Hasan Toor
- I'm deleting every codebase documentation tool because of this. Google launched CodeWiki and it turns any GitHub repo into documentation a normal human can actually understand. You paste a repository and it automatically maps the entire project, explains the architecture, builds diagrams, creates tutorials, and gives you a chatbot that understands the codebase. The difference from every other AI code explainer is the structure. Most tools summarize files. This turns the whole repo into an interactive wiki you can actually navigate. → Generates architecture diagrams automatically → Explains what each part of the codebase does → Detects dependencies and how files connect → Creates step-by-step tutorials from the repo → Turns complex systems into readable documentation → Lets you ask questions through a repo-aware chatbot → Makes onboarding to any codebase feel 10x faster Basically: you paste a repo you don't understand. CodeWiki turns it into something you can read, explore, and ask questions about in minutes. This is what documentation should have been all along. Link below 👇
- 🚨 OpenClaw just got an unfair advantage over every other AI agent on the internet. It's called Scrapling and it scrapes undetectable, adaptive websites without breaking when they update their structure. No bot detection. No selector maintenance. No Cloudflare nightmares. OpenClaw tells Scrapling what to extract. Scrapling handles the stealth. Clean data lands in your agent in seconds. → 774x faster than BeautifulSoup with Lxml → Bypasses ALL types of Cloudflare Turnstile automatically → pip install "scrapling[ai]" and your AI agent is scraping in 60 seconds Works everywhere: → HTTP + browser automation → CSS, XPath, text, regex selectors → Async sessions for parallel scraping → CLI with zero code required If you're building AI agents that need real web data, this is the scraping backbone OpenClaw has been missing. 100% Opensource. BSD-3 license. Link in first comment 👇
- 🚨 BREAKING: Someone just open sourced the missing layer for AI agents and it's genuinely insane. It's called LangWatch. The complete platform for LLM evaluation and AI agent testing trace, evaluate, simulate, and monitor your agents end-to-end before a single user sees them. Here's what you actually get: → End-to-end agent simulations - run full-stack scenarios (tools, state, user simulator, judge) and pinpoint exactly where your agent breaks, decision by decision → Closed eval loop - Trace → Dataset → Evaluate → Optimize prompts → Re-test. Zero glue code, zero tool sprawl → Optimization Studio - iterate on prompts and models with real eval data backing every change → Annotations & queues - let domain experts label edge cases, catch failures your evals miss → GitHub integration - prompt versions live in Git, linked directly to traces Here's the wild part: It's OpenTelemetry-native. Framework-agnostic. Works with LangChain, LangGraph, CrewAI, Vercel AI SDK, Mastra, Google ADK. Model-agnostic too OpenAI, Anthropic, Azure, AWS, Groq, Ollama. Most teams shipping AI agents have zero regression testing. No simulations. No systematic eval loop. They find out their agent broke when a user tweets about it. LangWatch fixes that. One docker compose command to self-host. Full MCP support for Claude Desktop. ISO 27001 certified. 100% Open Source. (Link in the comments)
I'm deleting every codebase documentation tool because of this. Google launched CodeWiki and it turns any GitHub repo into documentation a normal human can actually understand. You paste a repository and it automatically maps the entire project, explains the architecture, builds diagrams, creates tutorials, and gives you a chatbot that understands the codebase. The difference from every other AI code explainer is the structure. Most tools summarize files. This turns the whole repo into an interactive wiki you can actually navigate. → Generates architecture diagrams automatically → Explains what each part of the codebase does → Detects dependencies and how files connect → Creates step-by-step tutorials from the repo → Turns complex systems into readable documentation → Lets you ask questions through a repo-aware chatbot → Makes onboarding to any codebase feel 10x faster Basically: you paste a repo you don't understand. CodeWiki turns it into something you can read, explore, and ask questions about in minutes. This is what documentation should have been all along. Link below 👇
🚨 OpenClaw just got an unfair advantage over every other AI agent on the internet. It's called Scrapling and it scrapes undetectable, adaptive websites without breaking when they update their structure. No bot detection. No selector maintenance. No Cloudflare nightmares. OpenClaw tells Scrapling what to extract. Scrapling handles the stealth. Clean data lands in your agent in seconds. → 774x faster than BeautifulSoup with Lxml → Bypasses ALL types of Cloudflare Turnstile automatically → pip install "scrapling[ai]" and your AI agent is scraping in 60 seconds Works everywhere: → HTTP + browser automation → CSS, XPath, text, regex selectors → Async sessions for parallel scraping → CLI with zero code required If you're building AI agents that need real web data, this is the scraping backbone OpenClaw has been missing. 100% Opensource. BSD-3 license. Link in first comment 👇
🚨 BREAKING: Someone just open sourced the missing layer for AI agents and it's genuinely insane. It's called LangWatch. The complete platform for LLM evaluation and AI agent testing trace, evaluate, simulate, and monitor your agents end-to-end before a single user sees them. Here's what you actually get: → End-to-end agent simulations - run full-stack scenarios (tools, state, user simulator, judge) and pinpoint exactly where your agent breaks, decision by decision → Closed eval loop - Trace → Dataset → Evaluate → Optimize prompts → Re-test. Zero glue code, zero tool sprawl → Optimization Studio - iterate on prompts and models with real eval data backing every change → Annotations & queues - let domain experts label edge cases, catch failures your evals miss → GitHub integration - prompt versions live in Git, linked directly to traces Here's the wild part: It's OpenTelemetry-native. Framework-agnostic. Works with LangChain, LangGraph, CrewAI, Vercel AI SDK, Mastra, Google ADK. Model-agnostic too OpenAI, Anthropic, Azure, AWS, Groq, Ollama. Most teams shipping AI agents have zero regression testing. No simulations. No systematic eval loop. They find out their agent broke when a user tweets about it. LangWatch fixes that. One docker compose command to self-host. Full MCP support for Claude Desktop. ISO 27001 certified. 100% Open Source. (Link in the comments)
