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Product Hunt

Memory & observability for LLM apps

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Actual performance

148upvotes
11comments
Made the leaderboard

Traction signals

Makers1

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto
58%58% predicted probability of success on BetaList, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% 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, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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