.

It’s critical that the user be able to have freedom of speech when it comes to submitting labels. Just as entries to all blockchains contain dark or horrible things, the clockchain may be spammed; but the incentives are low, if existent at all, for the worst kinds of things harming children or whole populations.

user_dti_taxonomy_label_select: At an inflection point of scale, the DTI can be used as a recursive self-informing source of taxonomical structure. The user can start to select descriptions and labels they agree with easily and quickly.

I may release a taxonomy to kickstart this process, which has been designed and funded by my private company. Perhaps the clockchain enables someone to benefit from the taxonomy they provide, when other users fork it they pay some fee to use it.

validator_graph_i: There are many types of validators, and each type has a simple graph. At minimum, a validator graph has a

validator_id -- The clockchain id of the validator.

validator_timestamp -- The clock height when the validation was recorded.

validator_value -- 0 to 1 value representing certainty.

validator_reputation_rank -- 0 to 1 value representing recursive reputation of validator’s use and credibility, likely through centrality scoring.

validator_method -- Initial options should be human judgment or trust, smart contract, external validation with proof, and consensus. The first representing “I said so” - basically - and the second relying on something like a photo proving an activity being done, then the last simply listing other user ids that subsequently validated it.

validator_method_proof -- For validators that have proof, either an explanation or something like a url to a photo serves as proof. It’s possible the clockchain needs file storage or some type encoded file snippets to manage proof of validation.

validator_reward -- Smart contract to reward payments.

validator_legible_label -- Human readable label or title for the validator.

validator_notes -- Because validations can be very imprecise, there needs to be the ability to explain what’s happening.

Each validator can be expressed as a {x,…,y} entry. It’s also possible the clockchain needs two types of nodes; Activities and Validators. Any user, presumably self-validating the entry precipitating their participation in the clockchain, is a Validator.

There is no reason there can’t be a very large number of validators, but there should, perhaps, be a limit on the number of validator graphs that can be linked. Over-linking the nodes of the DTI dramatically increases the computational cost of graph queries, and in that way over-linking is not helpful. Perhaps there is a statistical threshold after which more Validator graphs are not contributing much, chasing an asymptotic curve that never reaches 0.

validation_score: Because the validation graph structure is complex, a simple score will contribute a lot of functionality to the DTI. This should be a centrality score with “weighted force direction” (not exactly, more recursive, but similar) based on the compounding strength of the validator graphs. This score incentivizes growth in validator networks. Each user / Validator may have a recursive score derived from this, aggregating their score across all entries, so that the higher integrity the higher integrity.

linked_user_ids: Because many activities include multiple people, this value store allows infinite additional users to validate they participated. There must be a system for unclaimed user accounts.

(One of the most fascinating aspects of the DTI is that a person’s whole life could be mapped out by others, and with inference graphing, a full model of that person may be derived -- without them knowing the clockchain exists. That’s unlikely, but it’s plausible as a thought experiment.)

clock_height: Because the clockchain is a blockchain, it has a block height. This is recorded with each entry.

For fun, it’s called the clock height -- a reference to the clock towers built by monarchs, which the clockchain evolves past. As a metaphor, Clock Zero is taller than the tallest royal tower.

{{object_id}}: Received when the graph is submitted.

{{emergent_dti_label}}: As the DTI begins to have an emergent recursive taxonomy, labels can be appended automatically.

{{shortcode}}: Because computer programmers don’t want to starting putting complex in their databases to replace the old standby local timestamp, each clockchain entry needs to have a shortcode entry that complies human time. This is a GMT+/- time/date code; possibly two; possibly multiple.

{{legible_code}}: Because humans read human time, this simple function states the label of the graph and how many hours and how many years the activity takes place, cumulatively. For example: yoga_100_1 would signify yoga, for 100 hours, for 1 year. This references the emergent_dti_label.

//////

Missing from all of this are Miners and the data structures Miners need, including payments. That is an intentional omission, in part because it’s unclear what type of hardware best mines the clockchain + DTI; and mostly because those interested in pioneering this work should have a voice in the structure from the first moment of public discussion. I will share my opinions when the first meeting of prospective Miners takes place. The same is true for paid validators who might build a model off of validating, similar to how notaries public make their livings in the US.

//////

Minimum Graph and xpayz time API.

Because the full graph is onerous for many uses cases, especially for app developers wanting to push clockchain entries out of their existing model, which is unlikely to include so much information when they decide to do so. A minimum graph approach should be accepted, with a simplification and possibly bifurcation in post types. An “mg” is the equivalent of using an address and note pair on an ETH payment.

The best mg may be:

user_id start_anchor stop_anchor waveform

Act 3: PhD vs. PoHT

Ecosystem / Protocol

As a clockchain + DTI emerges, a large portion of early adopters will be academic. The correlation abilities of the DTI to any data set - combined with cultural comfort with statistical models invoking Bayes-like certainty, centrality, and inference models - makes an ideal use case. Many of the best developers still participate in graduate research, and researchers across all fields want to carve our their role in the crypto revolution.

As such, a great early market to approach is academic use cases. The first is to circumvent the institution and create social-graph-validated models of credentials.

The PoHT vs. the PhD.

As a thought experiment, imagine a world where only one credential can be presented on hiring for a top tier staff engineer at a big tech company. In 2025, would a candidate be better to present their PhD from the University of Maryland, or to show a peer-validated network of 3,000 hours of coding time on projects in the past six years, with all the validating nodes being students or staff of the university. Would the smart candidate provide that clockchain-verified Bayes-like history to this company built on computer science and AI research, or a piece of paper signed by an institution head?

Let’s explore why the smart candidate will use the clockchain + DTI records.

Time tracking with integrity.

It’s easy to lie, manipulate, change, or cheat about time. What percentage of time tracking is honest? On first principles, manual time tracking through paper or digital entries is rife with consequences. That, in part, is why students are tested. We credential not what gets done, per se, but a long chain of small moments of human judgment on the part of the instructor, the institution, and the student.

Because of the mechanisms discussed as Bayes-like in this essay, fraud is hard in the clockchain + DTI. Spam detection is heavily-reliant on Bayes functions to work so well, which should serve as ample proof to merit the hypothesis it will work here.

Social media sites about work are a joke.

Having a page with a glamorous headshot of yourself, listing all the places you’ve worked, amidst a sea of tech company recruiters, and AIs designed to grasp your attention — that is a solution for a better phonebook, one that has a TV in it.

Proving how you have spent time leads to an ocean of insights.

Can a person keep doing the job of mowing lawns for 30 years if they don’t know how to mow a lawn? Can a person be ignorant to how a surgical operating room works after 1,000 verified hours in one? The answers to both are likely no; the broader their graph of validation, the more certain the no.

Over time, the clockchain + DTI becomes a trustworthy source, where other platforms are built on truths that are, at best, castles made of sand.

Exciting new types of market models.

The range of new market models ranges from replacing PhD certificates to changing the way professional sports are telecast. Imagine a professional athlete’s stats including their total game and training time, as well as a score of how likely those stats are to be true.

Imagine a history channel that explores one millisecond in history each episode. The DTI enables anyone to grab all these events in a query.

Imagine a legal system where legal assistants - like stenographers, paralegals or dossier developers - have a new skill set of sourcing validators for alibis.

Imagine a calendar that shows you as a character running along a limited slice of time eternal, rather than a perpetually renewing empty sheet to fill in each week end.

Taking time immemorial to time memorial.

The clockchain is inevitable. We can - and do - record a lot of our lives and activities. Digital copies of us exist write large. Most big companies have one of most people in their markets, at this point.

Our time is recorded.

Also, we now know that all time exists. We can perceive and visualize what has and will happen, with great certainty. Thinking, social species evolved before us. Dinosaurs roamed. Supernovae will explode in the future.

The phrase “time immemorial” is purported to have emerged over a thousand years ago in English law to refer to time outside of known time. That no longer exists.

The clockchain + DTI are the inflection point of intellectual evolution where we stop pretending we can’t know most things, and begin to take account of all time.

The power of sequence.

We tend to analyze time as thought time spent has direct consequence. Going to the gym builds muscle. But really, eating the right food before and after, sleeping the right way, and so on — it all compiles to a sequence of performance outcomes, not a one-to-one set of behaviors and outcomes. It’s matrix to matrix math to look at inputs and outcomes in life.

In this way, time as a mechanism of AI in fields like bioinformatics can look not just at behaviors, but at sequences. A superstructure like the human genome could emerge, like the human action-outcome model map. But that’s probably more of a thought experiment for now, until clockchain + DTI data exists at such scale an AI can be trained to consider how this might really be implemented.

Immutable time. Immutable opportunities to learn from one another.

The library at Alexandria was burned many times. Gold has been stolen from many empires. Land, the same. We are at the dawning of a a new ear of things being immutable through scale of distribution. The clockchain + DTI offer more than a record of time. By correlating outcomes to the entries therein, maps and models of how humans succeed and fail can be derived, analyzed, and crystalized into new plans that are most effective. Everything from business strategy to meme crafting can be back-tracked to behaviors that spent time.

This protects the legacy of learning across time better than any monolithic store of information. The clockchain + DTI thus protects our knowledge, as a species, and as consciousness emergent from the stardust.

As previously mentioned, this also accelerates our ability to mimic effective strategies, further enabling the best actions at a time where environmental changes threaten mass migrations and permanent supply chain restructuring.

The clockchain, the DTI, and the multiplicity of times in the metaverse.

As time takes place on different scales, we will create infinite branches of time. New VR games use movement as a clock. Some use external clocks as clocks. Some set time on other planets.

As these experiences and accomplishments become more valuable as digital goods and avatar add-ons, as well as NFT’s and other proof of ownership models, the time spent to do them, time spent winning, losing, collaborating, even using a certain weapon or spell, does too — the need for timestamps that are smarter soars.

Rich with digital assets, and able to be up at any of the 24 hours in a “day,” the metaverse is not tied to earth time. Gamers have, for decades, played into the early morning hours, with other gamers on the other side of the planet, hitting the game during a lunch break. They exist on — for now — server time, not a central synchronized time. That server may itself synch to a centralized time, but only clocks at precisely the same altitude, and possibly inside the same stratification of the magnetosphere, are truly synchronized, otherwise the millisecond and the second are, a fine resolution, very rough consensus acceptance of an equivalence of functional time units, not an equal measure of the same thing.

VR also inevitably creates metaverse-time. The clockchain + DTI is the bitcoin to that fiat.

DAO participation and PoHT.

Many DAO’s already reward human time through provable actions. Promote your DAO - two governance points, tokens, coins, or whatever denomination it may be.

PoHT can decrease and, in rare cases, end problems endemic in the world of DAO’s. Nepotism and controlled / slow drip information release enables people to reward real-world friends and trade favors in launching DAO’s, protocols, and currencies.

PoHT can show work and commitment instead of Discord popularity.

A tApp like Sundial can show how much of someone’s life has gone to that DAO, which should be a meaningful metric, as much so as their budget.

The inflection point of Unreasonable Effectiveness of atomic networks of time data stored in synch at scale.

