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1
How open model ecosystems compound
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2
Notes from inside China's AI labs
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3
The distillation panic
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4
Reading today's open-closed performance gap
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5
My bets on open models, mid-2026
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6
What I’ve been building: ATOM Report, post-training course, finishing my book, and ongoing research
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7
The inevitable need for an open model consortium
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8
Claude Mythos and misguided open-weight fearmongering
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9
Gemma 4 and what makes an open model succeed
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10
Latest open artifacts (#20): New orgs! New types of models! With Nemotron Super, Sarvam, Cohere Transcribe, & others
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11
Lossy self-improvement
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12
GPT 5.4 is a big step for Codex
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13
What comes next with open models
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14
Dean Ball on open models and government control
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15
Olmo Hybrid and future LLM architectures
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16
Latest open artifacts (#19): Qwen 3.5, GLM 5, MiniMax 2.5 — Chinese labs' latest push of the frontier
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17
How much does distillation really matter for Chinese LLMs?
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18
Open models in perpetual catch-up
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19
Opus 4.6, Codex 5.3, and the post-benchmark era
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20
Why Nvidia builds open models with Bryan Catanzaro
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