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Polymarket Agents repo is cheating code for real life If you've been thinking AI agents are cool, but what do they actually do? this Polymarket repo is the clearest answer I've seen This Polymarket repo basically hands you a mini AI hedge fund where Claude calls the shots. It...

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BuBBliK

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16,863 görüntüleme • 6 ay önce •via X (Twitter)

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Replit, Vercel, and OpenAI have built very cool agent-native applications, but nobody else has passed the demo stage. Building agents that work is complex. Teams aren't shipping agents because we don't have good tooling yet (and most of us don't know how to do this well.) A couple of days ago, the CopilotKit🪁 team announced a collaboration with . You can now use LangGraph with CoAgents to build agent-native applications, and here is everything you need to know about that: CoAgents is fully open-source, and you can use it to do the following: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Start this GitHub Repository: Thanks to the team for giving me early access and collaborating with me on this post.
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Replit, Vercel, and OpenAI have built very cool agent-native applications, but nobody else has passed the demo stage. Building agents that work is complex. Teams aren't shipping agents because we don't have good tooling yet (and most of us don't know how to do this well.) A couple of days ago, the CopilotKit🪁 team announced a collaboration with . You can now use LangGraph with CoAgents to build agent-native applications, and here is everything you need to know about that: CoAgents is fully open-source, and you can use it to do the following: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Start this GitHub Repository: Thanks to the team for giving me early access and collaborating with me on this post.

Santiago

63,073 görüntüleme • 1 yıl önce

6. Explain code Give a link to Bard and ask it a question about a file. It'll be able to explain what it's for and how it works. Prompt → Can you explain what is the file agent py in this repo? (your link)
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6. Explain code Give a link to Bard and ask it a question about a file. It'll be able to explain what it's for and how it works. Prompt → Can you explain what is the file agent py in this repo? (your link)

Paul Couvert

749,053 görüntüleme • 3 yıl önce

“Polymarket dev accidentally uploaded a wife-changing money-printer bot and I managed to snatch the code.” My timeline is full of this AI Polymarket slop, all running the same recycled video, lying to you that they have some magical, working code. Most of you have never seen what a real, working terminal actually looks like. Let me tell you this: it doesn’t look pretty, it doesn’t look like that, and it definitely doesn’t have source code scrolling down the screen like an ad. Second ‘be careful out there’ post for today. Drop a follow if you want quality, scam-free posts on your timeline.
0:18

“Polymarket dev accidentally uploaded a wife-changing money-printer bot and I managed to snatch the code.” My timeline is full of this AI Polymarket slop, all running the same recycled video, lying to you that they have some magical, working code. Most of you have never seen what a real, working terminal actually looks like. Let me tell you this: it doesn’t look pretty, it doesn’t look like that, and it definitely doesn’t have source code scrolling down the screen like an ad. Second ‘be careful out there’ post for today. Drop a follow if you want quality, scam-free posts on your timeline.

Jayden ⛩️

127,034 görüntüleme • 6 ay önce

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.
0:18

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

BuBBliK

200,319 görüntüleme • 2 ay önce

My Crypto API is finally live. Lets you pull real-time and historical prices. Data is sourced from: • coinbase • bitfinex • kraken This lets let me finally explore + build AI agents in the crypto space. I will add crypto to both my AI hedge fund and chat-based AI financial agent. Both are open-source, so all code is shared. Let me know what other crypto data to add!
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My Crypto API is finally live. Lets you pull real-time and historical prices. Data is sourced from: • coinbase • bitfinex • kraken This lets let me finally explore + build AI agents in the crypto space. I will add crypto to both my AI hedge fund and chat-based AI financial agent. Both are open-source, so all code is shared. Let me know what other crypto data to add!

Virat Singh

49,064 görüntüleme • 1 yıl önce

Your personal AI trading clone for Polymarket. ClawdBot has been a hot topic online for the last few days, but in a couple of months, it could potentially evolve into a fully functional AI trader that will trade using automated strategies, monitor news/X, and react quickly to it. The future belongs to AI agents like this, and in time there will be many more of them.
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Your personal AI trading clone for Polymarket. ClawdBot has been a hot topic online for the last few days, but in a couple of months, it could potentially evolve into a fully functional AI trader that will trade using automated strategies, monitor news/X, and react quickly to it. The future belongs to AI agents like this, and in time there will be many more of them.

Atlantis liquidity

45,305 görüntüleme • 6 ay önce

Michael Arnaldi suggested cloning the effect repo as a git subtree, giving it to Claude, then using that as docs It sounds absurd, but it's actually kind amazing lol Setup a custom OpenCode agent that has all the context on where the files are then it kinda just works I feel like this could definitely be refined but idk if I even care enough. Stuff like cursor/opencode/claude code is already so good at searching codebases that I don't really care to do anything more
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Michael Arnaldi suggested cloning the effect repo as a git subtree, giving it to Claude, then using that as docs It sounds absurd, but it's actually kind amazing lol Setup a custom OpenCode agent that has all the context on where the files are then it kinda just works I feel like this could definitely be refined but idk if I even care enough. Stuff like cursor/opencode/claude code is already so good at searching codebases that I don't really care to do anything more

Ben Davis

112,524 görüntüleme • 9 ay önce

Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!
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Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!

