The web was built for humans.
Search engines index it, we (humans) query it, we skim pages and piece together answers. That model works well for finding isolated facts.
It does not work well for AI agents.
Agents do not just need answers. They need structure, context, relationships, and semantic continuity across entire websites. And that is exactly where ReadyAI (Bittensor Subnet 33) is now focusing its efforts.
With the launch of Webpage Metadata v2, ReadyAI is moving from tagging individual pages to enriching entire websites at scale.
The ambition is to build an llms.txt version of “Common Crawl” and transform the open web into something agents can truly understand.
From Page Tagging to Site Level Intelligence
ReadyAI began its partnership with Common Crawl with a focused objective.
Tag web pages, make semantic web data widely available, and break down the barriers that prevent structured web organization.
That first phase addressed a foundational issue. The open web contains enormous volumes of information, but most of it is unstructured.
Without tagging, classification, and enrichment, large language models and agents are forced to infer context from fragmented signals.
Webpage Metadata v2 expands that mission.
Instead of treating the web as isolated pages, ReadyAI now processes entire websites as cohesive knowledge units.
The Problem With Page-Based Search
Search engines atomize the web. They surface individual pages optimized for specific keywords. This is efficient for retrieving a quick answer, but it is not sufficient for enabling autonomous agents to complete complex tasks.
Consider a simple example:
An agent searching for “best skis” may retrieve ranking lists from multiple review pages. It might learn which models score highly for value.
What it misses is the deeper knowledge scattered across the site:
a. How waist width affects flotation in powder,
b. How sidecut influences carving performance, and
c. How ski stiffness changes maneuverability in tight terrain.
That information often exists across buying guides, blog posts, technical breakdowns, and FAQs (Frequently Asked Questions). But because it is not structured at the site level, agents struggle to connect it holistically.
ReadyAI’s latest upgrade is designed to change that.
What Webpage Metadata v2 Actually Does
With Webpage Metadata v2, ReadyAI now enriches entire websites, not just individual pages.
Through a high-volume API (Application Programming Interface), full sites are pushed through the subnet and processed collectively. The enrichment pipeline includes:
a. Semantic tagging,
b. Named Entity Recognition,
c. Similar page clustering,
d. Cross page summarization, and
e. Context grouping across the entire domain.
Instead of isolated annotations, the output becomes site-level intelligence.
This is a meaningful shift. It transforms raw web content into structured, machine-readable context designed specifically for AI agents.
Why llms.txt is the Missing Layer
The llms.txt standard provides a summarized representation of an entire website in a single structured text file.
In practical terms, it allows:
a. Agents to understand what a site contains,
b. MCP (Model Context Protocol) tools to assess content scope without crawling every page, and
c. Systems to quickly determine relevance before deeper retrieval.
It is the bridge between the open web and the agent economy.
Adoption has been slow for one core reason as no one has been generating llms.txt files at scale.
ReadyAI aims to solve that supply side bottleneck.
Once the subnet reaches sufficient enriched site volume, it plans to publish llms.txt files at scale. The long-term objective is ambitious: become the largest producer of llms.txt files globally.
If successful, this positions ReadyAI as foundational infrastructure for AI navigation of the web.
Proven Demand for Structured Data
The appetite for high-quality structured datasets is already visible.
ReadyAI’s first open source release, the 5000 Podcast Conversations dataset, has surpassed 300,000 downloads on Hugging Face.
That dataset focused on conversations.
The current initiative targets something far larger: the entire open web.
This trajectory reflects a broader market signal. Developers, researchers, and AI builders are actively seeking structured, high signal data sources. ReadyAI is expanding into one of the most valuable frontiers available.
Why This Matters for the AI Economy
As AI agents become more autonomous, their bottleneck shifts from model capability to data structure.
Agents can reason.
They can plan.
They can generate.
But without well structured web data, they remain dependent on inefficient crawling and page by page inference.
ReadyAI’s enrichment layer offers three strategic advantages:
a. Efficiency: Agents can understand entire sites without processing every URL.
b. Context: Cross page relationships become explicit rather than inferred.
c. Scalability: llms.txt files can be generated systematically at web scale.
This is not about incremental SEO optimization. It is about reformatting the internet for machines.
What Comes Next
Version 2.28.63 is currently on testnet and is scheduled for mainnet release on February 23rd.
More open source releases are planned.
If ReadyAI reaches tipping point volume in enriched site data, the production of llms.txt files could scale rapidly. At that stage, the subnet would not just be tagging the web. It would be structuring it.
The Bigger Picture
The first phase of the web connected documents.
The second phase connected people.
The next phase connects agents.
For that transition to succeed, the open web must evolve from fragmented HTML pages into structured, machine interpretable knowledge.
ReadyAI’s Webpage Metadata v2 is a direct step toward that future.
If Common Crawl helped archive the web, ReadyAI is working to make it intelligible.
And in an era defined by AI agents, intelligibility may become the most valuable layer of all.
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