
BiasLens. Who rejected you, AI or human?
The bias was always human. We just gave it a name.
BiasLens. Who rejected you, AI or human? launched on May 30, 2026 with 3 votes, #3,449 of 20,538 launches that month and more than 50% of that year's launches.
What it does
50% fewer callbacks. Same resume. Different name. Algorithms don't discriminate, the humans who build them do. BiasLens scans your rejection for documented bias patterns under US employment law. 30 seconds. Free. Anonymous. The system failed you. Now you can prove it.
Paste your rejection. We scan it against documented discrimination patterns in 30 seconds. Free. No accountfrom biaslens-justice.vercel.app
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There are existing HN job aggregators, but I thought we could take it a step further. Inspired by an insightful comment on a previous thread (https://news.ycombinator.com/item?id=36163021), I built a tool that aggregates job postings and intelligently categorizes them based on user-specific preferences: * Country and remote work preferences * Employer type (e.g., startup, corporation, government) * Industry * Technologies used * Role type (developer, architect, product owner, etc.) * Salary range (where available) One of the superpowers of LLMs is reformatting information from…
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Our AI recruitment pipeline was auto-rejecting anyone who'd worked at companies founded after 2023. It didn't recognize names like Harvey or Snorkel AI, or didn't realize how important they'd become because of training data cutoffs. We had traces, evals, Langfuse dashboards - everything looked fine - but we kept finding failures we should have caught earlier. The pattern kept repeating: - ship an improvement - it works for a while - hit an edge case that breaks it - don't notice until we've lost good candidates That's when we realized - the problem wasn't just our recruitment pipeline -…
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