Eva

But it will raise the bar on what good decision-making even means.

Faster insights will stop being impressive - they ll become expected.
Broader analysis won t be a differentiator - it ll be table stakes.
Deeper visibility won t be optional - it ll be assumed.

And once that happens, a lot of traditional discovery work will start to feel outdated. Not because it was wrong, but because it was designed for a world where you could afford to be slow, partial, and reactive. That world is gone.

Something I built led to a design decision I want to get feedback on.

LineageLens is a free VS Code extension that captures every AI code insertion and scores it for risk on a 0 100 scale. Works with Cursor, Copilot, ClaudeCode, Gemini CLI. Zero config on install just start using your AI tools and your insertions start showing up in the sidebar.

The scoring is deterministic rules: +28 for credential-like material, +24 for eval/exec patterns, +22 for subprocess calls, +14 for landing in an auth or payments file, and so on. Fully traceable. No ML, no black box.

The design decision that surprised me: missing prompt capture when the extension records a file insertion but has no record of what was asked adds +24 to the risk score. Same weight as detecting an eval() call.

I don t want to rant here (but I probably will a bit)...

Almost every ad visual, but also a lot of organic content on social media, is very generic and created by AI (I get it, it s a cheaper and more scalable way of promoting). But when everyone does it, it becomes really hard to stand out and differentiate yourself.