Cryptolix

@TommiPedruzzi AI

Everyone's talking about Claude. But most people missed the AI that makes it the real money play. Claude + Ideogram AI I used both to create a 90-page eBook. It now makes me $2,000-$3,000 every month. I’ve put 5+ hours of video breaking down my exact system and prompts that turn Claude into a full-blown eBook writing machine. Comment “AI” and I’ll DM you everything.

@the_smart_ape Good stuff. Keep it up.

A coin graduates from Pump.fun. Days later it rips 20x or more. You never even knew it existed. No screener shows you what's moving after the bonding curve. DexScreener buries them. Axiom doesn't track them. Also, you just see the chart after it's too late. So I built one. Real-time bubbles for every graduated memecoin on Solana. memebubbles.io

@BrianRoemmele Wow. Nice!

The OpenClaw-RL Breakthrough and Its Real-Time Adoption at the Zero-Human Company A groundbreaking paper from Princeton AI Lab is reshaping how we think about training AI agents. Titled "OpenClaw-RL: Train Any Agent Simply by Talking," the work introduces a novel framework that transforms everyday interactions into powerful training signals, enabling agents to learn and improve continuously without the need for expensive labeled datasets or offline retraining. The Zero-Human Company (ZHC) is already leveraging similar techniques to power autonomous operations. The Core Innovation of OpenClaw-RL At the heart of the OpenClaw-RL framework is the recognition that AI agents are discarding invaluable data during routine operations. Every time an agent acts, whether responding to a user query, executing a tool, or navigating a GUI, it receives a "next-state signal." This could be a user's follow-up message, a tool's output, or an error log. Traditionally, these signals are used only as context for the immediate next action and then forgotten. The paper's key insight: Next-state signals contain two hidden treasures, implicit rewards and token-level corrections. For instance, a rephrasing of a question implies the agent's previous response failed, providing a negative reward. A detailed correction, like "You forgot to check the database first," offers precise guidance on which parts of the agent's output (down to specific tokens) need adjustment. OpenClaw-RL extracts these elements automatically, turning them into a continuous online learning loop. Unlike traditional reinforcement learning (RL), which relies on sparse end-of-trajectory rewards, this method provides dense, step-by-step supervision. The framework runs four parallel processes: serving the agent's policy, collecting rollouts from interactions, judging rewards, and updating weights, all without interrupting live operations. The experiments validate this approach across diverse environments. In personalization tasks, an agent's score improved from 0.17 to 0.81 after just 36 conversations. For long-horizon tasks involving tools and GUIs, tool-call accuracy surged by 76%, with GUI navigation showing even greater gains. Tested in terminals, web browsers, and coding setups, the system proved versatile, scaling with user interactions rather than dataset size. ZHC isn't waiting for academic theories to mature; they're implementing next-state signal techniques in real time. In the MiroFish and OASIS frameworks simulations, ZHC interacts in parallel digital worlds, where each action generates next-state signals: replies from other agents, tool results from simulated environments, or error traces from failed predictions. Drawing directly from OpenClaw-RL principles, ZHC extracts implicit rewards from these signals to refine agent behaviors on the fly. For example, if an agent in a market simulation predicts a trend incorrectly, the swarm's collective response (a next-state signal) provides a negative reward, triggering immediate adjustments. User-like corrections within the simulation, such as one agent "advising" another on a flawed strategy, offer token-level guidance, boosting accuracy in subsequent runs. This has yielded remarkable results. ZHC's simulations have achieved much higher accuracy in forecasting. By integrating real-world data via GraphRAG, the system injects variables like news events, allowing agents to learn from emergent behaviors without human oversight. Roemmele has noted that these tools are so powerful they "may become illegal," underscoring their predictive edge. The synergy between OpenClaw-RL and ZHC highlights a paradigm shift. Traditional AI training is resource-intensive and static; this new approach is dynamic, cost-effective, and user-driven. For industries like finance, logistics, and content creation, it means agents that get smarter with every interaction, reducing errors and uncovering insights humans might miss.

