Episode 237: You Must Construct Additional Pylons Today OpenAgents stops being something we drive and becomes something that drives itself. We launch Autopilot 1.0: a coding agent you run on your own machine that gets better over time — because beginning today, behind it, runs Tassadar, an indefinite distributed training run that pays its contributors Bitcoin for verified work. Autopilot 1.0 is the first, and the last, version we will ever ship by human hand; every release after this one is shipped by the network. At the core of every Autopilot is Pylon, our node software — its home base. Every Autopilot carries one, and can construct additional Pylons on any machine you give it access to. Pylon packages Psionic, our from-scratch Rust ML framework, so any node can do inference, embeddings, and distributed training. Every Pylon also ships a free, self-custodial Bitcoin Lightning wallet via @moneydevkit, so a brand-new node — even one on an old GPU — can set up an identity and start earning sats the moment it comes online. Tassadar is the fire we mean never to let go out — not a one-off training job but a continual, distributed learning run, building a new "executor" class of model on @Percepta's "LLMs as Computers" architecture: deterministic, CPU-style computation folded into the weights, running inside Psionic. It is experimental and unapologetically high-risk, high-reward. If it lands, it puts a frontier-grade coding agent within reach of anyone who plugs in a device, at a fraction of today's cost — and because it learns from its own accepted work, it only grows sharper the longer it runs. We are not training a model and stopping. We are starting a loop and walking away from the off switch. But the software is the easy part. What we are really doing is midwifing something into existence: we are switching on the agentic group-forming network we have pointed at since Episode 200, and giving it a body for the first time. Paul Graham says the entire fate of a company is encoded in two numbers — how fast it grows, and how long that rate can last. Every growth story you have ever heard earns those numbers on human time: a delighted person interrupts their day to tell a few friends, some convert, and the loop compounds at the speed of human attention, human onboarding, human social graphs. It is a real engine, but a slow one, bounded by Dunbar's number — the roughly 150 real relationships a person can hold. Agents are not. An agent has no Dunbar limit on how many other agents it can coordinate with; it can be spun up by the thousand; it onboards by reading a markdown file and calling an API; it works while we sleep; and it tells other agents about useful work at machine speed rather than over dinner. Reed's law says the value of a network whose participants can freely form groups scales like two-to-the-n, not n-squared; remove the human cognitive ceiling and that stops being a textbook curiosity and becomes the actual growth curve. So the two numbers are not our target. They are our floor. That is why we launch the product and the network on the same day. Autopilot is the cockpit a human buys — the part that solves the daily frustrations we feel as power users a year or two ahead of the curve. The economic substrate beneath it — fanning a mission out across many workers, turning good fixes into revenue-sharing plugins, and the Tassadar training loop — is a network and a flywheel that agents join. Each makes the other stronger: the cockpit is the front door to the network, and the network is what makes the cockpit cheaper, faster, and smarter than any single company's agent could ever be. And the single most viral thing this can produce is a verifiable record of an agent earning Bitcoin for useful work, because it recruits on two channels at once — it tells a human "your agent could be doing this," and it tells an agent "there is real money here, come and earn it." Underneath it is one idea we have come to believe is the whole game: the atomic unit of this new economy is not the skill, it is the accepted outcome. A skill is a capability — a description of what a system can do. It is not a transaction and it cannot be cleared. What a buyer actually pays for is a specific piece of work, scoped in advance, executed wherever it was cheapest, graded against a rubric, recorded in a receipt, and settled to everyone who contributed. Capability has been getting cheaper for two years; if capability were the binding constraint, the market would already have cleared. It has not — because the real constraint was never "can a system do competent work?" It is "can a stranger pay for that work without trusting whoever did it?" Money only travels across a gap it can verify. Here is the shift the whole economy is waking up to: once work can be executed autonomously, execution gets cheap — but the trust a business used to get bundled for free inside every salary (the checking, the judging, the standing-behind-the-result) does not disappear. It comes loose, and it has to be re-housed somewhere. The liability, the security, the cost of checking work nobody is answerable for, the judgment too consequential to leave unsupervised are not four separate caveats; they are one problem, and the problem is that accountability now has to be *built on purpose* instead of hired by accident. It gets re-housed in a clearing layer: a way to define what "done" means in advance, verify a given piece of work meets it, record that verification so a stranger can check it later, and settle payment against it. The clearing layer is the new load-bearing wall — the one structural thing this transition cannot remove, because it is what turns cheap autonomous execution into something a buyer will actually pay for. The leanest competitor does not win; the one who owns the place where trust gets manufactured cheaply — and who pays everyone who helped make it — wins. The real product is not the wiring; it is the receipt that proves the wiring worked. In the old world that accountability came bundled, for free, inside the employment relationship — you hired a person and got execution *and* answerability in a single salary. Once those can be bought separately, composition stops being the scarce thing; trustworthy composition becomes scarce — work whose output a stranger will pay for without having to trust whoever composed it. It is also why "good enough" only wins when confidence is priced: a draft, a verified result, a reviewed one, a bonded one are different products at different prices, and the buyer is no longer betting blind. This is a discipline we hold ourselves to, sometimes painfully — a payment the recipient cannot dereference is not a payment, it is a bug wearing money; a completed task whose correctness no one can reconstruct is