Stack Decoder

The idea started from a simple observation: most tech job descriptions are written by recruiters or HR using a template, and the real signal about a company's engineering culture, technical maturity, and day-to-day reality is buried in the language choices, the stack they mention, and the requirements they include. Engineers read dozens of JDs while job hunting and mostly go in blind.

Stack Decoder applies a structured analysis framework to surface what's actually there. Stack modernity is scored based on the technologies mentioned — Kubernetes, Terraform, OpenTelemetry, and Rust score high; SVN, WebSphere, and JSP score low. Tech debt signal estimates the maintenance burden based on what's listed and what's absent. Culture signals come from the language itself: "fast-paced environment" and "wear many hats" mean different things at a 15-person startup versus an 800-person enterprise.

The interview questions are the feature engineers find most useful. They're generated specifically for what's in the JD — not generic. If the description mentions "migrating from monolith to microservices", the questions probe how far along that migration is, who owns it, and what the timeline looks like. That turns a passive read into an active evaluation.