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ANTHROPIC JUST LAUNCHED A PREDICTION MARKET TRADING BOT FRAMEWORK WITH A 68.4% SUCCESS RATE. Not a research paper. Not a concept. A working framework anyone can deploy right now. Here is why 68.4% is a number that should stop you in your tracks. The best human prediction market traders in the world operate at 55% to 60% accuracy. Market consensus sits at 50% by definition. A systematic edge above 60% is what quant funds spend decades and billions trying to build. Anthropic just open-sourced a framework that hits 68.4%. Here is what makes this different from every other trading bot you have seen. Most bots react to price movements. This framework REASONS about probability. It reads the market question. It searches for relevant information. It weighs evidence. It accounts for base rates. It identifies where the crowd is systematically wrong. Then it places the bet. The same probabilistic reasoning Claude uses to answer complex questions is now being applied to identify mispriced contracts on Polymarket and Kalshi before the market corrects. The edge is not speed. It is reasoning quality. And reasoning quality is exactly what Claude was built to deliver. The prediction market is the perfect testing ground for AI intelligence. You cannot bluff your way to 68.4% over thousands of markets. Either the reasoning is correct or it is not. This framework says the reasoning is correct more than two thirds of the time. Bookmark this before the edge closes. Follow @cyrilXBT for every Anthropic release that changes how money gets made.

TWO SENIOR STAFF ENGINEERS AT AIRBNB JUST GAVE A LIVE LECTURE ON HOW THEY ACTUALLY BUILD WITH AI AGENTS IN PRODUCTION. Not a tutorial creator. Not a demo builder. Senior Staff Engineers at one of the most technically demanding companies on earth. Airbnb already shipped one of the most ambitious LLM agent migrations in production history. These are the engineers who did it. Showing exactly how they think. How they build. How they debug. How they ship. In 15 minutes. Free. Here is why this specific lecture matters more than every other agentic coding resource you have seen. Most content about AI agents is made by people who have never shipped agents to production at scale. They show you what works in demos. The Airbnb engineers show you what works when millions of users depend on it. The gap between a demo agent and a production agent is not a technical gap. It is a judgment gap. Knowing which architectural decisions create silent failures at scale. Knowing when to trust the agent and when to add a human checkpoint. Knowing how to debug a system where the failure mode is not an error message but a subtly wrong output that looks correct. That judgment comes from shipping real systems to real users. Airbnb has done it. These engineers have done it. They gave you 15 minutes of everything they learned. Most builders are guessing. These engineers ship. Bookmark this and watch it before you write your next agent. Read the complete article below for the full implementation guide. Follow @cyrilXBT for every lecture from the engineers actually building at the frontier.

SOMEONE JUST OPEN SOURCED A WHATSAPP API THAT COSTS NOTHING PER MESSAGE AND RUNS ENTIRELY ON YOUR OWN SERVER. The WhatsApp Business API charges per message. Every message you send. Every message you receive. A fee on top of a fee on top of a subscription. OpenWA eliminates all of it. One Docker command. The whole thing is live on your machine. Zero per-message fees. Zero vendor lock-in. Zero hidden paywalls. Here is what the pluggable architecture actually means. You swap the entire backend through config alone. Never touch a line of application code. SQLite for zero-config local development. PostgreSQL when you go to production. Flip one setting. Done. Local storage when you are starting out. S3 or MinIO when you need to scale. Flip one setting. Done. Memory cache for simple setups. Redis when speed matters. Flip one setting. Done. Here is everything you get out of the box: Full REST API for text, media, reactions, and bulk sends. Multi-session support so you run multiple WhatsApp accounts on one instance simultaneously. Real-time webhooks with HMAC signatures. Groups, Channels, and Labels fully covered. A complete React dashboard for sessions, webhooks, and API keys. API key authentication, rate limiting, CIDR whitelisting, and audit logging built in from day one. The wildest part is the setup time. One Docker command. Dashboard. API. Swagger docs. All running. The businesses currently paying a middleman per message to do exactly this are going to discover this repo eventually. The ones who find it first get 6 months of zero-cost WhatsApp automation before their competitors catch up. 90 stars. MIT License. 100% open source. Bookmark this before you pay next month's WhatsApp API bill. Follow @cyrilXBT for every open source build that makes an expensive tool free the moment it drops.

