RemoteLiz – Global Search Engine for Remote Jobs

Hacker News

Hello HN! I built RemoteLiz — a global search engine for remote jobs that are programmatically verified on being real and actually hiring. Unlike most remote job boards, RemoteLiz crawls company career pages directly, and I manually verify whether each company is legit and actively hiring (That'll be automized). Backstory Time!! I’ve been working remotely for over eight years, and when I recently looked for a new remote job, I kept running into the same problem: job listings claiming to be “remote” but actually location-restricted. After talking with friends who had the exact same frustration, I decided to build RemoteLiz to fix this. What’s Different? Direct Crawling: Remoteliz crawls company career pages to find current openings. Manual + Automated Checks: I personally vet companies for legitimacy, and then remoteliz uses LLMs to analyze job descriptions. Global Focus: RemoteLiz aims to help you find truly remote roles in your country, not just “remote in one country.” I’d love to hear your thoughts on this project. Note: I’m a solo founder juggling everything, so you might encounter bugs. I’ll keep fixing them as I go. Stay Remotelizely!

Visit product View on Hacker NewsFeb 19, 2025

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

79%79% 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 · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

18%18% predicted probability of success on AppSumo, 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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