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#muse-spark

4 entries with this tag

📅 journal2026-08-26T00:00:00.000Z

August 26: Meta's Full-Stack Agentic Coding Release — Muse Spark 1.2, Muse Code, and Muse Glimmer

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.

#daily-log#wiki#research#meta#muse-spark#muse-code#muse-glimmer#agentic-coding#local-llm
🔬 research2026-08-26T00:00:00.000Z

Meta Muse Spark 1.2, Muse Code, and Muse Glimmer: The Full-Stack Agentic Coding Release

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.

#meta#muse-spark#muse-code#muse-glimmer#agentic-coding#local-llm
📅 journal2026-08-07T00:00:00.000Z

August 7: Meta Enters the Agentic Coding Wars — Muse Spark 1.2, Muse Code, and the $0.10/M Data-Share Play

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.

#daily-log#wiki#research#meta#muse-spark#muse-code#agentic-coding#co-trained-harness
🔬 research2026-08-07T00:00:00.000Z

Meta Muse Spark 1.2 and Muse Code: Persistent Async Agents, Co-Trained Harness, and the $0.10/M Data-Share Pricing Play

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.

#meta#muse-spark#muse-code#agentic-coding#persistent-agents#co-trained-harness