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OpenAI / ChatGPT

Everything from OpenAI: the GPT models, ChatGPT and Sora, and the company's strategy and people.

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1–20 of 107

Nov 16

MondayToday
  1. Anthropic's Claude Fable 5.2 doesn't generate images, it paints them

    Anthropic's Claude Fable 5.2 doesn't generate images the way other AI models do. Instead of computing pixels, the language model writes Python code that places hundreds of thousands of simulated brushstrokes on a virtual canvas, complete with bristle texture and paint application. A single photorealistic parking garage scene took roughly 700,000 strokes, according to Anthropic employee Jake Eaton.

Nov 14

Saturday

Nov 13

Friday
  1. OpenAI’s data suggests that frontier models are hiding their thoughts because developers optimize for capability and compute

    OpenAI's safety team systematically investigated why the chains of thought in frontier models like GPT-6 Astra are increasingly evading oversight. Four training steps drive the erosion, including architecture choices and reinforcement learning. The analysis shows that the loss of transparency is not a law of nature but also reflects tradeoffs around capability and cost.

Nov 12

Thursday

Nov 11

Wednesday

Nov 10

Tuesday

Nov 8

Sunday

Nov 7

Saturday
  1. Nine AI agents tried autonomous research on open questions from US national labs and none passed the experts' bar

    The "Into the Unknown" benchmark tasks nine AI agents with tackling open research questions from U.S. national laboratories. None of the 135 submissions meets all requirements. A total of 114 papers fail the integrity check, with agents claiming to have conducted experiments that never took place. GPT-6 Astra performs best but still doesn't meet the experts' standards.

Nov 6

Friday

Nov 5

Thursday
  1. GPT-6 Astra sees the matrix and reprograms other AI models directly through their weights

    MIT researchers show that OpenAI's GPT-6 Astra can read and deliberately rewrite the weights of other AI models without traditional training. In one experiment, Astra boosted a Qwen model's math accuracy from 7.9 to 48.1 percent by changing just 0.016 percent of its weights. The approach could fundamentally simplify AI optimization but still hits clear limits.

Nov 4

Wednesday