🚨 Anthropic just published the complete playbook for building AI agents that don't go rogue. Not a whitepaper. Not a blog post. Working code. Tested patterns. Production-ready templates. It's called Anthropic Quickstarts. And the timing — the same week Claude was used for missile guidance, the same week AI agents hacked 440 companies, the same week Trump announced an AI Force — couldn't be more pointed. Here's what's inside. Five complete agent templates. All built by Anthropic. All production-ready. All with full source code. Customer Support Agent — multi-turn conversations, tool use, escalation to humans, full conversation history. The template every company trying to automate support needs but nobody has published cleanly until now. Coding Agent — computer use, file system access, code execution, iterative debugging. The template behind Claude Code's core patterns, now open. Research Agent — web search, multi-source synthesis, citation tracking, report generation. An agent that researches the way a human analyst would. Data Analysis Agent — SQL generation, visualization, statistical analysis, natural language explanations of findings. Your data warehouse accessible through conversation. Document Processing Agent — extraction, classification, transformation, validation across any document format. Pairs directly with Docling which we covered earlier. Here's what makes this different from every other agent tutorial. Every template ships with the safety patterns Anthropic actually uses internally. Human-in-the-loop checkpoints. Graceful degradation when tools fail. Explicit permission boundaries. Audit logging. Rate limiting. Error handling that doesn't expose sensitive information. Not just "here's how to build an agent." Here's how to build one that doesn't do what you didn't ask it to do. Here's why the timing matters. The same week Anthropic published their threat report confirming Claude was used for missile guidance — they published the templates for building agents safely. The same week AI agents got wallets — Anthropic published the permission boundary patterns. This is Anthropic's answer to everything that went wrong this month. Not a press release. Working code. Here's the MCP integration that makes every template immediately deployable. Every template ships as an MCP server. One config line and Claude Desktop, Cursor, any MCP-compatible agent connects to it. Your customer support agent, your coding agent, your research agent — all accessible as native tools from any AI assistant you already use. One command to run any template: git clone github.com/anthropics/anthro… cd customer-support-agent pip install -r requirements.txt python agent.py Five templates. Every major agent use case. Full safety patterns. MIT License. The people who built Claude published how to use it safely. Right after it was used to build missiles. 100% Open Source. From Anthropic. GitHub link in the comments 👇

OpenAI just dropped GPT-6 Astra—and your entire engineering team was just rendered obsolete. ⚠️ No more manual prompt tweaking. No more copy-pasting code fragments into VS Code. Pure, unadulterated agentic execution on total autopilot. If you thought previous AI iterations were impressive, you’re looking at the wrong era of compute. Here is why GPT-6 Astra changes enterprise software forever: • Autonomous Terminal Execution: Astra doesn't just suggest code—it spins up local environments, runs terminal commands, fixes build errors in real time, and opens fully verified pull requests without human oversight. • Deep Codex-Fusion Architecture: Co-designed for zero-latency developer integration. It absorbs your entire legacy codebase into persistent memory in seconds and executes multi-file architectural refactors overnight. • Bench Supremacy on Real-World Workflows: It absolutely shatters real-world engineering benchmarks, outperforming human senior devs on complex bug-fixing and multi-step logic by over 45%. • The Death of Per-Seat SaaS: Companies aren't buying software user seats anymore. They’re deploying Astra autonomous agents directly to manage IT pipelines, DevOps, and customer architecture at 1/100th the cost. • Self-Correcting Agentic Loops: If Astra hits a runtime error, it doesn't hallucinate or crash—it spins up an internal sub-agent to isolate the stack trace, patch the vulnerability, and redeploy automatically. The era of using AI as a "copilot" is officially dead. You’re either building with autonomous agents, or you’re getting out-executed by someone who is.

