Google just solved an old RNN problem. A new paper from Google Research introduces "Memory Caching," and the idea is almost too simple to believe. Here's the problem it solves: Modern RNNs compress the entire input into a single fixed-size memory state. As sequences get
@akshay_pachaar
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Three-Dimensional Agent Memory Systems Architecture
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Agent memory is three-dimensional. Most agent memory systems use a single store. Usually a vector database. It handles semantic similarity well, but it captures only one dimension of knowledge. Here's the gap. Store these three facts: → Alice is the tech lead on Project Atlas
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LLMs AI Agents Machine Learning Insights Tutorials
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If you found it insightful, reshare with your network.
— Akshay 🚀 (@akshay_pachaar) 14 avril 2026
Find me → @akshay_pachaar ✔️
For more insights and tutorials on LLMs, AI Agents, and Machine Learning! https://t.co/wOtVKubY0xIf you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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Claude-Mem GitHub Project Enables Persistent AI Agent Memory
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Calude-Mem GitHub: http://
github.com/thedotmack/cla
ude-mem
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Claude-Mem Plugin Persists Memory Across Sessions
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Make Claude Code 10x more powerful.
— Akshay 🚀 (@akshay_pachaar) 14 avril 2026
Claude-Mem is a free plugin to persist memory across Claude sessions.
It captures tool usage, so you always start where you left off.
Comes with a production-ready, 3-layer retrieval system that saves up to 10x tokens.
100% open-source. pic.twitter.com/9jToAEmDsEMake Claude Code 10x more powerful. Claude-Mem is a free plugin to persist memory across Claude sessions. It captures tool usage, so you always start where you left off. Comes with a production-ready, 3-layer retrieval system that saves up to 10x tokens. 100% open-source.
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MiniMax M2.7 Open-Source: Self-Evolution and Autoresearch at Scale
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MiniMax M2.7 is open-source!
— Akshay 🚀 (@akshay_pachaar) 14 avril 2026
The most interesting part of this release isn't a benchmark number. It's what MiniMax calls "self-evolution," and it's essentially Karpathy's Autoresearch applied at full scale.
Every AI agent today runs inside a harness: the scaffolding of skills,… pic.twitter.com/91lBJXZax5MiniMax M2.7 is open-source! The most interesting part of this release isn't a benchmark number. It's what MiniMax calls "self-evolution," and it's essentially Karpathy's Autoresearch applied at full scale. Every AI agent today runs inside a harness: the scaffolding of skills,
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Agent Memory Management: Filtering, Sharing, and Temporal Consistency
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Great questions. – No, not everything should be stored. The agent needs to filter what's actually useful vs what's not. – Yes agents can ahev shared memory, and most memory infra support multi tenancy. – For contradictions, timestamped facts let newer info override older
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Intelligent forgetting in AI systems memify strengthens useful paths
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That's a fair reframe. Knowing what to forget is arguably harder than knowing what to remember. memify() is exactly aimed at that, strengthening useful paths and letting stale ones decay. The title optimizes for the hook, but you're right that intelligent forgetting is the
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Graph Traversal Enables Multi-Hop Queries Beyond Vector Search
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Good questions. Graph traversal adds a small overhead but makes multi-hop queries possible that vector search alone simply can't answer.
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Retrieval vs Behavioral Learning: The Real Gap in AI Agents
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Really well articulated. The distinction between "finds the right fact when asked" vs "already changed behavior from experience" is the real gap. Retrieval is table stakes. Consolidation turning episodic traces into behavioral defaults is where agents actually start learning.