TQNN
Fault-tolerant inference for noisy and imperfect data.
TQNN exists because real-world data is rarely perfect. I'm building a fault-tolerant inference platform that helps applications make confidence-aware decisions from noisy, incomplete, or uncertain data.
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Launch Intel predictions
Analyze your own launch →71%71% 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.
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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