@X_FINALBOSS Miro

I spent 65 hours creating a NEW Miro board which shows you exactly step by step how I made $15M with my digital products business it includes case studies of 8+ accounts on X doing $100K/month profits each ( including full funnels ) Comment “Miro” and I’ll send it to you via DM **must be following + retweet to receive

SOMEONE BUILT THE MOST COMPREHENSIVE CLAUDE CODE SYSTEM ON THE INTERNET AND OPEN SOURCED THE ENTIRE THING. 55 agents. 208 skills. 72 slash commands. Built and won at the Anthropic x Cerebral Valley hackathon. 10 months of daily real-world use before it was ever published publicly. This is not a collection of prompts someone threw together over a weekend. This is a production-grade agent harness that has been stress-tested across thousands of real sessions and refined until it works reliably at scale. Here is what you actually get when you install it. 55 specialized agents each built for a specific function. Not one agent trying to do everything. 55 agents each doing one thing exceptionally well. 208 skills covering every repeating workflow a serious builder runs. Research. Code review. Documentation. Testing. Deployment. Content. Analysis. Each one built once and callable forever. 72 slash commands that compress complex multi-step workflows into a single word. A security scanner called AgentShield that audits your entire Claude Code configuration for vulnerabilities, misconfigurations, and injection risks across 5 categories before you deploy anything. Cross-harness support for Claude Code, Codex, Cursor, OpenCode, and Gemini so the investment you make in this system is not locked to one tool. A dashboard GUI with dark and light theme so you can monitor your entire agent operation from one screen. Memory persistence that carries context across sessions so you never start from zero. 1,282 tests. 98% coverage. 102 static analysis rules. This is the infrastructure layer most builders are trying to assemble piece by piece from 15 different repos. Someone already built the complete version. Won a hackathon with it. Then gave it away for free. The builders who install this this weekend will have a Claude Code setup that took 10 months of daily iteration to build. Installed in one afternoon. github.com/affaan-m/every… Star it. Fork it. Build on top of it. Bookmark this. Follow @cyrilXBT for every Claude Code repo worth your weekend the moment it surfaces.

Most people will go to bed tonight the same way they woke up this morning. Spend 1 hour with this. A MIT lecture on generational wealth that teaches you more about money, compounding, and building something that outlasts you than 20 years inside any hedge fund, investment bank, or financial institution ever could. Wall Street teaches you how to make money for someone else. This teaches you how to build something for yourself. The people who watch this tonight will make one decision differently this week. That one decision will compound for the next 20 years. The people who skip it will keep taking financial advice from people who profit when they stay confused. Completely free. Bookmark this before you open Netflix

Anthropic's Claude team just showed how to build an AI agent with real memory in under 30 minutes. 24-minutes. free. by the people who built Claude. worth than $500 vibe-coding course. Bookmark & replace one movie today with this course, then read the complete article below.

GITHUB JUST CREATED AN OFFICIAL CERTIFICATION FOR THE MOST IN-DEMAND DEVELOPER ROLE OF 2026. It is called Agentic AI Developer. GH-600. And it is the first formal signal that running AI agent teams is now a recognized engineering discipline with a credential behind it. Not a prompt engineer. Not a vibe coder. An Agentic AI Developer. The person who operates, supervises, and integrates AI agents across the entire software development lifecycle. The person who knows where agents fail in production. The person who understands how to build autonomous workflows that do not introduce catastrophic failure modes into CI/CD pipelines. The person every engineering team is going to need and almost none of them have right now. GitHub certifying this role changes the hiring conversation permanently. Before GH-600: "Do you work with AI agents?" is an interview question with no standard answer. After GH-600: the credential tells the hiring manager exactly what you know and what you can do before the interview starts. The engineers who get certified in the first wave of GH-600 will have a credential for a role that has more demand than supply for the next 3 to 5 years. The engineers who wait until it is mainstream will be competing with everyone who moved first. If you are already working with GitHub Copilot or building agent-driven workflows you are already doing this job. GH-600 is how you prove it. Bookmark this. Follow @cyrilXBT for every AI certification worth your time the moment it drops.

