Yandex released AliceAI-Foundation-80B, a fully custom 80-billion-parameter base model built without borrowing from existing open architectures. The model, posted to Hugging Face, uses a mixture-of-experts design with 3 billion active parameters per forward pass - the same efficiency pattern as Qwen's small MoE models - but on a custom architecture Yandex built from scratch. It is a base model only, meaning no instruction tuning or chat fine-tuning has been applied yet. There is no llama.cpp support at launch, which limits local deployment options for now. It positions itself against similarly sized MoE models from Chinese labs. (r/LocalLLaMA)
Brief
Tuesday, September 22, 2026 · 5 min read
Muse Beats ChatGPT's Launch Numbers, Then Hits a Wall
Good morning. Meta's Muse is beating ChatGPT's early download numbers while Amazon quietly locks it out of its own storefront. Yandex dropped an 80-billion-parameter base model that runs on a fully custom architecture nobody has seen before. And Alibaba announced a model somewhere between 5 and 10 trillion parameters, which is either a serious engineering ambition or a very large number in a press release. Busy Tuesday.
Today's reading time is 5 minutes.
INDUSTRY
Consumer AI agents live or die on distribution, and the biggest e-commerce platform in the US just decided it is not interested in helping a competitor's agent shop there.
Driving the news: Meta's Muse AI agent has outpaced ChatGPT's early mobile traction in the US and Canada, racking up more downloads and daily active users over the same post-launch window, according to Appfigures estimates. At the same time, Amazon has blocked Muse from accessing Amazon.com, cutting off one of the most obvious use cases for a shopping-capable agent. Amazon runs its own foundation models and one of the most-used inference platforms on the internet, so it has both the motive and the infrastructure to keep Muse out.
- Muse's download and DAU figures in the US and Canada exceed ChatGPT's over the equivalent post-mobile-launch period, per Appfigures.
- Amazon is under no legal obligation to grant Muse access, and has not done so.
Zoom in: The tension here is structural. An AI agent that can browse, compare, and buy on a user's behalf is only as useful as the sites it can reach. Amazon controls roughly 40 percent of US e-commerce by most estimates, which means blocking Muse is not a minor inconvenience - it is a meaningful capability gap. Meta built Muse to operate across the open web, but the open web increasingly has gatekeepers with their own AI products.
- ChatGPT's mobile debut is the closest comparable launch in consumer AI history, making Muse's outperformance a meaningful benchmark.
- The block applies to Amazon.com specifically; the TechCrunch report does not detail whether other Amazon-owned properties are affected.
Why it matters: If you are building on top of Muse or any third-party agent platform, Amazon's move is a reminder that access to major retail data is not guaranteed and cannot be assumed in product design. Workflows that depend on agents pulling live pricing, inventory, or product data from Amazon need a fallback. The agent era's biggest friction point may not be model quality - it may be which doors stay closed.
Bottom line: Muse is winning the download race and losing the shopping war at the same time, which is a reasonable summary of where AI agents actually are right now.
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Also happening
Alibaba announced Qwen 4 at its Apsara Conference and separately disclosed plans for a model with 5 to 10 trillion parameters, alongside a new in-house chip. The Qwen 4 announcement came with few technical specifics in the community reports, but the parameter count for the planned future model - 5 to 10 trillion - would dwarf anything currently public. Alibaba also unveiled a new AI chip at the same event, continuing a pattern of Chinese labs building their own silicon as export controls tighten on US hardware. The community has also noted that the expected small MoE model in the 35B-A3B range was not announced, leaving that slot open. (r/LocalLLaMA)
OpenAI formed an independent advisory group on mathematics and AI to review and communicate emerging results in the field. The group is described as independent and focused on guiding how OpenAI reviews and communicates math-related AI findings - a credibility layer for results that are otherwise hard for outside researchers to verify quickly. OpenAI did not announce specific members or a publication timeline in the initial post. The move comes as math benchmarks have become a primary arena for model capability claims, making independent review more consequential. (OpenAI)
oMLX creator Jun Kim joined Hugging Face to support the MLX community full-time. oMLX is a widely used toolkit for running models on Apple Silicon via Apple's MLX framework, making it a key piece of infrastructure for local inference on Macs. Kim moving to Hugging Face means the project gets institutional backing rather than depending on a solo maintainer. For anyone running models locally on Apple hardware, this is the kind of staffing news that tends to mean faster updates and better compatibility over time. (Hugging Face)
The thread
Distribution gaps are the new capability gaps
Two of today's biggest stories are not really about model quality. Muse is outperforming on downloads but getting blocked at the storefront level, while Yandex shipped a capable base model that cannot yet run in the most common local inference tools. In both cases, the model exists and works - the constraint is access, not architecture. For builders, that means evaluating an agent or model now requires asking not just what it can do, but where it is allowed to go.
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On our radar
- Xiaomi released MiMo-V2.6-Flash-RL on Hugging Face, a reinforcement-learning-trained model in its MiMo series, with minimal detail on benchmarks at launch.
- A new paper proposes Attention-Aware Routing for mixture-of-experts models, giving the router access to temporal and contextual signals rather than just the current token's hidden state.
- RBS-Attention, a new sparse attention method for long-context inference, targets the prefill bottleneck by using radius-bounded block selection to avoid missing relevant tokens that fall outside block centroids.
- V7 Labs published a case study with OpenAI showing how its platform uses GPT-5.6 to turn internal company documents into source-linked context for AI agents handling complex tasks.
- A research paper on ARC-like benchmarks argues that current evaluations cannot distinguish whether a model learned the intended rule or found a shortcut, and proposes test-time task embeddings to separate the two.
- New research on fine-tuning LLMs finds that internal representational changes and actual causal importance to outputs do not always move together, complicating assumptions about what fine-tuning actually does to a model.
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