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Generate a dev portfolio from your LinkedIn profile in under 5 minutes

Hacker News

Generate a dev portfolio from your LinkedIn profile in under 5 minutes

Creating a dev portfolio website can be daunting, especially for students and early-career professionals. Many have a solid LinkedIn resume but find it hard to showcase their skills online. So we built a tool to convert a LinkedIn resume PDF into customizable portfolio website in less than 5 minutes. It requires minimal web development knowledge, making it perfect for both seasoned developers and newcomers looking to build a professional web presence quickly and maintain it themselves. And it's open source. Key features: - Extracts information from your LinkedIn PDF - Creates a polished, customizable portfolio site - Easy deployment (local or cloud) - Maintainable in your own GitHub repo - Tech stack: NextJs, React, Vercel AI SDK, Bolt, Claude AI Demo with live code: https://www.youtube.com/watch?v=M9KssZnfZrw Code: https://github.com/levelup-apps/generate-portfolio Started this project this week with georgeck and liza for the World's Shortest hackathon hosted by Nvidia and Vercel. It's just the beginning - we plan to continue development. See the demo and let us know what you think! We welcome contributions and ideas to improve.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
88%88% 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: claude, apps, new · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
35%35% 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
29%29% 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
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.

Correct prediction on native model

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