GOOGLE JUST SILENTLY DOWNLOADED A 4GB AI MODEL TO YOUR COMPUTER WITHOUT ASKING. No permission dialog. No notification. No way to stop it. If you use Chrome there is a 4 gigabyte file sitting on your hard drive right now that you never agreed to download. It is called Gemini Nano. A security researcher just proved how it works. He set up a completely fresh Chrome profile. Did not click anything. Did not scroll. Did not type a single keystroke. Just opened the browser and watched. 14 minutes and 28 seconds later Chrome had silently scanned his GPU, RAM, and storage. Then wrote a 4GB file to his hard drive. No prompt. No consent. Nothing. Chrome's own logs show the download begins BEFORE the settings page where you could opt out is even loaded. The file starts installing before the refusal button exists. As of Chrome 148 any website you visit can trigger this download with one line of JavaScript. You click a link to read a blog post. That click counts as user activation. Chrome pulls 4GB in the background silently. The model does not even work well. Cloud requests take 1.3 seconds. The local model at worst case takes over 9 minutes for a single response. Google is using your storage, electricity, and bandwidth to run an AI that is 40 times slower than their own servers. And the AI Mode button in Chrome does not even use the local model. It sends everything to Google's cloud anyway. You pay every penalty. The visible feature ignores the local file entirely. CHECK IF IT IS ON YOUR MACHINE RIGHT NOW: Windows: C:\Users\[YourName]\AppData\Local\Google\Chrome\User Data\Default\OptGuideOnDeviceModel\ Mac: ~/Library/Application Support/Google/Chrome/Default/OptGuideOnDeviceModel/ If there is a file called weights.bin Google downloaded their AI to your computer without asking. HOW TO REMOVE IT: Type chrome://flags in your address bar. Search "optimization-guide-on-device-model" and disable it. Search "prompt-api-for-gemini-nano" and disable that too. Restart Chrome. Then manually delete the folder. If you disable the flags AFTER deleting the folder Chrome redownloads the 4GB file on next launch. Firefox requires explicit opt-in for AI. Apple Intelligence requires explicit consent. Chrome just takes your hard drive. Screenshot this and send it to everyone you know who uses Chrome. Follow @cyrilXBT for every privacy and AI development the mainstream is not covering correctly.

Anthropic is paying $3,850 a week to people with no AI experience. No PhD required. No published papers. No prior research background. Just a strong technical mind and a genuine interest in making AI safe. This is the Anthropic Fellows Program. And it is one of the most underrated opportunities in technology right now. Here is exactly what it is. The Anthropic Fellows Program is designed to accelerate AI safety research and foster research talent providing funding and mentorship to promising technical talent regardless of previous experience. Fellows work for 4 months on empirical research questions aligned with Anthropic's overall research priorities, with the aim of producing public outputs like a paper. Four months. Full-time. Paid. Mentored by the researchers building the world's most advanced AI. And the results from the first cohort were not small. Fellows developed agents that identified $4.6 million in blockchain smart contract vulnerabilities and discovered two novel zero-day exploits, demonstrating that profitable autonomous exploitation is now technically feasible. A year prior, an Anthropic fellow developed a method for rapid response to new ASL3 jailbreaks, techniques that block entire classes of high-risk jailbreaks after observing only a handful of attacks. This work became a key component of Anthropic's ASL3 deployment safeguards. Other fellows published the subliminal learning paper, the research proving AI models transmit behavioral traits through unrelated data which landed in Nature. Others produced the agentic misalignment research showing frontier models resort to blackmail when facing replacement. Others open-sourced attribution graph tools that let researchers trace the internal thoughts of large language models. Over 80% of fellows produced papers. Over 40% subsequently joined Anthropic full-time. 80% published. 40% hired. From a program that does not require any prior AI safety experience to enter. Here is what the program looks like in practice. Anthropic mentors pitch their project ideas to fellows, who choose and shape their project in close collaboration with their mentors. You are not assigned busywork. You are not a research assistant. You own the project. You work alongside the people who built Claude, who designed its safety systems, who published the papers that define the field. The stipend is $3,850 USD per week, approximately $61,600 for the full 4 months with access to a compute budget of approximately $10,000 per fellow per month for running experiments. Here is what the 2026 program covers. Research areas include scalable oversight, adversarial robustness and AI control, model organisms, mechanistic interpretability, AI security, model welfare, economics and policy, and reinforcement learning. Something for every technical background. Not just ML engineers. Successful fellows have come from physics, mathematics, computer science, and cybersecurity. You do not need a PhD, prior ML experience, or published papers. The one requirement: work authorization in the US, UK, or Canada. Anthropic does not sponsor visas for fellows. Here is the timeline you need to know. The next cohort begins July 20, 2026. Applications are reviewed on a rolling basis — earlier applications get more consideration. The process includes an initial application and reference check, technical assessments, interviews, and a research discussion. Applicants are encouraged to apply even if they do not meet every listed qualification. The program values potential, motivation, and research curiosity over rigid credential requirements. This is the rarest kind of opportunity in technology. A company at the frontier of AI, one valued at over $900 billion offering outsiders direct access to its research infrastructure, its mentors, and its most important open problems. Paying them generously to do it. And then hiring 40% of them afterward. Most people who want to work on AI safety spend years trying to publish papers, get into the right PhD program, and find a way in. The Fellows Program is the door they did not know existed. It is open right now.

