
briefroom
Share AI-generated HTML in 30s — client feedback comes back
Hey IH! 👋
Today I launched briefroom (https://briefroom.net) — solo-built in
6 weeks, from Japan.
The itch: I do client work with AI coding agents (Claude Code, Codex).
They generate HTML proposals and mockups in seconds — but then the workflow
collapses:
- Hosting a static HTML draft just to show a client = Vercel overkill
- Feedback comes back as vague emails ("can you fix the top part?")
- I manually translate every comment back into prompts
Generation takes 30 seconds; sharing and collecting feedback takes 30 minutes.
What briefroom does:
1. Drop an HTML folder (or npx @briefroom/cli deploy ./) → share URL in
30s. No signup for the first deploy.
2. Clients click/tap any element on the live page and comment right there.
No account needed, mobile-first (decision-makers review on phones).
3. briefroom feedback pull returns every comment as structured,
LLM-ready Markdown → paste into your agent → fix everything → redeploy
to the same URL. Loop closed.
It's llms.txt-compliant, so agents can learn the whole workflow themselves.
Business model: Free / Pro at ¥690 (~$5/mo) / Founders Edition —
one-time ¥4,800 (~$30) for lifetime Pro, capped at 50 seats as a launch
experiment. Targeting Japanese freelancers & consultants first (underserved,
non-English-first market), English UI is fully supported.
Some honest numbers & bets:
- 6 weeks from first commit to launch, built almost entirely with Claude Code
- Infra cost is ~$30/mo at current scale (R2 zero-egress does heavy lifting)
- Biggest risk I'm watching: abuse (it's "host arbitrary HTML" as a service —
isolated delivery domain, forced CSP, malware scanning from day one)
Also on Product Hunt today: https://www.producthunt.com/products/briefroom
Ask me anything — especially curious how others here handle the
client-feedback round-trip in AI-assisted work. 🙏
About
AI agents generate HTML in seconds — sharing it and turning client feedback into prompts is still manual. briefroom closes the loop: share in 30s, clients comment on the page, pull feedback as LLM-ready Markdown.

1 Comment
What I find interesting is that you're not really speeding up HTML deployment.
You're reducing the translation loss between client intent and AI execution. That's a different problem entirely. If that loop becomes reliable, the deployment part almost fades into the background because the real value is preserving context from feedback all the way through implementation.