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Flokkk – Discover Useful links through community curation

Hacker News

Flokkk – Discover Useful links through community curation

Hey HN, I'm Teja from Flokkk ( https://www.flokkk.com/home ). Flokkk helps you discover quality resources on any topic through community curation. The platform is designed around the idea that people don't just want resources, they want to understand the experience behind the recommendation. So instead of just sharing links, people add context explaining why each resource matters. For example - rather than posting "Great Photoshop tutorial," you'd write "This 10-minute video taught me the layer masking technique I used in my latest series." Here's how Flokkk works differently: - Every submitted link requires an annotation explaining why it's valuable. - Community votes based on actual usefulness, not just engagement. - You see exactly who endorsed each resource and their reasoning (full transparency). - Topics get organized into peer-validated learning paths and resource collections. The annotation requirement filters out low-effort submissions, while the voting system surfaces genuinely helpful content. We support all types of resources - YouTube videos, articles, courses, tools, even personal Google Sheets or templates. Built this as a non-technical founder who learned to code specifically for this project - took 9 months from zero coding experience to launch. The irony? Learning to code taught me exactly why we need better content curation - I wasted so much time on poor tutorials that good community filtering could have saved me. You can try it out at https://www.flokkk.com/home where it's free to use. Feel free to try it out and contribute your own curated resources. We'd love your feedback on the approach and how it could be made even better!

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, context, coding · Missing: mac, agents, macos
83%83% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
49%49% 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: personal, video, month · Missing: mobile apps, ios, entrepreneurs
28%28% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: need better · 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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