AI agents can write code. They still can't deploy it.

<!-- SC_OFF --><div class="md"><p>I’ve been in QA for almost a decade. My mental model for quality was always: given input X, assert output Y. Now I’m on a team that’s shipping an

<!-- SC_OFF --><div class="md"><p>The idea is simple, if one agent is good then multiple agents working together must be better. In practice coordination between agents is an

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1st2r1v/at_some_point_managing_agents_started_feeling/"> <img alt="At some point managing agents started

<!-- SC_OFF --><div class="md"><p>I've been thinking about why local-first AI agent architectures are getting serious enterprise traction in 2026, beyond the obvious &quot;keep

<!-- SC_OFF --><div class="md"><p>I've been thinking about this as local models get better at </p> <p>tool use — shell access, file system, HTTP requests, MCP. </p> <p>If a prompt

<!-- SC_OFF --><div class="md"><p>Been heads down building graphify since April 5th. The idea: instead of stuffing your entire codebase into context every session, build a

<!-- SC_OFF --><div class="md"><p>I'll be upfront about my endgame: I work at a large tech company, I have a niche picked out, and I'm making the move to build and sell AI agents

<!-- SC_OFF --><div class="md"><p>Most of my LLM cost was on the wrong tier of work.</p> <p>Classification, extraction, JSON formatting, summarization I'm going to review anyway.