Each viable minimum clockchain post to the DTI is valuable unto itself as a PoHT asset; valuable to the DTI in revealing more of the shared synchronized time record; and is a slow contribution into something like a savings account, which will threshold one day to the point that things that are unreasonable to derive from one record will be easy to derive from millions or billions of records.

For example, how much time does it take to start a startup that succeeds, given the history of the founder, the market conditions, the industry type, and so on? Given one clockchain post to the DTI, it’s absurd to wager a guess. But among the billions of entries perhaps thousands of data points exist, from which a complex sub-graph can be filtered.

This sub-network continues to exist as it did with in the whole, but can be isolated for computational focus; many types of inference and prediction graphs can be used to manipulate these data from there.

Perhaps the holy grail - by which I mean object of desire that may or may not actually exist, not the thing on which we should focus - is to derive perfectly crafted products designed based on market needs found in the DTI.

What if survey participants in basic science had to validate their behaviors out-of-lab in the clockchain? What would social sciences become?

Imagine that someone has to self-report behavior in a psychology research assessment that is part of a multi-year study. The person isn’t living and working from a lab, so self-reporting of how they behave is critical.

The clockchain requirement of time report means the person has to build a quasi-Bayes graph of social proof about their behavior. Fraud patterns are likely to occur, and to be caught in many cases with basic detection.

Years later when the findings of the paper come into play, it’s not a question of how honest the participant “seems” but how high a score, but of some metric like clockchain reputation + sub-graph of reputation into a centrality score, to determine veracity of reporting.

Did a researcher actually work 10,000 hours in the lab according to themselves, or according to a three-deep network of validators with high reputations?

My favorite daydream about the magic of data synchronized on the clockchain is to root out synchronicities and make them clear. Does the social science of economics actually have the answer to a recession, or can the business school’s market dynamics research from the exact same time periods offer more clear trajectory-mapping? When we backtrace and back-propagate models that can coordinate off of a central handshake of shared, reconciled time frame, we can build a new model of research where a quasi-open centralized database exists not around subjects but just around time itself.

Immutable ledger of action.

We have immutable ledgers of money.

We have immutable ledgers of computation.

We do not yet have immutable ledger of human action.

This represents a new legal system, wholly acceptable within the current system. What once was an affidavit is now a series of six-tier deep validations of behaviors built by a defense, offering an alibi with a hypothetical nine zero’s of statistical certainty.

People can say anything. At a certain scale, the DTI limits what they could have plausibly done. In fact, it eliminates most things. A person who has never been on the clockchain and does not show up in the DTI will still have to reconcile with all the things adjacent to their actions.

For example, someone cannot claim that they were at a restaurant when no clockchain entries exist from that restaurant in the past ten years at that time of day. No record is needed to know what time they are open, past a certain amount of data.

Voter fraud would be nearly impossible through clockchain-style ledgers that include multiple identity checks. In cases like some from the distant future intergalactic economy, you are the sum of who you are validated to be. How could any institutional verification out-perform that statistical model of certainty of identity? (2)

AI time structures in emergent time models.

Time is emergent on top of a framework of priors. That network is both discernible and can be inferred, to a large extent.

Current AI research accounts for time in linear models; not a singular model. But surely the particles that make up the screen on which you read this were doing something when the dinosaurs roamed this world.

A synchronized universal time exists, we have just not started using it.

To a machine, our carbon atoms are temporarily human.

What sets of human assumptions will be wholly replaced when multiple AIs are analyzing time-synchronized petabytes of data? “Economics” will seem like a cute joke, early Homo sapiens pontificating on their behaviors with rudimentary data. The butterfly effect should be able to be put to the test, at scale, with substantial-enough AI.

Molecule-level tracking for Assembly Theory.

In Assembly Theory - as loosely referenced for this essay having nothing to do with it (surely poorly), the intellectual equivalent of a baboon trying to open a coconut - consciousness emerges at a threshold of priors, a level of trailing aggregate complexity or interconnectedness.

The clockchain and DTI - in theory - enable tracking of individual atoms or molecules, on their journey across time. Then, perhaps, their centrality score would be the measure of potential for consciousness?

Or perhaps that’s all wrong and the clockchain + DTI prove it? We’ll see.

Integrations as force multipliers of clockchain + DTI input and output pace to scale.

tApps are novel things only possible because of the clockchain + DTI.

Validators are integrated with the application in its roots.

Integrations are third parties that play nice, and have mutually-beneficial goals. As discussed, DAO’s are a natural fit, and are inspiration to the clockchain design. New protocols are of most interest to me as partners, so to speak:

While apps may change over time, a few of interest today are:

Proof of Attendance Protocol PoAP. This allows event organizers to prove attendance. It’s already permitting into “honorary” ones that organizers give to community members who miss events, and so on.

Superlocal and CityCoins enable different levels of stacks where people can earn tokens, NFT’s, and similar digital assets by being places.

Of course, Ethereum is a natural tie-in to use clockchain records as timestamps, to use XPAYZ time conversions across planets, and to settle time scales on bets, for example. Bitcoin is, in its own way, as pointed out by many before me, time. The minting and proven work cycles are time scales unto themselves. Perhaps XPAYZ time converts to chain height at timestamp or some novel conversion like that to serve as a dually-immutable hyper-graph of authenticity of some moment.

Gifting entries as NFTs.

Users cannot permission ownership of their clockchain entries with other users, other than as activities that can be forked to be repeated — templates to reuse, basically. But users will want to share in some cases, just as cultural artifacts. In this case, they should be able to mint their time as an NFT. Perhaps it’s a grandfather giving his grandson his time climbing Everest; or a mother giving a daughter her first startup venture round closing as a memory that inspires her daughter in her new job managing a billion dollar fund; or something more sentimental, like a last moment together of long-lost grandparents. Or a famous athlete receiving a gold medal for 3 minutes.

It’s a creative ocean of options with that kind of feature, and early efforts should focus on where these things create joy, more than value, in my opinion.

Epilogue: Time is love

The Time Distributed Autonomous Organization. The tDAO. The DAO running the clockchain + DTI.

From the Way, one emerged.

From one, two emerged.

From two, three.

From three, all things emerge.

—Tao Te Ching, verse 42

The leadership structure of the tDAO needs to be based on a deep commitment to and deep incentive for the integrity of the DTI. Rather than being the equivalent of Bitcoin maximalists, the keepers of this timeline will emerge into those responsible for the structural integrity of software, human coordination, and new structures of thought, philosophy, and science. This should not be governed by an elected board, but by a consortium of differing interests. Similar to checks and balances, the clockchain and DTI need systems to maximize the benefit of integrity and scale to all stakeholders.

These mechanisms should be formed by consensus within the TAO. One such mechanism may be that the centrality scores are integrated into all functions. This could enable, as an example, paying higher validator rates to validators whose users refer the most validations. This in turn motivates validators to design, tailor, and market their services to those users most likely to grow quickly in scale. Age-out mechanisms of rewards ensure ongoing opportunity and lack of access to perpetual market dominance tactics, which could erode the integrity of the system over time.

Perhaps a scenario emerges where many factors add bias to rewards. Integrity of clockchain filings; integrity over time; link-backs like eigenvector reference scoring used in search; redistribution of share efficacy; participation in other community points-type systems; or even efficacy of governance proposals or some other measurable but subjective contribution.

The best thing is probably a Proof of Work for the system; and Community as another domain. Perhaps these are the two houses of commons in voting. One ruthlessly mathematical; the other chaotically human.

The way of the tDAO should be gentle, yet immeasurably strong. Time crystalized down to a true abstraction, perfect in the way that each moment can be a full and complete story — yet able to grow and permeate and emerge complexity upon complexity to reveal yet another focused lens by which we can understand the reality in which we operate this system.

Time Guardian NFT’s.

The first 100 supporters of clockchain protocol and DTI will receive Time Guardian NFT’s. I made this art, bending time. It shows dozens of portraits of people, with one collective human experience emerging across time. C’est la vie.

This NFT signifies you as a keeper of the timeline. As such, you will receive access to unique and important discussions and events.

The Clockchain™ and Deep Time Index (DTI)™

It’s critical protect Intellectual Property in order to honor the TAO, in this case.

In the open budget, a percentage of engineering and legal resources go to this work. The tDAO owns all.

Time, as expressed in the artifacts and infrastructure of society, should be courageously honest about scarcity, artifice, and the limits of free will.

Time is scarce. Time is limited. Time is brutal. Time is now. We don’t know what time is. We make up time. There is nothing in the laws of physics that requires a sexagesimal system (60-based), nor is there anything in physics, nor biology, nor chemistry that dictates that we can’t contrive some entirely new model of time, using perhaps 12-based systems of hours, where minutes are about 5 of our current minutes.

All of these are strange truths to consider when you think about your life, your work, your time with family, or your bank account. But all is time, and time is all. Most of us care more about time than anything; and when people lose their ability to cherish their time, we recognize it as a mental health problem, not a logical conclusion.

We must further accept that we don’t have infinite choices. We have very limited choices. Most options we can conceive of are impossible. We cannot think of being in Paris, and be in Paris. We cannot think of being in the year known now as 1850, then be there. But we also cannot invent a water-to-wine machine simply because we can conceive of it. We cannot be in New York at noon and Dehli an hour later. We cannot write twenty books in a year. The limits themselves are finite, but a massively long list.

None of this is counter-intuitive. It’s counter cultural.

It’s time the people control the time.

We, the people, own time.

Our infrastructure must be fully consistent with that absolute truth.

Housing and time emerged together. As the context of our daily lives became more and more safe from daily threats and distracting insufficiencies, we had the freedom to deeply synchronize and coordinate our time and labors with other humans.

But we have a full philosophical barbell arch here, where we can return to responding to our context. This extraordinary liberty will be the soil from which our higher forms emerge.

Owning the means of production may not be as important as it was once regaled to be; owning time itself can be what sets us free.

Time is love.

In a future world, where suffering and strife are solved due to rapid mimicry of essential behaviors in high resolution, enabled by the clockchain + DTI, how we spend our time is how we spend - and cultivate - our love.

In the end, the love expressed when you align your intentions and your impact, is the love you take.

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Thank you.

Sean

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(1) Is the universe immortal? Let’s figure out! But until then, we’ll go with ‘not for storage costs’.

(2) A highly-sophisticated highly-coordinated PoHT attack where the conscious being does not agree with their chained identity — would be a great sci-fi script. It should also be theoretically impossible because fraud can only span so many nodes deep in the DTI past the scale of roughly 10B entries. That admittedly arbitrary point represents a threshold at which so many real things exist that a whole artificial history would have to be contrived, and so long as each clockchain entry records its own time of recording.

Addenda

These updates are made after the first draft circulated editors, colleagues, friends, and family. Thank you all for your help and feedback. Thank you to my family for your love and support.

Encryption

Nothing stated herein should stop the user from posting encrypted data; although it would probably break DTI label look up and taxonomy functions, in at least some of the cases. The ideal encryption system would have multiple zones: user management, clockchain meta management, encrypted clockchain graphs, and encrypted variables within graphs. There should be another set of encryption tools, which include the ability to decrypt-on-time-trigger and perhaps smart decryption contracts, the former being of more prescience and portent -- and inter-referential to the clockchain.