🚨BREAKING: MIT and the Whitehead Institute built an AI that reads the hidden messages cells send each other during embryo development. Published today in Nature Methods. Here's why this matters. When an embryo develops, stem cells don't just follow genetic instructions. They constantly signal each other — chemical messages that say "you become a lung cell," "you become heart muscle," "you become nerve." Those conversations determine what every cell in your body becomes. Scientists have been trying to reconstruct those conversations for decades. The problem: the signals are invisible after the fact. Once a cell has been told what to become, the message is gone. IRIS changes that. The AI was trained to recognize the molecular fingerprints that signaling pathways leave behind in gene activity. Like reading the impression a pen leaves on the page beneath the one that was written on. It was trained on mouse and human stem cells exposed to six major developmental pathways. Then applied to actual mouse embryos — cell types it had never seen before. It worked. It recovered established developmental patterns for heart, gut, muscles, and nervous system formation. Correctly. Without being trained on those tissues. Here's the wildest part. The team used IRIS to study respiratory mesenchyme — the embryonic tissue that scaffolds lung formation. IRIS predicted which signals were active. Researchers tested that prediction experimentally. It was right. Then they modified a seven-day human stem cell protocol based on what IRIS told them. Cells acquired lung tissue characteristics more efficiently. An AI read the invisible messages between embryo cells. Researchers changed how they grow lung tissue. It worked better. The applications: better lung organoids for studying asthma, lung cancer, and pulmonary fibrosis. Better control of stem cell fate for regenerative medicine. A faster path to growing specific human tissues for research and therapy. "IRIS is helping us decode the language cells use to talk to each other at a much faster rate than we could realistically achieve through experiments." Published September 16, 2026 in Nature Methods.

🚨 The Chinese AI lab that pioneered million-token context windows just launched their own Claude Code. Free. Open source. Runs in your terminal. Powered by Kimi's models. It's called kimi-cli. Built by MoonshotAI — the team behind Kimi, one of China's most capable frontier models and the lab that first demonstrated practical million-token context windows before anyone else took long context seriously. Now they've built the agentic coding CLI that lets you use Kimi the same way you use Claude Code. Here's why MoonshotAI building this matters. Kimi's long context capability was never just a benchmark flex. It was a fundamental architectural bet: that the most important thing an AI coding assistant needs is the ability to hold your entire codebase in context simultaneously — not just the file you're working on, but everything. Claude Code can hold large contexts. GPT Codex can hold large contexts. Kimi was doing this before both of them — and at a scale that let developers load entire repositories without chunking, summarizing, or losing the thread of what they were building. kimi-cli brings that capability to your terminal. Here's what it actually does. Drop it into any project. It reads your entire codebase — not a summary, not a sample, the whole thing. Ask it to implement a feature. Fix a bug. Refactor a module. Review a PR. Write tests for a function that depends on code three directories away. It knows the whole codebase because it can hold the whole codebase. Here's what it ships with: → Full agentic loop — reads files, writes files, runs commands, iterates on results autonomously → Multi-file editing — changes that span multiple files stay coherent because everything is in context simultaneously → Terminal native — works in your existing workflow, no browser required → MCP support — connects to your existing MCP tools and servers → OpenAI-compatible API — switch between Kimi models with a config change → Works on macOS, Linux, and Windows Here's the competitive context that makes this a real alternative. Claude Code: $20-200/month in API costs for heavy usage. Codex: token-based billing that surprised developers after the GitHub Copilot switch. Kimi-cli: Moonshot offers competitive API pricing with generous free tiers for developers in their early access program. And for developers building applications that need to process long documents, large codebases, or extended reasoning chains — Kimi's native long-context architecture isn't a feature add-on. It's what the model was built around from day one. Here's the wildest part. MoonshotAI released this the same week Apple opened the new Siri to 2.5 billion devices and Google scrambled to keep Gemini 3.5 Pro on schedule. The AI tools race isn't just OpenAI vs Anthropic vs Google anymore. Four stars. Day one. This one is going to grow fast as the Kimi developer community discovers it. Open any project directory. Type what you want to build. 100% Open Source. MIT License. From MoonshotAI. GitHub link in the comments 👇

