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Redcache_AI – open-source memory framework for LLMs and Agents

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Redcache_AI – open-source memory framework for LLMs and Agents

While building my AI-powered dating app, I couldn't find a memory backend that was affordable,accessible and efficient. I built Redcache-ai to meet this need. Redcache-ai is also available as a Python package. Happy to receive feedback and answer questions. Note: I am not a native English speaker. Apologies for the typos and grammatical errors.

Visit productView on Hacker NewsJul 30, 2024
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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, open · Missing: mac, macos, cursor
77%77% 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 · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, 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
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
24%24% 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.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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