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I built a job board that scrapes the internet for Remote roles

Hacker News

I built a job board that scrapes the internet for Remote roles

Hi folks. My wife has been looking for a job and has been frustrated with existing job boards because they either don't have enough jobs (because companies need to pay to list their openings) or they don't have good information/filters on the companies. To solve this, I scraped recruiting sites like Workable, Greenhouse and Lever where most job openings are hosted. I then parse them for useful information such as salaries and hiring location. I then combine this with more useful information I scrape online about the companies e.g. funding, company size. The result is that I have a lot more open positions on my site than most other job boards. I'd love to hear your thoughts and if you have any feedback! Fun fact: I noticed that about 11% of job listings specify a salary.

Visit productView on Hacker NewsNov 29, 2022
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Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: wife · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
36%36% 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
33%33% 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
24%24% 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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