I built a search engine for 800 niche job boards

Hacker News

I started JobBoardSearch about four years ago as a simple directory of niche job boards (A static HTML file with a dozen job boards) One particular think I'm proud of: I didn't scrape these niche job boards and add them to a dabatabase. The 800+ job boards currently listed were submitted organically by their ownres. Lots of their job boards provide a job feed (API, XML or RSS), I fetch those feeds and use the data in serveral different ways. Each job board has its own page in JBS, where I show jobs from that board. I call this the "meta" part of the site. The same feed powers a few other things I built: - A subreddit bot that automatically post jobs every hour to the /r/jobboardsearch subreddit, which has grown to 26k memebers. - A free telegram bot that sends personalized job alerts based on custom settings: keywords, locations, industry, candence, etc. The Telegram community passed 6k members. - The main job search itself. The interesting part is that the same data creates several distribution channels (Including AI citations that keeps growing). Over time, I turned it into much more than a simple directory. I've add

Visit product View on Hacker NewsAug 25, 2026

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

80%80% 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.

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

57%57% 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: personal, way · Missing: mobile apps, ios, entrepreneurs

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · 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 · Missing: plus, platform, intuitive

37%37% 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

26%26% 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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