Why documentation fails (and how I’m trying to fix it with AI)
Hey Indie Hackers
I've spent the last few years watching engineering teams silently lose something incredibly valuable: the reasoning behind their own code.
Not the code itself but the WHY.
Why did we pick PostgreSQL over MongoDB?
Why is there Redis in the auth flow?
Why does this "temporary" workaround still exist 2 years later?
The answer is usually: "Ask Zain." But zainleft the company six months ago.
The Uncomfortable Truth:
Every team knows this is a problem. Every team tries to fix it with Wikis, ADRs, or Notion docs. But they all fail for the same reason: Documentation requires a behavior change.
Engineers are under deadline pressure. Asking them to stop their flow to write a doc is a losing battle. Behavior change doesn't survive a sprint deadline.
So, I stopped trying to change engineer behavior.
I built WhyLog to capture decisions passively, from work the team is already doing.
The workflow is dead simple:
Connect your GitHub repo.
Merge PRs like you always do.
When a meaningful PR is merged, WhyLog’s AI analyzes the code changes, the description, and the discussion. It then drafts a decision record (The "What," the "Why," the tradeoffs, and the affected files).
You get a Slack notification. One click to approve. Done.
Six months later, when a new engineer asks why you switched to session cookies, they don't hunt through 2022 Slack threads. They search WhyLog and get the exact reasoning and the original PR link in seconds.
What’s under the hood:
GitHub Integration: Automatic PR capture.
Gemini-powered AI: To draft the decision logic.
Slack Workflow: For low-friction approval.
Semantic Search: Ask questions in plain English.
I’m currently offering a free plan (no CC required) to get more teams onboard.
I'd love the IH community's take on this:
For those of you leading teams or scaling products what is the one decision in your codebase that you wish was documented, but isn't?
Check it out here: https://whylog.nubrotech.com
