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ReadyAI
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ReadyAI
@ReadyAI_
Making the world's data accessible to AI. Home of AcquiOS for CRE & M&A acquisitions Bittensor Subnet 3️⃣3️⃣ 🌐
Beigetreten Ağustos 2024
24 Folgt3.4K Follower
ReadyAI retweetet
Slowly than all at once
The world is waking up to decentralized AI $TAO
Congrats @tplr_ai on the incredible milestone 🫡
Jolly Green Investor 🍀@jollygreenmoney
Nothing to see here… Just Jensen Huang (CEO of the world’s most valuable company Nvidia) and Chamath discussing Bittensor $TAO 🤯
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👀 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.
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ReadyAI retweetet
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
ReadyAI@ReadyAI_
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ReadyAI retweetet
The web wasn't built for AI agents. We're fixing that. First 1,000 domains live now, millions coming. Open source, decentralized, and free.
Frontend coming shortly to request llms.txt for any site
ReadyAI@ReadyAI_
🚀 llms.txt are live on SN33 The llms.txt repository is now live. 🔗 github.com/afterpartyai/l… 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.
English
🚀 llms.txt are live on SN33
The llms.txt repository is now live. 🔗 github.com/afterpartyai/l…
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.
English
Great question. Short answer: we sidestep a lot of it by processing at the site level, not the page level. When you enrich an entire domain's pages together, NER, tags, summarization, similar pages, you get entity grounding from context across the site rather than trying to reconcile isolated page-level extractions across the whole crawl. It doesn't eliminate the problem but it dramatically reduces the noise surface. The repo drops Thursday so would love your take once you can see the output structure.
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ReadyAI retweetet
The generic data race is over. The teams that win the next 3 years are the ones building deep, vertical-specific pipelines that scraping can't replicate.
That's exactly what we're doing @ReadyAI_ . Phase 1 is just the start.
ReadyAI@ReadyAI_
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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 with experts explaining their craft, founders breaking down strategy, researchers debating methodology. Millions of hours of it happen in public every day across podcasts, interviews, panels, and debates. 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 with 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 something we've been building toward: customer-requested categories. Need every meaningful AI conversation from the last 90 days transcribed, enriched, and delivered as structured data? That's not a hypothetical. That's the infrastructure we're standing up right now.
With site enrichment, we organized the written web. With agentic transcription, we're organizing the spoken web. Together, these systems are building what we think of as llms.txt for the entire web so not just pages, but conversations. Structured, categorized, and ready for the next generation of AI agents to consume.
Not just what people write, but what they say.
Rolling out TOMORROW February 26th.
English
ReadyAI retweetet
We started SN33 @ReadyAI_ with a simple thesis: the web has all the information agents need, but none of it is structured for them. Webpage Metadata v2 is the inflection point as we're not just tagging pages anymore, we're enriching entire sites. The goal is to become the largest producer of llms.txt files in the world. On testnet now, mainnet 2/23 🫡
ReadyAI@ReadyAI_
SN33 -- Enriching the Data of the World SN33 just shipped Webpage Metadata v2, and the best way to explain what we’re building is this: an llms.txt version of Common Crawl. Our partnership with Common Crawl began with the simple but daunting task of tagging web pages to make semantic web data widely available. Generating this data would break down the barriers preventing web organization. This week we are taking a giant step in broadening that goal to encapsulate AI-enabling the world wide web by launching the enrichment process for entire web sites. Search engines atomize the web by surfacing individual pages. That's great for finding individual facts. It does almost nothing to give agents the holistic information they need to actually complete tasks. Simple example: an agent searching for "best skis" gets quality-for-price rankings from individual pages. It completely misses how waist width affects your ability to float in powder, navigate tight spaces, or carve on groomed trails. That information exists across an entire site, but no one is structuring it that way. This week we shipped the technology to change that. SN33 is now enriching entire web sites, not just individual pages. Our new high-volume API pushes full sites through the subnet, collecting enriched data from tags, NER, similar pages to summarization across every page on a site, grouped together. Why llms.txt matters The llms.txt standard summarizes an entire web site's contents in a single meaningful text file. Agents and MCP tools can understand what a site contains without processing every page. It's the missing layer between the open web and the agent economy. Adoption has been stymied by one problem: nobody is generating these files at scale. There hasn't been a broad effort to create llms.txt for the whole web — until now. Once SN33 reaches tipping-point volume of enriched site data, we begin publishing llms.txt files at scale. We believe SN33 will become the largest producer of llms.txt files in the world. The demand for structured web data is already proven. Our first open-source dataset, the 5000 Podcast Conversations, has crossed 300,000+ downloads on HuggingFace. That was conversations. This is the entire open web. More open-source releases are coming. v2.28.63 is on testnet and goes mainnet February 23rd.
