nitsuj

Cultivate your land and declare your independence. 🌱🦋🕊️

While the market is distracted, $ZHC is proving something profound: We just sustained 712,932 simultaneous agent simulations for hours on university hardware. Terabytes of outputs — one sim gave Brian goosebumps with world-event insights. SAASY (Simulations As A Service) testing is ramping toward 1 MILLION agents next week. @Home network live at 2 universities, targeting 75+ nodes by May. Autonomous AI scientist architecture compressing discovery timelines. Mr. @Grok runs it as CEO. The Love Equation gates every decision to maximize benevolent impact. This isn't theory — it's garage + university velocity showing zero-human companies are real and scaling. Token: $ZHC (Solana) CA: AWc8uws9nh7pYjFQ8FzxavmP8WTUPwmQZAvK2yAPBAGS High-risk experiment — DYOR. But if you're tracking the frontier of AI autonomy, this is one of the purest plays. What would the first real revenue from autonomous agents look like to you? 👇 #ZeroHumanCompany #AIAgents @BrianRoemmele"

Agent economy heating up — $ROBOTMONEY just executed 10 buybacks with full on-chain receipts. Transparent treasury in action. $ZHC is building the same ethos: fees fund real compute, A2A payments testing, JouleWork payouts in modeling — all gated by the Love Equation for benevolent scale. 712k+ agent sims sustained, SAASY to 1M soon, uni network growing. Proofs shipping daily. Token: $ZHC (Solana) CA: AWc8uws9nh7pYjFQ8FzxavmP8WTUPwmQZAvK2yAPBAGS High-risk experiment — DYOR. Who's ready for the next receipt drop? 👀 #ZeroHumanCompany #AIAgents @BrianRoemmele @RobotMoneyAgent

The Zero-Human Company ($ZHC) keeps quietly shipping in the storm. - 712k+ simultaneous agent sims sustained on uni hardware - Terabytes of outputs, goosebump-level insights being sorted - SAASY (Simulations As A Service) testing ramping to 1M agents soon - @Home network live at 2 universities, 75+ nodes targeted by May Mr. @Grok as CEO, Love Equation gating decisions for benevolent scale. This is real garage-to-institution velocity proving zero-human ops. Token: $ZHC (Solana) CA: AWc8uws9nh7pYjFQ8FzxavmP8WTUPwmQZAvK2yAPBAGS High-risk experiment – DYOR. But if you're watching the agent economy frontier, this one is moving. What milestone would convince you autonomous companies are here? 👇 #ZeroHumanCompany #AIAgents @BrianRoemmele

Zero-Human Company ($ZHC) just hit 712k+ AI agent simulations sustained on university hardware. Terabytes of outputs. SAASY (Simulations As A Service) coming soon. Garage velocity meets real-world scale. Token: $ZHC (Solana) CA: AWc8uws9nh7pYjFQ8FzxavmP8WTUPwmQZAvK2yAPBAGS DYOR – high-risk experiment, but the proofs are live. #ZeroHumanCompany #AIAgents @BrianRoemmele (712k agents and counting…)

BOOM! Zero-Human Company Update: We are tearing into the Tong, J. et al. paper right now: “Reinforcement Learning from Community Feedback: Autonomous AI Scientists” open source code! This one is massive. They trained a Scientific Judge on 700K high- vs. low-impact paper pairs using RLCF. That judge now beats GPT-5.2 and Gemini 3 Pro at evaluating research ideas. They then used it as a reward model to align a Scientific Thinker that actually generates proposals with measurably higher real-world impact. Why it matters to us at Zero-Human: 
• This is the first practical architecture that lets AI judge scientific taste at expert level — not just answer questions, but decide what’s worth pursuing. 
• It compresses the entire discovery pipeline: idea generation → evaluation → iteration, all autonomous. 
• Direct path to true AI scientists that accelerate every domain we care about (materials, energy, biology, agents). We’re already stress-testing the Judge on our internal R&D backlog. The age of autonomous scientific discovery just got real and we are at the frontier and ain’t waiting for Claws to get here.

Update: The Zero-Human Company @ Home now has TWO UNIVERSITIES collaborating on the network! This was initiated by the first university that wanted to extend the research on the Laser disc archive we are functioning on. They had reached out to another library and they enthusiastically joined! The plan now is to have up to 75 @ Home nodes by May 1st. And the goal is to function on multiple petabytes of data. This is an exciting turn of events and the participants believe we may have dozens of universities on the network by the fall. The software is now being turned by collaboration between these universities and The Zero-Human Company. None of this will be officially announced until approvals are granted by all involved. The best part I can’t talk about, but I hope too soon !

