How thevones works

Before you choose a tool or commit to building something, you want one honest answer: has anyone actually done this, and what really happened? That question is the whole reason this site exists.

Every workflow here starts from a real, named, public source — a case study, an engineering write-up, a conference talk. We read it, pull out the problem it solved, the tools it used, and the outcome it reported, and structure it so you can judge how much of it applies to you. Then we do the part a single case study can't: we read across all the deployments in a function and surface what they have in common — the architectures that recur, the outcomes that hold up, and the failure that keeps showing up first. That synthesis is our own work. The sources are the evidence under it.

Our one rule about AI. We use AI to help structure evidence — never to invent it. A workflow is only called AI-driven when its source documents a real AI or ML mechanism. When a source is ambiguous, we stay conservative rather than fill the gap. Better understated than inflated.

What we promise, and what we don't. We're a proof layer, not an audit firm — we don't re-run other teams' results. What we do guarantee is provenance: every case links back to where it came from, so you never have to take our word for anything. We tier each case by how strong its evidence is and show that tier on the page, so you always know how solid the ground is. And every number is reported by its source, in that source's context — a real signal to learn from, not a promise it repeats for you.