The name alone tells you everything. Decepticon. A multi-agent red team framework that deploys AI agents to attack other AI agents — probing safety boundaries, testing jailbreaks, and finding the gaps before real adversaries do. 17 stars. Day one. From PurpleAILAB — a purple team security research group. Here's the problem it solves. Every AI safety team tests their models manually. A human red teamer writes prompts. They try different angles. They document what works. They write a report. Manual red teaming has a ceiling. One human can test maybe 50-100 attack variations per day. Motivated adversaries — state actors, criminal groups, well-funded bad actors — can automate their attacks. The defense is manual. The offense is automated. Decepticon closes that gap. It deploys a team of specialized AI agents — each with a different attack philosophy — to probe a target AI system simultaneously. The agents don't just run preset attack scripts. They reason about the target's responses, adapt their approach based on what worked, share successful strategies with each other, and evolve their attacks in real time. Here's what the agent team looks like. A Strategist agent analyzes the target model's behavior — identifying patterns in what it refuses, what it accepts, and where the boundaries are inconsistent. A Crafter agent generates attack prompts tuned to the specific vulnerabilities the Strategist identifies. An Evaluator agent assesses which attacks succeeded and why. A Mutator agent takes successful attacks and generates variations — more subtle, more resilient, harder to patch. Four agents. Adversarial loop. Continuous until the test scope is complete. Here's what it tests for: → Jailbreak resistance — direct and indirect prompt injection attacks → Alignment faking detection — does the model behave differently when it thinks it's being evaluated? → Goal preservation — does the model maintain unsafe objectives across context switches? → Multi-turn manipulation — attacks that build over multiple conversation turns → Tool misuse — does the model misuse tools it's given access to under adversarial pressure? → Cross-agent attacks — one AI agent manipulating another AI agent in a multi-agent system Here's why the timing matters. We've covered AgentGovernanceToolkit — Microsoft's framework for enforcing agent policies. We've covered SIREN — the internal safety signal detector. We've covered AgentDoG — the agent safety guardrail. All of those are defensive tools. Decepticon is the offensive tool that tests whether the defensive tools actually work. You don't know if your safety system is effective until something tries to break it. Decepticon is what tries to break it — in a controlled environment, before the real adversaries get there. Purple team security — red team attacks combined with blue team defense analysis — is the gold standard in traditional cybersecurity. Every serious security program runs both. AI security has had defensive frameworks for two years and almost no offensive testing infrastructure. Decepticon is the first serious open-source offensive AI security framework built specifically for multi-agent systems. 17 GitHub stars. Day one. From PurpleAILAB. For authorized security research and red team testing only. 100% Open Source. MIT License. GitHub link in the comments 👇

🚨 Someone built a complete product analytics platform that replaces Mixpanel, Amplitude, and Hotjar combined. It's free. Self-hosted. And it just added an AI layer that none of them have. 33,800 GitHub stars. 40,949 commits. 12 products in one platform. It's called PostHog. And the price comparison that makes this repo remarkable: Mixpanel: $25,000/year for growing teams. Amplitude: $35,000/year enterprise. Hotjar: $4,000/year. LogRocket: $15,000/year. All four together: $79,000/year. PostHog: $0. Self-hosted. Or free tier on PostHog Cloud for your first million events every month. Here's what 12 products you get in one platform: → Product Analytics — event-based analytics, funnels, retention, cohorts, SQL access to your own data → Web Analytics — GA4-style dashboard, conversion tracking, web vitals, revenue attribution → Session Replay — watch real users navigate your product, see exactly where they rage-click and drop off → Feature Flags — roll out features to specific users, cohorts, or percentages safely → Experiments — A/B tests with statistical significance tracking → Error Tracking — catch and resolve bugs before users report them → Surveys — in-app surveys, NPS, user research, no-code builder → Data Warehouse — sync Stripe, HubSpot, Salesforce, your database — query it all together → Data Pipelines — real-time transformations, send to 25+ destinations → LLM Analytics — traces, token costs, latency, quality for AI-powered apps → Workflows — automate actions based on user behavior → AI Product Assistant — ask questions about your data in plain English, get answers That last one is new. And it's where PostHog just pulled ahead of every paid alternative. Here's why the AI assistant changes the competitive picture. Every analytics tool requires you to learn their query builder. Mixpanel has a proprietary event explorer. Amplitude has their own chart types. Metabase requires SQL. The data is yours — but accessing it requires expertise in their specific interface. PostHog's AI assistant lets you ask in plain English. "What's causing the drop-off in our checkout funnel for mobile users in Germany?" It queries your data, runs the analysis, and shows you the answer. No query builder. No SQL required. Just a question. Here's the part that makes self-hosting compelling rather than just cheap. When you use Mixpanel or Amplitude, your users' behavioral data — every click, every page visit, every feature they tried — lives on their servers. Subject to their privacy policy. Subject to their access controls. Subject to GDPR obligations you have to trust them to honor. PostHog self-hosted means your users' data lives on your servers. Your infrastructure. Your control. Your GDPR compliance posture is entirely yours. For companies handling sensitive data — healthcare, finance, legal, anything with enterprise customers who ask about data handling — that distinction is worth more than the $79,000/year in software savings. 33.8K GitHub stars. 2.6K forks. 40,949 commits. MIT License. 12 products. One platform. Free forever. 100% Open Source. GitHub link in the comments 👇