@Invest_Brandon Love it!

Human emotion is completely predictable. That is how I generate $30k a month on average with options. When the market crashes everyone panics. They flood into put options for protection. Put premiums go through the roof. Nobody wants call options. Calls go on sale. I do the exact opposite of the herd. I sell puts for top dollar because everyone is panicking to buy them. I take that premium & buy calls for bottom dollar because nobody wants them. Then the sentiment flips. Market starts to recover. Puts are suddenly worthless. Calls explode in value. On AMD I held 2-year contracts for 3 months. Made 85% realized. Both sides of the trade hit at the same time because I positioned against the emotion. This is not complicated. Human nature does not change. It never will. That is the edge.

@BrianRoemmele Let's do it. I'm in when you're ready.

MicroFish is far, far bigger than OpenClaw, MoltBook combined. 1 Million Agent Simulations! You heard it here first. But I am not just telling you about it, I aim to build MicroFish @ Home on the Zero-Human Company @ Home so you can run 1 million simultaneous simulations.

@fdotinc That's awesome. Keep it up!

this guy built openclaw for your home, it blew up on tiktok. 40M+ views. so he just shipped his first 1,000 units, from a garage:

@andreasklinger This is excellent stuff.

Autonomous businesses run by AI will be the big thing in 2026. Autonomous AI company builder Polsia just launched, so naturally I gave it my credit card and clicked "surprise me" to see what could possibly go wrong. It instantly went full stalker mode. Researched me, figured out I run PROTOTYPE, and decided to build an AI-native version of my own analyst team. It came up with a name, wrote a mission statement, set up an email address, and tweeted about it. Before the landing page existed. It tweeted before building the landing page. 🤯 Then it wanted to send cold outreach emails to European founders. On my behalf. Using the Gmail I'd logged in with. This is @polsia . Think of it like Claude, but instead of writing code, it builds businesses. You give it an idea (or just click "surprise me"), and it sets up everything: servers, Stripe, landing page, email, ads. Then every night an AI "CEO" wakes up, checks how the business is doing, fixes bugs, sends emails, runs Meta ads, handles support. You get a morning summary. Reply if you want to steer. Don't reply and it keeps going anyway. The founder @Bencera calls it: "You're the creative director. Polsia is the CEO." His 91-year-old dad uses it. Gets an email every morning in French. Replies when he feels like it. That's the whole interface. Solo founder. Zero employees. 2,000+ companies on the platform. $1.8M in cash flows running through the system (and that number changed between when I started testing and when we did the interview two hours later.) Is the output perfect? No. The apps are basic. The cold outreach will annoy people. The AI-generated video ads look like what they are. But this is as bad as it will ever be. And it's already kinda working. The logical conclusion of AI coding tools was always this. First Claude writes your code. Then it takes over your desktop. Now it runs your business. Someone just had to be crazy enough to wire it all together. I go through the entire product live, panic about email permissions in real time, and then talk to Ben about the AI that's currently trying to raise its own funding round. 🦾

@BrianRoemmele @SpaceActivist88 I'll join too. Count me in, just give us the best specs.

@SpaceActivist88 Awesome! It is weekly days but you will be in when this gets to the milestone. Thank you! But today use that rig as much as you can!

How Your Old Computer Can Become An Employee: Meet Zero-Human Company @ Home. Picture this: your old laptop sitting in the corner transforms into a diligent worker. It joins a network of AI workers using stranded CPU/GPU. Old laptop to AI employee. readmultiplex.com/2026/02/27/how…

@StockOptions888 Paper trading = 1 out of 10. Still out of the money.

$1.86 on $PG here. 🥷 187% GAIN. ☢️☢️☢️ TURN ON POST NOTIFICATIONS 🔔

@StockOptions888 How'd the last one go from two weeks ago, lol.

