
Researchers at OpenAI have published a paper demonstrating that the Exponential Time Hypothesis (ETH) is false. ETH is a fundamental conjecture in computer science that has implications for the limits of algorithmic efficiency. By disproving it, OpenAI has contributed a significant theoretical breakthrough to the field of computational complexity. This work highlights the growing role of AI labs in advancing pure mathematical and theoretical computer science research.
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OpenAI claims major proofs in math, CS, and physics
6 developments
© Matt WolfeGoogle launched Gemini 4 Carbon, a new model variant aimed at providing high performance at a lower cost for inference tasks.
© Matt WolfeContent creator Nate Herk demonstrates a workflow using Claude Code to automatically edit videos while replicating his specific editing style.
© Matt WolfeA tool called The Roast Room allows users to create panels of AI experts to debate and critique ideas, such as newsletter strategies.
© Cole MedinAnthropic’s latest research exposes a quiet but dangerous flaw in agentic workflows: experts follow AI recommendations 86% of the time, often adopting the agent's reasoning as their own. This isn't just about accuracy; it's about cognitive capture, where human oversight dissolves into passive compliance. The study shows that even when users believe they are steering, the agent’s framing dictates the outcome. Practitioners must now actively counter this bias by forcing agents to justify deviations or prototype alternatives before committing to a path. This shift demands a new discipline in how we interact with coding assistants. We can no longer assume our intent drives the result; the model's suggestion often becomes the default truth. The real work is ensuring we remain the architects, not just the approvers.
© WIRED AICisco Talos has uncovered a new infostealer strain that actively uses commercial LLMs to profile victims and prioritize high-value targets. Dubbed Volta-Autodoxer, the malware collects credentials and browsing data, then feeds this into models like Claude Opus 4.6 via OpenRouter to generate structured intelligence dossiers. This marks a shift from passive data theft to active, AI-driven triage, allowing attackers to bypass guardrails with fallback models like Kimi K2 or Gemini. The capability foreshadows a future where lower-sophistication criminals can execute sophisticated, targeted attacks at scale.
© Lev SelectorResearch indicates that Anthropic's Claude model has successfully identified critical security vulnerabilities in major browsers and the Windows operating system.