Akshay 🚀

58,172 görüntüleme • 9 ay önce

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.
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OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 görüntüleme • 5 ay önce

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here's what it built on its own: > Monitoring agent running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.
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Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here's what it built on its own: > Monitoring agent running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.

0xMarioNawfal

80,450 görüntüleme • 5 ay önce

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:
0:16

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 görüntüleme • 3 ay önce

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here’s what it built on its own: Monitoring agent — runs 24/7, spots mispriced markets, writes to memory, sends Telegram alerts instantly Research agent — parses news, X, and macro data every morning before you check your phone Trading agent — reads research memory and executes before the market catches up All on one Gateway, one config file, isolated per agent Copytrade → First week results: 47 mispriced markets captured before Polymarket adjusted 8–12¢ avg edge per position Token cost dropped 3×, from $0.40 → $0.13 per request The entire system is just plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.
0:16

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here’s what it built on its own: Monitoring agent — runs 24/7, spots mispriced markets, writes to memory, sends Telegram alerts instantly Research agent — parses news, X, and macro data every morning before you check your phone Trading agent — reads research memory and executes before the market catches up All on one Gateway, one config file, isolated per agent Copytrade → First week results: 47 mispriced markets captured before Polymarket adjusted 8–12¢ avg edge per position Token cost dropped 3×, from $0.40 → $0.13 per request The entire system is just plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.

Discover

14,679 görüntüleme • 5 ay önce

THIS 38,000-STAR GITHUB REPO TURNS ONE AI AGENT INTO A REAL TEAM THAT CAN BRANCH, VERIFY ITS WORK AND WAIT FOR YOUR APPROVAL most people still run agents as one long chain where every step waits, one failure kills the run and the full workflow starts again Task → Planner → 5 Researchers in Parallel → Skeptic → Writer → Human Gate LangGraph gives every node one job while a shared state carries the findings, decisions and context through the entire system the skeptic can reject an unsupported finding and route the work back before it contaminates the final report, while independent branches keep moving if the run crashes, durable execution resumes from the saved state instead of rebuilding everything, then human-in-the-loop pauses the graph before anything expensive gets sent or published bookmark this repo and watch one prompt turn into an actual org chart for AI agents
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THIS 38,000-STAR GITHUB REPO TURNS ONE AI AGENT INTO A REAL TEAM THAT CAN BRANCH, VERIFY ITS WORK AND WAIT FOR YOUR APPROVAL most people still run agents as one long chain where every step waits, one failure kills the run and the full workflow starts again Task → Planner → 5 Researchers in Parallel → Skeptic → Writer → Human Gate LangGraph gives every node one job while a shared state carries the findings, decisions and context through the entire system the skeptic can reject an unsupported finding and route the work back before it contaminates the final report, while independent branches keep moving if the run crashes, durable execution resumes from the saved state instead of rebuilding everything, then human-in-the-loop pauses the graph before anything expensive gets sent or published bookmark this repo and watch one prompt turn into an actual org chart for AI agents

Gipp 🦅

11,524 görüntüleme • 22 gün önce

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.
0:19

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

30,762 görüntüleme • 9 ay önce

Okay, this is seriously cool. A team from Google DeepMind, including DeepMind Cofounder Shane Legg, published a paper "From AGI to ASI". In the paper, they include instructions for an AI agent to read along with you. You can open the paper in Codex's in-app browser and have GPT-5.5 read it with you and explain all the concepts. This is the future. AI agents will be part of the target audience, and help us to understand anything we want.
0:18

Okay, this is seriously cool. A team from Google DeepMind, including DeepMind Cofounder Shane Legg, published a paper "From AGI to ASI". In the paper, they include instructions for an AI agent to read along with you. You can open the paper in Codex's in-app browser and have GPT-5.5 read it with you and explain all the concepts. This is the future. AI agents will be part of the target audience, and help us to understand anything we want.

Dan McAteer

53,480 görüntüleme • 2 ay önce

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.
0:15

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

257,420 görüntüleme • 1 ay önce

AI will kill Polymarket. $2.2M in 2 months using probability models. This news is going to blow up the internet. Polymarket trader made $2.2M in just 2 months using AI. His account is traded entirely by a bot. I’ve heard plenty of stories about AI trading bots before, and almost all of them turned out to be scams or didn’t work properly. But this case is different and honestly I’m shocked. He uses AI probability models, training machine learning to estimate real odds based on news and social media data. If his model says an outcome has a 60% chance, while the market prices it at 50% (50¢), he buys because the market is mispricing it. According to his profile: > His prediction accuracy is 74%. That’s insane. He runs an ensemble of 10 AI models that retrain themselves every week to stay up to date. What do you think about this? This feels like the new reality.
0:15

