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First2Apply – job scraping desktop app

Hacker News

First2Apply – job scraping desktop app

Inspired by the latest tech layoffs and all the conversations about how bad the job market is, I decided to build a tool to solve a part of this problem. I’ve had this hypothesis that if a job has more than 200 applications it’s pretty much useless to apply anymore because they will probably find a candidate amongst those first few hundred. This is how I came up with the idea for https://first2apply.com/ an app that continuously scans job boards customised by the user and alerts when it finds a new opening. I built a prototype over one weekend to see if I could actually pull it off and then created a landing page and started spreading the word. Two weeks later there have been 200+ signups on the waiting list and the MVP is ready to be shipped. How it works? You go to a job site like LinkedIn, search for your desired role/tech stack, filter by country or remote and apply the last 24h filter. Then copy paste the URL in the app. The app will then periodically scrape the links you saved and check if there are new listings from last time and send a native desktop notification. It's also very easy to organise your job hunt when using multiple sites since you have one dashboard to check new entries from all of them and recently added a tab to save the ones where you applied in order to manage them more easily. I've been testing this with my wife who is currently looking for a job as a junior react dev and am starting to get some positive results after using it for a while: - https://imgur.com/a/LFOdAOD here is an example of a job from LinkedIn which got 50 applications in the first 12h and then they closed it. I think it's safe to assume they have enough to pick from - https://imgur.com/a/2Coi1KQ finally the holy grail, a job with no applications. Unfortunately it was for a Java position because even though you filter by React in LinkedIn they still show you jobs for other tech :/ She has been using the app for the past 2 weeks and was able to find ~5 valid jobs that she could apply for. Unfortunately there are not a lot of companies hiring juniors. One other benefit that she noticed was that she doesn't have to constantly refresh her job sites, just wait for the notification to pop up and check the latest results. This way she has more time to spend working on her portfolio projects. So from my one beta tester, I have gotten positive feedback, but it's very much a textbook case of the Mom test. Looking forward to hear what other people think and hopefully this could help someone get that extra edge needed to land a job.

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3comments
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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started, wife · Missing: supports, reddit linkedin, podcasting
92%92% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, 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
51%51% 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: way · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% 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
15%15% 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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