Built a Resume Optimizer to Improve Your Resume Based on Job Role

Hacker News

Recently, I was exploring RAG systems and wanted to build some practical utility, something people could actually use. So I built a Resume Optimizer that helps you improve your resume for any specific job in seconds. The flow is simple: → Upload your resume (PDF) → Enter the job title and description → Choose what kind of improvements you want → Get a final, detailed report with suggestions Here’s what I used to build it: LlamaIndex for RAG Nebius AI Studio for LLMs Streamlit for a clean and simple UI The project is still basic by design, but it's a solid starting point if you're thinking about building your own job-focused AI tools. If you want to see how it works, here’s a full walkthrough: https://www.youtube.com/watch?v=J8qIR29zCHU And here’s the code if you want to try it out or extend it: https://github.com/Arindam200/awesome-ai-apps/tree/main/rag_... Would love to get your feedback on what to add next or how I can improve it

Visit product View on Hacker NewsJun 24, 2025

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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.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, code · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, io · Missing: https docs, excited, just released

44%44% 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: apps · Missing: mobile apps, ios, personal

27%27% predicted probability of success on TrustMRR, 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.

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