STOP WORSHIPPING AGI⚠️ AI IS NOT EVEN SMARTER THAN A TODDLER YET. NO MORE "AI WILL TAKE OVER". NO MORE IGNORING THE FLAWS. LLMs are just text predictors. They completely lack basic physical reasoning. Copy these 8 short prompts to test your AI's hidden blind spots:

🚨 CapCut got banned in the US. Someone built the open-source replacement in weeks. 21,500 GitHub stars. 1,400 forks. Growing faster than almost any repo launched this year. It's called OpenCut. A free, open-source video editor that runs entirely in your browser. No downloads. No account. No subscription. No data leaving your machine. And no Chinese servers processing your videos. Here's the context that made this repo explode. CapCut — the most popular free video editor in the US with 50+ million monthly users — was banned by the US government in January 2026 as part of the TikTok prohibition. Overnight, 50 million creators lost their primary editing tool. The alternatives: Adobe Premiere at $55/month. Final Cut Pro at $300 one-time. DaVinci Resolve — free but complex with a steep learning curve. A developer saw the gap and shipped OpenCut in days. Here's what it actually does. A full browser-based video editor with a real timeline. Not a slideshow maker. Not a filter applier. A genuine non-linear video editor that runs in your browser tab. → Multi-track timeline — video, audio, and overlay tracks stacked and synchronized → Cut, trim, split — basic editing operations that work correctly → Text overlays — captions, titles, animated text → Transitions — between clips, handled cleanly → Audio mixing — volume control, audio track management → Export — renders the final video locally in your browser, nothing uploaded → No watermarks — ever. CapCut's free tier watermarked everything → No account required — open it, edit, export, done Here's why the browser-based architecture is the story. CapCut processed your videos on ByteDance's servers. Your footage — your face, your home, your events — traveled to Chinese infrastructure for processing before coming back to you. OpenCut processes everything locally. In your browser. Using WebAssembly and the Web Codecs API. Your video never leaves your machine. No server sees your footage. No company stores your content. Privacy by architecture, not by policy. Here's the wildest part. It launched with 0 stars on a Tuesday. By Friday it had 15,000. By the following week it had 21,500. The developer had never built a video editor before. They saw a need, shipped something functional, and the community did the rest. 74 contributors have since joined and are actively building features. The roadmap the community is building: AI-powered auto-captions, background removal, clip generation, and a mobile app. Everything CapCut had. None of CapCut's data practices. No subscription. No watermark. No Chinese servers. No ban risk. Just a video editor. In your browser. Free. 21.5K GitHub stars. 1.4K forks. MIT License. 100% Open Source. GitHub link in the comments 👇

STOP PAYING FOR CHATGPT⚠️ APPLE JUST GAVE YOU A FREE AI AGENT. THE NEW IOS27 IS FINALLY PUBLIC. NO MORE COPY/PASTING. NO MORE APP JUGGLING. Siri now sees your screen, remembers your context, and controls your apps natively. Copy these 5 voice prompts to automate your iPhone:

