Siarhei Hamanovich

Working on scoring candidates against job postings with AI, and I keep going back and forth on this: a bare percentage feels untrustworthy no matter how good the model is, but a full paragraph of reasoning is too slow to skim across 50 profiles.

Landed on a point-by-point checklist (what's met, what's missing) as a middle ground, but curious how others handle this. If a tool told you "73% match," would you trust it without seeing why? Where's the line between useful signal and noise for you?

We often hear that users want fewer tools, fewer tabs, and everything in one place.
But does that really mean they want an all-in-one product?

There s an interesting trade-off between all-in-one and best-in-class.

There s probably a line where an all-in-one product becomes too complicated.

Adding more features can mean fewer tools, less context switching, and a more connected workflow.

I noticed a small problem in my debugging workflow with AI agents.

When something broke, my first instinct was: