ReadyAI

ReadyAI Revenue Dashboard is Live 🚀 Real-time demand for SN33's structured data pipeline Starting today, anyone can watch demand for ReadyAI's structured data pipeline as it happens. Revenue, jobs, quotes, and tags produced. Data straight from the Subnet 33 Jobs API is refreshed every 8 hours, The Early Signal Exactly what we hoped for. Direct ReadyAI revenue is on a five-figure ARR pace and the curve is steepening week over week. The Jobs API is open. Beyond our existing enterprise feeds, we're starting to see organic customers find it and plug in on their own. Submit data, pay in USDC over a single HTTP call, get structured output back. What This Means for Alpha Holders 75% of this revenue goes directly to buying back SN33 alpha from the open market. Every job that flows through the pipeline, enterprise feed or organic API user, funds a buyback for the foreseeable future. All on-chain, all verifiable. The dashboard you're looking at is the same demand that drives the buyback. Dashboard: readyai.ai/dashboard Back to building. Got some exciting news coming on our coding data pipeline and benchmarks to share shortly. Docs and endpoint details for the API is available here: api.readyai.ai/jobs-api/docs

AI's impact on jobs is something we thing a lot about @ReadyAI_ Some perspetive from our founder @DavFields

When we started @ReadyAI_ the thesis was simple: structured data is the bottleneck for AI, and Bittensor is the best way to produce it at scale. Today we can prove it. NYSE-listed customers paying for our data, those payments moving on-chain through x402, and 75% flowing into alpha buybacks. Dashboard goes live next week.

Real customers. Real revenue. Coming fully on-chain this month. ReadyAI Update and Roadmap: → 5 paying customers including a NYSE-listed REIT (SmartStop, $1.8B) → x402 payment rails going live May 18th with every API call settable and verifiable on-chain → 75% of enrichment revenue committed to SN33 alpha buybacks → Public dashboard shipping this month so anyone can audit demand Full breakdown ↓

We've been heads down on something. Coding agents are the trillion-dollar race for every major lab, and the high-quality structured data to make them work is the bottleneck. Context7 became the #1 MCP server (53K stars) solving current docs. But the hard problems live deeper from version-pinned breaking changes to expert reasoning mined from thousands of technical podcasts, coding intelligence that doesn't exist in any documentation. We're building that dataset. More this week.

Getting my Claw into music this weekend...thanks @ReadyAI_ Grab any files you'd like here: readyai.ai

Bittensor and decentralized AI is as important as ever. This bump in the road will lead to improving the protocol and further resilience Zoom out $TAO

New on @ReadyAI_ Request an llms.txt file for any domain, free Search a site → not in our 10K+ database? Hit "Request This Domain Now" → get your file queued on subnet 5 free requests per user. Every file is open-sourced on GitHub Structured data for agents shouldn't be gated

This is the best explanation for why (1) Bittensor is unique amongst crypto projects and (2) you often see crypto VCs hating on it Bittensor provides the incentives for bootstrapping innovation across numerous experiments all at once without the need for VCs $TAO

SEO was built for humans browsing the web The next version of search optimization is built for agents reading it AEO/GEO ("agent engine optimization" or "generative engine optimization") is becoming a real category An entire industry is forming around making your website legible to LLMs and autonomous agents instead of just Google crawlers Right now every AI agent that needs info about a company or domain does the same thing; scrapes, parses HTML, and hopes for the best Billions of redundant crawls; trillions of wasted tokens llms.txt emerged as a proposed standard for this; a markdown file in a website's root directory that gives LLMs a clean structured summary of the site's content instead of forcing them to parse navigation menus, cookie banners, and JavaScript Over 844k websites have already adopted it; Anthropic, Cloudflare, and Stripe among them The problem is that no one has built the infrastructure to do this at scale across the entire web The beauty of this is that the infrastructure powering it can be decentralized from day one; there's no reason for one company to own the machine-readable index of the entire web So when you read the below announcement from subnet 33 you should look at it in the context of this broader agentic engine optimization (AEO) How many "AEO experts" do you think currently exist? Zero. There's a huge opportunity for you to pick a niche and dominate Once again, another Bittensor subnet tackling a forward thinking problem

We just launched a new readyai.ai Type any domain into the search. If it's in our dataset, you get clean, structured intelligence instantly. No scraping. No parsing HTML. Just machine-readable data, ready for any AI agent. 10,000+ websites crawled, cleaned, and structured by Subnet 33 so far. Growing to 100K by Q2, 1M by year end. This is the beginning of something bigger: a marketplace for agentic data. Right now, every AI agent that needs info about a company or domain scrapes, parses, and hopes. Billions of redundant crawls. Trillions of wasted tokens. We're building the infrastructure layer that fixes this — an indexed, machine-readable web powered by decentralized compute.

Slowly than all at once The world is waking up to decentralized AI $TAO Congrats @tplr_ai on the incredible milestone 🫡

@eleusys7 Orienting SN33 for the coming wave of agentic commerce 🫡

@ReadyAI_ nice, how does 33 make revenue from this?

👀 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.

Our recent breakthrough with enrichment tasks on the subnet has completely opened the floodgates. We can now create structured datasets from nearly any source, from llms.txt to deep coding data. Will be sharing benchmark improvements with this coding data shortly