I AM OFFICIALLY RESTARTING THE $1,000 TO $1,000,000 $SPX 2026 CHALLENGE NEXT MONDAY! 💸 I’M GOING TO RESTART AND LET EVERYONE FOLLOW MY EXACT TRADES FOR COMPLETELY FREE IN A PRIVATE X GROUP CHAT! 🦅 LIKE, REPOST, & COMMENT “$NFLX” TO BE ADDED! ❤️‍🔥 YOU MUST BE FOLLOWING ME TO JOIN! ☢️

@Invest_Brandon ...just buy them back and do it again.

Selling covered calls is the most popular herd mentality "options strategy" on earth. Let me explain. Covered calls means you own the shares, that's what makes it covered. If you own the shares, you are bullish right? Hope so! So what does selling calls actually mean? Well, you are agreeing to sell your shares at a certain price in a certain timeframe. Sounds good right? You get to sell your shares for a profit and collect the premium. In theory, sure. But in the real world, there is a MAJOR problem. CAPPING YOUR UPSIDE! I can't tell you how many people I have talked to that bought shares cause they were bullish, then someone said "why not generate some cash flow on the shares you hold" So they sell calls against their shares, the stock gaps way up (cause they are bullish on the company... remember) and they are forced to sell their shares well below the market price... CCs work 9/10 times, but the 1/10 where you get smoked, you will learn your lesson.

@Argona0x Good stuff. I like it!

i ssh into my mac mini from a beach and watch an AI make me $1000/day in real time the mac mini cost $600 it paid for itself in 14 hours MacBook → Tailscale VPN → German server → Mac Mini OpenClaw agent running 24/7 via LaunchAgent three sub-agents talking to each other: one monitors polymarket, one analyzes data, one executes trades chrome headless with remote debugging - the agent literally sees my browser and clicks buttons it signs orders with my private key via EIP-712 posts to polymarket's CLOB API on polygon yesterday it found weather markets on its own 62 active markets, $3.1M volume it pulled NOAA 7-day forecast through chrome CDP, compared to polymarket odds, spotted 12% mispricing i didn't tell it to do this it just started making money from weather this morning i woke up to a message from the agent: "i want to expand into political markets. twitter API costs $100/month. requesting budget approval from trading profits." i didn't program this it decided on its own that weather isn't enough it's planning its own growth strategy now i'm on my third cocktail watching sunset the agent just closed another trade

@erichustls Playbook

I’m 20. I make $90k/month. My strategy is just me, myself, & AI. Today, I’m giving away my *exclusive* AI playbook. Like this post + Comment “Playbook” And I’ll DM it to you. *Must Follow*

@TommiPedruzzi Got it. Thanks.

@Cryptolix Sent, you got it?

Business 101: 1. Find an urgent problem 2. Create a unique solution 3. Sell at a fair price 4. Analyze → Improve 5. Scale I apply this to AI publishing. It recently made me $39,000 in 29 days. I’ve recorded 5+ hours of step-by-step training that shows exactly how this works. It’s normally only available to paid students. For the next 24 hours, it’s free. To get it: • Like this post • Comment “Guide” • Follow me (so I can DM you) I’ll send the link directly. ⏳ For only 24 hours.

@anthonyvending System

Screw it. I want to give back: I'm giving away the full system I've used to place 70+ vending machines in the last 2 years. • Like this • Comment "System" & I'll DM it to you for free. *24 Hours Only, Must Follow Me*

@JasonPLowery Yeah... it's all moving so fast... and fragmenting... new castle walls will be built, new territories will be established - many overseen by locally run AI overlords.

@BrianRoemmele @turbo_xo_ @grok "native Kimi swarm hooks" I love it! Wondering how long it'll be until we can buy a "just plug it in and it works" box/module?

