What if your LLM kept learning after deployment—even without changing its weights? Researchers from Jilin University, King's College London, and UCL introduce CASCADE, a framework that equips LLM agents with an evolving episodic memory. It treats experience reuse like a
SYSTEMS
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Claude: The Emerging Universal Interface for All Your Tools
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Claude is slowly becoming the interface for everything: – Presentations: PowerPoint → Claude in PowerPoint
– Editing: Premiere Pro → Remotion
– Browser: Chrome → Claude for Chrome
– Spreadsheets: Excel → Claude in Excel
– Image / Video: Midjourney → Higgsfield
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AI system outperforms human-written code in scientific software design
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#AI system automates scientific software design, outperforming human-written code in key benchmarks
by Anne J. Manning @TechXplore_com Learn more: https://
bit.ly/4dqI91k #GenerativeAI #ArtificialIntelligence #MachineLearning #DeepLearning -
16 Local AI Agents Running on DGX and MiniMax M2.7
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(2x DGX Sparks) + MiniMax M2.7 NVFP4 = 16 local AI agents running simultaneously 👀 https://t.co/Oaf5J1dyuF
— NVIDIA AI (@NVIDIAAI) 25 mai 2026(2x DGX Sparks) + MiniMax M2.7 NVFP4 = 16 local AI agents running simultaneously
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New Book: Mastering AI System Design with Domain Driven Blueprints
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"Mastering AI System Design: Architect, Build and Deploy AI Systems Using 10 Domain Driven Blueprints and Interview Strategies" available at http://
amzn.to/3NFNOWF 𝙏𝙖𝙗𝙡𝙚 𝙤𝙛 𝘾𝙤𝙣𝙩𝙚𝙣𝙩𝙨:
1. Introduction to AI System Design
2. Crafting Intelligent Systems Using Prompt -
Book recommendation: AI Agents in Practice by Valentina Alto
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Another great book by @AltoValentina
, from @PacktDataML at http://
amzn.to/4p98LYl "AI Agents in Practice — Design, Implement, and Scale Autonomous #AI Systems for Production" Book Description (from Amazon): As AI agents evolve to take on complex tasks and operate -
New book on designing multi-agent AI systems with MCP and A2A
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New release from @PacktDataML available at: http://
amzn.to/40Sp4O9 "Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based Agentic AI Framework with tool use, memory, and multi-agent workflows" Table of Contents:
Introduction to Generative AI and AI -
Boring context compaction beats multi-agent systems for most use cases
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I've built multi-agent systems for clients. I still think they're overrated for most use cases. What's underrated is boring. Context compaction. The reason your AI app feels slow and expensive isn't the model. It's everything you keep stuffing into the prompt before it even
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τ Scaling: Beyond Transistor Shrinking for AI Systems
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The proposed “τ Scaling” approach moves beyond traditional transistor shrinking and focuses more on reducing latency and improving efficiency across chips, interconnects, and AI systems. Concepts like LogicFolding, hybrid bonding, Unified Bus, and advanced system-level
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Agent Architectures: Thin Orchestrators Over Direct Work
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This matches what's happening in agent systems beyond research too. You would find similar patterns in Claude Code as well. I think the best agent architectures keep converging on thin orchestrators that delegate instead of doing the work themselves.