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Discontent – Extension to combat garbage search engine results

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Discontent – Extension to combat garbage search engine results

Creator here. In a nutshell this extension is a "Like / Dislike" system but for website results. I miss the days of things like the YouTube dislike bar, where you can quickly assess if something is good or not. Currently sitting at ~90 users. To get it off the ground I've scraped the last year of HN to make a set of good links, and used a few content farm blacklists to make a set of bad links. Right now it's semi-useful and should only get better with time. There are some simple measures in place to prevent spam & abuse, but will cross that bridge if it ever comes. All feedback welcome!

Visit productView on Hacker NewsMar 15, 2023
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% 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: user · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
52%52% 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: users · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
45%45% 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
16%16% 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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