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A blog post argues that the 'Astra can read and write weights to improve Qwen' claims are misleading, showing the headline Qwen math result actually relies on writing Python code and running the model, not directly reading raw weights as advertised.
Sean Goedecke argues that a recent paper showing frontier LLMs reading and editing neural network weights is not as superhuman as claimed, since a smart ML researcher could replicate the tasks with basic analysis.
Matt Pocock shares a brief critique claiming the AI agent 'skills' specification is broken, linking to further details. The post is a single-tweet reaction with no substantive elaboration.
一篇技术评论文章，批评 OpenAI 在数学论文集中声称的更快快速傅里叶变换（FFT）算法，指出其改进微乎其微，更像是利用理论模型在硬件上并不实际可行的“恶意利用”，而非真正的突破。
Miles Brundage compares the vague promise of 'recursive self-improvement' to Tesla's 'Full Self-Driving (Supervised)', arguing both use appealing keywords to deflect critics while misleading about the actual goal.
A commentary discussing the mathematician backlash against OpenAI's released AI-generated mathematical proofs, arguing the real issue is not lack of human credit but the lack of comprehensibility — models only close proofs without conveying understanding needed to advance mathematics.
A tweet critiques the common use of 'order a plane ticket' as an example for AI agents, arguing it doesn't represent typical user behaviors.
The author updates his AI policy to refuse AI use in professional work, criticizes the AI industry for dehumanizing labor, and commits to building websites for real people while distancing himself from AI trends.
This article critiques the iLands AI platform, questioning whether its economic and social architecture fully supports its research claims about autonomous agents and persistent consequences.
The article critically reviews a Netflix AI documentary, highlighting its superficial analysis, biased portrayal of Sam Altman, and neglect of deeper societal and environmental issues related to AI.
TypeSafe AI launched its Jev model claiming no hallucination and calibrated probabilities, but the article questions the lack of public evidence for calibration while noting rapid developer adoption.
A skeptical deep dive finds numerous errors in the Humanity's Last Exam benchmark, with the official o3-mini grader incorrectly marking correct answers as wrong.
The article critiques the panic over AI regulation, arguing that existing laws are sufficient but not enforced against powerful tech companies, making new regulations like an FDA for AI potentially ineffective.
The article critiques Bend 2, a programming language designed for the AI coding era, for falling into a 'vibe-coding trap' and compares it unfavorably to formal verification approaches like SPARK.
The author criticizes the AI model Astra for poor code architecture decisions and resistance to deleting unwanted code, arguing it should be better trained for long-term software engineering practices.
The article argues that Anthropic has built a financially dependent network of organizations to promote AI doom and secure regulatory capture, compromising independent safety assessments.
The post criticizes effective altruism as hubris and quotes a tweet alleging that effective altruists are promoting AI safety as a political ideology to restrict technological progress.
The article critiques the overuse of 'AI agent' in the tech industry, advocating for terms like 'shard' for simpler, bounded components to avoid misleading perceptions of autonomy.
The author critiques OpenAI's research-intern milestone, suggesting that AI agents should be measured on their ability to advise against or abandon bad experiments to save researcher time, as a form of intelligence.
The author attempted to use the MaleCNS v1.0 fly connectome to play Pong, but it failed to learn. Auditing the failure revealed critical issues with circuit connectivity and highlighted shortcomings in viral AI projects like Doom, Minecraft, and Beat Saber mods.