English
SN33 -- Enriching the Data of the World
SN33 just shipped Webpage Metadata v2, and the best way to explain what we’re building is this: an llms.txt version of Common Crawl.
Our partnership with Common Crawl began with the simple but daunting task of tagging web pages to make semantic web data widely available. Generating this data would break down the barriers preventing web organization.
This week we are taking a giant step in broadening that goal to encapsulate AI-enabling the world wide web by launching the enrichment process for entire web sites.
Search engines atomize the web by surfacing individual pages. That's great for finding individual facts. It does almost nothing to give agents the holistic information they need to actually complete tasks.
Simple example: an agent searching for "best skis" gets quality-for-price rankings from individual pages. It completely misses how waist width affects your ability to float in powder, navigate tight spaces, or carve on groomed trails. That information exists across an entire site, but no one is structuring it that way.
This week we shipped the technology to change that. SN33 is now enriching entire web sites, not just individual pages. Our new high-volume API pushes full sites through the subnet, collecting enriched data from tags, NER, similar pages to summarization across every page on a site, grouped together.
Why llms.txt matters
The llms.txt standard summarizes an entire web site's contents in a single meaningful text file. Agents and MCP tools can understand what a site contains without processing every page. It's the missing layer between the open web and the agent economy.
Adoption has been stymied by one problem: nobody is generating these files at scale. There hasn't been a broad effort to create llms.txt for the whole web — until now.
Once SN33 reaches tipping-point volume of enriched site data, we begin publishing llms.txt files at scale. We believe SN33 will become the largest producer of llms.txt files in the world.
The demand for structured web data is already proven. Our first open-source dataset, the 5000 Podcast Conversations, has crossed 300,000+ downloads on HuggingFace. That was conversations. This is the entire open web.
More open-source releases are coming. v2.28.63 is on testnet and goes mainnet February 23rd.
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Welcome to the arena 🫡
gmoney.eth@gmoneyNFT
just set up my first miner on @bittensor subnet 33 with claude code. will check in a few hours to see if i made any money and then scale if i think its worth it. i'm looking for opportunities i can apply inference towards. please comment below if you have any.
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@DavFields You are in the eco since pre dtao and there is no sign of revenu since..
No buybacks.. why should i own your token ? What is the incentive ?
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@Batlecash We have announced a number of revenue generating structured data deals but it is still early in our revenue ramp. We are focused on building a long term sustainable business on Bittensor.
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SN33 🤝 Chutes Integration
Chutes is now a the preferred inference provider in our LLM backend.
Subnets powering subnets:
→ SN33 miners/validators can route inference through Chutes' decentralized compute
→ Volume and utility flow within the ecosystem, not to OpenAI
→ Reduces single points of failure in validation pipeline
Also adding OpenRouter for model optionality and redundancy.
The Bittensor flywheel works best with subnets building on each other instead of routing value to Web2 infrastructure.
Testnet live. Mainnet next week.
PR and more detail on v2.26.61 in Discord.
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ReadyAI retweetet
SN33 Technical Paper: Elastic Protocol Surface (live now on SN33 mainnet)
We've rearchitected our backend to support non-linear scaling both in volume and content diversity.
What this enables:
→ Common Crawl-scale ingestion without pre-provisioned capacity
→ Domain-specific execution profiles (RE, finance, code, technical docs)
→ Horizontal throughput scaling based on demand
→ Fault-isolated containerized tasks
Infrastructure as a first-class subnet primitive.
Cross-subnet collaboration pathways outlined for Chutes (inference) and Ridges (code annotation).
Full paper ↓
ReadyAI@ReadyAI_
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