Gutsy, command line app to parse HN Who is Hiring Threads (in Perl)

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https://github.com/gryftir/gutsy Since in about 11 hours, there will be new job postings, I thought I'd show this to people. It is about half way done, but is usable, despite mostly being an excuse to practice coding. You can search by job type: remote, HIB, internships/ by location/ by language, and pull the postings into a separate html or text file. It's better then Ctrl/Cmd +f. You can construct arbitrary function references (see GutsyExamples) to pick which postings you want, so I can reject postings which say no remote, or remote control when searching for remote positions. It also searches comments on pages linked with a More. I plan to allow && between job type, location, and languages, maybe add a few locations (suggestions or pull requests welcome), as well as allow doing your own searches. Hopefully I can get some of that done before midnight tonight. Anyway I'm curious if anybody besides me would find this useful.

Visit product View on Hacker NewsJul 31, 2013

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

55%55% 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: way, para · Missing: mobile apps, ios, personal

45%45% 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

37%37% 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

27%27% 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

22%22% 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.

Incorrect prediction on native model

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