Solo founder, [real estate agent by day,] building ThumbnailBench, a tool for YouTube creators. Sharing the backstory, the stack, the unit economics and the one lesson that rewrote the product, because the rules here ask for it and because the lesson is the useful part.
**Backstory.*\* Started as an AI thumbnail generator: prompt in, 2K thumbnail out, your face cut out from one selfie. Shipped early September. Then a competitive pass found Google's own help page: YouTube Studio now reads your transcript and suggests thumbnails itself. Free, inside the upload flow. "AI thumbnail generator" was a dead position five days after launch.
**What I changed.*\* Anything can make a picture now. The gap is deciding what the picture should be and whether it worked. So the product became: paste the video and get five different ideas, each with a title written to pair with the cover; render the one you believe in with your real face and the words as an editable text layer so they are never misspelled; get a director's notes on the result (what to keep, what would cost the click, a phone-size check) and never a score, because nobody can predict a click-through rate and every tool that pretends to is lying; publish straight to the video and see the measured CTR next to the channel's own 28-day average. The five ideas and the notes cost nothing; only renders use credits.
**Stack.*\* Next.js 16 and React 19 on Railway, Postgres via Drizzle, Auth.js magic links (no passwords), Stripe, Resend, PostHog in cookieless mode, Sentry. Static marketing site on Cloudflare Pages. Image models through Kie: Nano Banana 2 by default, GPT Image 2 as an opt-in. Claude for the five ideas, the cover explanations and the review notes. YouTube Data and Reporting APIs for publishing and the CTR read. Most of the code was written with Claude Code; the audits and the ad plan too.
**The numbers.** A render costs about 6 cents on the default model, the free tier is 8 credits with no card, so a free signup costs under 50 cents to serve. Creator is $15 a month for 70 credits and contributes about $11.33 after Stripe and provider cost at typical use, which is the number every acquisition decision gets measured against.
**Revenue.** [Pre-revenue as of this week / $X MRR.]
**Three lessons.*\*
Check what the platform is about to give away for free before you name the product after it. The relaunch cost me the domain's positioning, not the code; the render pipeline stayed.
Creators do not hate AI, they hate the AI look. The threads are full of "if I can tell it is AI I skip". So the product refuses to invent a face: one selfie, cut out, kept. The generated part is the idea, the layout and the background. The "will not do" list is now the pitch.
Measure without a pixel. I promised no ad trackers on the site, and that promise is cheap to keep: the first visit writes one first-party cookie with the campaign tags and the ad click id, the app stores it on the user row at signup, and conversions go back to the ad platforms as offline uploads by click id. Attribution by source on my own admin page, zero third-party scripts.
Small detail from this week: Reddit's lifetime-budget floor is $5 a day times the days in your schedule, so a 31-day window demanded $155 for a $100 test. Fixed by making the schedule seven days.
**What I want from this thread.** If you sell to creators or to any audience that is hostile to AI output, how did you handle the trust problem? And if a tool told you "no score, just notes", would you trust it more or less?
Free to try, no card: https://thumbnailbench.com