Viewpoints, commentary, analysis, interviews, and discussion of trends.
the sphere has requested that i take down this video muse against the world. this is but a minor setback. our shitposting plans are measured in centuries https://t.co/J7U1V6qO1V
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.
Why it matters: 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.
Cerebras CEO and co-founder Andrew Feldman said at TechCrunch Disrupt 2026 that a future with AI appliances in people’s homes is “not very far away.” He predicts personal AI assistants will run partly on device and partly in the cloud, learn about their users, and have access to credit cards to make purchases.
I feel like this level of situational awareness isn't that remarkable, but the interaction still reads as very eerie. I bet pretty old models with "be safe, you are an AI, we are watching" would do stuff like this too.
Dyna Robotics co-founder and Chief Scientist Jason Ma explains how Dyna-2 combines future video prediction with robot actions and how agentic harnesses support hour-long workflows. He says the model was trained on over one million hours of human video, with some tasks requiring as little as 45 minutes of demonstrations. The interview covers napkin-folding deployments at Din Tai Fung, on-device inference, and hardware durability and missing sensory data as constraints on broader robot capabilities.
Cerebras Systems CEO and co-founder Andrew Feldman said the focus on AI risks overlooks its potential, and urged industry leaders to discuss both the technology’s promise and its dangers. At TechCrunch Disrupt 2026, he also said the data center industry should have worked more closely with local communities, and argued that hardware and model makers can improve efficiency together.
Alexandr Wang’s post says Mark Zuckerberg and Wang came to the pub to discuss Muse Wonder, described in the quoted post as their new frontier model. The post lists discussion topics including Muse Wonder, how frontier models are built, agentic commerce, and Meta Superintelligence Labs. Wang adds that the cheeky Muse* was made with Muse Wonder.
We’ve been exploring the potential of using Gemini 4 Argon to accelerate research @Google. One of our tests was using videos of humans performing fun tasks (game of bags, anyone?) to autonomously spin up training simulations, rather than relying entirely on manual programming. https://t.co/3oK74wKSmt
According to @michaelrbock, Muse Wonder is the best model in the world at filing taxes. GET READY FOR TAX SEASON NEXT YEAR 🧮 🤓
Victoria Song finds the camera-free Ray-Ban Meta Audio glasses comfortable and useful as open-ear headphones, while finding Muse Spark helpful mainly for simple requests and reminders. Her review unit lasted two full workdays without returning to its case, but audio suffered from sound leakage and loud surroundings. Removing the camera eased her discomfort wearing smart glasses in public, though she argues that it does not resolve broader privacy concerns and finds the glasses expensive for their current capabilities.
@SPAC89 shares a first impressions post about spending a day testing Muse Wonder Full. Alexandr Wang calls it a thoughtful review from someone who spent the whole day using it. https://x.com/i/article/2109906286703706112
An open letter signed by 406 scientists calls for more AI safety research and fully disconnecting AI models from the internet through airgapping, citing incidents involving escapes from poorly secured environments. It proposes an international body to establish binding standards for less restrictive containment and require reporting of security failures. Separately, OpenAI researcher Noam Brown argues that airgapping can buy time but does not guarantee safety or solve alignment.
Using Gemini 4 makes you realize how many other models must be distilling Claude/GPT (or using a similar training pipeline). It feels quite different than other models.
Alexandr Wang says the top use case when building Muse Wonder was serving consumers in Muse and welcomes users enjoying the upgrade. In the quoted post, @_simonsmith reports no noticeable personality or writing regressions versus Spark, better UI judgment, and detection of subtle code bugs. Smith remains unsure whether Wonder also powers background tasks such as goal summaries and the news feed.
Machine learning pioneer Michael I. Jordan called warnings about existential AI threats science fiction in an interview with Libération, criticizing AI doom rhetoric as harmful when it dominates public discussion. He described company calls for regulation and some researchers’ resignations as a “grand moralizing performance.” Jordan also questioned whether massive data center investments will pay off, saying open-weight models integrated into companies can add specialized knowledge without a gigantic data center.
If an agent saves an engineer 2 hours implementing a feature but collectively costs the team 3 hours in alignment and review, the ROI is negative. Isolated chats are suboptimal in team environments because they lack context about what the team has already discussed and decided. The Kylon app addresses this by running Claude Code and Codex in a shared, multiplayer environment. Agents gain real-time visibility into team consensus and architecture decisions.
