Sabitlenmiş Tweet
ReadyAI
226 posts
ReadyAI
@ReadyAI_
Making the world's data accessible to AI. Home of AcquiOS for CRE & M&A acquisitions Bittensor Subnet 3️⃣3️⃣ 🌐
Katılım Ağustos 2024
24 Takip Edilen3.5K Takipçiler
ReadyAI retweetledi
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.
ReadyAI@ReadyAI_
English
ReadyAI retweetledi
Getting my Claw into music this weekend...thanks @ReadyAI_
Grab any files you'd like here:
readyai.ai
English
ReadyAI retweetledi
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
Chamath Palihapitiya@chamath
If Martin is right, he also just wrote the product spec for open source + distributed compute where broad swaths of groups, individuals and organizations contribute their compute resources to training runs for large param open source models. There are lots of issues in figuring this out: homogeneity vs heterogeneity of the training clusters, orchestration, financial incentives etc etc etc but some early projects are good signal as to where this can go and that these limitations can be overcome (folding@home, Venice, Tao). An attempted oligopoly on intelligence is the perfect boundary condition for a bottoms up uprising of fully open, fully distributed AI.
English
ReadyAI retweetledi
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
English
ReadyAI retweetledi
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
Algod@AlgodTrading
Yes emissions are used to bootstrap innovation, same as Uber, Amazon and countless of other big companies You can chose between these 2: -give those emissions to VC’s -give those emissions to builders who devote their whole time to build out the network Vc’s hate it because they can’t apply the VC playbook/had discounted access compared to the masses
English
ReadyAI retweetledi
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
David Fields@DavFields
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.
English
ReadyAI retweetledi
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.
ReadyAI@ReadyAI_
English
ReadyAI retweetledi
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 🤯
English
👀 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.
English
ReadyAI retweetledi
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_
English
ReadyAI retweetledi
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.
English
@ReadyAI_ LLMs.txt? Interesting. How are you handling entity resolution across the Common Crawl's noise? That's always been a bear for us.
English
ReadyAI retweetledi
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_
English