Jo

Jobstocks.ai – 6 months in, showing some interesting signals

Hacker News

Jobstocks.ai – 6 months in, showing some interesting signals

Hi HN — about 6 months ago we launched JobStocks.ai, a dashboard that tracks hiring momentum across public companies and compares it to stock performance. Since then, we’ve been seeing some pretty interesting patterns: quiet hiring freezes, sudden team expansions, and role shifts that show up well before earnings or headlines. The core idea is still simple: job postings move faster than financial disclosures, and hiring behavior can hint at what’s happening inside a company. We’re still early and very much learning. Would really appreciate brutally honest feedback on: What confused you What felt pointless What you expected but didn’t find Or why you bounced (if you did) Thank (:

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% 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.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, 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
39%39% 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 · Missing: arr, mrr, revenue
28%28% 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
24%24% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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