why not? what in the current harnesses are not bitter lesson-pilled? off top of head: will go away:
– planning tools
– compaction (or at least will matter less) not going away:
– sub agents
– skills
– filesystem access
– bash
– websearch
– mcps what else?
SYSTEMS
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Essential Agent Components: What Survives the Bitter Lesson
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Claude Multi-Agent Collaborative Workflow
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8. Claude Multi-Agent Workflow You are 4 collaborative agents:
• Architect (system design)
• Engineer (development)
• Reviewer (quality control)
• Optimizer (performance improvement) Return:
• Architecture
• Implementation
• Review feedback
• Final optimized version -
HyperMem: New Memory Architecture for Long Conversations
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A new paper has been released, HyperMem, a memory architecture for long conversations. The goal is to enable dialogue systems to better preserve, organize, and retrieve long-term memories, avoiding the fragmentation of relevant information in traditional RAG or ordinary
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Interesting Innovation Happening at Harness Layer
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Totally agree, all the interesting stuff is happening at the harness layer
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SpaceX Future: Space Datacenters and Distributed Robot Control Systems
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The new SpaceX to me will be: 1. Way to get datacenters and robots to space.
2. Datacenters in space.
3. Distribution from datacenters to planets they serve. 4. Control systems for the robots and humans they are distributing to. These four things alone are worth many -
Discrete symbols and mathematics foundations of AI modeling
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To make sense of the world is to model it in the simplest possible way. And simplicity requires discrete symbols. This is why we developed mathematics in the first place.
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Why Enterprise AI Strategies Fail: Alignment and Governance
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This article explores why most enterprise AI strategies fail before execution—highlighting how misalignment across architecture, governance, and business operations prevents AI from scaling and delivering real value. https://
tinyurl.com/yefmyzw2 #ArtificialIntelligence -
Meta Neural Computer: Model as Integrated Computing System
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What if the model didn’t just use a computer, but actually was the computer? Meta AI introduces "Neural Computer", a model where computation, memory, and I/O are all inside one learned system. Their early prototype learns from screen recordings of terminals and desktops, and it
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New video on model scaling and Tensor Parallelism performance.
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New quick video, more coming up
— Ahmad (@TheAhmadOsman) 9 avril 2026
Goal isn't just to show how large models perform but also to show how small and medium models scale up as you multiply # of nodes and how Tensor Parallelism performs on Unified Memory hardwarehttps://t.co/U9XhuLmJHfNew quick video, more coming up Goal isn't just to show how large models perform but also to show how small and medium models scale up as you multiply # of nodes and how Tensor Parallelism performs on Unified Memory hardware
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RAG is an Ecosystem: Building Modular Production-Grade Systems
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This is one of the cleanest visual summaries of a production-grade RAG (Retrieval-Augmented Generation) stack I’ve seen. What it highlights clearly is an often-ignored reality: RAG is not a single tool — it’s an ecosystem. A solid RAG system spans multiple, interchangeable layers: LLMs (open & closed): Llama, Mistral, Qwen, DeepSeek, OpenAI, Claude, Gemini Frameworks: LangChain, LlamaIndex, Haystack — orchestration is the real differentiator Vector databases: Chroma, Pinecone, Qdrant, Weaviate, Milvus Data extraction: Web crawling, document parsing, structured ingestion Embeddings: Open (BGE, SBERT, Nomic) vs proprietary (OpenAI, Cohere, Google) Evaluation: RAGAS, TruLens, Giskard — because “it sounds right” is not a metric Key takeaway for leaders and builders: RAG success is less about which model you choose and more about: data quality retrieval strategy chunking & indexing evaluation loops cost / latency trade-offs This is why mature AI teams design modular stacks, not one-vendor pipelines. RAG is no longer experimental. It’s becoming foundational infrastructure for enterprise AI. #RAG #AgenticAI #EnterpriseAI #LLMs #AIArchitecture #GenAI #DataEngineering X (Twitter) RAG isn’t a tool. It’s a stack. LLMs Frameworks Vector DBs Embeddings Extraction Evaluation Winning teams design modular RAG systems — not single-vendor pipelines. This is how enterprise AI actually scales.
→ View original post on X — @ingliguori, 2026-04-09 17:25 UTC