Alexandr Wang posts “muse after seeing my email inbox”

Alexandr Wang thanked Meta Superintelligence Labs’ research teams after a week of launches, including Muse Wonder [full] and Muse Spark open. He said MSL and TBD began a little more than a year ago with the goal of producing a frontier model within a year or so, despite the ambition of that target given where Llama 4 landed. Wang said the teams have also built a scalable system for continuing to advance models at the frontier.

DeepSeek apologized for an API outage on October 15 and said it would return users’ API spending from October 8–14 as account credits. Users shared refunds ranging from a few cents to more than 3,000 yuan, depending on their usage. The company did not explain the outage’s cause; the report says the platform had experienced at least 18 widespread service disruptions so far this year.

Doubao Phone Assistant says it will gradually open third-party app GUI automation after the 30-day public comment period for its Screen Automation Protocol ends, but only for apps that have not refused. Its published refusal list names 151 apps, including WeChat, DeepSeek, Taobao, Kuaishou, and Meituan. Users can still install, log into, and operate those apps manually on the listed phones.

Modal crossed $1B in annualized run rate and raised a $750M Series D led by Accel at a $15.75B valuation, according to the company announcement cited by Latent Space. Usage grew more than 15x year over year. Modal’s Erik says Sandboxes alone reached a $300M run rate, with close to 3 billion sandboxes launched and support for a million concurrent sandboxes across uses including background agents and reinforcement learning.

Google announced that users can build personal Android apps with Google AI Studio and run them privately through Google Play using a standard Google account. The apps remain accessible only to their creators, must comply with existing Play policies, and will initially be available to U.S. users age 18 and up. Google says sharing with trusted friends will follow in the months ahead, with a path to public publication after trust and safety reviews.

Andrej Karpathy describes four tiers of LLM exposure, arguing that public perceptions lag behind capabilities experienced by frontier-system users. He estimates roughly six billion people have barely encountered LLMs, one to two billion use casual chatbot products, 20 million use frontier models professionally in math and code, and 5,000 access internal frontier systems. He claims those internal systems coordinate thousands of agents on projects that would take experts years, while human review falls behind, and attributes progress in coding and math to reinforcement learning on verifiable rewards and commercial training priorities.

muse after seeing my email inbox