Bl
Blokfeed – A simple crypto news aggregator
Hacker NewsBlokfeed – A simple crypto news aggregator
Hi everyone! Scratched an itch and solved an issue I had while trying to stay up to date with crypto and blockchain news. I realized I was spending too much time bouncing around various sites to find recent news articles. So I built blokfeed.com to help me sift through crypto news more efficiently. I have plans on building in more features. Check it out - hope it helps you as well! blokfeed.com
Share cardActual performance
2points
4comments
Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting68%68% 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released35%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.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos16%16% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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