Name-classifier – infers attributes about a person from a name

Hacker News

This was based on an old project that I resurrected, improved, and repackaged with claude code. It's useful for estimating demographics in large datasets from limited information, i.e. just a name. It's also fairly good at separating given name and family names across a wide variety of languages and contexts. Contains a standalone binary, embeddable shared lib, and a python wrapper. Examples CLI: user@box » ./build/name-classifier -j -c "Carlos Eduardo Fernando Salazar Montemayor" | jq . { "input": "Carlos Eduardo Fernando Salazar Montemayor", "script": "latin", "components": [ { "token": "Carlos", "role": "given", "index": 0, "surname_score": 0.009 }, { "token": "Eduardo", "role": "given", "index": 1, "surname_score": 0.001 }, { "token": "Fernando", "role": "given", "index": 2, "surname_score": 0.01 }, { "token": "Salazar", "role": "family", "index": 3, "surname_score": 0.998 }, { "token": "Montemayor", "role": "family", "index": 4, "surname_score": 0.975 } ], "attributes": { "gender": { "male": 0.9938, "female": 0.0062, "neutral": 0 }, "origin": { "english": 0, "french": 0, "germanic": 0, "nordic": 0, "iberian": 1, "italian": 0, "eastern_european": 0, "arabic": 0, "east_asian": 0, "south_asian": 0, "southeast_asian": 0 } }, "calibrated": true, "model_version": "embedded", "provenance": { "gender": { "lexicon": 0.598, "ngram": 0.302, "neural": 0.101 }, "origin": { "lexicon": 0, "ngram": 0, "neural": 0 } } } Python: from name_classifier import NameClassifier nc = NameClassifier(args.model_dir) nc.classify("Kateryna Olha Mykhailivna Shevchenko")

Visit product View on Hacker NewsFeb 28, 2026

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Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: claude, model, user · Missing: mac, agents, macos

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

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