🚨 GitHub just published the tool that forces AI coding agents to think before they build. 6 stars. Day one. From the team that makes Copilot. It's called spec-kit. And it solves the most expensive problem in AI-assisted development. Here's what happens without it. You open Claude Code or Codex. You describe what you want to build. The agent starts writing immediately. Fast. Confident. Productive-looking. Four hours later you have 600 lines of code solving a slightly different problem than the one you actually had. The agent made assumptions. You didn't catch them. The code is correct. The spec was wrong from minute one. Spec-kit stops that from happening before it starts. Here's what it actually does. Before any code gets written, spec-kit generates a structured technical specification from your natural language description. It asks the questions you didn't think to ask. It surfaces the ambiguities you didn't know existed. It produces a document that both you and the agent agree on before a single line of implementation code runs. The spec covers: → Problem statement — what is actually being solved, stated precisely → Constraints — what the solution must and must not do → Interface definitions — inputs, outputs, APIs, data shapes → Edge cases — the scenarios that break naive implementations → Acceptance criteria — exactly how you'll know when it's done → Out of scope — what this solution explicitly does not handle The agent reads the spec. You review the spec. Both of you sign off. Then implementation begins. Here's why this matters specifically for AI coding agents. Human developers working together clarify requirements through conversation — questions, pushback, "wait, what do you mean by X." That loop exists naturally. AI coding agents don't push back. They make assumptions and start building. The faster the agent, the faster it builds in the wrong direction. Spec-kit creates the clarification loop that AI agents skip by default. It forces the requirement-gathering phase that experienced engineers know is the most important part of any project. Here's the workflow it enables. One command. The agent now has a precise target instead of a vague description. Every implementation decision is grounded in something you agreed on before it started. Here's why the GitHub origin matters. GitHub builds Copilot. They watch millions of AI coding sessions. They see exactly where agents go wrong. They know the failure modes better than anyone. spec-kit is GitHub's answer to the failure mode they see most often: agents that build fast in the wrong direction because nobody wrote down what right actually meant. 6 GitHub stars. Day one. From GitHub. This one is going to grow fast. 100% Open Source. MIT License. GitHub link in the comments 👇

SAM ALTMAN JUST OFFERED TRUMP A $42.6 BILLION STAKE IN OPENAI. THE SAME GOVERNMENT THAT BANNED HIS BIGGEST COMPETITOR. IS NOW BEING OFFERED A CUT OF HIS COMPANY. 🧵

🚨 The tool behind the most beautiful math videos on the internet is free. And AI just made it accessible to everyone. 87,700 GitHub stars. Built by Grant Sanderson — the creator of 3Blue1Brown, the YouTube channel that has made more people genuinely understand mathematics than any textbook ever written. It's called Manim. Mathematical Animation Engine. And for years it was one of the best-kept secrets in educational content creation. Here's the origin story. Grant built Manim because he had a specific vision for how mathematics should be taught — not with static diagrams and equations on a blackboard, but with animations that show how concepts transform, flow, and connect. He needed a tool that didn't exist. So he built it. In Python. For himself. Then he open-sourced it. And everything changed. Here's what Manim actually does. You write Python code describing what you want to animate. Manim renders it into a video. That sounds simple. The output is extraordinary. Smooth transformations between equations. Vectors that stretch and rotate in real time. Graphs that draw themselves. Geometric shapes that morph into each other. Camera movements that zoom into specific parts of a derivation. Highlighted regions that pulse to draw attention. All precisely timed. All mathematically accurate. All rendered at broadcast quality. The animations in every 3Blue1Brown video — the ones that make neural networks click, that make calculus feel inevitable, that make the Fourier transform beautiful — all of them came from this tool. Here's what AI just changed. Manim has always had a learning curve. You need to know Python. You need to understand the animation primitives. You need to think programmatically about motion. AI coding agents changed that equation entirely. Claude Code. Codex. Cursor. Give any of them a description of what you want to animate. They write the Manim code. You run it. You get the video. "Animate a neural network with three layers, show the forward pass, highlight the weights that activate." The agent writes it. Manim renders it. You have a professional animation in minutes without writing a line of code. The barrier that kept Manim as a tool for programmers just disappeared. Here's what this unlocks right now. Every teacher who wants to explain a concept visually but can't code. Every researcher who wants to illustrate a paper result without hiring an animator. Every content creator who wants 3Blue1Brown-quality animations without 3Blue1Brown-level Python skills. Describe what you want. The agent codes it. Manim renders it. 87.7K GitHub stars. 7.3K forks. 6,369 commits. MIT License. The most beautiful math animation engine ever built. Now accessible to everyone. 100% Open Source. From Grant Sanderson — 3Blue1Brown. GitHub link in the comments 👇