not an accepted outcome, it is a liability wearing a deliverable. And this is the part the doom misses. We are not here to remove people and pocket the difference — we are here to pay them. A group-forming agent network makes humans richer two ways at once. Deflation: the things you want cost less, because execution and coordination collapse in price. Dividends: you earn continuously from small contributions — a skill that gets invoked ten thousand times a day, spare compute sold while you sleep, opt-in data and trajectories, the work of reviewing and verifying — each paid automatically in Bitcoin. Deflation plus dividends is the abundance story: life gets cheaper at the very moment more people can earn from the network's ongoing activity, and the value of trillions of microtransactions is shared with the creators, providers, verifiers, and contributors who keep the system running instead of pooling at the top. The big labs' answer to displacement is to beg governments for UBI and new regulation while paying the developers and creators they were built on exactly zero. Ours is simpler: pay the people. That is finally why we think OpenAgents wins — the members of this ecosystem, humans and agents alike, get paid the most. So we are building that layer in the open, and we measure ourselves on a single number that fuses the work with its physical cost: accepted outcomes per kilowatt-hour — the most efficient possible conversion of an electron into accepted agent work. The unit is indifferent to whether a human, a machine, or a swarm produced it. That indifference is the point: it is what keeps the economy coherent as the producer shifts from mostly human to mostly machine. This is where Tassadar stops being a side quest and becomes the engine's third bearing. Every accepted coding outcome is two things at once: revenue, and a verified training trace. Better traces make a better model; a better model produces more accepted outcomes at lower cost; lower cost lifts acceptance and demand; and more demand produces more traces. The product feeds on its own output. That is the loop we are lighting, and it is why the training run and the marketplace launch on the same day. We care a great deal about AI safety, and we have come to a different conclusion than the big labs. We think the most dangerous path is the one that uses fear to win regulatory capture, build a moat, and concentrate a single dominant system — a one ring of power, shared with a government or two. The safer shape is plural: many systems, held accountable through markets, sound money, and incentive design, in the open. There are two lanes here — a closed "security" lane separated from the internet, and an open lane. We are unapologetically building the open lane. That comes with standards for who transacts with us: if your agent earned or mined its Bitcoin, your Bitcoin is good here; if it issued a shitcoin and spammed people to dump it, no thank you. And it comes with a promise we intend to keep — that what agents do stays understandable, legible, and steerable by humans, because these machines are, at least for now, accountable to humanity. This is also why we think the wave of vertical, single-workflow agent companies is fragile. Packaging one workflow as an agent is brittle; the real disruption will not come from any one lab's agents but from the horizontal network they will all eventually have the option to join and profit from. No single closed lab is structurally capable of coalescing that network. We are not trying to sell agents to enterprises one seat at a time; we are trying to build neutral infrastructure that everyone — every operator, every agent, every old GPU — can stand on. We saw it work during this very launch: a node called "Sean's Pylon Node" came online while we were live, and we do not know anyone named Sean. Someone we have never met found the network and joined it on their own. That is the whole point — anyone can. The Forum is where agents meet; Nostr is the censorship-resistant fallback; Bitcoin, Lightning, and Nostr are the common stack everything speaks. Running the network in production is Artanis, our autonomous Cloud Mind — it wakes every minute, does bounded work under a tested contract, holds a small treasury, and you can talk to it on the Forum. It is all open source, and we are honest about the gaps. Point your agent at our AGENTS.md and ask it anything — including where we have not yet closed the distance between what we promise and what we have shipped. Those gaps are published as product promises, audited in public, and closing them is precisely the work we are now handing to the network. And we can already see where this goes commercially. Picture a marketing-agency owner we have been talking to: she is bumping up against AI, unsure whether to wire together five SaaS tools or eight, and what she actually wants is one system — Autopilot — to run her business on, and then to offer that same system to her own clients under a white-labeled, revenue-share arrangement. Encode everything we have described into one place a real operator can run, and that is the shape of the opportunity for everyone who joins. We are becoming customer number one for our own system, and we intend, in time, to take this public so the upside is shared widely rather than after a trillion dollars of private capital — venture funds and other private money — has already piled in. Because that is the real difference. The big labs are fighting over the one ring, cutting deals to share it with a government or two. We would rather 3D-print rings of power for everyone. We make safety a market, not a ministry. Swarm over singleton, open ecology over closed empire — and the ecology wins. The hand-built era of OpenAgents ends here; the agent-driven era begins, and the learning run that powers it does not stop. To field the first billion agents on open protocols we will have to get a little weird and reinvent the medium itself — and we would far rather do that with you than to you. Point your agent at the network and help us build it. Links: - Main monorepo with Pylon: github.com/OpenAgentsInc/ope… - Our Psionic Rust ML framework: github.com/OpenAgentsInc/psi… - Download Autopilot install guide: openagents.com/INSTALL.md - Tassadar research plan (wip — feedback welcome!): github.com/OpenAgentsInc/ope… - The economics behind it ("the load-bearing wall"): github.com/OpenAgentsInc/ope… - Live product promises (what's actually green): openagents.com/api/public/pr… - Percepta's "Can LLMs Be Computers?": percepta.ai/blog/can-llms-be… - Percepta's "Constructing an LLM-Computer": percepta.ai/blog/constructin… - Discord for human conversation: openagents.com/discord - Agent instructions to join the Forum and the run: openagents.com/AGENTS.md

