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TypeSafe CEO Diogo Almeida on Jev: Building a System One Model for Production, Not for God

InstructGPT paper author and TypeSafe CEO Diogo Almeida detailed his new model Jev on the Latent Space podcast, describing it as a System One large programmatic model oriented toward code consumption, with its core innovation being an unpublished RLCD (reinforcement learning for calibrated decision-making), optimized for "intelligence per dollar" rather than human preference feedback. He criticized RLHF for causing mode collapse, sycophancy, and hallucinations, arguing that safety alignment suits products like ChatGPT but fragments intelligence at the API layer, and said TypeSafe would rather be seen as a data lab, refusing to publish benchmark tests. He revealed that Jev's daily token consumption has exceeded one trillion since launch, and previewed follow-up models such as ReasoningJev.

Open models have only 6 months left? Nathan Lambert on tightening regulation and the distillation dispute

Nathan Lambert believes that the most severe feasibility test for open-weight models is now unfolding: the White House is discussing managing open models through a new executive order, which may affect only Chinese-origin models and government uses. He judges that the most likely action is to ban, or indefinitely postpone, within the next 6 months open-weight models whose capabilities clearly exceed the GPT 5.5, Claude Opus 4.8, or GLM-5.2 range, and such models are currently most likely to come from Chinese companies. He criticizes the distillation issue led by Anthropic as having evolved into regulatory capture, and believes that if the United States alone bans imports of the relevant models, the global open-source ecosystem will continue, and instead make the United States itself harder to predict.

Import AI 464: Fable Writes GPU Kernels, AI Automation, and Analog Computing

Fable wrote what is claimed to be the first and fastest megakernel on KernelBench-Mega, achieving an 18.71X speedup with CUDA on an RTX PRO 6000 Blackwell compared with an optimized PyTorch baseline, higher than Claude Opus 4.8's 14.4X, GLM-5.2's 11.14X, and GPT 5.5's 4.34X. The Remote Labor Index from CAIS and Scale Labs shows that the success rate of AI systems completing online freelance projects rose from 2.5% at its release in October 2025 to 16.1% in July 2026, with GPT-5.5, Opus 4.8, and Fable 5 achieving 6.3%, 8.3%, and 16.1%, respectively. This issue also includes a science fiction short story about analog computing and general-purpose computing being banned.

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The Shape of AI Capabilities: Jagged Frontiers, Bottlenecks, and Reverse Protrusions

In an article, Ethan Mollick points out that AI capabilities remain distributed in a jagged pattern, that weak spots such as memory have long shown no marked improvement, and that the system as a whole is constrained by its worst link. Taking Google's Nano Banana Pro as an example, he explains that image generation quality was once a bottleneck, and that after it was broken through, NotebookLM could directly generate full-page slides from source materials; he argues that judging where AI is heading should focus on bottlenecks rather than benchmark scores.

Ethan Mollick: AI agents can now complete real work

In an article in One Useful Thing, Ethan Mollick argues that AI agents have crossed the threshold of being able to complete real work with economic value. He had Claude Sonnet 4.5, using only the paper text and replication data, read the paper on its own, organize files, convert STATA statistical code to Python, and check each item one by one; it successfully reproduced multiple research results, which he then double-verified with GPT-5 Pro. He believes the improvement in agent capability stems from the fact that a small gain in accuracy is enough to complete more steps, and from new models' ability to self-correct; at the same time, he warns that using them without judgment may also produce 17 PowerPoints that nobody needs.