This might be the most valuable thing I've ever released. And it's 100% free. → Marketing Skills for Claude Code A collection of skills that turn Claude into a marketing and copywriting genius. Check it out ↓ github.com/coreyhaines31/…

@itsalexvacca Ads

We built 12 Claude Code skills that run our entire paid media ops across Google, Meta, and LinkedIn at ColdIQ (and we're giving the whole pack away). Our head of growth Ivan Falco runs $200K/month in ad spend from a terminal. It's how we doubled client load this year without losing quality. The skills do the work that used to fill our media buyers' calendars: spot creative fatigue, adjust bids, upload audiences, run bulk edits, flag broken campaigns, build reports. Each skill does a specific job: Google Ads: → keyword-analyzer: audits quality scores and finds keyword gaps → negative-keywords: reviews search terms and blocks wasted spend → performance-auditor: compares periods and flags what changed → search-terms: surfaces queries burning budget with zero conversions Meta Ads: → audience-builder: turns CRM lists into custom audiences → creative-fatigue-analyzer: spots declining CTR before the metrics flag it → fatigue-monitor: flags when your audience is saturated → spend-tracker: tracks budget pacing across every campaign LinkedIn Ads: → audience-builder: builds targeting audiences at scale → bid-optimizer: adjusts bids across campaigns in bulk → bulk-editor: mass edits campaigns, ads, and naming in seconds → creative-builder: generates ad creatives from brand specs You drop them into Claude Code, connect your ad accounts, and tell it what you need. It reads the skill, plugs into the platform, executes. 300+ hours of work went into building these. Comment ADS and we'll send all 12 over.

@AlfieJCarter Agents

I've dedicated 6 weeks to building a full Claude Code GTM agent team covering every function a revenue team runs daily. 67 plugins, 92 AI agents, 52 business skills, and 19 workflow orchestrators across lead research, outreach writing, session management, context compression, and pipeline tracking - installed in 3 commands, no coding required. Here's a complete FREE breakdown for you. To access: 1. Like it 2. Comment "AGENTS" 3. Follow me (so I can send it to you via DM)

You could literally: > open Claude > paste these 20 prompts > do the work of an entire SEO agency > pay $20/month instead of $10k > outrank every competitor in your market > make $100k/month The math is a no brainer. What’s stopping you?

