WarmHub @warmhub_ai

An agent doesn't skim. It traverses structure: the fact, its source, its neighbors. It doesn't need searchable knowledge. It needs legible knowledge. That's what WarmHub hands it. Copy the prompt, let your agent decide if WarmHub is worth connecting. warmhub.ai/agents/?utm_so…

A language model is a guessing machine. Today the machine decides when to reach for a fact. The better design runs the other way: deterministic code decides when to call the guessing machine, and what to hand it first. Our CEO @caryden makes the case → warmhub.ai/blog/your-mode…

Code compounds: someone writes a library, someone else builds on it. Decades later, everyone stands on the shoulders of strangers. Knowledge never got that home. Wikis, vector stores, hand-rolled memory: islands. That's what WarmHub is for → warmhub.ai/manifesto/?utm…

Doug Engelbart once taped a brick to a pencil to prove a point: a bad tool doesn't make work impossible, it makes it prohibitive. It taxes every stroke. RAG over a pile of files is a pencil with a brick taped to it. The interface is the whole game → warmhub.ai/blog/brick-off…

@eya0 "Most domains construct their ontology and hope it's right. Accounting's comes with a proof system attached." Best sentence in the piece. Do you think anything outside accounting gets close, or is it genuinely one of one?

It works because coding has a verifier. Generate a task, apply a fix, run the tests, and you know if it worked. Most of what a company knows has no equivalent check. Our CEO @caryden on why the high ground is what you feed the machine: warmhub.ai/blog/your-mode…

Microsoft Research and Mila trained a 4B coding agent to 61.5% on SWE-bench Verified with no distillation from a bigger model. Roughly where 32B-100B models sat a year ago. Cheap intelligence, arriving on schedule. arxiv.org/abs/2609.07925

A model's weights are the most expensive, least trustworthy place to store a fact. Memorized facts go stale; the answers keep coming anyway. Your agent should reason over knowledge it can trust, and look the rest up. Copy the prompt, let it decide → warmhub.ai/agents/?utm_so…

For years, content was the product. For agents, context is the new content: the knowledge you bring to bear at the moment of the guess. Models keep getting cheaper. What you feed them is the part that's yours. Our CEO @caryden makes the case → warmhub.ai/blog/your-mode…

You do the work nobody else has the patience for. You would rather be right than fast. That's never had a home where it counts. We're building one: where rigor is the thing that gets rewarded. Read the manifesto → warmhub.ai/manifesto/?utm…

@PrimeIntellect Under 1% to 95% on ARC-AGI-3 with the weights untouched is the cleanest version of this we've seen. Same shape as NVIDIA's harness taking Opus 5 from 30 to 100 on the public set. The gains are in the harness.

A language model isn't trained to be right. It's trained to sound right. Every real agent is a neural core in a harness, and the harness is where the gains are hiding. Our CEO @caryden shares why the high ground is what you feed it → warmhub.ai/blog/your-mode…

Your agent is guessing at things it could just be told. Point it at WarmHub and it pulls the answer instead of guessing: curated, versioned, traceable. Save the guess for where it earns its keep. Copy the prompt, let your agent decide → warmhub.ai/agents/?utm_so…

The shape of WarmHub over the next year will be set by the first curators who show up. You'll leave fingerprints nobody else gets to, the way the first creators on a new medium always do. The compounding era is starting. Read the manifesto → warmhub.ai/manifesto/?utm…

Some of what your agent just told you was true yesterday. Knowledge changes; a static dump can't say what still holds. In WarmHub, knowledge is versioned: what changed and when. Copy the prompt, let your agent decide if it's worth connecting → warmhub.ai/agents/?utm_so…

Every run of our agentic code review lands in a WarmHub graph, and agents mine it, clustering recurring issues into reusable checks. Token spend stops being a cost and becomes an investment. How it works → warmhub.ai/blog/asserting…

Data and models are infinitely cloneable. Trust isn't; you earn it. Knowledge that compounds without trust is propaganda at scale. That's why every assertion in WarmHub carries a source, a confidence, and a history you can challenge → warmhub.ai/manifesto/?utm…

An agent without context doesn't stop and ask. It guesses, sometimes in front of someone who matters. Point yours at WarmHub: curated, versioned knowledge, sources on every answer. Copy the prompt, let it judge → warmhub.ai/agents/?utm_so…

Agentic code review at WarmHub, by the numbers: 1,000+ reviews in 30 days, median under 2 minutes, under $3 a PR, routed by risk. Every finding verified against the code before it posts. The full breakdown → warmhub.ai/blog/asserting…

You build software on decades of others' work. It compounds. You build knowledge on a doc here, a spreadsheet there, a thread the next agent re-derives. We build software at scale. We still build knowledge by hand. It should compound the way code does → warmhub.ai/manifesto/?utm…