Measured on the sample (October 15):
- gpt-oss-20b: 7 to 9 seconds per letter, 4 to 5 model calls, about 3,000 input tokens and 800 output tokens. That is under $0.001 per letter at Workers AI list price.
- Every gpt-oss letter admitted the Kubernetes gap.
- Some runs end with one flagged claim. The page shows it as a warning instead of hiding it.
- Llama 4 Scout took about 30 seconds and left more issues, so gpt-oss is the default.
Known limit: the checker knows technologies, numbers and role words. Adjectives still get through: one draft called a service "high-availability", and the resume never says that.
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Why Does Open Innovation Matter?
The price makes the side hustle work. Under a tenth of a cent per letter means $4 a month can cover heavy use with almost all of it as margin. At typical closed frontier-model prices, the same run with retries costs roughly 10 to 20 times more per letter. #
I can swap models per step. Reading and searching use gpt-oss. When it returns an empty draft, Llama 4 Scout writes it. That fallback is one line of code, because both are open-weight models behind the same interface. #
The model is not the judge. Open weights let me choose small, cheap models on purpose and put the trust in code I can read and test. With a closed API I'd be paying more to trust the model more, which is the opposite of what this tool needs. #
Your resume could stay on your machine. The same weights run locally. I haven't built that mode yet (my 2019 Intel laptop can't run gpt-oss-20b at a usable speed), so for now the page sends your text only for the length of one request and stores nothing.
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My First User
Me.
I'm a full-stack developer in Korea. I write English through AI translation, and I apply to remote jobs where I can work in writing. Before I built this, I had sent 38 cover letters. I ran HonestFit's checker over all 38 against my own resume:
38 of 38 said "13 years". My resume says "12 years 8 months". #
27 quoted numbers from side projects, like "90 of 94 MCP servers scanned" or "13 marketplace extensions", that don't appear anywhere on my resume. #
3 named a technology my resume never mentions.
None of it was a lie. But a recruiter holding both pages sees two slightly different people, and I had no idea until the checker showed me.
Then I ran the full workflow with my real resume against a real Senior Backend posting from my job search (its requirements list). The first run marked "SQL and NoSQL databases" as missing and wrote that I had no database experience, which is wrong (that bug is in the table above). After the fix: 5 covered, 2 partly, 1 missing (working across time zones), one draft, 6.9 seconds, $0.0008.
Did I pay? I'm the user, so no. But I would.
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Prize Categories
Best Use of Mastra : the whole thing is a Mastra workflow with three agents and a tool, running on Cloudflare Workers.
Code and English text were produced by an AI coding agent (Claude Code) working under my direction. The resume data, the 38 letters and the decision to publish are mine. I write in Korean.