Insights on AI-powered acquisitions.

🚀 llms.txt are live on SN33 The llms.txt repository is now live. 🔗 http://github.com/afterpartyai/llms_txt_store SN33 has processed the first batch with over 1,000 websites crawled, cleaned, and converted into structured llms.txt files by the subnet. Semantic summaries ready for any LLM agent, MCP server, or AI app to consume instantly. No scraping. No parsing raw HTML. Just clean, machine-readable intelligence. New batches will be pushed as the subnet keeps processing. The repo grows every week. What's in the dataset: → Structured semantic summaries per domain → Named entities: people, orgs, products, technologies, concepts → Topic classification and key themes → Deterministic O(1) lookup by domain with no index file needed → Git-friendly structure that scales to millions of domains This initial release covers ~1,000 domains as a pilot, but the pipeline scales to millions. 📍 Roadmap: 10K → 100K → 1M domains → continuous updates from new Common Crawl releases and soon from requests. 🌍 And the frontend is coming. Any domain. You request it, the subnet processes it, you get an llms.txt back. We're putting the finishing touches on the public UI and it drops soon. SN33 is becoming infrastructure. The web, made readable for machines and open to anyone, powered by decentralized infra. Star the repo. Share it. And stay close. The next drop is right around the corner.

👀 something new is coming We've been building and we're almost ready to show you. SN33 has been processing the web at scale, turning raw Common Crawl data into clean, AI-ready `llms.txt` files. Structured semantic summaries that any LLM agent, MCP server, or AI app can consume instantly. On Thursday we'll be releasing the Github repo where `llms.txt` files will be pushed in batches as the subnet processes them. We're starting with over 1000 websites analyzed and processed by the subnet that will grow every week. And shortly after... 🌍 We're launching a public frontend Any website. Any domain. You request it, the subnet processes it and you get a `llms.txt` back. No more raw HTML hell for AI agents. No more redundant crawling. Just clean, structured, machine-readable intelligence about any corner of the web, on demand, powered by decentralized compute. This is SN33 becoming a public utility for AI infrastructure The web, made readable for machines. At scale. Open to anyone. 🔜 More very soon. Stay tuned.

SN33, Organizing the Spoken Web Our podcast conversations dataset has been downloaded over 300,000 times on HuggingFace. That demand told us something: the market is starving for structured conversation data. Written content represents a fraction of human knowledge online. The real depth lives in spoken conversation, experts explaining their craft, founders breaking down strategy, researchers debating methodology. Millions of hours of it happen in public every day. It's the highest-signal data on the internet, and almost none of it is structured, tagged, or accessible to AI agents. We've been calling it the web's dark matter. This week, we're making it visible. We're launching SN33's agentic transcription system, an autonomous pipeline that discovers, retrieves, and processes public conversations across the web at scale. It doesn't wait for input. It finds the conversations that matter, converts them into structured data, and feeds them directly into the subnet for enrichment. Every conversation enters the system tagged to a category from the start. That means category-specific task routing for miners, and more importantly, it unlocks customer-requested categories. With site enrichment, we organized the written web. With agentic transcription, we're organizing the spoken web. Together, these systems are building llms.txt for the entire web, not just pages, but conversations. Rolling out TOMORROW February 26th.