Fi

Find and manage Hacker News fractional jobs

Hacker News

Find and manage Hacker News fractional jobs

Hi HN, I love all sorts of flexible jobs, whether it's freelancing, part-time, fractional, or whatever we call it. I have strong reasoning behind it, but that's another story for another time. One of the best places to find these types of works is HackerNews. But it's hard to keep track of the applications or skip the ones you don't like and return the next day to continue. There are some nice works done to solve this but a) they focus on full-time jobs, and b) they don't allow you to sync your data on multiple devices. Essentially, there's no lightweight CRM-like solution for freelancers/consultants to manage their job applications unless it's offered by the job listing websites. I managed to find a great part-time job in HN by following this strategy: Fetched the past 3 months of whoishiring submissions, loaded them into a database, and divided them into two priorities: 1. Freelance jobs, posted under "Ask HN: Freelancer? Seeking freelancer?" with "SEEKING FREELANCER" title. 2. Part-time or contract jobs, posted under "Ask HN: Who is hiring?" with specific keywords (part-time, consultant, contractor, etc.) and without "no contract(s)". I reviewed the first priority, either applied or skipped, and marked their status accordingly, then moved on to the second priority. Now, I turned this into a small web app, built using Django, HTMX, and Postgres all running on a single DigitalOcean VM. You don't need to sign up, just click on "Login as Guest" to start using the app. Or watch the video on the about[1] page. This is my first Show HN post, would love to hear your feedback. Thanks! [1] https://fraction.works/about

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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: hacker news, ide, io · Missing: https docs, excited, just released
67%67% 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 HuntUnlikely to reach the leaderboard · Strong signals: new, single, using · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal
41%41% 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
40%40% 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
19%19% 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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