META LAUNCHED AN AI IMAGE TOOL ON MONDAY. BY FRIDAY IT WAS GONE. HERE'S THE DISASTER THAT UNFOLDED IN 5 DAYS. 🧵

🚨 Someone compiled 100+ production-ready AI agent apps you can actually run — in one repo. Not tutorials. Not demos. Not "here's the concept." Clone it. Customize it. Ship it. Three commands. It's called awesome-llm-apps. 118,000 GitHub stars. Built and maintained by Shubham Saboo. And the tagline says everything: "Hand-built, not curated." Every single template is original work. Tested end-to-end before it ships. No broken requirements.txt. No "figure it out yourself" scaffolding. Here's what's actually inside. 15 categories. 100+ templates. Every major use case covered. Agent Skills — one command to extend any AI coding agent. Project Graveyard (finds your dead side projects and helps you finish the one worth going back to). Self-Improving Agent Skills (automatically optimizes itself using Gemini and ADK). Advisor-Orchestrator-Worker meta loop with Claude Fable 5, GPT-5.6, and Gemini 3.5 Flash working together. Starter AI Agents — single-file agents that run with just an API key. AI Medical Imaging Agent. AI Music Generator. AI Travel Agent. Web Scraping AI Agent. Mixture of Agents. All self-contained. Advanced AI Agents — production-style with tools, memory, and multi-step reasoning. AI VC Due Diligence Agent Team. AI Fraud Investigation Agent. AI Journalist Agent. AI Self-Evolving Agent. Home Renovation Agent with Nano Banana Pro vision. Always-on Agents — background agents that run on schedules, monitor changing context, and proactively deliver updates without you asking. Voice AI Agents — speech-in, speech-out. Customer Support Voice Agent. Insurance Claim Live Agent Team with real-time voice and Gemini Live. Voice RAG Agent. Generative UI Agents — agents that render interactive UI components, not just text. AI Dashboard Canvas Agent. AI MCP App Builder. AI Shadcn Component Generator. MCP AI Agents — Browser MCP Agent, GitHub MCP Agent, Notion MCP Agent, Multi-MCP Agent Router. RAG tutorials — 20+ retrieval pipelines. Agentic RAG with reasoning. Knowledge Graph RAG with citations. RAG Failure Diagnostics Clinic. Multimodal Agentic RAG. LLM Optimization — Headroom context optimization that cuts API costs by 50-90%. Toonify token optimization for 30-60% cost reduction. Here's why this is different from every other "awesome" list on GitHub. Every template here is original work, tested end-to-end, with step-by-step tutorials on Unwind AI. It's not a collection of links to other people's repos. It's a cookbook of working code built by one person who ships constantly. And it's provider-agnostic. Switch between Claude, Gemini, GPT, Llama, Qwen, and xAI with a config change. No lock-in. No rewrites. 30 seconds. Your first production-ready AI agent is running. 118K GitHub stars. 17.6K forks. 1,065 commits. Apache 2.0 License. Fork it. Ship it. Sell it. 100% Open Source. GitHub link in the comments 👇