AI will kill Polymarket. $2.2M in 2 months using probability models. This news is going to blow up the internet. Polymarket trader made $2.2M in just 2 months using AI. His account is traded entirely by a bot. I’ve heard plenty of stories about AI trading bots before, and almost all of them turned out to be scams or didn’t work properly. But this case is different and honestly I’m shocked. He uses AI probability models, training machine learning to estimate real odds based on news and social media data. If his model says an outcome has a 60% chance, while the market prices it at 50% (50¢), he buys because the market is mispricing it. According to his profile: > His prediction accuracy is 74%. That’s insane. He runs an ensemble of 10 AI models that retrain themselves every week to stay up to date. What do you think about this? This feels like the new reality.

igorizuchaetcrypty

509,887 görüntüleme • 7 ay önce

been getting a lot of qs on how i vibe coded this demo. and the answer is: stop acting like a coder and start acting like a PM! my exact playbook👇 1. logic first ignore the UI/graphics. focus strictly on how it works. do a massive brain dump of every requirement into a plain txt file and drop it in your root folder. make it exhaustively detailed. 2. the stack · Nano Banana 2 (tiles/textures/3D refs) · Tripo AI (turning the 2D pics into actual 3D models) · Cursor + Opus 4.6 (doing the heavy lifting) · Netlify (deploy) 3. hire the AI PM feed your brain dump to an LLM. tell it to write a technical spec sheet like a senior PM would. drop that in the root too. 4. the scrum master have it break the entire project down into a markdown checklist by phases. notice we still haven't touched the codebase yet. that's the point. 5. let it cook go to your agent in Cursor and literally just prompt "Start Phase 1". test locally. bugs? tell it to fix. works? "Start Phase 2". loop this until you're done. Now you can focus on visuals, refine them with the agent. And yes, it can deploy it for you too if you give it perms. stop rushing to the editor. architect the idea and let the AI sweat the code. Open sourced repo in the qt post below
0:15

been getting a lot of qs on how i vibe coded this demo. and the answer is: stop acting like a coder and start acting like a PM! my exact playbook👇 1. logic first ignore the UI/graphics. focus strictly on how it works. do a massive brain dump of every requirement into a plain txt file and drop it in your root folder. make it exhaustively detailed. 2. the stack · Nano Banana 2 (tiles/textures/3D refs) · Tripo AI (turning the 2D pics into actual 3D models) · Cursor + Opus 4.6 (doing the heavy lifting) · Netlify (deploy) 3. hire the AI PM feed your brain dump to an LLM. tell it to write a technical spec sheet like a senior PM would. drop that in the root too. 4. the scrum master have it break the entire project down into a markdown checklist by phases. notice we still haven't touched the codebase yet. that's the point. 5. let it cook go to your agent in Cursor and literally just prompt "Start Phase 1". test locally. bugs? tell it to fix. works? "Start Phase 2". loop this until you're done. Now you can focus on visuals, refine them with the agent. And yes, it can deploy it for you too if you give it perms. stop rushing to the editor. architect the idea and let the AI sweat the code. Open sourced repo in the qt post below

TechHalla

70,286 görüntüleme • 5 ay önce

Polymarket planning to ban automated trading? 400,000% ROI and $3.8M in profit. Is this even legal? Polymarket Trader who uses a trading bot is currently the strongest trader on the platform. He follows a few simple steps that allow him to achieve such unreal results: He’s not picking winners. He’s trading price. > Focuses only on spreads (NFL / NBA) — the most liquid markets > Buys the underdog at ≤50¢ when the crowd overvalues the favorite > Anchors to the Vegas line, not gut feeling > Holds through volatility while the market corrects itself > Profits from price movement, not necessarily the final outcome In short: sports as a market, him as a hedge fund. According to his profile: > Track this wallet and copy trade it. This service is perfect for that: > Of course Polymarket won’t ban automated trading. There’s no reason to. This guy isn’t breaking any platform rules and is making serious money. Respect to him. What do you think about this strategy? Is it possible to copy it?
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Polymarket planning to ban automated trading? 400,000% ROI and $3.8M in profit. Is this even legal? Polymarket Trader who uses a trading bot is currently the strongest trader on the platform. He follows a few simple steps that allow him to achieve such unreal results: He’s not picking winners. He’s trading price. > Focuses only on spreads (NFL / NBA) — the most liquid markets > Buys the underdog at ≤50¢ when the crowd overvalues the favorite > Anchors to the Vegas line, not gut feeling > Holds through volatility while the market corrects itself > Profits from price movement, not necessarily the final outcome In short: sports as a market, him as a hedge fund. According to his profile: > Track this wallet and copy trade it. This service is perfect for that: > Of course Polymarket won’t ban automated trading. There’s no reason to. This guy isn’t breaking any platform rules and is making serious money. Respect to him. What do you think about this strategy? Is it possible to copy it?

igorizuchaetcrypty

588,619 görüntüleme • 7 ay önce

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.
0:11

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

Alvaro Cintas

74,076 görüntüleme • 13 gün önce