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Tech jobs on the command line

Hacker News

Tech jobs on the command line

Hi HN! I'm Nico and lately I've been struggling looking for jobs. I've gotten specially frustrated with having to spend hours reading through listings just to find good matches. So I built something to scratch my own itch: Command Jobs, a terminal-based job finder and application tracker designed specifically for me, and maybe also software engineers The app scrapes web listings, saves them, processes them with GPT[1], then based on your resume and job preferences, it gives you the best matches This is my first open source project, and also my first public project using LLMs I'm really excited to share this with the HN community and would love to hear your feedback, suggestions, and any features you'd like to see in the future. Please check it out and let me know what you think! https://github.com/nicobrenner/commandjobs I'm currently working on adding more sources for job listings, periodic scraping via cronjobs, and alerts for new matches Looking forward to your questions, feedback and requests. Thank you [1]: Bring Your Own (OpenAPI) Key

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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, using, open · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, io · Missing: https docs, just released, exist
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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.

Correct prediction on native model

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