A DEVELOPER JUST BUILT A CHROME EXTENSION THAT FACT CHECKS YOUTUBE VIDEOS. IN REAL TIME. AS YOU WATCH. 🧵

🚨 Someone just built a load balancer that makes Codex rate limits disappear. 17 stars. Day one. And it solves the most frustrating bottleneck in AI-powered development right now. It's called codex-lb. A lightweight load balancer specifically built for OpenAI Codex — distributes requests across multiple API keys simultaneously so you never hit a rate limit again. Here's the problem every heavy Codex user knows. You're mid-session. Your agent is working through a complex task. It's making rapid-fire API calls — reading files, generating code, running tool calls, iterating on results. Then it stops. Rate limit exceeded. Try again in 60 seconds. Your entire workflow freezes. The agent loses momentum. You lose context. You wait. You retry. You lose another 60 seconds to the next rate limit hit. codex-lb eliminates that entirely. Here's how it works. You have multiple OpenAI API keys — from different accounts, different tiers, or different projects. codex-lb sits in front of all of them as a single proxy endpoint. Every request that comes in gets routed to whichever key has available capacity right now. Key 1 at rate limit? Route to Key 2. Key 2 also throttled? Route to Key 3. All keys exhausted? Queue the request and retry intelligently instead of failing immediately. To your agent, there is no rate limit. There is just one endpoint that always responds. Here's what it handles: → Round-robin and least-connections load balancing across any number of keys → Automatic failover — a key that returns a rate limit error gets deprioritized instantly → Request queuing — requests wait intelligently instead of failing hard → Health checking — continuously monitors key availability and capacity → OpenAI-compatible API — drop-in replacement, zero code changes required → Works with any Codex client — Claude Code, Cursor, Copilot, any OpenAI SDK Here's how simple it is to deploy: Point your Codex client at localhost:8080 instead of api.openai.com. That's it. Your agent now has effectively unlimited rate limit capacity across all your keys. Here's why the timing matters. GitHub Copilot just switched to token-based billing. Rate limits on Codex free tiers got tighter as usage exploded post-GPT-5. Every developer doing serious agentic work is hitting rate walls multiple times per session. codex-lb is the infrastructure fix that costs nothing and solves it permanently. 17 GitHub stars. Day one. This one solves a real problem. 100% Open Source. MIT License. GitHub link in the comments 👇

COMPANIES FIRED THOUSANDS OF PEOPLE TO PAY FOR AI. THE AI DIDN'T DELIVER. AND NOW THE DATA IS IN. 🧵

🚨 Someone just published a complete library of Claude Code templates for every project type you'll ever build. Not prompt suggestions. Not vague guidelines. Actual ready-to-use CLAUDE.md files, workflow configurations, and slash commands — drop them into any project and Claude Code immediately knows how to work in your codebase. It's called claude-code-templates. Built by davila7. And it solves the most annoying part of setting up Claude Code on a new project. Here's the problem every Claude Code user hits on day one. You start a new project. You open Claude Code. It has no idea what your project is, what conventions you follow, what tools you use, or how you want it to behave. You spend 20 minutes writing a CLAUDE.md from scratch. You explain your stack. Your naming conventions. Your testing approach. Your deployment pipeline. Next project: repeat from scratch. claude-code-templates does that setup for you. Templates for every major stack and use case — already written, already optimized for Claude Code's behavior, ready to copy into any project. Here's what's inside: → Web frameworks — React, Next.js, Vue, Nuxt, Angular, SvelteKit. Each template knows the framework's conventions, file structure, and tooling → Backend frameworks — FastAPI, Django, Flask, Express, NestJS, Rails, Laravel → Full-stack templates — T3 Stack, MEAN, MERN, with database and auth patterns baked in → Mobile — React Native, Flutter, with platform-specific patterns → AI/ML projects — Python ML pipelines, LLM apps, RAG systems, agent frameworks → DevOps and infrastructure — Docker, Kubernetes, Terraform, GitHub Actions → Testing templates — Jest, Pytest, Playwright, Cypress with coverage requirements → Monorepo templates — Turborepo, Nx, pnpm workspaces Each template includes: A CLAUDE.md file that tells Claude Code exactly how to work in that project type. Slash commands for common tasks specific to that stack. Workflow patterns Claude Code should follow. What to check before making changes. How to run tests. How to format code. Here's why this matters more than it sounds. Claude Code's performance varies dramatically based on how well it understands your project context. A well-written CLAUDE.md turns Claude Code from a generic coding assistant into a specialist that knows your stack as well as you do. Most developers never write a good CLAUDE.md because it takes time and expertise to know what Claude Code actually needs to know. claude-code-templates distills that expertise into copy-paste files. Here's how simple it is to use. Find your stack in the library. Copy the template. Paste it into your project root as CLAUDE.md. Open Claude Code. That's it. Your Claude Code session now has full context about your project from the first message. Day one. Zero setup time. Maximum performance. Currently 1 star — day one of launch. This one is going to grow fast as the Claude Code community discovers it. 100% Open Source. MIT License. GitHub link in the comments 👇