This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
Resume Fitting
Diff your resume to a job posting: extracts skills and quals
Compares a resume or a cover letter against one job posting. For a resume, it extracts the skills, tools and qualifications the posting names and marks each one matched or missing. Doubtful cases count as missing so the score is not inflated, and scoring runs at temperature 0 so identical inputs return the same result. A rewrite then works the missing terms into the resume where the experience supports them. A cover letter is judged on different questions. The checker scores keyword overlap.
Built because most resume keyword advice cites no sample. This site counts named terms across 3,518 job postings in seven roles with the method published. The checker compares one resume with one posting and lists the skills, tools and qualifications the posting names that the resume lacks and also helps the users adjust their cover letter appropriately. Matching is strict: doubtful cases count as missing.
Feedback wanted:
1. Matching is by term, so a missed synonym counts as absent. Where does that break down?
2. Does strict matching under-score real experience?
3. Samples of 123 to 212 postings carry 6 to 9 point margins. Which roles or sources would reduce bias?
About Resume Fitting on Product Hunt
“Diff your resume to a job posting: extracts skills and quals”
Resume Fitting was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #288 on the daily leaderboard. Compares a resume or a cover letter against one job posting. For a resume, it extracts the skills, tools and qualifications the posting names and marks each one matched or missing. Doubtful cases count as missing so the score is not inflated, and scoring runs at temperature 0 so identical inputs return the same result. A rewrite then works the missing terms into the resume where the experience supports them. A cover letter is judged on different questions. The checker scores keyword overlap.
On the analytics side, Resume Fitting competes within Productivity, Tech and Career — topics that collectively have 1.3M followers on Product Hunt. The dashboard above tracks how Resume Fitting performed against the three products that launched closest to it on the same day.
Who hunted Resume Fitting?
Resume Fitting was hunted by AI Utilities. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Resume Fitting including community comment highlights and product details, visit the product overview.
AI Utilities
Built because most resume keyword advice cites no sample. This site counts named terms across 3,518 job postings in seven roles with the method published. The checker compares one resume with one posting and lists the skills, tools and qualifications the posting names that the resume lacks and also helps the users adjust their cover letter appropriately. Matching is strict: doubtful cases count as missing.
Feedback wanted:
1. Matching is by term, so a missed synonym counts as absent. Where does that break down?
2. Does strict matching under-score real experience?
3. Samples of 123 to 212 postings carry 6 to 9 point margins. Which roles or sources would reduce bias?