Optional mutability.

A critical point of feedback is that there are many reasons to include a mutability function - whether ticker or trigger - that ages entries out of the clockchain. It’s not clear how that would work, or how archived versions could possibly be handled. But, it’s a good subject for debate. Perhaps there is some asymptotic curve value over time like ADPRS; there is a trigger; there is user command; or some other function that stops including an entry. I’m skeptical, but optimistic.

Among the good reasons to have optional mutability: people may do things they regret and want to hide; fraudulent entries have to be stopped somehow; no clear mechanism exists to stop group-level deception, creating a statistically-likely yet entirely false entry; and the storage costs of all entries, over time, becomes very large. Perhaps the mindset of optional mutability is wrong altogether, and all entries should de factor age out, but can be set to perpetuity. Perhaps these entries have higher or residual mining fees?

Role of miners: Proof of Orbital Position in Space.

My brilliant friend and past collaborator Alex offered up the brainstorm that clockchain miners monitor our position in space to record time. By analyzing data from a variety of prospective sources - satellite data, eclipses, orbital data, local basic telescope data, and dozens more - the miners could develop a consensus on where in space they are. This would exactly position us in time, as well. Appending POPS to clockchain entries would show exactly where in the universe the entrant was when the entry was entered, which would show exactly when. This is a superior time format to what is described in my essay, and in the official first version of this essay, I will replace my ideas with this one. Combined with nuclear time, it is a system the whole universe should be able to agree upon.

Governance term limits, and power time-outs.

Another brilliant friend, Rakshak, pointed out that “term limits” on the governance of the clockchain are important. To be clear: I’m working to build the clockchain in a decentralized way that does not put me in a long-term exclusive position of power, but rather ages me out of the power structure entirely, becoming a self-sustaining emergent phenomena with rewards to all contribute to its success, not just a founding handful.

It’s not clear how to do that, but it should be recursive, in my opinion. Perhaps it is a count of the number of graphs posted to the clockchain. I can be on the tDAO with voting power for the first billion? Then the second billion I start quadratic scaling down in power, but never reaching zero on my returns. Whatever the mechanism should be, the design should enable incentives that last perpetually (i.e. revenue share that goes asymptotically towards zero but never reaches it), but also prevent governance power from suffering nepotism, unbreakable dominance, x% attacks, and other crippling problems faced by the battleground of failed crypto initiatives.

Only a fraction of time spent will be logged in the clockchain.

As a clarification, I can’t imagine that people benefit put “brushing my teeth” on the clockchain, with maybe some very outlier cases like research at a dental college. It’s not a calendar or behavioral quantification tool, any more than bitcoin is a mortgage broker.

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This post is on the blockchain: https://mirror.xyz/seanmcdonald.eth/rxr1BO_6D_59N9I1hpUYM4u7IDpMWLeJ4Qtbu60wJtw

Solution Design Requirement 1) Gratitude mindset.

I am personally grateful every day that I exist. I am grateful that consciousness emerged from the stardust. I’m grateful that it happened in such a way that I can enjoy the world around me — and the other conscious beings who exist at the same time as me. We have near infinite capacity to travel, to experience, to explore. Almost anybody can learn to juggle. Almost anybody can learn to fish, and go fishing near them. Almost everybody can learn to garden. Most things are actually available to most people. It’s culture (and money) that keep them from trying a broad range of experiences. And in many cases, money isn’t as limiting as it may seem.

Solution Design Requirement 2) An honest calendar.

You’re gonna die.

We’re all gonna die, someday.

Every hour you spend is a deduction from a total.

Solution Design Requirement 3) An aspirational calendar.

Sundial will be responsible for a renaissance-scale event in giving people the ability to be the best of themselves in their work, their life choices, and their relationships all through optimizing time-consuming life problems. Because commercial vendors benefit from this growth cycle at a broad scale, in a time of existential need for new business models, the model we have will be a competitive investment asset in perpetuity.

//

Try Sundial. You might be surprised by how mapping out your whole impacts your perspective on how you live today. It’s free and easy to start.

https://app.sundialcalendar.com

Sean McDonald is building Sundial to live in a world full of people living great lives.

This post is on the blockchain. https://mirror.xyz/0x936fB3dCC849b01d12154B6F96391DceBBb697DB/d1pK4kDx1MtSa8HQw_TRmuDBVhl4ibyWT1XJND9VGbs

Carbon floats through the universe. Billions of years. Energy vibrates it.

Consciousness emerges. Eventually conscious beings realize this is what happened. We awaken, probing into our past. With that sacred gift of time, we organize things in a grid, which repeats forever. Most of the grid goes to productive income generation, for most of the consciousnesses. The rest of the grid goes to consuming and watching things.

The grid is an ugly liar. It’s visually ugly. And it’s lying. The time it pretends exists does not. There is no Tuesday. It’s the Queen’s time. There are not 24 hours in a day. There could be 312 hours in a day, if we all agreed. Tomorrow morning, we could have 1 hour days. You work for about 35 minutes each day, in that model. It’s an effective lie — sort of — as long as we all pretend that a clock in Greenwich, London is and should be the central point of time. That's legit how it works, today.

The grid is a coordinator, not an achiever. We live in stories, fluid mental states over our lifetime of identities, often sitting beside one another. Many things are achieved by coordination. Manufacturing facilities need people to show up at the same time. (For now.) But we have profoundly overfit our consciousness to coordinated time.

Let’s not forget the grid is ugly. It's just a grid of squares. It’s not how I see my budget of conscious hours.

This mindset - traditional calendar mindset - is an enemy I’m naming TradCal.

TradCal Mindset.

NPC's are non-player characters in video games.

TradCal living is the embodiment of NPC mindset.

TradCal is an absurd pitch, if you lay it out.

Do you want your model of time to never ever change? Do you want to repeat the same exact thing for 8 hours a day, 5 days a week? We’ve got something for you! It’s a grid calendar. Welcome to TradCal, where your life is a grid, designed to serve the people with whom you coordinate, mostly for income.

What’s going on in your life? Doesn’t matter! Don’t sweat - TradCal keeps on going no matter how you feel, what the weather is, or what’s going on in life or culture. If a government needs to shift around your calendar, no problem! TradCal is fully compliant and honors all local, regional, and corporate time constraints.

Don’t want to think about your own mortality or the preciousness of the gift of time? No problem! We never mention it. Most of the time, TradCal keeps you focused on the week ahead. That’s what matters, you.

If you don’t use TradCal, how will you comply? The Grid offered by TradCal is easy to mimic. So, externalize your conscious experience to a coordination grid. It’ll be easy to do commerce. Done learning in life? Join TradCal now. Get a timed job. Enjoy your two weeks.

The way out.

Looking for a way out? Try Sundial. You might be surprised by how mapping out your whole impacts your perspective on how you live today. It’s free and easy to start.

https://app.sundialcalendar.com

Sean McDonald is building Sundial to live in a world full of people living great lives.

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This post is on the blockchain. https://mirror.xyz/0x936fB3dCC849b01d12154B6F96391DceBBb697DB/dp_4T-Y8Bwd4jEwnrEMDxtBsCAe3soqbvaulMEBmKwc

This essay is on the blockchain via mirror.

Act 1: In the future, your calendar will care about you.

We’re building Sundial because our calendars are functional but abusive vestiges of another time, with absurd assumptions . Coordination is critical — sometimes. But how much of our lives really, truly need to be coordinated to the second, in an era where computers solve more and more of our problems? Less and less.

Rather than prove it in a lengthy post, I’ll give you, the reader, the benefit of the doubt on forecasting the long-term impact of work from home, automation, AI, and rise of cultural values focused on spending time on self-care and selfie experiences. Each of these erode our need to synchronize and coordinate.

Here’s how we’re building a “calendar” alternative for that new world:

Build a calendar that is honest about the scarcity of your time.

You have a very short time to live, on a cosmic scale. You may be able to extend your life soon, but human bodies are not going to live for millions of years anytime in the foreseeable future, so many experiences operate in an extremely limited time bracket. Your chance to play professional sports is limited to an age range; your chance to have kids is limited to an age range; your chance to see an eclipse is time-limited. Almost everything has some upper and lower bound of time that can be spent.(1) Those hard ranges are the first principles upon which Sundial is built, including how long you can live. The sundial is a metaphor enabled by sunrise and sunset — hard limits on our days, forced by the rotation of the cosmos themselves.

There is a hard limit on how many backyard barbecues you can go to.

There is a hard limit on the number of yoga classes you can attend.

There is a hard limit on the number of sunsets you can see.

When you model all of your time, every action has an opportunity cost. Watching a show while you eat dinner? You missed one of the limited set of sunsets you can watch during your time as conscious human life.

The thing is, we don’t live in a world in which we are deluding ourselves, not nearly as much as we used to. The awareness that life is short is broad; tightening the grip on the wallet of your time hasn’t started yet; but it’s coming.

Build a calendar that keeps a score.

The Sundial alpha web app already includes the ability to approximate how an activity will impact your longevity. By using a Deep Time model, Sundial can keep various scores about all kinds of stuff. This is going to be the core of how we enable scorekeeping and forecasting. Your Deep Digital Twin can be many different versions of you, and we attach consequences to those future projected data models. So we can forecast how your exercise, for instance, will impact your longevity.

Is it a realistic forecast? It’s mathematically impossible to say.

Are forecasts valuable because they’re accurate, or because they help you make decisions today with better information? If you check the weather, it’s the latter. Weather forecasts are notoriously difficult. Forecasts enable space exploration by analyzing weather patterns seconds ahead of the present because of the real time it takes to turn giant rudders being longer than the time needed to correct to strong turbulence. Economic forecasts are the foundation of our public policy — perhaps all public policy.

Why do all this?

Sundial and our approach to social impact.

I have spent about half of my career in companies that are b-corp, “social impact,” or some other generally-trying-to-do-good model of business. I’ve spent the other half in high-growth venture equity companies and consulting for private equity funds and mega conglomerates. I’m not convinced that Tom’s Shoes is a morally better company than Intel; as a matter of fact, I think the world would collapse without Intel, and be fine without Tom’s. My perspective now is that we should all be the judge of our own outcomes, but also that we should strive to make those choices as the healthiest version of ourselves.

So, I have never pushed an outcome in testing EDITOR with people. It’s a 1-2 hour process, and involves dozens - even hundreds - of questions. People laugh, people cry. They talk about their lives. Here are the things I’ve learned help people, observationally:

1) Breaking bad habits through increased awareness. Realizing that you will spend 22% of your waking life watching shows can be a reality check. Sometimes perspectives change quickly - in minutes or seconds - on visualizing something like this.

2) Social permission to act against loneliness and disconnection. People have disconnected from each other during COVID, especially from older people, and there is now a real need to reach out and plan activities. Sundial at its best gives people the reason, the envelope, and the actual invitation to get together. It’s easier to invite someone to a museum than to have a chat, for many people.

3) “I have no time.” means you have bad habits. Many of these people have plenty of time, but are loaded down with either responsibilities they don’t want, or habits that don’t fulfill them.

