@ak14053 · X

@harryjwang · X

I've used many of the major conference systems. Plenty still have docs as an unsearchable static site — or worse, as PDFs that stopped tracking the product three versions ago. Somehow, still true in 2026. We took an AI-first path with instead. Our docs are generated from the codebase itself. A custom Claude Code skill writes each page and retakes the screenshots whenever a feature changes — so the docs don't drift from the product. Every page still gets reviewed by a human before it ships. Now you can talk to them, too. We just shipped "Ask AI" over our docs: This feature also turned out to be a nice teaching case for agentic RAG vs. classic RAG: • Classic RAG: chunk → embed → vector DB → retrieve once, by fixed rules • Agentic RAG: hand the model a search tool and let it drive — write a query, read the results, rewrite, retry At more than 100 pages, still no vector database needed. And every answer cites the exact page it came from. #Age

@michaelaplesner · X

hey :) building @trymaze_app <3 it's like Pinterest for the things you actually bought.

@AmeerHaio · X

your content everywhere!

@nameisnotanoun · X

@rubs_murga · X

@shuvo_xyz · X

@trackowork · X

u/Rcoo232 · Reddit

I built a council of 5 historical figures that debates your hardest life decision (based on Karpathy's LLM Council) A few weeks ago, I came across Karpathy's "LLM Council" concept. You ask multiple Agents the same question independently, have them anonymously peer-review each other, then synthesise a final answer. The peer-review round is the genius part; models get surprisingly honest when critiquing anonymised responses. I turned it into a consumer product where the council members are hi

@DDominella · X

@edgarwritesai · X

Your personal linkedin ghostwriter

@AjayGB4 · X