A ten-year-old neural network beat GPT-6 and Claude at spotting skin cancer. What the result says about where a general-purpose model belongs on your AI roadmap.
Kimi K3.1 removed cyber data from training before release and published the score it cost. Why open weights no longer mean unrestricted, and what to ask a model vendor now.
A compromised LiteLLM release spent about two hours on PyPI this week. It shows why the box you built for AI governance is also your most valuable target.
Public AI benchmarks cover the work that is cheap to grade, not the work that pays your bills. Why the unmeasured majority is where private evidence becomes an advantage.
A developer says a near-free model changed what he bothers to attempt. For leaders, the useful question is which work never got funded, and who checks the results.
Meta and Microsoft are reportedly cutting internal Claude use. The cut is a symptom: AI budgets run on usage counts until finance steps in, and a cap is a blunt tool.
A harness around a model is where a company's judgment will live. Most firms can buy the loop and should keep the two parts that compound: corrections and review rules.
Google's AI-era products keep breaking an old trust contract with users, and the model isn't the reason. It previews what fast AI shipping costs any team.
Microsoft folded its consumer AI companion into the enterprise product. The adoption number behind that decision says more than the U-turn itself.
Anthropic, OpenAI, and xAI all updated their flagship models within 48 hours. The benchmark charts are the least useful thing in any of the three announcements.
