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Fully Automatic Job Application Tracker via Gmail

Hacker News

Fully Automatic Job Application Tracker via Gmail

Hey HN, I'm a recent grad who's been unemployed for the past 6 months, spending that time building web apps. During my own job hunt, I got incredibly frustrated with the amount of manual work involved in tracking applications across different platforms. Even with tools like Huntr.co (my university provided a subscription that recently expired), I was still spending too much time updating the kanban board after checking the relevant email; worst of all is most of the time it's just updating the company to the rejected side of the kanban board. So, I built RunMagi: an automatic job application tracker that completely monitors your Gmail inbox. The goal is to eliminate that tedious manual work. Unlike many other trackers, RunMagi doesn't require any browser extensions – it just works by analyzing your emails. I'm sharing this here because I know many of you have been through the job search and might relate to this pain. As a solo developer, this has been a passion project, and I'd love to get your feedback. What are your biggest frustrations with tracking job applications? Does this approach of automatic Gmail monitoring sound useful to you? Any thoughts on the landing page, or the core idea would be greatly appreciated! My goal is to iterate and eventually be able to take care of all the little things that people find annoying that has nothing to do with the actual interview itself.

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, inbox, email · Missing: mac, agents, macos
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, 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
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
18%18% 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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