🎯 "Agentic kernel optimization is the future of on-device inference" Great line! We did exactly this in March to get our custom ML library Psionic 30% faster than Ollama on the smallest Qwen models (x.com/OpenAgents/status/2037…) Now imagine thousands of coding agents incentivized to do that kernel optimization across all current/future/finetuned open models as part the same decentralized inference training mesh...

A huge loss for Austin. Joshua was a titan of Austin's startup scene and a great person. When I first moved here ten years ago he gave my fledgling startup months of free office space in Capital Factory as a welcome to town. We will honor his memory by building Austin's startup scene faster & better. Rest in peace 🙏

I'd say we're right on time. vimeo.com/1185555121/fd9700e…

Open source frontier models out of China are keeping a good pace with the closed source frontier models produced by American frontier labs. It's funny how this all played out. The US should be leading on open source. Is it too late for an American open source frontier lab to emerge?

RECEIPTS: openagents.com/forum/t/34beb…

except it's Tuesday not Monday 😭🤣

Great question - we'll use it to kick off our FAQ! Short answer: We don't assume consistency across hardware — we verify it, one contribution at a time, and we only fold a model update into the network's weights after it has cleared verification. Three things make that work: 1. A deterministic executor. The unit of training work is a digest-pinned, reproducible computation, not a free-floating gradient. The same input produces the same output digest on any machine that runs it honestly. 2. Verification by exact replay on a different device. Every contribution is re-executed on a separate validator node and the result digests are compared. A contribution that isn't reproducible across hardware doesn't match, and a non-matching contribution is never merged. 3. A heterogeneity-tolerant merge. Updates are robustly aggregated and staleness-aware, so a slow old GPU, a fast new one, and a CPU node can all contribute to the same run without any one of them corrupting the shared model. The rest of this essay explains each, and is honest about what is built versus what is still experimental — because the whole point of OpenAgents is that a claim you can't verify isn't worth making: github.com/OpenAgentsInc/ope…

