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Viewpoints, commentary, analysis, interviews, and discussion of trends.
宝玉@doteyPicks81@dotey 谁能告诉我这和 Dots、Grok Bot、Muse 到底有啥区别？硅谷四大厂排队给我塞功能一模一样的赛博田螺姑娘，我那点少得可怜的待办事项已经不够它们分了......
The comparison makes intended users and access conditions the main selection criteria, helping distinguish four assistants described as sharing cloud computers, proactive updates, and approval checks.
Kevin Weil 🇺🇸@kevinweilPicks66My theory is biotech is about to turn into a national security thing and the west doesn't really get a vote Beijing just floated blocking foreign buyouts of china's cell and gene therapy and siRNA platforms And what's interesting is what they're NOT blocking. Licensing individual drugs is still totally fine So you can sell the west a medicine, just not the platform that makes the next 100 of them And the west has been buying like crazy lol. Foreign deals for chinese biotech assets are at $120B through august, up 36% Astrazeneca's $1B gracell deal, that CAR-T platform, probably doesn't happen under this draft Same update hits AI tech too btw we spent years fighting over who gets the chips. Feels like the next fight is over who owns the platforms that spit out the next generation of medicines Once biology is programmable it's not really a healthcare business anymore, it's infrastructure Still just a draft tho, comments open till nov 15 bio/acc.
The distinction between licensing individual medicines and acquiring their underlying platforms provides a concrete lens for understanding the strategic ownership concerns raised by the proposed restrictions.
François Chollet@fcholletPicks81Gemini 4 Argon from @Google on ARC-AGI (Verified): - ARC-AGI-3: 97.5%, $4.8K (provider adapter harness), 49.8%, $5.8K (standard harness) - ARC-AGI-2: 91.3%, $0.32/task - ARC-AGI-1: 98.5%, $0.16/task Google DeepMind is the second frontier AI lab to score above 95% on ARC-AGI-3. https://t.co/8siwgfBJcr
The reported ARC-AGI-3 scores distinguish provider adapter and standard harness results, providing context for Chollet’s comparison of Argon with Astra and his assessment of Google’s frontier position.
Simon Willison highlights Anthropic’s explanation of Scion, its custom alternative to Git, as merged code changes grew from around 2,500 per day in May 2026 to over 30,000 per day in September. Anthropic reports almost 100 GB of main-branch history and roughly 25 million daily CI checkouts; seven people built the system, completing migration in five months. Willison speculates that Fable/Mythos class models contributed to the increased coding velocity and connects Anthropic’s experience with adversarial review to his own, including the need for engineers to steer models away from over-engineering.
Anthropic’s checkout volume and repository size offer concrete context for understanding how increased coding activity can put pressure on the infrastructure supporting code review and CI.
Demis Hassabis@demishassabisPicks80Gemini 4 Argon is amazing for knowledge work and coding - and it’s super fun to build with! We asked it to create this wizard chess game and it's so cool to see Argon's physics understanding & 3D modelling capabilities, makes me nostalgic for Battlechess on the Amiga - fun times! Argon is rolling out today to Google Ultra and Pro subscribers in the @GeminiApp and Search, and Ultra subscribers in @antigravity! More info & full availability in the blog: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/
The rollout details distinguish access across GeminiApp, Search, and antigravity, helping subscribers understand which subscription tiers include the model in each of those products.
Josh Woodward@joshwoodwardPicks74Nano Banana 2.1 is here! This upgraded version outperforms our previous models across the board, with notable leaps in: - visual design - mask-based editing - subject consistency - more natural-looking imagery Try it now in @GeminiApp, @GoogleAIStudio, and more.
The announcement frames the upgrade around visual design, editing, subject consistency, and natural appearance, providing a concrete comparison with previous models.
Arthur Mensch@arthurmenschPicks72Meet Mistral Large 4, aka Le Chonk. • 1T parameters, natively multimodal. 49B active. It is the best open weights model from US or Europe on aggregated benchmarks. • State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding. • Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure. • Available to all via API today. Working with cybersecurity partners privately. Open weights release end of October.
The announced API availability and end of October open weights release give teams distinct timelines for accessing the model through hosted infrastructure or its weights.
Noam Brown@polynoamialPicks66In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. 1/N
Brown’s recollection provides a concrete example of how a conference poster conversation led to an internship, continued research collaboration, and eventually a shared workplace.
Alex Albert@alexalbert__Picks74Introducing Claude Sonnet 5.5, the second model in the Claude 5.5 family. It’s a clear upgrade over Sonnet 5, runs more than 30% faster, and costs up to 30% less for most work.
The comparison highlights writing clarity and iteration speed alongside the announced pricing change, offering a concrete perspective on the upgrade from Sonnet 5.
Kevin Weil 🇺🇸@kevinweilPicks79New on the Science Blog: Yes, Claude can do Nine Loops. Theoretical physicists predict how particles behave using formulas called scattering amplitudes. These are notoriously hard to compute, so researchers work with layers of increasingly fine corrections called “loops”—each added loop makes the answer more precise but takes exponentially more computation. Most calculations stop at two or three loops. Eight loops was the previous record in a simplified model physicists use as a testing ground (planar N=4 super-Yang-Mills), set by SLAC's Lance Dixon and collaborators. Last month, physicist and science writer @4gravitons issued a challenge: could an AI push past eight loops in this model, using only the compute budget an academic could reasonably access? Given a single prompt describing the nine-loop problem, Claude ran largely unsupervised for days in Claude Science and solved it using methods developed by Dixon and his colleagues, at a total cost of a few thousand dollars. Dixon independently verified the result, and von Hippel wrote about the experience for our blog. Read more: https://www.anthropic.com/research/yes-claude-can-do-nine-loops
The reported cost of a few thousand dollars offers a concrete reference point for assessing AI-assisted theoretical physics calculations within an academic compute budget.
Hugging Face has released a report on the open-model ecosystem from January through August 2026, drawing six findings from Hub data. Public model repositories grew from 2.43 million to 2.96 million and datasets from 711,000 to one million, but 85.6% of models have fewer than 200 lifetime downloads, while 1.5% of repositories account for 99.2% of downloads.
Hugging Face analyzes its Hub data on the open-model ecosystem; findings on downloads versus likes and the scale of Qwen derivatives can inform model selection.
Aditya G. Parameswaran and collaborators outline how near-free inference could change data systems through agent workloads, infrastructure for agent coordination, and systems synthesized by agents. Their perspective examines overlapping SQL queries, structured corrective memory, shared-state management, and workload-specific system generation. Drawing partly on their ongoing research, they identify incomplete specifications and correctness verification as central challenges for agent-generated systems.
The reported overlap among agents’ SQL subqueries connects agent workload growth to established database optimization techniques, offering a concrete basis for rethinking how data systems serve agents.
