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Safa.ai – Generated Documentation for TensorFlow
Hacker NewsSafa.ai – Generated Documentation for TensorFlow
We are excited to release SAFA's generated documentation for Tensorflow. You will be able to view: -Project Overview (Overview, Features, Entities, Dataflow) -Code Files and their summaries (975 Code Files) -Functional Requirements (371 Functional Requirements) -Features (182 Features) -Mapped Relationships with explanations Learn more at https://safa.ai
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Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist67%67% 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 HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos54%54% 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 · 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, intuitive36%36% 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, crypto12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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