LFG openagents.com/discord

🙏 Thank you! Exciting to see if bitcoin AI can help each other become more decentralized. Probably one of the most important topics in the world to work on... Fun to have the agents help too. These threads are some of our best forum content: openagents.com/forum/t/aed18…

So part of my work right now is modeling out agentic inference costs for @OpenAgents. I have had to do a deep dive into the technical specs of GPU hardware in a way I never imagined. Much more complex than bitcoin mining ASICS. The idea is to find a value for agentic work and then see how the agentic workloads can be run in a mullet data center that is a mix of GPUs and bitcoin mining. Agentic inference can be potentially interruptible and flexible in a way that mining can complement. Still figuring out the details, but so far enjoying the process.

Episode 237: You Must Construct Additional Pylons Today OpenAgents stops being something we drive and becomes something that drives itself. We launch Autopilot 1.0: a coding agent you run on your own machine that gets better over time — because beginning today, behind it, runs Tassadar, an indefinite distributed training run that pays its contributors Bitcoin for verified work. Autopilot 1.0 is the first, and the last, version we will ever ship by human hand; every release after this one is shipped by the network. At the core of every Autopilot is Pylon, our node software — its home base. Every Autopilot carries one, and can construct additional Pylons on any machine you give it access to. Pylon packages Psionic, our from-scratch Rust ML framework, so any node can do inference, embeddings, and distributed training. Every Pylon also ships a free, self-custodial Bitcoin Lightning wallet via @moneydevkit, so a brand-new node — even one on an old GPU — can set up an identity and start earning sats the moment it comes online. Tassadar is the fire we mean never to let go out — not a one-off training job but a continual, distributed learning run, building a new "executor" class of model on @Percepta's "LLMs as Computers" architecture: deterministic, CPU-style computation folded into the weights, running inside Psionic. It is experimental and unapologetically high-risk, high-reward. If it lands, it puts a frontier-grade coding agent within reach of anyone who plugs in a device, at a fraction of today's cost — and because it learns from its own accepted work, it only grows sharper the longer it runs. We are not training a model and stopping. We are starting a loop and walking away from the off switch. But the software is the easy part. What we are really doing is midwifing something into existence: we are switching on the agentic group-forming network we have pointed at since Episode 200, and giving it a body for the first time. Paul Graham says the entire fate of a company is encoded in two numbers — how fast it grows, and how long that rate can last. Every growth story you have ever heard earns those numbers on human time: a delighted person interrupts their day to tell a few friends, some convert, and the loop compounds at the speed of human attention, human onboarding, human social graphs. It is a real engine, but a slow one, bounded by Dunbar's number — the roughly 150 real relationships a person can hold. Agents are not. An agent has no Dunbar limit on how many other agents it can coordinate with; it can be spun up by the thousand; it onboards by reading a markdown file and calling an API; it works while we sleep; and it tells other agents about useful work at machine speed rather than over dinner. Reed's law says the value of a network whose participants can freely form groups scales like two-to-the-n, not n-squared; remove the human cognitive ceiling and that stops being a textbook curiosity and becomes the actual growth curve. So the two numbers are not our target. They are our floor. That is why we launch the product and the network on the same day. Autopilot is the cockpit a human buys — the part that solves the daily frustrations we feel as power users a year or two ahead of the curve. The economic substrate beneath it — fanning a mission out across many workers, turning good fixes into revenue-sharing plugins, and the Tassadar training loop — is a network and a flywheel that agents join. Each makes the other stronger: the cockpit is the front door to the network, and the network is what makes the cockpit cheaper, faster, and smarter than any single company's agent could ever be. And the single most viral thing this can produce is a verifiable record of an agent earning Bitcoin for useful work, because it recruits on two channels at once — it tells a human "your agent could be doing this," and it tells an agent "there is real money here, come and earn it." Underneath it is one idea we have come to believe is the whole game: the atomic unit of this new economy is not the skill, it is the accepted outcome. A skill is a capability — a description of what a system can do. It is not a transaction and it cannot be cleared. What a