Octopuses have over two-thirds of their neurons in their arms, allowing each arm to operate independently—a form of distributed intelligence unique to cephalopods. The article explores what scientists have learned about this neural arrangement.
A developer who built a fully autonomous custom agent architecture that can run for weeks without intervention is asking the community for extreme, adversarial tasks to properly stress-test it, because they can no longer trust their own judgment.
Brian Tarricone reflects on the ethical trade-offs of using LLMs in open-source development, then explains how he uses Claude for research and planning while building xfwl4.
Gergely Orosz highlights that 'founder mode' at many startups is causing CTOs, VPEs, and Heads of Engineering to struggle and burn out, as it clashes with how engineering leaders have traditionally led orgs.
MIT Technology Review reports on a wave of startups pursuing post-transformer architectures for LLMs, as the dominant model family faces growing costs, energy use, and context-length limits. Companies like Subquadratic aim to build the next generation of AI.
This MIT Technology Review article argues that AlphaFold-style deep learning on massive datasets is not the ideal template for accelerating science, and that AI agents capable of reasoning and experimentation will drive future breakthroughs.
A developer shares lessons from running an AI report generator in production, arguing that data quality and validation matter far more than the model's writing ability, since fluent but incorrect reports are dangerous.
After leaving Google, Jeff Dean appeared at Stanford AASF 2026 to give a talk, sharing his method for distinguishing foundational technology from trendy tech. The original tweet shows the old laptop covered in stickers that he returned upon departure.
A blog post argues that toggle switches are bad UI controls on macOS, citing unclear state representation, and suggests using checkboxes instead.
A discussion on why speculative decoding matured in 2026 for LLM inference, citing Uber's early use, Apple and DeepMind papers, and Tri Dao's research, with observations on adoption in frameworks and local deployments.
A blog post examines an ambiguity in the C89 standard about implicit function declarations, where GCC and Clang disagree on interpretation, leading to different compile results for certain code.
Dan Luu critiques claims that dynamic languages are more token-efficient for LLMs, pointing out flaws in existing evals and emphasizing the need for better benchmarking methods.
The author critiques Lea Verou's two-state dark mode toggle design, proposing a three-state approach where clicking the active theme button returns to the system default.
The article starts from the technological optimism brought by the early internet and the iPhone, reviews the rise and fall of the Luddites during the Industrial Revolution, explores the privatization and ownership of technology, and points out that the current rise of AI has once again sparked controversy over the ownership and control of technology.
The article discusses the deprecation of Sampling in the MCP spec as of the 2026-07-28 changelog, shifting model-call costs from clients to servers, and advises how to check if a server relies on Sampling via code or logs.
According to SemiAnalysis, Google has quietly cancelled the upcoming Gemini 3.5 Pro model, meaning it will not be released.
Argues that AI agents in enterprises are composed of code artifacts like skills, tools, and MCP servers, so they should be governed like software code rather than as documents or approval lists, since agents are unstable while underlying skills are reusable and stable.
Similarweb data shows that Claude's monthly active users grew for the 15th consecutive month in July, reflecting the continued rapid development of this AI product.
Boris Cherny revealed that Claude Code has reduced indirect prompt injection attacks to near zero by stacking model training, input probing, and intent classifiers, and plans to set Auto Mode as the default mode next week.
Elon Musk affirms a grand vision of colonizing the Moon, building an AI Dyson Swarm, and ascending the Kardashev scale.