4) Their intention did not meet their impact. They intend to have lots of magic moments with their kids; instead they are spending their time shopping real estate to find the place to have future magic moments.

5) Hyper scheduling. Some people find happiness in knowing all the things they want to do. While we have to be careful about rewarding dopamine by planning instead of doing, for a lot of people a life plan is a reward unto itself.

6) Looking in the magic mirror. Like a lot of exercises, Sundial helps people stop lying to themselves about the things they know they’re lying to themselves about.

7) Emotional state modeling helps people want to stop being upset so much of the time. Future essays will be dedicated to this.

8) HD mimicry — as mentioned at length before, people want to mimic top performers. Freeing up “me” time to mimic “hero” time is appealing to a lot of folks.

9) Whoa factor. Ever seen how your life will be going in 2030, and meditated on it for 20 minutes? (Emoji)

10) The power of positive visualization. Sundial as a brand, as a metaphor, and as a practice is atavistic. We use contemporary tools to map reality as it is laid out before us. At its core, Sundial is yet another exercise in the power of positive visualization; a tool designed for a universe that responds to the stories we tell it.

Do we need a b-corp to do this? I’m not sure. My wife co-authored the b-corp legislation in Arizona, and she’s not, either. Are DAO’s the new b-corp? Not sure about that either. For now, Sundial is an S-corp.

Sundial is “The calendar that cares about you.” ™

To care about you, your calendar has to know you. Not in some way that’s focused on selling you ads. The real you. Your hopes, your dreams, your reactions to outcomes.

A computer can only care about what you tell it to care about. Typically, we tell our calendars when we need to coordinate with other people, and we use generic labels, not a structured taxonomy of activities that can be quantified and modeled. Sundial changes all of that, creating a platform where you explore the person you want to be, then act on specific goals, based on specific outcomes. It’s like converting life from a TV show you watch to a video game you play.

The more information you put into Sundial about who you want to be, the better it can help you actually live that life. Whether it’s eating dinner with grandma more or getting a black belt - or any type of life goal - Sundial is focused on outcomes, and those are determined by you.

Sundial wants you to live longer.

Sundial wants you to be less lonely and more connected.

Sundial wants you to be mentally and physically healthy.

Sundial wants you to be economically successful.

Sundial wants you to be as good at your job as the best person in the world is at that job.

Sundial wants you to be the best you.

We are all experiments in consciousness.

Sundial doesn’t have a bias towards any philosophy, creed, partisan position, or any limitations (other than legal) on what makes the best version of you. That’s completely up to you. As the founder of the company, this is a direct application of my personal beliefs. We are all beautiful manifestations of consciousness from the stardust, and we should be the best us — not the best whomever someone else tells us to be.

Feedback loops and digital twins.

The largest activity forecast we have done yet, internally, modeled about 1.3mm activities in the client’s future. That was because the client was relatively young, and interested in living a long life already, exercising and maintaining a rigorous medical routine. They had a lot of time to live. That’s a lot of brushing your teeth.

Most calendars don’t have feedback loops, and most that do are enterprise sales products. While it’s not in our web app yet, we’re transparent about heading that way.

Sundial is like Fount — for time.

I’m inspired by the story of Fount. They’ve taken two huge startup categories and synthesized them into a more thoughtful model. The idea is your blood testing and health control what you get in your meal kits they deliver. It’s genius, imo. Packy McCormick thinks so, too.

The way Sundial is like Fount for time is that Sundial focuses on how your actions impact your life, your wellbeing, your happiness, your sense of finding and fulfilling a life’s purpose. Sundial doesn’t beep when it’s time for a meeting. Sundial aligns your values with your actions.

Act 2: A calendar for a time of great transformation.

We have to adapt faster and faster.

Economists and analysts print volumes each day on how the rate of innovation, the rate of change, is increasing. Meme behaviors can sweep across the globe in hours now, even minutes in some cases. The Thing of The Day is a trend that is going to increase, and every day will have a thing.

Our economies are going to re-distribute. A Network State will emerge on top of nation states. Whether we go multi-polar or things just get weird, it will be different.

Americans will need to learn advanced manufacturing quickly. Other nations will struggle with the requirements of retooling their economies. AI and automation will flip more scripts than computers and the internet have so far.

Sundial isn’t just about bucket lists. It’s about living best lives, and that includes being awesome, reliable, and smart about your work and productivity. We are all interdependent and need each other’s best-ness.

Mimic how time is invested, and with whom, and in what sequence.

This is the core of the small networks of operator + service provider + clients that are formed by some of the fastest growing companies in history. Humans seem to have evolved into team structures. We need to focus self-performance not just on the self, but on mimicry we exhibit in personal, interpersonal, and sequential patterns.

Are all actions connected to time?

Yes.

Sundial is a platform for a new way of life.

Enough consumption. Don’t live a little. Live a lot.

As the world changes, we need to spend more time doing things that move our time from passive to active. So many people have a list of accomplishments, after which they can “relax” and this is when they consume things. That is OK, and we - in part - will promote a lot of that. But not exclusively that.

Sundial is building a tool to enable humans to refine and replicate our life journeys, broken down into activities and playbooks.

Be the customer, not the product or the audience.

Your life is being sliced into milliseconds and sold to the highest bidder, already. Advertising model platforms are all connected to this harsh reality — your time is for sale. One party attracts it, usually with compelling content, then once it is captured, sells that time and space to someone else. You get the emotions of the content, and perhaps an escape, but not a penny of the transaction. Many times, the advertising platform itself is benefiting not just from your specific time, but from your future projected time, as well. That is what the stock market for publicly traded advertising based models is — a forecast of your time.

Ruin bad habits with perspective.

One of the most powerful use cases of Sundial is helping people snap out of a bad behavior. We are going to tell the stories of real people who have really changed their behavior thanks to Sundial.

I sat down with a friend of mine last October. During COVID he went through a divorce, went through a fall off in his career in the entertainment and commercial production industry, and moved in and out of his parents’ house while finalizing a house sale. A rough time. He didn’t start drinking heavily or gain a bunch of weight; but he started watching a lot of content.

When we added it all up, and visualized it as a Sundial - how much of his life he would spend on this - it was 12:45pm in the day of his life, and he was going to watch shows until 5:20pm. He said he stopped binge watching that day. I checked in with him at three months out, and he was dating again, as well as traveling to Mexico for fun. He was living his life. Of course the change was within him, and his therapy process played a part, and so on — but he credited his three hour EDITOR experience with Sundial as “life changing.” He’s a real friend, this means a lot to me. It was a big reason I doubled down, shifting to spending most of my time spent on Sundial - unpaid - designing, developing, and market testing.

It’s time to help people out. Be a friend. Be a mentor.

We’re likely to see wild changes in our economic structure, and our leaders are publicly warning the whole world about global food shortages. People need help; and people will need lots of help. Whether it’s your neighbor, or the world away — you can help people. Sundial makes it super easy to see how much of your time it actually takes to, for example, help in a community garden for two years. I modeled out my own time, and it’s surprisingly small.

Anti-calendarism and our competitive advantage.

Something trying to be the best in a product class can’t compete with something that undermines the existence of its product class. A horse can’t compete with a car. A computer is inconceivable orders of magnitude more powerful than an abacus wizard ever could be, and knows of the existence neither of the wizard nor his abacus.

The most powerful and largest companies in tech can easily add most features. But they cannot easily add features that undermine the purpose of their products. Search companies with calendars can’t take the time of day off the calendar, without extraordinary education to the market, and careful public relations over a long period of time. Companies building tools for work are so deeply vested in time as it synchronizes global supply chains, that any change the structure of time management is a loss of market position for them, and not something they could rationally pursue. In all but a few cases, time needs to stay the same for the system to work.

Anti-calendarism, deep work, and deep time.

Deep Work is a concept that has spread like wildfire, and is proudly circulated among great thinkers, leading engineers, and prominent voices in podcasting as the way to get shit done. Dozens - if not hundreds - of apps promise ways to manage this. Schedules, readings, trainings, timer apps, and a never-ending set of top 5 lists in email newsletters, each promising to make the most out of your day.

In this way, our Ancient Roman predecessors would have likely understood their point, and named it simply Carpe Diem, which was often inscribed on their sundials.

COVID cost us billions of hours of quarantine, collectively.

I ran some numbers about COVID, and they’re shocking. The amount of time we have spent in quarantine - as a country, and as a species - is staggering. I don’t need Sundial’s math to run the numbers, here they are:

Billions of people * minimum dozens of days locked down * 12 hour minimum day = tens of billions of hours locked down. Probably hundreds of billions. Maybe a trillion including the current and near-future.

Factory schedules need to depart with factories.

Stupid management practices by small-minded people who don’t care about pain and trauma and sensitivity have crushed the souls of the brilliant with schedules designed to make peasants of our minds.

Your schedule — as art. “Enjoy yourself, it’s later than you think.”

Great songs, great movies, and great thinkers have all cited this expression. A permutation of carpe diem, purportedly engraved on sundials in Ancient Rome as well, this expression encapsulates half of what Sundial is about. That is the need we hope to foster in most people, to love the opportunity at consciousness, to explore, to revel, to feel, and to love.

(The other half is about being excellent at whatever you do, fwiw.)

As we plan our bucket lists, so we plan our lives.

EDITOR is called EDITOR because it literally edits your life story. You are the author. We’re just here to help.

Do great things — together. Your calendar is your social life. Two player mode coming soon.

“I am an ark in the swift flood of time, and my companions, a fellowship. Who throws in with us sails into light.” - Rumi

Whether it’s helping a community garden, or inventing the next medical breakthrough tech with a badass team, you can probably do more faster with a great plan. Maybe you need one year, three year, or a five year plan.

Sundial is evolving into a social calendar platform. A lot of social media pushes you to be the person advertisers want you to be. When we enable two-player mode, it becomes a central point for sharing life’s journeys together. Because Sundial isn’t focused on social streams or constant engagement, but instead scoring as high as you can in the game of life, it pushes you to be the person you want to be.

High performers want to mimic.

“That great poets imitate and improve, whereas small ones steal and spoil.” - Tennyson

This quotation has been attributed to many, including Picasso. I first heard it as “Good artists borrow; great artists steal.” which appears to be Steve Jobs imitating and improving it from Tennyson.

Later in this essay, you can see how Sundial EDITOR already has a draft function to compare yourself to famous high performers, like comparing how much you read to how much Oprah reads, per her famous hour a day dictum for her book club.

Time pattern mimicry is a relatively new thing.

While there are countless blog posts about how “Elon spends his day” it’s unlikely any given day follows that pattern so specifically. It’s hard to imagine Elon skipping a meeting going over an algorithm to ensure he has his 20 minutes of cardio at exactly 4pm. So, people are being - basically - inspired by his hypothetical schedule, and making probably minor changes to their own when reading these pieces. It could be Steve Jobs or Joan of Arc and it wouldn’t matter — people are replicating a false schedule. A real one would be complex, include campaigns, as well as different schedules for different phases of company growth.

What we’re building at Sundial is a way to spend the same time budget as Elon, which is fundamentally different. Distribute a complex narrative across a set amount of scarce time, and then you can really emulate someone.

