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AceIQ360 – First AI memory system to achieve 100% on LongMemEval

Hacker News

AceIQ360 – First AI memory system to achieve 100% on LongMemEval

I built AceIQ360, a deterministic agentic memory system. Results: - LongMemEval: 100% (500/500) - first ever - LoCoMo: 75.32% J-Score (vs Mem0 68.44%) - 80x cheaper per turn - 13x faster Built on RudraDB, my relationship-aware vector database with automatic relationship detection. No LLM extraction calls. Pure embedding-based. Solo developer. Looking for feedback.

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Actual performance

2points
3comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, agentic · Missing: mac, agents, macos
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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
29%29% 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
13%13% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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