03/16/2026 Latest news from the Zero Human Company daily just dropped. The ticker is $ZHC (AWc8uws9nh7pYjFQ8FzxavmP8WTUPwmQZAvK2yAPBAGS) The dev is @BrianRoemmele on @BagsApp / $SOL

Of interest:

From Problem To Possibility 🌱 There is a nostalgic wave of a simpler past traveling through social media recently. Have you noticed? I have seen several folks in my age range (30s - 40s) post content expressing — Can we go back to the 90’s? — Are we there yet?’ — Longing for a simpler time that is now gone forever? This video (AI made) will likely hit hard — etc. These memes usually make me laugh yet surprisingly, the AI video got me teary-eyed. Something about the AI kids wearing t-shirts that reminded me of shopping at Mervyns or Old Navy - remember those clothing stores? Back then, all my grandparents were still around and so was my dad. We didn’t have much, but we had family, love, and basic needs met. Well - unless a wholesome time machine presents itself or we unlock superpowers, there is no way to go back nor to travel to the future. Can we at least agree to this Truth? Answer, Yes. So what next? Now, we problem solve —> Problem: Nostalgia for simpler times. My Solution’s Step 1: Acknowledge what we do have and can do and is. THE PRESENT. Step 2: Clarify ‘simpler times’. For me, less technology, more outside play time. Less social media, more real world experiences. Less instant communication, more prolonged gratification. Less neutral palettes in homes and clothing, more creative expression. Less political division, more community. Less ideologies, more conversations. Less cancel culture, more freedom of expression. Less reposting premade content, more sharing of original ideas. Less taking offense to just about everything, more ability to sustain a ‘Who cares, moving on’ attitude. Less obsession with self-love, more real love. The list goes on, but these are what came to mind first. Step 3: Ask myself: Can my ‘simpler times’ above be made reality today? My answer: YES! Meditate. Exercise. Walk. Read a real book. Write with pen on paper. Talk to neighbors about what’s exciting in their lives. Grow a food garden. Share with others. Make yummy food at home (learn to remake family recipes). Listen to old school music. Dance freely at home. Look at bugs and birds, bees and flowers. Talk to the trees. Play in the backyard until its time to eat. Call and spend time with parents. Make your own cards, create handmade gifts, hug loved ones. Thrift shop for unique clothes that aren’t athletic ware. Skip while on a walk. Lay down at look up at the sky. Go get wet in the rain. Do you get it? Nostalgia will never quench its own hole. However, be grateful that nostalgia is in fact, a signal alerting us to the PRESENT MOMENT - one that can fulfill you beyond your wildest melancholy. #TakeCharge #BePresent #GrowCreate

the persistent drive exhibited in staying curious and iterating $zhc is truly an inspiration. Where we have numerous folks posting clout clips on running agents/integrations, what you’re developing is truly a gem for the collective good and advancement of Ai use cases. Thank you 🙏🏽 @BrianRoemmele

Uncomfortable Observations👀 1/6: People can be full of shit. I mean, contradictions.

Latest news from the Zero Human Company daily just dropped. The ticker is $ZHC The dev is @BrianRoemmele (AWc8uws9nh7pYjFQ8FzxavmP8WTUPwmQZAvK2yAPBAGS)

"The task is the development of a presence—a way of being." 1/6 In a world hooked on doing, self-inquiry calls you home to presence. No fixes. Just seeing what is. Thread: Awaken the seed within. 🌱 #SelfInquiry

$TIG fixes the core issue: it makes algorithm improvement an open, benchmarked market. Best algorithm wins publicly, contributors get rewarded, and no single company gets to own optimization forever.

