🧠 Preconfigured Brains: How Neural Patterns Shape Our Understanding of the World
A fascinating bridge between neuroscience and computational theory, suggesting that the brain’s intrinsic organization could inspire new models in AI and cognitive simulation.
Researchers at UC Santa Cruz found that the human brain may come with built-in electrical activity patterns even before sensory input begins. Using brain organoids, they observed self-organized neural activity resembling a ‘default mode,’ hinting at an innate computational framework for interpreting the world. These findings could revolutionize early neurodevelopmental research and improve detection of fetal disorders and toxin effects.
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🛡️ LLVM Adds Constant-Time Security: A New Era for Cryptographic Safety
A milestone for secure compiler design—merging cryptographic rigor with practical software engineering to redefine industry standards in secure compilation.
Trail of Bits integrated constant-time coding support into LLVM 21, protecting cryptographic implementations from timing attacks caused by compiler optimizations. The new intrinsic `__builtin_ct_select` maintains constant-time behavior across compilation stages, eliminating data-dependent branches. Early adopters from Rust Crypto, BearSSL, and PuTTY report minimal performance loss with strong cross-platform security benefits.
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🤖 What Does It Mean to Be Massively Against AI?
A sharp reflection on the ethical and cultural divide shaping the AI debate, questioning whether technological progress aligns with social responsibility.
This essay examines the growing skepticism toward artificial intelligence within the Mastodon community. Reflecting on Armin Ronacher’s query about AI resistance, it argues that the backlash stems from hype-driven and ambiguous uses of ‘AI.’ The piece critiques large-scale AI deployment, highlighting ethical and ecological concerns such as water consumption and corporate influence.
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🔗 The Promise of P-Graphs: Faster Matching in Graph-Based Rewriting Systems
An insightful deep dive into graph-based optimization and term-rewriting, offering valuable perspectives for compiler research and automated reasoning frameworks like egglog.
This article explores the evolution of E-matching into relational e-matching, where expressions are flattened into p-nodes to enable more efficient pattern matching. It details how variable ordering and trie-based joins streamline the elimination process, laying the groundwork for advanced P-matching in rewriting systems.
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