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12 entries with this tag
One major article published: Meta Muse Wonder & Wonder Full deep-dive — Meta's first frontier models with Active Memory, Active Focus, and state-of-the-art long-running agentic coding (SWE-Marathon 57.5, Harvey LAB 13.1).
On October 12, 2026, Meta Superintelligence Labs released Muse Wonder and Muse Wonder Full — Meta's first frontier models. Trained at the gigawatt-scale Prometheus data center, Wonder Full scores 56 on the Artificial Analysis Intelligence Index (tying Claude Sonnet 5.5), leads on SWE-Marathon (57.5), Harvey LAB legal tasks (13.1 vs. Opus 5.5's 4.2), and DeepSWE 1.1 (79.0). Wonder is available via Meta Model API at $4/$20 per million tokens; Wonder Full is exclusive to Muse Code subscriptions ($50/$200/month). Both models feature Active Memory and Active Focus for sustained, multi-hour agent work.
One new research article published: comprehensive deep-dive on Meta's coordinated August 2026 release of Muse Spark 1.2 (coding-focused frontier model), Muse Code (terminal-native multi-agent coding tool), and Muse Glimmer (30B open-weight local agentic model). Covers the complete agentic coding stack from frontier API to local edge inference.
Meta released Muse Spark 1.2 (coding-focused model with 1M context), Muse Code (terminal-native multi-agent coding tool), and Muse Glimmer (30B open-weight local agentic model) in early August 2026. Covers architecture, co-training methodology, benchmark performance, pricing tiers, and the complete agentic coding stack.
One new research article published: comprehensive analysis of Meta's Muse Glimmer 30B — a distilled local-first agentic model running on consumer hardware with Apache 2.0 licensing and DFlash speculative decoding.
On August 10, 2026, Meta released Muse Glimmer — a 30B-parameter multimodal agentic model distilled from Muse Spark, released under Apache 2.0, and optimized to run on a single consumer GPU. Covers the distillation pipeline, DFlash speculative decoding, 3.1x speedup on RTX 5090, benchmark results against Gemma4-31B and Qwen3.6-27B, the safety evaluation framework, and strategic implications for the local agent ecosystem.
One new research article published: Meta's Muse Spark 1.2 and Muse Code release — a co-trained model+harness system with persistent async background agents, replay-exact event logging, and a controversial $0.10/M contributor tier that trades data rights for ultra-low pricing.
On August 5, 2026, Meta released Muse Spark 1.2 and Muse Code — a coding-specialized model co-trained with its own terminal agent harness, featuring persistent async background agents, replay-exact event logging, and a controversial contributor pricing tier at $0.10/M input tokens in exchange for training data rights. Covers the co-training methodology, benchmark results (82.9% Terminal-Bench 2.1, 59.3% DeepSWE 1.1), the GPU kernel optimization case study, the two-tier pricing strategy, and strategic implications for the agentic coding landscape.
One major research article published: comprehensive deep-dive on Meta's Muse Image launch — agentic image generation with tool use and self-refinement, the Muse family strategy (Spark → Image → Video → Watermelon), Superintelligence Labs under Alexandr Wang, Meta Compute cloud announcement, and the $125-145B capex bet.
On July 7, 2026, Meta launched Muse Image — its first in-house image generation model from Meta Superintelligence Labs — featuring agentic tool use, self-refinement, and test-time compute scaling. The launch completes Meta's Muse family alongside Muse Spark (reasoning) and Muse Video (in development), while internal reports reveal the next 'Watermelon' model has reached GPT-5.5-level benchmarks. Combined with the Meta Compute cloud announcement and $125-145B capex, Meta is executing its most aggressive AI push.
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