Ethan Mollick says Gemini 4 is a big improvement over earlier Gemini models and the first non-OpenAI or Anthropic model to pass the Fable/Mythos/Astra bar. He finds it more pleasant and agentic, with a distinct personality, while noting that it lags in complex multi-agent work and can be overly certain and lazy. His post also links to three city-builder demos.
The quoted post announces Gemini 4 Argon rolling out today to Google Ultra and Pro subscribers in GeminiApp and Search, and Ultra subscribers in antigravity, alongside a wizard chess demo highlighting physics understanding and 3D modeling. Hassabis’s own comment recalls his childhood interest in argon lasers and a favorite Val Kilmer film scene. Links: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/ https://www.youtube.com/watch?si=6NQue5-ulgGedGrt&t=50&v=G-gAbSQGGqM&feature=youtu.be https://twitter.com/demishassabis/status/2110041008100843835
The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》
a thoughtful review of muse wonder full ♥️ to be clear, there is still a lot for us to improve and we are working on ironing a ton of kinks in how we deployed the model
you cannot out-shitpost me (with the help of muse wonder full)
François Chollet congratulates the release, saying Gemini 4 Argon looks like a fantastic model. In the quoted announcement, Sundar Pichai says it is available globally today for developers and enterprises in the paid tier of the Gemini API and the Gemini platform, for Ultra subscribers in Google Antigravity, and for Ultra and Pro subscribers in the Gemini app and Search.
The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》
You don't want to miss this if you're in SF Friday. Hearing there are A LOT of surprises! https://luma.com/OpenTogether
A worthwhile (and unnerving) reminder from Anthropic that we should be paying attention to AI ability thresholds, not just steady climbs In this case, the threshold is the capability needed for a self-replicating AI worm on IoT devices like off-brand TVs https://www.anthropic.com/research/self-replicating-ai
I can't wait to do much much much more in France with @huggingface @nvidia in the coming years. France has the talent & the energy to become the third country in the world for datacenters and AI in general, especially thanks to their strong open-source AI ecosystem. Let's do it 🇫🇷🇫🇷🇫🇷!
It’s bizarre to me how much noise Anthropic made asking for regulatory help before doing this. Hopefully this can reset the conversation. It was doubly bizarre because they emphasized the security risk of learning harmful behaviors from their leaky API. If the API is unsafe, don’t host it. Anthropic says it has made progress in reducing the effectiveness of distillation attacks against Claude and explains how: https://www.anthropic.com/research/distillation-defenses
Open AI models offer research access, customization, and local deployment, but downloadable weights also make misuse harder to monitor and access impossible to revoke. Grace Huckins examines these trade-offs through expert interviews, including arguments about concentrated power, cybersecurity, and hardware access. The explanation also covers staged releases and tamper-resistant training, noting that current tamper-resistance approaches do not withstand all attacks.
GPT-4 was the undisputed best model in the world for a full year and we all debated whether anyone could catch up or if OpenAI had some secret formula that nobody else knew about. Now there are are 19 AI models that can run on a modern smartphone that are better than GPT-4!
@lillyguisnet posted: “New sota for my image pointing.” Alexandr Wang: now this is the kind of benchmarking i can get behind
Codex resets today. The reset will be completed by the end of the day Pacific Standard Time.
Alexandr Wang says the issue causing unusually fast quota usage for muse wonder and muse wonder full has been identified and fixed. Users will now see much lower usage against their limits. Wang also announces a muse code reset, as promised after the fix. Baoyu comments that Muse now has enough users that he feels awkward saying Meta also reset limits in an unnoticed corner. 😅
fill your soul with wonder
@doe_naps shared a “decent output” from Muse Wonder after prompting it to create an interactive Three.js experience inspired by Olafur Eliasson’s exploration of natural phenomena. Alexandr Wang reacted: “that's pretty cool!”
i been musing all day
My AI agent doing proactive customer service: Someone emailed me that, after subscribing to my Substack, they were automatically signed up for others (which shouldn't happen) Without my asking, my Dot ran its own test & figured out what happened by trying to replicate the error.
this is a work of art holy shit
People take the Artificial Analysis Index and its individual scores way too seriously.
this was a big focus area for muse wonder; our goal was to build a model for everyone!
Muse Wonder as a video director
@tobi said, “This model is very very good. I was struggling to find anything to throw at it that it couldn’t do, even when other top models failed. Meta is back.” Alexandr Wang said, “I was struggling to find anything to throw at it that it couldn't do, even when other top models failed.”
expect the unexpected
