Even DeepSeek is now making fun of Anthropic's Claude.
LLMS
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Cross-Architecture Distillation Recipe for Mamba Models
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Attention to Mamba: A Recipe for Cross-Architecture Distillation Paper: https://
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Mamba Models Match Transformer Performance Without Attention
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Can you get a Mamba model to perform like a Transformer without adding Attention? Researchers from Apple, MILA, and Flat Iron Institute (including Abhinav Moudgil and Ningyuan Huang) have a breakthrough answer. They introduce a two-step distillation recipe: first, they convert
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QClaw International Beta Now Free with 40M Daily Tokens
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4/ Their international beta is now accessible and completely free if anyone wants to test it out. Capacity: 40M tokens/day. Relationship with OpenClaw: QClaw is built on the OpenClaw open-source framework with an end-to-end consumer encapsulation layer. All QClaw-developed
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DeepSeek v4 Performance Comparison and Visual Generation Capabilities
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If you want to see what DeepSeek v4 is like, there's no better way than looking at my face alongside the dozens of generations I did side by side with other models
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Architectural Principles for Building AI Agents and Automations
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What this means practically for anyone building agents, prompts, or automations right now: – Stop treating memory as a storage problem – Stop renting your agent's intelligence from the labs – Instrument outcomes, not just inputs – Every interaction should be a labeled
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The Architectural Trade-off Between Retrieval and Learning Systems
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The moat math is brutal once you see it. A retrieval system plateaus at the quality of its extractor. As good on day 1,000 as day 1. And most of what it "knows" belongs to whichever lab hosts it. A learning system you control compounds every interaction into a corpus of (state
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The Risks of AI Agent Data Lock-in and Model Dependency
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every major model provider wants you hooked to them. Not to your agent. Not to your data. To them. The more your agent "remembers" inside their walls, the less of it is actually yours. You're not building a moat. You're renting one.
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Critique of platform lock-in strategies in AI memory features
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Now look at what the labs are actually shipping: Useful? Sure.
But notice where the memory lives. Inside their platform. Tied to their models. Portable nowhere. Every "memory" feature the big labs ship is designed to make leaving more expensive. -
Neuro-inspired Three-tier Memory Architecture for AI Agents
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The clearest frame I've seen on this comes from these guys @midbrain_ai. Their point: the brain doesn't run one memory system. It runs three. – Episodic: what happened (raw traces) – Semantic: what it means (abstracted patterns) – Procedural: how I now behave (baked into the