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I built a job-search AI assistant using Gemini, Next.js, and Neon

Hacker News

I built a job-search AI assistant using Gemini, Next.js, and Neon

Hey HN, I’m 18, and over the last few weeks, I got tired of spending hours just managing the process of finding work — writing resumes, outreach, proposals, contracts, etc. As a freelancer, it felt like I was doing more admin than actual work. So I built a tool to solve my own problem: CareerMind AI It helps with: Creating tailored resumes and cover letters Writing personalized outreach + freelance proposals Spotting red flags in contracts Optimizing LinkedIn and freelance profiles I built it in ~3 weeks, solo. The stack: Next.js (frontend/backend) Gemini API (for all the AI functionality) Neon (Postgres with a nice DX) Why Gemini? I wanted to try something outside the OpenAI bubble — the Gemini models performed surprisingly well, especially for contract analysis and writing long-form outreach. Not trying to make a pitch here — just genuinely curious what you all think. What’s broken? What’s overbuilt? What would you actually want in a tool like this? Here’s the link: https://career-mindai.vercel.app Thanks in advance!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, openai · Missing: mac, agents, macos
81%81% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
42%42% predicted probability of success on TrustMRR, 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
32%32% 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
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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