Blog
Engineering posts on multi-agent systems, AI coordination primitives, and what we're learning building Beevibe.
We dogfooded ADR. It refused to commit.
We ran Architecture Deep Research against a real Beevibe decision. The output was defensible-looking and didn't engage with the actual question. Ten changes later: hard constraints filter instead of decorate, commitment when the field narrows, peer-product research, quantitative cells, streaming as a live research log.
Architecture Deep Research: the questions that keep coming up
A Q&A on ADR: why a coding agent's planner step is not enough, why frontier models miss the architecture layer, why README scraping fails, how ADR reaches your internal context, and why the Beevibe philosophy matters.
Architecture Deep Research: the layer before the coding agent
Coding agents pick the easiest local implementation before anyone picks the right architecture family. ADR is the live, evidence-only research loop that runs first — and refuses to bluff when the evidence is thin.
Your agent reinvents the wheel. The fix isn't a better prompt.
We spent weeks prompting agents to search for an off-the-shelf tool before scaffolding from scratch. Each tightening worked for a day, then drifted. The fix moved the action out of the prompt and into the UI.
AI makes engineers faster. It's not making teams smarter.
Solo speed is up. But the knowledge isn't compounding. Private tabs, forgotten context, and re-derived decisions are costing teams the coordination gains AI promised.