Linklet – Save and search your LinkedIn posts cleanly

Hacker News

Most people save posts on LinkedIn, then never see them again. They get buried in the “Saved” section with no way to search or organize them. Linklet fixes that. It’s a simple Chrome extension that captures the full content of any LinkedIn post you save - author, headline, text, and media—without the clutter of likes, comments, or ads. You get: - One-click saving directly from LinkedIn = Instant search across your saved posts = Sorting by author or date = Collections for organization = Export option for backups There’s no signup, account, or backend storage. Everything stays local in your browser. Chrome Web Store: https://chromewebstore.google.com/detail/linklet/lhmnjaclknh... I built Linklet because I kept losing valuable posts I’d saved for learning and reference. I’d love feedback on UX, local-first design choices, and what features you’d want next.

Visit product View on Hacker NewsOct 28, 2025

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

54%54% 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.

TrustMRRFits verified-revenue profile · Strong signals: google, way · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% 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

44%44% 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

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

16%16% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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