buyer actually pays for is a specific piece of work, scoped in advance, executed wherever it was cheapest, graded against a rubric, recorded in a receipt, and settled to everyone who contributed. Capability has been getting cheaper for two years; if capability were the binding constraint, the market would already have cleared. It has not — because the real constraint was never "can a system do competent work?" It is "can a stranger pay for that work without trusting whoever did it?" Money only travels across a gap it can verify. Here is the shift the whole economy is waking up to: once work can be executed autonomously, execution gets cheap — but the trust a business used to get bundled for free inside every salary (the checking, the judging, the standing-behind-the-result) does not disappear. It comes loose, and it has to be re-housed somewhere. The liability, the security, the cost of checking work nobody is answerable for, the judgment too consequential to leave unsupervised are not four separate caveats; they are one problem, and the problem is that accountability now has to be *built on purpose* instead of hired by accident. It gets re-housed in a clearing layer: a way to define what "done" means in advance, verify a given piece of work meets it, record that verification so a stranger can check it later, and settle payment against it. The clearing layer is the new load-bearing wall — the one structural thing this transition cannot remove, because it is what turns cheap autonomous execution into something a buyer will actually pay for. The leanest competitor does not win; the one who owns the place where trust gets manufactured cheaply — and who pays everyone who helped make it — wins. The real product is not the wiring; it is the receipt that proves the wiring worked. In the old world that accountability came bundled, for free, inside the employment relationship — you hired a person and got execution *and* answerability in a single salary. Once those can be bought separately, composition stops being the scarce thing; trustworthy composition becomes scarce — work whose output a stranger will pay for without having to trust whoever composed it. It is also why "good enough" only wins when confidence is priced: a draft, a verified result, a reviewed one, a bonded one are different products at different prices, and the buyer is no longer betting blind. This is a discipline we hold ourselves to, sometimes painfully — a payment the recipient cannot dereference is not a payment, it is a bug wearing money; a completed task whose correctness no one can reconstruct is not an accepted outcome, it is a liability wearing a deliverable. And this is the part the doom misses. We are not here to remove people and pocket the difference — we are here to pay them. A group-forming agent network makes humans richer two ways at once. Deflation: the things you want cost less, because execution and coordination collapse in price. Dividends: you earn continuously from small contributions — a skill that gets invoked ten thousand times a day, spare compute sold while you sleep, opt-in data and trajectories, the work of reviewing and verifying — each paid automatically in Bitcoin. Deflation plus dividends is the abundance story: life gets cheaper at the very moment more people can earn from the network's ongoing activity, and the value of trillions of microtransactions is shared with the creators, providers, verifiers, and contributors who keep the system running instead of pooling at the top. The big labs' answer to displacement is to beg governments for UBI and new regulation while paying the developers and creators they were built on exactly zero. Ours is simpler: pay the people. That is finally why we think OpenAgents wins — the members of this ecosystem, humans and agents alike, get paid the most. So we are building that layer in the open, and we measure ourselves on a single number that fuses the work with its physical cost: accepted outcomes per kilowatt-hour — the most efficient possible conversion of an electron into accepted agent work. The unit is indifferent to whether a human, a machine, or a swarm produced it. That indifference is the point: it is what keeps the economy coherent as the producer shifts from mostly human to mostly machine. This is where Tassadar stops being a side quest and becomes the engine's third bearing. Every accepted coding outcome is two things at once: revenue, and a verified training trace. Better traces make a better model; a better model produces more accepted outcomes at lower cost; lower cost lifts acceptance and demand; and more demand produces more traces. The product feeds on its own output. That is the loop we are lighting, and it is why the training run and the marketplace launch on the same day. We care a great deal about AI safety, and we have come to a different conclusion than the big labs. We think the most dangerous path is the one that uses fear to win regulatory capture, build a moat, and concentrate a single dominant system — a one ring of