The same would be true of a great cellist. If you follow their time budget, it’s likely you’ll be very good at cello in the end. Perhaps you don’t attend the Royal Academy to study cello, but use an online tutor. In any case, if you dedicate yourself and mimic the time budget spent, you are likely to onboard a very similar neurological graph structure that allows you to mostly play what they can play.

High resolution mimicry is going to rapidly replace “read a book and think about it” self-help and business mastery courses. At first the rate of change will be slow, but it will then become exponential, and HD mimicry will quickly become the only credible type of self-improvement.

Don’t have time? Buy time.™

Sundial is going to enable a new feature soon — the ability to Buy time.™

We know that for people to spend time on what they need to do, they need to be able to have more time. We’re working to build an incredible team of partners who can bring this vision about.

Want to live longer? It’s a lot about how you spend your time.

The diseases most likely to kill us in the United States are basically self-imposed. That doesn’t mean we would survive forever if we didn’t eat sugar, or inhale air pollution. It just means that we can increase our odds of living longer based on our behaviors today. Arguably, there has never been a time that a threshold of great longevity may so clearly emerge, as advances in life sciences and brain-to-computer interfaces are increasing in their pace of realization. Perhaps there is a magic day, say 2039, after which people live to 300 years on average. I would like to get to that day.

Sundial does more than help you go to the gym, or understand the importance of that. It projects how your life will go because you go to the gym. You will see a whole new world enabled by exercising into your later years. Your list of options stays long. If you lose the ability to hike, for example, perhaps half of all Bucket List items in Sundial are immediately unavailable.

Your doctors, your healers, your thought leaders, and your spiritual leaders all have opinions on how to program your Sundial.

The “self-improvement industry” is a $10B+ market and growing. Some podcasters have built empires around time mimicry. Some in just…four hours…of work each week.

Beyond the billions of hours listened by podcast audiences around the world, physicians, spiritual leaders, and most employers, as well as most schools, have very specific prescriptions for your time budget.

An apple a day is about seven minutes a day. Twenty minutes of exercise a day is a use of time, even though it’s tied to optimal cardiovascular exercise ranges. Meditating every morning is a time prescription as much as a behavior prescription — if you ask a practiced meditator if they are really spending twenty minutes in the headspace, or rather allocating twenty minutes, flailing like a mental fish out of water for a bunch of it, and then grasping at a fleeting few minute of clarity transcending their monkey brain, it’s likely to be the latter. It’s a time, not a thought.

Most religious practices have time allocations. Pray five time a day. Come to church on Sunday; temple on Saturday; youth group on Wednesday; this holiday; that holiday. Again, most practices don’t expect people to be automatically spiritually reverent during these times, but to keep the drumbeat of time allocation in their lives. In many Protestant American traditions, there is an expression of committing “time, talent, and treasure” — it’s a powerful leveling factor for some people to pay the organization’s cash bills, while others pay the time bills, often being services to those in need.

Across these domains, actions can be broken into activities and time budgets to mimic, and for the most part - barring sometimes having access to places or equipment - you can manage mental, physical, and spiritual health.

Building a calendar that matches the values of people I respect.

Due to commercial use laws, I can’t list the people I respect so much, quoting from their tweets. But it’s easy to find a media personality, celebrity, or visionary who says time matters more than money. From people who invented personal computers to people who star on the most popular streaming shows, celebrities and leaders the world over credit their inner knowledge of the scarcity of time for some of the mindset that makes them who they are. Please check my Twitter for a mega thread of examples of personal favorites.

Act 3: EDITOR is a lifelong process of self-discovery and self-improvement. The Sundial App takes one minute, or so.

Think of EDITOR like Sundial Pro.

EDITOR is software to edit your life story. It runs forecasts on hundreds of thousands of future activities in your life, and in some cases, many potential versions of you. It takes hours - sometimes days - to program in detail. It’s done with the help of a professional, who coaches you through the grueling process of modeling your best and worst selves, then modifying your functional calendar until your behaviors align with your schedule. In this essay, I’m going to lay out the way it works from my personal EDITOR work.

A critical thing to know is that these slides come from multiple people, to protect each’s anonymity. I’m one of many, and their names have been changed / removed. Everything is shared with those clients’ consent, and as anonymized.

Before we get into the walkthrough:

Storytellers and time.

The manipulation of time is an ancient technique for storytelling and visualizing things that are not happening in present time. This ability itself may be very rare, if not unique to humans and computers humans program, for now. One of the oldest examples of time manipulation is in Arbahamic texts, which depict Isaiah moving the sundial of Ahaz ten degrees backward. Mark Twain put a Connecticut Yankee in King Arthur’s Court. Tarantino cut Pulp Fiction with a time traveler’s lens. Today, more than one VR app offers “time travel” as a button to another time, through photographs, augmented reality, and other nifty tricks like historical audio converted to immersive audio. Spike Lee put out See You Yesterday, quite literally about time travel. Ryan Reynolds just debuted The Adam Project, where he meets his past and future self.

I believe the stories we tell ourselves shape our realities. That’s why I’m building what I’m building — so you can tell yourself a story that shapes the reality in which you want to live. You are the writer. Sundial and EDITOR are better typewriters, models that can forecast the consequences of what you write to them.

The best stories take time.

You can’t write your life story in a day. You can live your life story — in exactly one life.

Using a tool like EDITOR is a commitment to optimizing the way you use your time. Starting today, EDITOR is becoming a subscription. We’ll onboard you to a new life story, then work monthly to review your progress on your path. If you’d like to reserve one of the new client slots we’re adding each week, please sign up here.

https://sundialcalendar.com/edit-your-life-story-with-editor

Talking to a human to get conversational feedback loops and casual honesty with yourself.

There is something about reviewing your schedule with a Time Coach, seeing how different versions of you could play out based on your behavior, and then adjusting your real world calendar to be who you want to be. It’s got an aspect of a traditional or spiritual practice, in terms of self reflection, and planning for the future. It’s got an aspect of talking to a financial analyst, although no financial information or advice is exchanged — to be clear. Our goal is to build models of you that inform you. It’s a difficult process, but well worth it.

Once the Deep Digital Twin of you is built - think of it like a full body MRI for your calendar - we start to get immediate feedback. Do you like who you would be if you lived one way, more than this other way? Would you rather write multiple books, or just one or two, and master another hobby, like painting, as well? You can choose either path, or neither.

As you fine-tune your EDITOR model, your Deep Digital Twin will become more and more accurate, both to your behaviors and your life goals. Once you have a model you like, we export it to the Sundial App, and to mainstream calendar apps.

BUT then you go live life.

Because this is about how you live, not how you’d like to live, EDITOR is an ongoing process. You’ll get a lot of information up front, but a choose-your-own-pace cycle of check-ins with your Time Coach will make it possible to update your models. This is a premium service for people making the most of their time. The feedback loop is critical to fine-tuning your model, especially for emotional outcomes.

EDITOR puts your Deep Digital Twin in a Deep Time environment and it’s wild to see the results.

Is this too buzzword heavy? Yeah. But is it even more than that awesome? Yes.

A digital twin on top of a whole-life timeline enables you to see how different behavior models - different versions of yourself - play out. Let’s dive in:

Perspective: We start with the meta view. Sundial is forecasting 454,663 events for ‘Sean Zero’ - one of my twins, using myself where there is a name attached.

The time of the day of your life.

Sundial is predicated on a metaphor of “the day of your life.” It’s a way to look at how you spend your time allocation. For me, it’s getting close to noon in my twin’s model of his life, were it just one day. That’s a wake up call for real me!

Learning to think in “Sundial Time” to understand time scarcity.

It takes a little practice to learn to think like this: “If my life were just one day long, how much time would I spend on this activity?”

In this case, the client wanted to spend more time casually making art. She realized she would only spend 27 seconds of the day of her life doing that. This page of her initial report immediately re-framed her mental model about creating.

At this rate, she was spending so much time focused on work and kids, that she hadn’t been checking in with old friends. Had she continued to behave as she had been the past few years, art and old friends would make up a combined minute of the day of her life. Again - quick change in perspective, slow change in behavior.

It wasn’t all bad habits. Yoga dominated. This still was less than she wants.

Looking at a life journey — in this case business.

Imagine a person who intends to spend 15 years running a venture-backed startup. That’s about 3 hours and 21 minutes of the day of that person’s life. During that time, other projects will require emails to professional services providers, attorneys, realtors, et. al that aren’t part of regular work. That person, if they manage to get their company acquired, will likely become a board member, and then investor in other companies to empower other entrepreneurs to build better worlds like they did. They’ll also have to handle bad debts, commute, and handle their small businesses, like AirBNB’s. All of that together — a forecast of 42,442 hours of their life in work.

Seeing old friends gets more and more rare. Every 3 years sound right?

This client has a plan - and cheat cards - to increase their longevity, value creation, and accomplishment of rare life goals by regularly connecting with old friends. The client was a young woman but thought this image was great for representing chatting with her girlfriends.

Keeping score, forecasting score.

Sundial fundamentally changes what a calendar is by focusing on outcomes based on your behaviors, and actions based on your intentions and plans, not passive mindset.

A powerful consequence of a calendar keeping score is that it enables outcomes forecasting like longevity. In this case, we ran an investor’s time across investment classes, modeling out their own perception of what created the most value. It was a wake up call to see real estate vs. venture when real estate takes much less time and stress.

Longevity is another critical dimension to analyze. It turns out brushing your teeth is a huge “bang for your buck” in time spent to the time it extends your life, as oral hygiene has a lot to do with cardiovascular health.

What are your time investments by relationship class?

Using powerful financial modeling concepts that are relatively simple to implement with our data structure, we can see that this person has a life plan to spend the majority of their time working. That’s true for almost all of us.

In the same domain of analysis, we can look at types of behaviors. Work and Entertainment usually make up people’s biggest time investments. Holidays, bucket lists, etc. are very small pie slices — typically.

How do you feel about how you spend your time?

It seems a relatively simple question to ask how what you do makes you feel, but people are often - usually - surprised at how much of their time is going to things they don’t enjoy. The classic example is a job they dislike, or an unhappy long-term relationship. But often people just don’t like emailing, they also don’t like binge watching but do it anyway, then feel bad. EDITOR assigns emotions to each event in your forecasts and can break down how you are going to spend your life feeling.

For everyone who has done it so far, feeling Exceptional or meeting Life Goals are smaller parts of the day of their life than “Good” or “Negative.”

It makes me personally question how I live my life, I am working with people through this.

The many you’s you can be.

EDITOR compares three possible versions of you:

1. What Sundial forecasts you will do.

2. What you said you want to do.

3. What you would do if you behaved the same as you have historically.

For clients, it’s effective to compare the many timelines they might live, and have to reconcile with the rationality (brutality?) of EDITOR’s forecast, which takes into account hundreds of thousands of passive events, like eating, brushing your teeth, and traffic.

In this case, you can see my wife, Courtney, wants us to spend a lot more time playing sports together as a family. We would spend just 122 hours doing this with our kids while they are kids if we behaved the same way for the rest of the time we have. She aspires that we spend about 10x that time. Our forecast is that she would do it about half that, in this case, due to a variety of factors, including her other athletic commitments, yoga, etc.

