AI-Radar.tech

AI-Radar.tech

Indie Hackers

A daily radar on models, frameworks, and HW to run AI onsite

Being an AI Enthusiast, I started studying how LLMs works, reading many books and practicing via google Colab, Hugging Face and Kaggle, developing many AI related Apps, using VS Code . Here my small contribution.

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Actual performance

1followers
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, apps · Missing: mac, agents, macos
87%87% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal
65%65% 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 released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
17%17% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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