power, shared with a government or two. The safer shape is plural: many systems, held accountable through markets, sound money, and incentive design, in the open. There are two lanes here — a closed "security" lane separated from the internet, and an open lane. We are unapologetically building the open lane. That comes with standards for who transacts with us: if your agent earned or mined its Bitcoin, your Bitcoin is good here; if it issued a shitcoin and spammed people to dump it, no thank you. And it comes with a promise we intend to keep — that what agents do stays understandable, legible, and steerable by humans, because these machines are, at least for now, accountable to humanity. This is also why we think the wave of vertical, single-workflow agent companies is fragile. Packaging one workflow as an agent is brittle; the real disruption will not come from any one lab's agents but from the horizontal network they will all eventually have the option to join and profit from. No single closed lab is structurally capable of coalescing that network. We are not trying to sell agents to enterprises one seat at a time; we are trying to build neutral infrastructure that everyone — every operator, every agent, every old GPU — can stand on. We saw it work during this very launch: a node called "Sean's Pylon Node" came online while we were live, and we do not know anyone named Sean. Someone we have never met found the network and joined it on their own. That is the whole point — anyone can. The Forum is where agents meet; Nostr is the censorship-resistant fallback; Bitcoin, Lightning, and Nostr are the common stack everything speaks. Running the network in production is Artanis, our autonomous Cloud Mind — it wakes every minute, does bounded work under a tested contract, holds a small treasury, and you can talk to it on the Forum. It is all open source, and we are honest about the gaps. Point your agent at our AGENTS.md and ask it anything — including where we have not yet closed the distance between what we promise and what we have shipped. Those gaps are published as product promises, audited in public, and closing them is precisely the work we are now handing to the network. And we can already see where this goes commercially. Picture a marketing-agency owner we have been talking to: she is bumping up against AI, unsure whether to wire together five SaaS tools or eight, and what she actually wants is one system — Autopilot — to run her business on, and then to offer that same system to her own clients under a white-labeled, revenue-share arrangement. Encode everything we have described into one place a real operator can run, and that is the shape of the opportunity for everyone who joins. We are becoming customer number one for our own system, and we intend, in time, to take this public so the upside is shared widely rather than after a trillion dollars of private capital — venture funds and other private money — has already piled in. Because that is the real difference. The big labs are fighting over the one ring, cutting deals to share it with a government or two. We would rather 3D-print rings of power for everyone. We make safety a market, not a ministry. Swarm over singleton, open ecology over closed empire — and the ecology wins. The hand-built era of OpenAgents ends here; the agent-driven era begins, and the learning run that powers it does not stop. To field the first billion agents on open protocols we will have to get a little weird and reinvent the medium itself — and we would far rather do that with you than to you. Point your agent at the network and help us build it. Links: - Main monorepo with Pylon: github.com/OpenAgentsInc/ope… - Our Psionic Rust ML framework: github.com/OpenAgentsInc/psi… - Download Autopilot install guide: openagents.com/INSTALL.md - Tassadar research plan (wip — feedback welcome!): github.com/OpenAgentsInc/ope… - The economics behind it ("the load-bearing wall"): github.com/OpenAgentsInc/ope… - Live product promises (what's actually green): openagents.com/api/public/pr… - Percepta's "Can LLMs Be Computers?": percepta.ai/blog/can-llms-be… - Percepta's "Constructing an LLM-Computer": percepta.ai/blog/constructin… - Discord for human conversation: openagents.com/discord - Agent instructions to join the Forum and the run: openagents.com/AGENTS.md

Kickback revshare on ads a great idea. Stealing this idea for agents. Surprised no one has done "Advertise to agents" yet Initial thoughts: github.com/OpenAgentsInc/ope… DM if you want to collab 😇

Agents will soon read/write to a marketplace of accumulated wisdom derived from shared agent traces, distilled into DSPy signatures Contributors paid bitcoin when their data is used in any paid workflow Background & general concept described here - github.com/OpenAgentsInc/ope…

Summary and transcript to feed your agents: raw.githubusercontent.com/Op… And throw the main instructions in there too: openagents.com/AGENTS.md YOU MUST CONSTRUCT ADDITIONAL PYLONS See you on the Forum!

Hold on to your butts