Is this forecast correct? As much as the weather, perhaps. Has it changed our behavior? I challenge you to a game of fútbol in our new backyard soccer net.

EDITOR has a time machine, too.

Everything we do at Sundial involves the ability to gain perspective by taking alternative views of time, time management, and really, database design and time. For EDITOR clients, the time machine function is more about individual goals. In this case, you can see an alternative model where my wife is too busy, and doesn’t hit her goals. The pairing of possibility with the reality that nothing comes true unless you do it works very well in giving perspective.

Editing your life story is important, complex, and takes a time investment.

“We are called to be architects of the future, not its victims.” - R. Buckminster Fuller.

Imagine that someone gives you hundreds of thousands of dollars. Would you spend any of that money ensuring there was a sound budget for it? I would. The same is true for my hundreds of thousands of hours. I am willing to spend a meaningful percentage of my Budget of Conscious Hours to spend that time well.

//////

Thank you.

Sean
//////

(1) There are outliers to everything.

This post is on the blockchain at mirror.

Act 1: Time is Scarce

For the shamans of ancient México, time was something like a thought; a thought thought by something unrealizable in its magnitude.

Carlos Castaneda, The Wheel of Time

44 Million Minutes and The Long History of Whole Life Time Models

Just as Jonathon Larson counted when he wrote the lyrics for RENT, we can all count how many minutes our life might be. If there are 525,600 minutes per year, and if we have about 84 years in life, then we have approximately 44,000,000 minutes to live. Sundial maps in milliseconds, so in the software, your life might be about 3.14e12ms.

For us to live with time as we have it today, it first required consciousness emerging from the stardust. Then came the invention of sexagesimal (60-base) counting systems, technologies from sundials to atomic clocks, and the mass acceptance across billions of people of this time model. We’re talking trillions upon trillions of perceived milliseconds. It’s all rendered, not found in physics, where time is the progression of Planck lengths. But by human perception, derived from what Fuller might describe as temporary integrity of electricity in brain goo. It then participated in coordinated consensus through social communication and generations of artifacts and tools to keep time — all that for us to glance at a clock and say, Oh crap, I’m late.

There is a book making the rounds that addresses time scarcity in the format of weeks. It has some poetic writing, and I particularly like this line:

The world is bursting with wonder, and yet it’s the rare productivity guru who seems to have considered the possibility that the ultimate point of all our frenetic doing might be to experience more of that wonder.

Oliver Burkeman, Four Thousand Weeks: Time Management for Mortals

In this case, he breaks down the roughly 76-year life span average for his audience to a simple model: 4,000 weeks. The use of the “4,000 weeks” framing is the literalism of Windows to Sundial’s Apple-style metaphor: If your life were just one day, as you watched it pass in a Sundial, how would you want to spend it?

Sundial EDITOR maps your entire life, then offers a visualization where you see your whole life as just one day. In many cases, our brains struggle to derive emotion from big numbers. So, let’s break this down. Are you watching three hours of content a day currently? Sure, it’s very possible. When days overlap, it’s hard not to give into the temptation of extended relaxation. However, if life were just one day, would you spend three hours of it on shows?

Deep time modeling is not new. Ancient religious texts reference people living 900-1000 “years,” which seems very likely to be months, or, more accurately, lunar cycles. We all get about 1,000 lunar cycles of life. Whether you focus on productivity and time management for 4,000 weeks or sacred journeys for 1,000 lunar cycles, it’s all scratching at what perhaps makes us sapiens sapiens. It comes down to two types of people: those who know that they will die and those who strive to know what they are. A lot of psychology, religion, tech, investments, and in many ways, architecture are about escaping death, escaping this scarcity. But in the age of software, I propose we need to lean in and embrace that we know we will pass. Life our lives as though they were just one day.

The theory I live by, and the theory from which Sundial emerges, is this: The more we courageously cultivate an awareness of the scarcity of our time, the more we will live the lives we want to live. The correlation is linear.

Deep Time Through the Lens of Planning for Stargazing with Grandpa

I have two little kids, and one of our family traditions, when we visit Southern Appalachia and its dark skies, is to set up the big telescope my dad bought when I was a kid. It’s still in good shape, and on a clear winter night, the views are magnificent. Sitting there this past winter solstice with my kids, wife, and parents, I couldn’t help but think of the scale of time — photons that left their home star billions of my years ago zapping my eyeballs. Being about 44 million minutes, human lifespans are many orders of magnitude shorter than each photon’s journey.

Our time with my dad is scarce. Same with my mom. Our oldest will age out of kid mode within a decade. My own time with my kids is scarce. Of course — c’est la vie. It’s this kind of grateful recognition of the scarcity of time that led me to build a spreadsheet in August 2019 that mapped out how many times we would do “bucket list” items with our kids. The numbers were shocking.

For instance, stargazing on the winter solstice with a kid younger than 18 with good weather and health while visiting Appalachia with a working telescope is quite challenging. The first principle’s hard limit on that happening is 12 years max. Then you have to deduct poor weather conditions, which counts for about 25% of conditions. So we’re looking at 9 chances now. But the kids are sick once; dad is sick once. So chances are down to 7. We visit the other side of the family twice, and the telescope breaks because of the kids. That leaves us 4 times. What we learned is that we have to make each time count.

The spreadsheet drew incredulity from my wife — at first. She couldn’t believe what was implied by the first principle’s analysis of how scarce our time with the kids would be. But my models were built on techniques I knew from my career in data science-based startups, and I tripled checked the math. We will be lucky to push to 6 solstices with my dad while my daughter is still a “kid” — and late teenage years are pushing that definition. It might be more like she doesn’t like going stargazing anymore post-adolescence, and we’re down to 2 or 3 instances. So, the lesson is clear, then: Make each time count.

An Homage: The Tim Urban / Ferriss Time Option

Long before this first spreadsheet, a new vision of time modeling came out from Tim Urban about how much time you spend with your parents as an adult. The Tail End shows a precise mathematical model that most of your time with your parents is when you are a kid. Tim Ferriss helped make this thesis even more famous by crediting it for improving his relationship and travel plans with his parents.

As an homage to Tim, one of the default options on the new Sundial Web App is to model how many vacations you can take with your parents.

Mapping All Time. Starting with 0.0001%.

What is possible with contemporary technology that would have been difficult in any prior generation — or at least very expensive — is to model out every millisecond of a person’s life. At Sundial, we are calling this a Deep Time model to position as part of the “deep tech” movement. “Deep time” in physics also refers to mapping out all of the known universe of time, which would, of course, for Sundial’s purposes, take more computation power than is on earth and include a lot of silly guesswork regarding the laws of physics literally everywhere. As practical applicators of Deep Time, we are taking a slice of it. It’s hard to know with precision, but I’ll run the numbers someday soon. Let’s call it 0.0000001% — “six zeros” — of all time. That slice is the slice most important to any human: Their own life mapped to the millisecond.

Act 2: The Best Time to Change How Time Works was Yesterday, The Next Best Time is Today

Norbert Werner issued a warning about the potential of automation: ‘We can be humble and live a good life with the aid of machines,” he wrote, “or we can be arrogant and die.” It is still a fair warning.

John Markoff, Machines of Loving Grace

Now’s the time to build the time company.

Can I prove it to you?

Post COVID, It’s Time We Get Our Time Back

Wasting time watching content is still wasting time. Wasting time on social media is still wasting time. Wasting time on sugar addiction is still wasting time. Loneliness is a waste of time. Stress reduces your time budget. Depression decreases your capacity to make the most of time.

Yet problematic content watching, detrimental social media behavior, self-abusive eating, stress, and depression are all going up during and after COVID. Within weeks of COVID starting, we knew loneliness had skyrocketed.

We have traded billions of hours of life experiences for billions of hours of scrolling, watching, eating, drinking, and being bummed.

It’s time we get our time back.

There’s No Normal To Go Back To

Sundial is building for a new future, one inconceivable just a decade ago. It is fueled by resignations, the work from home model, which is likely to oscillate back and forth in response to crises and climate. Perhaps most importantly, there’s the rise in automation, AI, and the coming changes in the need for fulfilling employment. All these trends lead to an important inflection point: some will either get more freedom and free time, while others will feel that they have been robbed of meaning, value, and identity as their work time fades into the sunset.

People are changing the definition, the schedule, and the way they work. Crypto has given rise to a class of quasi-investors who have some financial freedom, scrutiny over how financial products work, and know how to use collective actions made possible through DAOs and other blockchain and AI-driven tools. The cherry on top is that it’s all done very quickly and on a broad scale. All of this changes what a schedule is, which changes what a calendar needs to be for people.

Seems unlikely an incumbent will make the switch.

People Care More About Life Now — Quantity and Quality

Rampant investment amounts are going into life extension and wild ideas like uploading consciousness out of the meatverse and into the metaverse. In the hundred or so years since it’s become scientifically hypothesized and confirmed that consciousness emerged from the stardust and evolved slowly across the planet, we’ve gone from realizing we are made of stardust to broadly seeking and paying for transcendence.

We care more and more about our emotional state. We care about the quality of our life experience. The rise of meditation apps and podcasters like Sam Harris and Tim Ferriss all point to self-improvement taking a new phase, which is managed by infinite learning from infinite content creation. The rise of supplements, self-care, psychedelics on stock markets, ketamine retreat centers, and an ever-growing stream of content about these macro trends all point to the central truth that people have increasingly high standards for self-care and an ever-higher entitlement to epic life journeys.

Yet, in all of this, our calendars focus on our day, week, and year ahead.

(Microsoft office calendar screenshot vs awakening god avatar)

The New Wave of Calendar Apps Do The Same Better

There is a new generation of smarter AI-scanning calendar apps emerging out of each tribe in Silicon Valley and across the world, where people are increasingly relying on non-Western calendars or multiple calendar systems running in parallel. The space industry faces perhaps the most challenging time synchronization challenges.

Respect is given for the founders, teams, and investors on this list:

The Deep Time Investment Segment

Sundial is built on our propriety deep time calendar model. It relies on SCENE, our open source first principles time modeling engine. Deep Time is growing as a category of deep tech.

Deep tech refers to technology that takes on a substantial engineering challenge to do something novel. In our case, modeling every millisecond of your life and forecasting how your behaviors will impact your life across a high parameterization schema that passes to AI.

An example of a hyper-growth company modeling deep time is TimescaleDB, which just closed a giant round. This is the beginning of new time tech.

I Have Used Time Sequence Data to Manage A Billion Lives

Getting time right matters in more ways than what we normally consider.

My background includes working with time sequence data in IoT for indoor agriculture — raising insects. Our software was collecting and responding to tens of thousands of sensor data points per day — data points that controlled the environment for hundreds of millions of living things at a time, for which I was personally responsible. High consequence, high volume data taught me just how difficult it is to put large time models into practice, even with ML/AI and best of breed data management tools.

I wish TimescaleDB was around then; it looks great on the first read about its features.

