I made a LinkedIn Post Tagger to highlight posts based on preferences

Hacker News

Looking for a specific job? Trying to avoid certain kinds of messages? Just want a cringe filter? You can do all this using this chrome extension where you can fill in your exact preferences and every time you open LinkedIn you will get posts labelled to draw your attention to the right ones and keep it away from the wrong ones. I initially made it as a cringe filter but people are telling me that they are filling in specific job aspirations or target customer profiles in there and the AI is helping them filter out the noise and jump on the posts that would be most important to them. It's in testing so please try it out and let me know feedback!

Visit product View on Hacker NewsAug 21, 2025

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

63%63% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · 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

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: io · Missing: https docs, excited, just released

21%21% 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

Similar products

Peakedin – archiving LinkedIn's most unhinged posts as satire29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Peakedin – archiving LinkedIn's most unhinged posts as satire

Hacker News1

Extension to highlight new posts on HN frontpage since your last visit38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Extension to highlight new posts on HN frontpage since your last visit

Hacker News2

Extension to highlight new posts on HN frontpage since your last visit

Hacker News1

Extension to highlight new posts on HN frontpage since your last visit

Hacker News1

Extension to Highlight new posts on HN frontpage since your last visit38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Extension to Highlight new posts on HN frontpage since your last visit

Hacker News1

LinkedIn Post Generator44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LinkedIn Post Generator

Hacker News4

Liner – Highlight Everything (Now on ProductHunt)43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Liner – Highlight Everything (Now on ProductHunt)

Hacker News1

Past Post - LinkedIn Summarizer53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Discover Your Top-Performing LinkedIn Posts

Indie Hackers1$20/mocontent

LinkedIn Semantic Post Search32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Rapidly discover relevant LinkedIn posts

Indie Hackerscommitment-full-time

Voice to post48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

One idea - many posts

Indie Hackers1ai

Browse all products Analyze your launch