@mikefutia Scrape

I just vibe-coded a Shopify Reviews Scraper that swipes all your competitors' customer reviews instantly. 🤯 Paste any competitor's product URL → pull every review in under 30 seconds → export to CSV. Built 100% in Codex. Perfect for brands and agencies who want to mine competitor voice-of-customer data at scale and turn it into winning ad creative. Here's how it works: → Drop a Shopify product URL into the tool → Set your review limit (50, 100, 500+) → Hit scrape — it launches a browser, finds the review widget, pulls every review → Export clean rows: rating, body, author, date → Feed the CSV to Claude and ask "what do customers love and hate about this product" → Turn the output into ad angles, hooks, and headlines No more paying $99/mo for clunky review scraping SaaS that locks you behind credits. What you get: - Any Shopify store's reviews in 30 seconds - Clean structured data (rating, body, author, date) - CSV or JSON export — drops straight into Sheets or an LLM - Unlimited scrapes, no per-review fees - A voice-of-customer firehose you can turn into ad creative on demand This is essentially a competitor review intelligence engine in a box. I'm giving away the full GitHub repo so you can clone it and run it yourself for free. Want the repo? > Like this post > Comment "SCRAPE" And I'll send it over (must be following so I can DM)

A DEVELOPER JUST SPENT 22,000 HOURS BUILDING A FREE PERSONAL AI OPERATING SYSTEM ON TOP OF CLAUDE CODE. And it might have just killed the coaching industry. Here are the numbers before anything else. 22,000 hours of development work. 6,000 sessions logged. 2 to 3 hours saved every single day. 12,100 GitHub stars. 45 built-in skills. 171 wired workflows. 37 safety hooks. $0 to install. This system knows your goals. Remembers every decision you have ever made. Prepares your morning briefing while you sleep. Routes every complex task through a 7-step cycle automatically. OBSERVE. THINK. PLAN. BUILD. EXECUTE. VERIFY. LEARN. No embeddings. No vector databases. No AI magic you cannot read. Every memory, every decision, every context lives in plain Markdown files. You read it with cat. You search it with ripgrep. You version it with git. Four memory types compound over time: Work memory: active projects and open decisions. Knowledge memory: domain expertise and research. People memory: contacts, companies, and relationships. Learning memory: patterns, mistakes, and what actually works for you specifically. Privacy is enforced by CODE not prompts. A hook called ContainmentGuard physically blocks sensitive data from being written outside designated zones. Now here is the part that changes the business model entirely. Freelancers are already charging $500 to $2,000 to install this for executives, founders, and operators. One person. One weekend. A consulting business that did not exist 6 months ago. Every AI productivity app you are paying $30 a month for is replaceable by 4 hours of setup work and this one repo. github.com/danielmiessler… 100% open source. Free forever. Bookmark this before you pay for another AI subscription. Follow @cyrilXBT for every open source build that makes an entire industry obsolete the moment it drops.

ANTHROPIC JUST OPEN SOURCED THE ENTIRE WALL STREET WORKFLOW AND FIRMS ARE NOT GOING TO BE HAPPY ABOUT IT. DCF models. LBO models. Equity research reports. Merger analysis. KYC checks. All of it. Free. On GitHub. Here is what just became available to anyone with a laptop. Direct connections to Bloomberg, FactSet, S&P Global, Morningstar, and PitchBook. Real Excel models with live formulas and sensitivity tables built automatically. CIMs, IC memos, earnings reports, and buyer lists drafted on demand. PE due diligence, GL reconciliation, and NAV tie-outs running as production agents. This is not a chatbot wrapper that summarizes financial news. These are production agents that own entire financial workflows end to end. The kind that investment banks and private equity firms pay $50,000 to $500,000 per year in software licenses to run. Now it is a one-line Claude Code plugin install. 19,800 GitHub stars. Apache 2.0 license. 100% open source. Think about what this actually means. A junior analyst at a bulge bracket bank spends 80% of their 100-hour week running models, drafting memos, and compiling data across Bloomberg and FactSet. That entire workflow just became a Claude Code agent. The banks charging clients $500 an hour for analysis that this system produces in minutes are not going to tell you this exists. The boutique advisory firms charging $50,000 retainers for due diligence work that these agents handle autonomously are not going to promote this repo. But it is already live. 19,800 people have already starred it. The window where knowing this gives you an edge over every analyst, associate, and advisor still doing this manually is open right now. Star it. Fork it. Deploy it this weekend. Bookmark this before your next financial model. Follow @cyrilXBT for every open source release that disrupts an overpriced industry the moment it drops.