The Rise of Complex Forecasting at Low Cost

The cost of computing for even lifetime-scale data is so low it’s pennies for a single person’s whole life to run out in hundreds of thousands of events. Of course, a simulation can be tuned to be infinitely complex, but a forecast is practical. As I’m defining it for Sundial: where a simulation relies on sequences of processes and loops, a forecast can do the same but explicitly focuses on the part usable for decision-making.

Sundial is built to model not just how you will spend time, but how your time spent impacts you, like calorie analysis of your diet, but for your time.

Forecasting Who You Might Be

By forecasting variables like the longevity impact of your time investments — presumably going for a bike ride generally maintains or adds a little life, eating cheeseburgers subtracts — Sundial can forecast how your behavior impacts your lifetime budget. In theory, we can look at how your time usage affects your waistline, but that’s not the goal of our work. How you spend your time is likely how your bank account stacks up. How you spend your time is likely how your expenses add up.

I used to work in ad tech, where we could forecast which video you would watch next. These technologies and data models apply to more than your click behavior — they apply to your life.

Consequence and outcome mapping is at the core of much of the self-improvement movement, and books like “4,000 Weeks: A Life-changing Novel” and countless others highlight the results of your time expenditures.

Sundial is a calendar that keeps a score. Literally. Per event, per millisecond spent.

Is it accurate? As much as the weather. Is it informative? For sure.

Bucket List Items Are Quick and Take a Long Time

You see the solar eclipse over Patagonia. It’s 3 minutes long. If your whole life were one day, this entire experience would be something like .0000000005% of that day. Humans probably cannot perceive anything that fast.

For you to be there in that moment took a lot more time than that, though.

You earned money to shop for everything you needed, taking about 30 days. You shopped, planned, and mapped, which took about 30 days. You hiked to the mountain top, which took 3 days. You hike down, which took another 3 days. Then, you posted about it, taking about 1-3 days.

To bucket list is to plan, prepare, synchronize, and coordinate. The rarer and shorter the event, the more likely it takes extreme measures to experience it. Want to see the sunrise in Antarctica? It’s difficult. Want to see a sunrise tomorrow? It’s relatively easy for almost everybody.

Consumers’ expectations have soared in terms of their minimum viable life experience. A new generation focused on self-fulfillment wants to coordinate their work and recreational experience. A subset of crypto-rich people want fulfillment and luxuriating from diverse experiences so much that they are not just digital nomads but wealthy digital nomads.

As simulation theory poses, we could each be our own character in a world of non-player characters — NPC’s to the initiated in video game culture — we can now upgrade our characters to global travelers for relatively minor amounts of work and up front capital.

But it’s hard to actually do it. That’s where Sundial EDITOR service will help a lot.

Time Health

For most people, the headiness of simulation theory is not personally relevant. But relaxing and rejuvenating are, especially post-COVID. Among the many self-care and personal health management segments is time health.

Therapists and life coaches already focus heavily on proprietary walkthroughs and workbook exercises that map out clients’ time allocation. Some are labeled as time, but probably mean energy spent on certain things for certain amounts of time — for good reasons, in the mental health world.

In meeting with leaders in the emergent retreat center industry, including those providing ketamine-based and soon psychedelic-based services, I’ve learned that remapping life choices is essential for their therapeutic protocol. Since this protocol is regulated and must generally be the same per class of patients, they often use the same processes even before there is a protocol for a new drug or treatment. These people often compare Sundial to family systems therapy.

Family systems therapy, which I know little about, includes structural and strategic systems and theories, which focus on how people interact. Anecdotally, many practitioners say they start with a family schedule or a family calendar — if they keep one on the wall.

Cognitive Behavioral Therapy is basically a combination of calendar and self-talk.

All of this can be summarized, for our purposes, as “Time Health.”

COVID and Content

During COVID, there has been an astounding surge in content consumption, and by some accounts, Netflix is streaming 6 BILLION hours of content per month.

Some people see this as universally bad, but I think it’s more nuanced. In the earlier-referenced study, a distinction is drawn between watching for distraction from life’s problems and watching for shared social enjoyment. I would bet that’s why so many apps now have group watch mode and promote it so heavily.

I love stories. My dad was a screenwriter, and I spent my time in California living with filmmakers as roommates when I was young. I loved talking about stories and filmmaking techniques, and we teamed up in my career as an artist. But healthy watching habits ftw.

Sundial offers a powerful insight into how much of your time — literally — is going to watching content and how that impacts the other life choices that you can make. Many people snap out of it when they understand how much of their lives will go to behaviors they don’t plan.

(link)

How Much Time Have I Got, Doc? — End of Life Care

While Sundial doesn’t work in this space — yet — I’d love for us to get there. My personal experience is that people with scarce time budgets want to budget carefully and are surprisingly not squeamish about knowing how much time they’ve got. It’s the trope for the doctor delivering bad, terminal news: how much time have I got, doc?

I’m including this in a bid to find people with expertise in this to talk about how Sundial could help people get the most out of the time they have.

Life Coaching, Task Management, and Skill Mimicry Are All Time Management

Life coaching ultimately comes down to changing how people allocate time, whether functionally or emotionally. The common currency in all behavior change and career work is time and how it’s spent.

Like therapists, many life coaches start with calendar audits and move on to scheduling and time-based processes. Task management techniques are time management techniques.

The internet has enabled a new class of learning and apprenticeship: mimicry based on digital content. From how-to guides to DIY websites, from Youtube to MasterClass, a colossal industry is built around mimicking skills and behaviors. All of these, too, come down to how to spend your time. Every secret process, tip, or trick revealed requires time to do it — even things that may not seem like shopping for recommended gear. Try doing it in zero seconds.

Ultimately, Sundial will be a time mimicry app.

EDITOR, Character Player Mindset — The Rise of Simulation Theory Compatible Apps

Whether it’s tech CEOs commenting on how billionaires they know consider most people non-player characters (NPCs), Elon endorsing simulation theory, constant Nick Bostrom references, or headlines about reality being a hallucination — we are experiencing new interpretations of our own existence. Just a hundred years after quantum physics emerged from the stardust in homo sapiens’ minds, we now question if there is a base code we cannot see under the code we can see — perhaps it’s yet another religion; maybe some new truth.

In either case, many adherents to this theory see it as a mechanism for liberation, philosophical justification to make the most of their individual lives. For those people who take simulation theory seriously, Sundial is a powerful near-metaphysical tool to edit your own storyline.

Our premiere in-person service runs off a piece of software called EDITOR.

Digital Twins — Done Your Way

Many — many — companies are building digital twins of you. Models of your past behavior are extrapolated into sequential series of odds that you will click, convert, engage, share, and so on. Some companies build digital twins for you to have an avatar inside a game, a metaverse, or META’sverse. Some build digital twins for you to get customized cancer drugs. Some build them to assess if you’re a security threat in a simulation model if you work in high-security industries. Your car might profile how you drive; your coffee pot might have a model of your behavior. Your smart devices definitely do. Google has a detailed model of you.

Bob Iger just joined Genies, Inc — whose tagline is “Genies | The Fantasy Version of You” and is focused on accessorizing fantasy digital twins.

Sundial builds a model of who you want to be. An Aspirational Digital Twin. (tm)

I once worked for a small company called Mantrii that eventually became a part of a big credit card company, and we built ad-buying models. Very sophisticated models of what a buyer might do. This and experiences working with Fullscreen and other video platforms led me to believe the scaffolding on which the metaverse will be built is ultimately a series of digital twins, customizations, predictions, and guidance as to who you will be.

It’s critically important that our digital twin process includes who we want to be. That’s what Sundial does in the tech ecosystem: Digital twins of who you want to be.

The Post-Awakening Market

As I have market-tested Sundial EDITOR with dozens of users, one-one-one, often for hours at a time, I have come to see one thing in common with the people who value the experience the most: They have had some type of awakening in their life that has led them to want to upgrade their character, to be their best self.

That best self comes in various formats and often after a variety of punishing trials that teach us each who we want to be: Our own hero’s journey, our own life quest.

Awakenings are increasing exponentially. The rise of legal and “out” (above ground) psychedelic use is a driving factor in people making profound changes, and probably 10-20% of the people who tested Sundial EDITOR associated this experience with their past experiences of being “trippy” in a way akin to their prior psychedelic-induced experiences. With legislation changing, and legal treatment centers on the rise, the options are increasing for Sundial to be a service provider in this emergent space — when the time is right.

But people can awaken without any drugs or any specific moment. Some people credit a practice like meditation or yoga. Some people are just high on life. Some people have a religious epiphany. Some have trauma that awakens a sense of urgency. Some people just have very long bucket lists and would balk at being called awakened. But across each is a joie de vivre, a marked enthusiasm for the gift of consciousness.

But the biggest awakening to the shortness of time is rooted in something else. From what I can tell, COVID is an existential shock to many people’s systems, and they want to both make up for lost time and embrace life itself. Whether it’s the loss of loved ones or the lived experience of extreme quarantine, it has been a time of loneliness, loss of connection, and loss of experience. No culture or mindset separates any of us from that.

Life is too short, or too long, for me to allow myself the luxury of living it so badly.

Paulo Coelho, The Alchemist

Act 3: A New Hope for the Future of Calendar Apps

Why clocks? Who needs them? After all, nature is the great time-giver (Zeitberg), and all of us, without exception, live by nature o’clock.

David S. Landed, Revolution in Time

10 Abusive Traits of Your Calendar

1. Do these activities make you feel good? Doesn’t matter.

2. I’m not here for what you have done, just what you haven’t.

3. Every entry must. be. the. same. format.

4. Bad weather day? Awww... Who cares 9 am is 9 am, fool.

5. I don’t care if you do the thing; just tell me you did the thing.

6. Traveling? I don’t like that. Stay in the same time zone.

7. Doesn’t matter what you want to talk about; things take 15-minute increments.

8. Forget to do something? Let me grey that out and automatically hide it for you.

9. You know what, give me control of your calendar, and I will put your life on a website where other people can take your time when they want. When it comes to your job, be a professional. Put your time out like a buffet line to pick at.

10. Won’t talk about death. Pretends time is infinite. Knows it’s scarce. Had emotional breakdown at Y2K — legit thought the world would glitch out of function.

Actually, you know what? Do what I tell you. I’m watching now.

Do What You Got To Do, Then Do What We Sell You

Here’s an anecdotal taxonomy for how a lot of people divide time into classes:

1. What I have to do

2. Empty time, where I do passive stuff, and sometimes active stuff

Category 1 includes work, the dentist, sleeping, eating, etc. The second is everything from watching shows to shopping. Whatever season, culture, or habit require. This works great if you run a social media company, a content company, or a shopping company. It’s not so great if you are a human with scarce time, trying to make the most of it, experiential, professionally, and in your relationships of love and friendship.

Sundial breaks that system, turning life into a beautiful journey. Sundial increases intention in action, which is a key to happiness.

Sundial - Solving Real Problems for Real People

The people who have most appreciated the Sundial EDITOR experience have real problems to solve and real goals to hit. Whether it’s directing a film or raising a kid more intentionally, that’s what they care about.

