The money fight around Alex Bores ended without a clean winner: Bores narrowly lost the NY-12 primary, but attacks from a pro-AI PAC made him a visible symbol of regulation. For AI companies, it is a warning that political influence through super PACs can backfire.
The AI industry tried to press a warning button for regulators in NY-12. Instead it lit up a billboard over its own wallet, and every future candidate now knows where to point.
Google Research examines why chain-of-thought helps LLMs answer simple factual questions. The study on Gemini 2.5 and Qwen3-32B points to two mechanisms: extra computation in generated tokens and factual priming.
For factual recall, reasoning is more flashlight than diary: it can illuminate the model's memory, but if the beam hits the wrong shelf, the user gets a confident label on an empty slot.
At Config 2026, Figma put Motion, upcoming code layers and shader tools closer to the core design canvas. Product teams get a more powerful workspace, but also a new place for handoff problems to hide.
Figma wants design and code to stop sending postcards from opposite shores. The real test starts when someone carries the beautiful prototype into a pull request and CI lights up red.
OpenAI and Broadcom unveiled Jalapeño, OpenAI's first custom inference chip for running LLMs. For ChatGPT, this is less flashy than a new model, but potentially more important for the unit economics.
Jalapeño is the agents era invoice landing on Sam Altman's desk: if you want to hand out billions of tokens a day, every watt becomes a coin you either keep or burn.
Microsoft Research and partners described Talos, an open-source tool for automated genomic reanalysis in rare disease. In a prospective cohort of almost 5,000 patients, it added diagnoses in 5.1 % of cases.
Talos is the quiet night guard in the genome archive: it does not open every door, but when new evidence lights one up, it sends the human reviewer to the right handle.
Lilian Weng revisits scaling laws and shows why they are most useful as a tool for allocating compute, data and parameters. The practical lesson is sober: extrapolation helps only while you remember how small the underlying experiments were.
Scaling laws are a ruler placed on a map, not a navigator that drives to the destination. Spend millions by the ruler without checking the terrain, and you can draw a beautiful straight line into a swamp.
