I don’t disagree about other specific responses that can/should we use. I agree the current approach is extreme and, as I said, illegal. The point of the article is that no matter the president this is the real alignment problem: super powerful AI vs government.
SAFETY
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Context engineering is the new bottleneck
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the broader observation matters more than this specific paper. context engineering is quietly becoming the real bottleneck. not model capability, not training data, not inference cost. the unglamorous plumbing of what information reaches the model, when, and how. Anthropic
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Three Tiers of Persistent Context Explained
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the paper defines three tiers of persistent context, each with different lifecycles. scratchpads (/context/pad/) are temporary working notes scoped to a task. think of them as the agent's rough draft space. episodic memory (/context/memory/episodic/) holds session-bounded
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Agent frameworks’ context handling flaws
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the problem is real and underappreciated. right now, most agent frameworks handle context like this: load memory at session start. stuff it into the prompt. when the window fills, summarize and compress. hope the important parts survive. the paper calls this out directly. once
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QueryWeaver GitHub Project Promotes AI-Related Categories
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QueryWeaver GitHub: (don't forget to star )
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Grok’s truth-seeking expectation contradicted by hallucinations
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I thought Grok was truth seeking, this is just plain old hallucinations
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Biased human prompting leads to more biased outputs
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Depends on how it is prompted. So probably more since biased humans are prompting the system.
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TorchLean: First Fully Verified Neural Network Framework in Lean
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Super excited to release TorchLean!! I’m happy to answer questions and would love to discuss verified NNs + theorem proving especially what it’ll take for the field to become widely usable in real ML systems. Blog post + codebase release soon! Prof. Anima Anandkumar (@AnimaAnandkumar) We’re excited to release TorchLean which is the first fully verified neural network framework in Lean. The Lean community has largely focused on pure mathematics. TorchLean expands this frontier toward verified neural network software and scientific computing. With the recent release of CSlib, we see this as another step toward a fully verified ML stack. We support features: 1. Executable IEEE-754 floating-point semantics (and extensible alternative FP models) verified tensor abstractions with precise shape/indexing semantics 2. Formally verified autograd system for differentiation of NN programs Proof-checked certification / verification algorithms like CROWN (robustness, bounds, etc.) 3. PyTorch-inspired modeling API with eager-style development + export/lowering to a shared IR for execution and verification Project page: leandojo.org/torchlean.html Paper: [2602.22631] TorchLean: Formalizing Neural Networks in Lean Work done @Robertljg, Jennifer Cruden, Xiangru Zhong, @huan_zhang12 and @AnimaAnandkumar. #MachineLearning #ScientificComputing #Lean — https://nitter.net/AnimaAnandkumar/status/2027907453908857298#m
→ View original post on X — @animaanandkumar, 2026-03-01 22:38 UTC
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AI Hallucinations and Reasoning Errors Linked to Civilian Deaths
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hallucinations and reasoning errors that lead to accidental civilian deaths. which may have already happened, yesterday.
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OpenAI’s Unreliable Tech and War: An Ethical Debate
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tl;dr of something worth reading: OpenAI is full of shit, and perfectly happy to be using their unreliable tech for war. #DeleteChatGPT