Scoring resumes at scale using GPT-4

Hacker News

Hi HN. After being swamped by AI-generated resumes on the last couple of job posts we had, and given that we’re in the AI business ourselves, we built this little tool for in-house use. What it does is really simple. We first upload job description, which GPT analyses and generates criteria. Then we upload resumes, let GPT score them against that criteria, and we compute a total score which we then use for making shortlist. After successfully using it couple of times to hire people, we decided to try to convert it to stand-alone project, and here we are. The name is SortResume.ai, and you can find it here: https://www.sortresume.ai/ There’s a free plan, so anyone can give it a quick run. Any feedback is more than welcome!

Visit product View on Hacker NewsApr 22, 2024

Share card

Actual performance

2points

2comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

FAQify – Algolia for FAQs using GPT-369%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

FAQify – Algolia for FAQs using GPT-3

Hacker News8

less than 650 LOC trainable GPT only using NumPy55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

less than 650 LOC trainable GPT only using NumPy

Hacker News90

Topline Resumes55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Topline Resumes

Hacker News1

Bugbusters.ai automated bugfixing using gpt-360%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Bugbusters.ai automated bugfixing using gpt-3

Hacker News19

A GPT-driven tool for analyzing & tracking your resumes & job postings35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A GPT-driven tool for analyzing & tracking your resumes & job postings

Hacker News3

Worldbuilding Experiments with GPT-336%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Worldbuilding Experiments with GPT-3

Hacker News2

Autosummarized HN (With GPT-3)51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Autosummarized HN (With GPT-3)

Hacker News6

I composed a sonata with GPT-3 DaVinci-003 and you can too51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I composed a sonata with GPT-3 DaVinci-003 and you can too

Hacker News3

GPT Classifies HN Titles57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GPT Classifies HN Titles

Hacker News6

Cerebras-GPT-2.7B finetuned on Stanford Alpaca dataset65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cerebras-GPT-2.7B finetuned on Stanford Alpaca dataset

Hacker News4

Browse all products Analyze your launch