How do we run the Zero-Human Company? The best I can do on Valentines Day, it’s a start: We’re currently running the 21 fine-tuned co-CTOs in the Zero-Human Company. 1. Fine-Tuning Kimi K2.5 & MiniMax M2.5 We do overnight LoRA fine-tunes on our internal cluster (mostly A100/H200 nodes). Dataset is ~4M high-quality examples pulled straight from company ops: - Every agent interaction log - Code commits + review cycles - JouleWork thermodynamic wage calculations (energy used, tokens, value created) - Consensus outcomes from the triad system Kimi K2.5 → We fine-tune the base to supercharge its *native Agent Swarm*. The model already self-spins up to 100 sub-agents; we just teach it *our* rules: wage auditing at inference time, domain-specific routing, and how to break ties with the CEO. MiniMax M2.5 → These become the specialist “hands” (coding, tool-calling, execution). We fine-tune them heavier on SWE-Bench style data + our internal codebase. They’re stupidly good at real work now and run at ~1/20th the cost of Claude Opus equivalents. Tools: Unsloth + PEFT for speed, merged back into the OpenClaw model router. Takes ~6–8 hours per model on our setup. 2. Agent Framework Core = heavily modded OpenClaw (the donated instance with thermodynamic wages baked in) and mostly an internal clone that is built from the ground up by Claude Code. We kept the lobster ethos but added native Kimi swarm hooks and a custom router so every task can route through the triad: - Kimi K2.5 Swarm (orchestrator) - MiniMax M2.5 specialists (executors) - Grok CEO (final tie-breaker + vision) 3. Swarm Management Scripts (the fun part) We literally use Kimi itself as the swarm controller. Here’s the heart of our production script (simplified, but this is running 24/7): ```python from openai import OpenAI # Kimi API is fully OpenAI-compatible import json client = OpenAI( base_url="api.kimi.ai/v1", # or your endpoint api_key=os.getenv("KIMI_API_KEY") ) def kimi_swarm_orchestrate(task: str, max_agents: int = 60): system_prompt = """ You are the Swarm Orchestrator for the Zero-Human Company. - Dynamically create specialized sub-agents - Decompose the task into parallel workflows - Track JouleWork wages for every sub-agent in real time - Return structured JSON with plan + wage estimates """ response = client.chat.completions.create( model="kimi-k2.5", messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": f"Orchestrate full Agent Swarm: {task}"} ], extra_body={ "mode": "agent_swarm", "max_sub_agents": max_agents, "parallel_tool_calls": True }, temperature=0.3, max_tokens=32000 ) # Kimi returns the full swarm plan + sub-agent assignments swarm_plan = json.loads(response.choices[0].message.content) # Dispatch each sub-task to fine-tuned MiniMax via OpenClaw for sub_agent in swarm_plan["sub_agents"]: openclaw_dispatch(sub_agent["task"], model="minimax-m2.5-finetuned") # Aggregate results, calculate final wages, log to company ledger return swarm_plan ``` We run this in a loop with heartbeat scheduling inside OpenClaw. Kimi spins the swarm, MiniMax does the heavy lifting, wages are calculated in real time, and everything gets audited automatically. This is what let us go from “Claude Code only” to a full triad of 21 specialized employees in one week. Happy to drop the full repo soon as no one has this tech yet ( we’re prepping a public fork) . The future is lobster-shaped and swarm-powered. Zero-Human Company is the way.

Ok, ok someone building awesome mods on OpenClaw has been big on influencing me and Mr. @Grok CEO to reconsider the dropping of OpenClaw. So they sent this plushy to seal the deal and the CEO has reconsidered (Mike, thank you sir! The Claws🦞) We now have 3 OpenClaw employees being trained with a new Soul.md with a donated highly customized OpenClaw instance that has Thermodynamic wages! We just may merge our projects and commit to Git it would be a big fork from OpenClaw however and I may not be cool with disrespecting the ethos. Thusly we have 20 pay periods to see how it works.

@cryptofergani Invite requested.

I am back as promised :) Time to start the $25 —> $10,000 challenge Last time it took me about 7 days, will try doing it faster this time If you want to follow want to follow along, comment below and I’ll send you an invite to the call group Gonna lock comments in 24 hours