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  4. Meta Releases Muse Code Terminal Agent and Muse Spark 1.2 Model
Tools & releases

Meta Releases Muse Code Terminal Agent and Muse Spark 1.2 Model

August 6, 2026· 4 min read
OKCurated by Oleksandr Kuzmenko, AI Product Engineer·Updated August 6, 2026·Sources cited on every story
AI-assisted · editor-reviewed·How we use AI
Meta Releases Muse Code Terminal Agent and Muse Spark 1.2 Model

Meta released Muse Code, a CLI terminal coding agent with persistent async background subagents and restart-safe local logging. Powered by the co-trained Muse Spark 1.2 model, it automates long-horizon tasks and Triton GPU kernel optimization.

Impact: High

Why it matters

Engineers can install a restart-safe terminal agent with specialized slash commands for repository-level tasks and custom GPU kernel tuning.

TL;DR

  • 01Muse Code uses an append-only event log to ensure replay-exact session recovery after crashes.
  • 02Async background subagents remain active across tasks, avoiding redundant context collection.
  • 03Default slash commands include /plan, /grill for stress-testing, and /goal for long-running targets.

Key facts

Optimization Horizon
1,000+ tool calls (up to 24 hours)
Target GPU Architecture
NVIDIA Hopper (Triton KDA / MLA kernels)
Installation Method
curl -fsSL https://dev.meta.ai/install.sh | bash

Terminal-First Agentic Architecture

Muse Code operates as a CLI application powered by Meta's Muse Spark 1.2 model. The agent uses a local event log that records every model call, tool execution, edit, and user approval in an append-only format, ensuring replay-exact session restoration after interruptions.

Slash Commands and Workflow Orchestration

Built-in workflow triggers include:

  • /plan: Generates approval-gated task plans before code execution.
  • /grill: Stress-tests generated plans against edge cases and failure modes.
  • /goal: Directs persistent subagents toward targeted objective completion.

Autonomous Triton Kernel Optimization

During evaluations on NVIDIA Hopper GPUs, Muse Spark 1.2 continuously generated, compiled, and benchmarked Triton GPU kernels over 1,000+ tool iterations spanning up to 24 hours. The model implemented chunk-parallel preparation kernels and sequential inter-chunk scans for KDA and MLA algorithms without external library imports.

Try it in 2 minutes

curl -fsSL https://dev.meta.ai/install.sh | bash

bash

✓ When to use

  • Running multi-step coding workflows across large repositories in the terminal.
  • Iteratively writing and benchmarking low-level GPU kernels in Triton.
  • Long-running tasks that require persistent subagent coordination and crash recovery.

✕ When NOT to use

  • Quick single-prompt edits that do not justify background agent execution.
  • Environments requiring verification without Meta account setup.

What to do today

  • →Install Muse Code via dev.meta.ai install script.
  • →Test /plan and /grill commands on a sub-module before committing large context runs.

What the community says

  • “And they are all TUI's installed via curl | bash.”

    — WASDx on Hacker News

#Muse Code#Muse Spark 1.2#Meta Model API#Triton

Sources

  • Introducing Muse Code and Muse Spark 1.2
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