Microsoft just mass-compressed LLM reasoning. their new paper introduces MEMENTO, a method that teaches reasoning models to manage their own context. instead of letting chain-of-thought grow into a flat 32K-token stream, the model learns to segment its reasoning into blocks,
@akshay_pachaar
-
Agent Engineering Evolution: Environment Over Model Power 2022-2026
By
–
from weights → context → harness engineering (evolution of agent landscape from 2022-26) the biggest shift in AI agents had nothing to do with making models smarter. it was about making the environment around them smarter. here's how agent engineering evolved in just 4
-
Agent Memory Systems: The Challenge of Forgetting Irrelevant Data
By
–
Knowing what to forget is harder! Most agent memory systems focus on ingestion. Add more documents, build more embeddings, extract more entities. The graph only grows. But a memory that never forgets isn't actually useful. Stale nodes and unused connections pile up over time,
-
Google’s Memory Caching Solves Classic RNN Limitation Problem
By
–
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
-
Three-Dimensional Agent Memory Systems Architecture
By
–
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
-
LLMs AI Agents Machine Learning Insights Tutorials
By
–
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!
-
Claude-Mem GitHub Project Enables Persistent AI Agent Memory
By
–
Calude-Mem GitHub: http://
github.com/thedotmack/cla
ude-mem
… (don't forget to star ) -
Claude-Mem Plugin Persists Memory Across Sessions
By
–
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.
-
MiniMax M2.7 Open-Source: Self-Evolution and Autoresearch at Scale
By
–
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,
-
Agent Memory Management: Filtering, Sharing, and Temporal Consistency
By
–
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