WhatA platform that instruments how engineering teams actually use AI tools — distinguishing productive use (research, boilerplate, refactoring) from risky use (autonomous agents writing core logic) — and correlates usage patterns with code quality outcomes.
SignalEngineers consistently describe two very different modes of AI usage with vastly different outcomes: skilled augmentation (using AI as a research and acceleration tool while maintaining architectural control) versus hands-off delegation (prompting agents and hoping for the best), and organizations currently have no visibility into which mode their teams are operating in.
Why NowAI coding tool adoption has become near-universal in engineering orgs but management has zero instrumentation to understand how these tools are actually being used or whether they're improving or degrading output quality.
MarketEngineering leadership at companies with 50+ developers; adjacent to $8B+ developer productivity/analytics market (LinearB, Jellyfish); no one tracks AI-specific usage patterns yet.
MoatFirst-mover data network effect: the more teams instrumented, the better the benchmarks for what 'good' AI-assisted engineering looks like, creating an industry standard others reference.
Ask HN: Why is the HN crowd so anti-AI?View discussion ↗ · 421 pts · June 6, 2026
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