I got my first 10 users for a job matching tool

Hacker News

The idea came from watching my girlfriend's frustrating job search experience. As a developer, I built a tool to match job seekers with positions that actually align with their skills and aspirations. Here's how I reached my first 10 users: Week 1-2: Started with my girlfriend as user #1 She found relevant matches within days (compared to weeks of manual searching) This validated the basic concept Week 3: Showed it to 3 college friends who had just graduated They were immediately interested because they were actively job hunting Got detailed feedback on UX and missing features they wanted Week 4: Implemented their suggested features (if the job is remote or not) Original users shared it in their college WhatsApp groups 4 more graduates joined and started using it daily Week 5: Word spread through the initial users 2 more users joined from different universities Reached 10 active users milestone Key learnings from first 10 users: Real problems = motivated users Recent graduates are great early adopters Word of mouth works when the product solves a real pain point Quick implementation of user feedback creates loyal users All 10 users are still active after 3 weeks, using the platform 2-3 times per week on average. Try it out: https://yourjobfinder.website Looking for HN feedback on: Scaling beyond word-of-mouth Features you'd expect in a job matching tool Technical architecture suggestions Tech stack details in comments.

Visit product View on Hacker NewsFeb 23, 2025

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

90%90% 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.

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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: user, using · Missing: mac, agents, macos

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

13%13% 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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