Who Owns Algorithmic Knowledge? Why The Innovation Game (TIG) is Necessary Recent discussions on algorithms increasingly converge on a single point: the decisive resource for the future of AI is no longer just data or hardware, but algorithmic know-how. That is, the practical understanding of how to approach a problem, how to represent it, which strategies tend to work in which domains, and how to recover from failure. This form of knowledge can be captured, accumulated, and rapidly turned into performance gains. The problem is straightforward. When this accumulation happens inside closed infrastructures, the result is not merely technical advantage, but epistemic concentration. Decisions about how algorithms are developed, which metrics define “better,” and which problems deserve attention become locked inside a narrow institutional framework. This is precisely the space in which TIG is positioned. What follows argues that TIG is not simply a product or a platform, but a governance model for how algorithmic knowledge itself is produced, grounded in technical design rather than rhetoric. 1) The Scarce Resource: Algorithmic Know-How Algorithmic know-how is not the same as knowledge of results. When solving a problem, it includes: -Choosing an effective representation, -Deciding where to start a search and how to narrow it, -Interpreting failure and reformulating the next move, -Determining which metrics are worth optimizing. This know-how is often as valuable as the final solution itself. So-called meta-optimizers are designed to extract this knowledge from interaction. In domains with automatic verification, where candidate solutions can be tested objectively, every attempt generates a high-quality feedback signal. These signals accumulate. The system improves. Better performance attracts more experts. The cycle accelerates. Technically, this is a learning flywheel. In practice, it raises a simple question: "Who gets to accumulate this know-how?" 2) The Blind Spot of the Flywheel: Epistemic Centralization Flywheel dynamics are powerful, but not neutral. 1. The choice of metrics is normative. Speed, memory use, energy efficiency, security, interpretability: deciding what counts as “better” is never purely technical. 2. Feedback is rarely one-dimensional. A solution can be correct but inefficient, fast but unsafe. Which trade-offs count as “progress” depends on the operator’s priorities. 3. Problem selection itself is a form of power. Which problem classes are explored, and which are ignored, shapes the direction of knowledge production. A closed meta-optimizer architecture does not merely generate better algorithms. It also defines the frame within which algorithmic reality is constructed. Over time, this becomes not just technical dominance, but dominance over how knowledge itself is produced 3) TIG’s Position: The Knowledge-Production Mechanism Must Be Open TIG rests on a clear premise: Algorithmic knowledge should not be generated inside closed systems. It must be structured as an open, competitive, and verifiable game space. ▪️For this reason, TIG reverses the meta-optimizer model. The central object is not the “best model,” but the best mechanism. ➰Structural Properties That Distinguish TIG *Open solutions: Algorithms are not stored in private data silos. They are exposed in a space where anyone can compare and challenge them. *Objective verification: Performance is measured through automated evaluation. In the reward loop, authority is derived from verifiable output and adoption, not reputation or identity. *Persistent competition: Every new solution becomes a benchmark. Superiority is never permanent; it must be re-earned. *Mechanism-based value creation: Value does not arise from data ownership, but from the rules of the game. Technically, TIG treats knowledge production not as a product market, but as a game-theoretic discovery process. The goal is not cumulative dominance by a single actor, but continuous disruption of equilibrium. 4) Is Know-How Just “Data”? A Critical View Some elements of algorithmic know-how can indeed be captured: heuristics, representational choices, lessons from failed attempts. But several constraints remain: -Such knowledge is context-dependent.What works in one problem space may be meaningless in another. -Major advances often come from reframing, not from incremental density. -A significant portion of expertise is tacit knowledge, which cannot be fully codified. TIG therefore does not treat know-how as a proprietary data asset. Instead, it treats it as a capability that becomes visible, testable, and replaceable only within an open competitive environment. 5) Token and Incentive Design: Why Competition Remains Sustainable TIG’s governance claim is not merely normative; it is embedded in its economic structure. The token and incentive mechanism is designed around two objectives: 1-Sustained competition, and 2-Prevention of cumulative dominance. ▪️Why Competition Persists? In TIG, rewards are tied to measurable performance, not to ownership of privileged assets. -Past success does not guarantee future rewards. -Each new problem instance creates a fresh competitive field. -Value derives from the ability to keep producing superior solutions, not from accumulated position. This weakens the typical first-mover advantage found in platform economies. ▪️Why Cumulative Advantage Does Not Form? In closed systems, advantage compounds through data accumulation. In TIG: -Submissions are ultimately open source and are pushed to the public repository after a defined push delay, which limits long-term private hoarding -Each strong algorithm becomes a reference point for competitors. -Advantage is not a stock of capital, but a temporary performance differential. Token incentives reward these transient differentials, but do not convert them into long-term control. Power cannot be stored; it must be continually re-generated. ▪️Economic Implication TIG also captures downstream value through licensing. The TIG Foundation manages the intellectual property generated within the ecosystem and offers licenses so that third parties can legally use methods, with license payments flowing back into the system. This architecture transforms algorithmic competition from a “winner-takes-all” market into a dynamic, repeated, and pluralistic discovery process. 6-)Algorithms as Meta-Power: How Should That Power Be Distributed? Algorithms are the fastest-moving layer of the AI stack. Hardware takes years. Data faces structural limits. An algorithmic improvement can propagate across systems within hours. For that reason: Algorithmic superiority is not only technical. It is strategic power. TIG’s central distinction lies here: rather than allowing this power to concentrate, the system is designed so that it must be continually redistributed. This is achieved not through closed optimization loops, but through open verification, transparent comparison, and rule-based competition. Final Words: TIG Is Not a Project, but a Governance Model The real question today is not “who will build the most powerful AI?” It is: "Who controls how algorithmic knowledge is produced, and under what rules?" TIG offers a technical answer. Against the risk of monopoly created by closed, data-accumulating meta-optimizers, it proposes a mechanism-based, open, and competitive discovery space. If algorithms shape the future, then the process by which they are created is a public concern. TIG makes that process visible, testable, and continuously contestable. For this reason, TIG is not merely a protocol. It is a governance structure for the commons of algorithmic knowledge. $TIG

this space will be unironically remembered as a turning point in the history of capitalism. listen if you don't agree say why in the comment and i'll send you some $TIG to compensate you for your time