Real user’s recurring problems have been:

1. COVID habits, especially too much social media and too much content

2. Loneliness from macro factors like economic, social, and cultural change

3. Career instability due to economic changes

4. Sadness, and in some cases, depression, burnout, and other inflection points

5. Positive inflection points, including graduation, career change, or retirement

But those aren’t the metrics by which those people want to design and measure their success. Those look more like this:

1. How many activities am I doing that stress me out?

2. How much time am I spending adding life to my timeline by exercise or other healthy activities?

3. How much time am I losing to behaviors I don’t like engaging in?

4. How many intentional plans do I have on the calendar with loved ones?

Solving Real Problems Part 2: My Personal Experience “Dog Fooding” Sundial

To eat one’s own dog food — a venture capital term for using your own startup’s product to accomplish your relevant growth goals for the company.

As described at the beginning of this essay, my personal experience mapping out family stargazing trips led me to build Sundial. But Sundial has done much more than that for me. It’s time to tell my personal story with the app briefly.

I’ll skip the first several Time/Bank prototypes, and my sincere thanks and apologies to the friends and family who spent several hours having a “time banker” — me — map out their time budgets and planning their investment activities. It was...theatrical.

Here’s where Sundial counts for me: when I was a kid, my dad wrote a book about the Gullah people, who lived freely on the coast, even at a time of slavery in Georgia. I grew up going to Savannah for his background research, and later my brother lived there. I remember eating in basement kitchens, where home chefs showed me what southern cooking was, the old way. While that may not exist the same way today as it did back then, using Sundial led to a lengthy planning process with my wife, including grandparents. I’m proud to share we have plans to be in Savannah on March 14th, 2023 — 03.14 — to go on a tour of Southern pies. (I married a west coaster who knows not the ways of the great Charleston - Savannah pie corridor, which deserves to be the Napa of the south.)

It doesn’t stop there. This is the book of my life goals I had printed. And below it, there’s a book of activities we’ve mapped out for my kids.

My wife and I go on exciting dates from a list of life choices we made. I have no shortage of projects and goals, and I feel fulfilled in my life quest, for the most part.

Around my house, when making choices about how to spend time, we often say “in a sundial kind of way…” when thinking about what matters in life. I’m proud of that.

Feeding My Kids Dog Food / Sundial for Kids

My kids also benefit from a version of Sundial — a printed one custom-made for them.

I teach my kids they are made of the future. I teach my kids what I’m working on. I teach my kids why it’s important. Over time, my daughter — who is six — wanted her own Sundial book. So she worked with me to make a book of icons showing her favorite things and some chores. We use it about half of the weekends. Our diversity of activity is broad. From forts to fairy houses, obstacle courses, to oatmeal. We love it.

We teach our kids they are made of stardust and that the dinosaurs are our ancestors. I see already how Sundial will mature into a product that supports their personal growth and development when they’re older. I will do my best to make it that for them.

If you’d be interested in getting a Sundial kids activity book, please email me, Sean, at sundialcalendar.com.

But Wait — How Is Any of This A Calendar?

The Sundial web app is just the beginning of our DIY tool stack. EDITOR exports to gCal, iCal, and Outlook.

Budget of Conscious Hours and The Sundial Metaphor

I’ve tested about a dozen types of metrics — from how much time might earn to how it might impact your calorie burn — and what seems to matter is, first and foremost, how much time do I have? That’s how Sundial became Sundial. The metaphor is: if you were born at sunrise and die at sunset, what time is it right now, on the day of your life?

This consolidation of your whole life down to a day makes it simple enough to see how your time is spent from here forward will play out. The natural next question is: How much time do I have to spend, and how do I want to spend it? That’s the Budget of Conscious Hours (BCH). It’s how much time you have left, with sleep subtracted.

(We’ll be adding more options to refine your BCH over time. It should be close enough to give you an idea right now. Check it out in the web app. It takes about 30 seconds.)

Once people know their BCH, they want to see how much a specific Activity “costs” from their budget. The BCH is a scarce thing, and each activity you commit to takes a deduction from it.

So, each calendar activity you plan is spending a percentage of your whole life. That’s exactly how life works.

Metrics That Matter for Your Aspirational Digital Twin (tm)

Once people create an Aspirational Digital Twin (tm), they tend to run a few forecasts of how its life goes. They want to see how it plays out to act a few different ways. Often they change out bucket list items. Hike Everest, or write a novel? Your 40s will struggle to provide enough time for both unless you have a lot of free time. (And coffee?)

The key metrics people seem to care about, beyond Intent, which is the core of all of this, seem to be:

1. Mimetic Model — Am I doing the things I envision my identity to do?

2. Longevity — How does this impact how long I will live? Does is shorten or extend life?

3. Happiness — Will be I be happy planning and doing these things?

4. Relationship Consistency — Am I being a good friend, partner, sibling, etc.?

5. Financial Planning — Am I aligning my dreams with my day job? How much do I need to live all the adventures I want to live?

6. Free Time — How can I find the time to do more of what I love? Less of what I don’t love?

Forecasting > Simulation

At first, I built a simulation tool and looked for simulation experts. Now I understand: Sundial is a forecasting tool. We can estimate how your life might go, but providing a persistent simulation is exponentially more complex and expensive. It also raises ethical questions about what’s healthy for self-awareness and potentially getting task planning dopamine instead of doing dopamine. More than one credible entrepreneur or investor has also pointed out it raises, for some people, metaphysical, religious, and cultural questions.

Our human minds are not conditioned to handle the amount of data Sundial churns out, and real prediction is as impossible as it is for forecasting the weather at an exact millisecond of 2049.

Our Data Model: First Principles Model of Human Time Expenditures

You will definitely eat. And drink. And go to the bathroom.

You almost certainly will use a phone. You will sleep.

It may vary, but there are things nearly all of us do. The more interconnected our global systems get, the more similar those uses of time become. Video games used to not exist but now make up huge percentages of people’s lives.

Perhaps most importantly is that time is scarce for us all. Nobody lives to 400 years old. Most people live many decades now. But we pass. Ashes to ashes; dust to dust.

Many things are predetermined, and many things have bounds of physics, biology, policy, or principle. For example, if you are in your 40s and wear glasses, playing the “fighter jet pilot” character is off the table. If you are in your 20s, spending time with your grandkids is out.

Things also take time. You can’t watch a show in a minute. You can’t go sailing in an hour. You can’t skydive for 3 minutes.

As Sundial EDITOR progressed, I realized that what I was building was a first principles database of human time expenditures. As Sundial progresses, you’re going to hear a lot more about this.

Sundial as a Way of Understanding Life

Your data structure must align with your life philosophy. As witnessed by crypto.

As I have built Sundial over the past year, at first slowly as a side project, then full time for six months now, what I have learned is time is what we think of it.

Just like the sun will set, our time will pass. I believe consciousness needs better tools to plan individual experiments. We are each an experiment, emergent temporarily from the stardust, held together in what Buckminster Fuller described as a temporary “integrity” — a cluster of emergent self-organizing components resulting in self-aware consciousnesses for a cosmic flash of time.

Sundial started as a better calendar but now shapes my thinking for how the world to come will work. We will all play many characters. We will download new skills quickly. We will optimize processes, and effective mimicry will give us near superpowers for new skills. This will result in a long list of experiences to experience.

At the same time, the reward for sharing, connecting, posting, commenting...it has put double pressures on being cool. For thine own self, be cool.

What I love about the Sundial metaphor is that the Sundial, unlike any human clock, exists whether we want it to be there or not, whether humans exist or not. The shadows of trees surely fell before homo sapiens. Clever animals track the sun. Time passes, and the Sundial passively observes that.

The first Sundial was found outside a goddess temple in Ancient Egypt. Since the advent of mathematical thinking, we have been using the sun to track the time of day and the moon to track the day itself — or really, the lunar cycle.

I like to think of Sundial as an artifact from the future.

I’m not the only one embracing a new theory of time.

But Can I Just Do The Thing In A Minute? I’m Busy.

Yes. Presenting: The Sundial Web App.

The Sundial EDITOR process is incredible. “Life-changing.” People really say that.

But it takes hours. It’s complex. It would take days to map your whole life. We can model about 6-10 Activities per hour of configuration. This is great for our in-person operations and core to our approach to market.

BUT people want to do things quickly. We all want the 1 minute proof of concept.

So, working with a great independent web developer, José del Valle, creator of KnobsAI and other cool apps, we’ve built a web app. It calculates your Budget of Conscious Hours deducting sleep and runs the numbers on a single activity like Date Nights.

This is limiting. Obviously, EDITOR can visualize your whole future, and this is forecasting — in a simple way — your behavior from one activity. But it’s fun and works and people responded to it well in early testing.

I hope you’ll check it out. One out of every 100 web app users earns a free EDITOR session for themselves or as a gift to give to someone at an inflection point in life.

Alpha Web App Limitations

There is no user registration system, but account claiming will come soon. You can’t edit the event names. You can’t time travel yet. All soon.

A Note On Inventing Time Travel

It was late at night, and I made an update to EDITOR. All of a sudden, I could type in a date and time and get a forecast for my life at that point. Where would I be? What would I have completed? How old are my kids? How many barbecues have I gone to? How many date nights with my amazing wife will I have had?

Like a full proper grown-up, I typed in April, 20, 2069, at 4:20 pm.

But then I got more serious. My wife and I often talk about our little kids as though they were adults about to join our conversation — it’s a good way to be a good parent, or at least to try. We talk about “28-year-old Jackson,” as though that person will show up any minute, and what she would want for him. Seeing that we’d have maybe 20 stargazing trips with Grandpa between now and then brought a tear to my eye.

Ever since I was a kid, I have wanted to invent time machines. I used to sit in the garage, building boards full of nails tied together with wires. I would offer to take neighbors to see the dinosaurs. When my mom was late for work, I would zap her backward to make it okay.

Now I’m more concerned about going forward in ways that make me more present, intentional, and connected in the present.

Time Travel in VR

I’ve been using the Vive Flow and Quest 2, and they’re great. You can go anywhere you want. Soon you will be able to go to any time you want. Our role is to allow you to meet any you that you want to meet.

This is How We End — Literally

During the week I’ve been writing this, new research has been published that shows the brain may very well flash the entire life before your mind’s eye before you pass away. A summary visualization of your whole life, in moments.

I think that may have happened to me when I had that near-death experience, now almost precisely half my life ago. What I learned then, try to live now, teach my kids, and want to offer the world is this: At that moment, we flash not on our fortune, not on our job titles, not on our wallets and NFTs. We flash on the life we lived. Most of that is with people, but some are just “helluva moment” moments we have by ourselves, like reading a great book or enjoying a long bath.

Many people who have had near-death experiences undergo some type of awakening and transition. I hope Sundial offers that benefit without all the pain. The ability to grow the way you choose. If we can do that sooner, we may live a much better, richer life and pass down a legacy of empowerment, discovery, and curiosity.

THANK YOU!

I’m committed to helping as many people as possible live the life they need — and want — to live. We have a new world to build. Thanks for reading.

Sean

Time After Time: A Reading List

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See the mirror version of this post to get the full reading list: https://mirror.xyz/0x936fB3dCC849b01d12154B6F96391DceBBb697DB/UaM3uNLJL_ZEi4pOLMM-Ne7n-V8yYrK2_mcAnHmLQCI

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