In
Intrgr – A new way to read/discover articles and blogs
Hacker NewsIntrgr – A new way to read/discover articles and blogs
Link: https://intrgr.com Hey HN, Intrgr is a way to discover, submit, and discuss articles from around the web with other readers; to be kept up to date by article recommendations--of not just news but all text content; to declutter websites; and to find related articles of any content Intrgr can parse. The recommendation algorithm weights blogs better, if you reply with a blog url(s) I can add it to the global article source pool.
Share cardActual performance
4points
2comments
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released50%50% 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, macos48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive40%40% 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, revenue16%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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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