Obserf

Find useful places to mention your product

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September 26, 2026 I built a CLI to find the few places where mentioning your product might actually help

I kept running into the same dumb problem with side projects: building them was the fun part. Figuring out where to mention them without becoming That Guy™ was not.

Search for a problem your product solves and you get 200 vaguely related pages. Then you spend an hour figuring out which ones are current, relevant, and actually have a reasonable place to reply.

So I built Obserf.

It searches Hacker News, Reddit, GitHub and the wider web, filters the obvious junk, then uses Claude to rank the survivors and explain why each one might be worth your time. When you find a good one, it can draft a response.

Importantly: it never posts anything. You read the thread, check the rules, edit the draft, and hit submit yourself. I have enough robots making questionable decisions on my behalf already.

It’s local-first. Your project profiles and history live in a SQLite database on your machine. Model calls go through Claude Code, so there’s no separate Anthropic API setup if you already use it. Obserf itself is free and open source; you just need Bun + Claude Code. GitHub

Getting started is basically:

bunx @obserf/cli init ~/dev/marketing
cd ~/dev/marketing
bun install

Then edit the generated project profile with what you’re building and the kinds of problems/conversations you want to find.

Before spending any model quota:

bun run obserf scan --dry-run

If the results look sane:

bun run obserf scan
bun run obserf serve

That gives you a little local inbox for reviewing and triaging findings. GitHub

It’s early. The whole discovery → ranking → review → drafting loop works, but I’m still figuring out whether the ranking is consistently good enough to save more time than it creates. Which, conveniently, is now a measurable problem instead of a vibes problem.

https://obserf.com

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Finding good places to talk about a product is surprisingly hard. The relevant conversations are scattered across communities, search results, GitHub, and the wider web – and checking them manually gets repetitive fast.