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A tech job board I've been building over the last 8 months

Hacker News

A tech job board I've been building over the last 8 months

Hey HN! Quick TL; DR: I set up scrapers for the websites of ~20k tech companies, fed the results through GPT, spent some sleepless nights building a web app, and voilá. I started this after getting fed up with how slow LinkedIn felt. I finally got it working and easy to use, so I wanted to share it with you guys. The job list is free. But I’m currently paying a lot of money to OpenAI for all the data I’m passing through GPT, so I may end up eventually charging for some advanced features, or putting ads on it or something. Please let me know what you think! I’m actively developing this so I’m happy to add requested features. Some technical details you may be curious about: - I began gathering the career pages for each company manually, but I ended up hiring a few Filipino contractors to speed up the process. - I run the web scrapers once per day. - I do the web scraping with Node.js. I use cheerio for basic web pages, and puppeteer for more complicated ones. - My OpenAI API dashboard tells me I process 7 million characters per day. - The list doesn’t encompass every single tech company (yet). I have most of the major ones (~20k), but the process of adding new companies is pretty labour intensive so it’s still ongoing. - The web app is built with React, and I got the infinite scrolling to be so smooth using a great library called React Query. For feature requests, please leave a comment here or email me at dorfmanjames@gmail.com

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Actual performance

3points
2comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
89%89% 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: new, email, openai · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
69%69% 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 · Strong signals: month · 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
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
21%21% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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