Official Technical Resources: Meta AI (page 5)
Official Technical Resources · Meta AI: 141 entries in total; this page lists entries 61–75.
Streaming responses
Render Muse Spark tokens as they generate and read the final usage chunk.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Model Deployment Options · AI Agent · Tool Calling
Link last checked: 2026-09-24
Subagent fanout
Fan one job out to parallel Muse Code subagents, each in its own isolated git worktree.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: AI Agent · Workflow Orchestration · Multi-agent Orchestration
Link last checked: 2026-09-24
Speech to text
Transcribe live audio or an existing recording with Muse Voice Transcribe — streaming over a WebSocket, dictating from a microphone, labeling speakers with diarization, or posting a whole file in one HTTP request.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Multimodal Models · Speech Recognition and Synthesis · Multimodal Models
Link last checked: 2026-09-24
Control Apple Chess with Voice
Turn exact spoken chess moves into locally validated Apple Chess actions with Muse Voice Transcribe — speech-turn detection and vocabulary biasing feeding a deterministic parser and a fail-closed macOS computer-use layer.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Multimodal Models · Speech Recognition and Synthesis · Multimodal Models
Link last checked: 2026-09-24
Muse Code
Muse Code is Meta's coding agent for the terminal and CI, built for Muse Spark, with approvals, sandboxing, sessions, and multi-agent orchestration.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Large Language Model (LLM) · AI Agent
Link last checked: 2026-09-24
Configuration and context
Configure Muse Code with the settings file, project instruction files, model and reasoning-effort selection, launch flags, and durable project memory.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: AI Agent · Workflow Orchestration · Multi-agent Orchestration
Link last checked: 2026-09-24
Extending and automating
Scale Muse Code beyond a single interactive session — parallel subagents, reusable skills, lifecycle hooks, MCP servers, and headless runs for CI.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Large Language Model (LLM) · AI Agent
Link last checked: 2026-09-24
Permissions and safety
Control what Muse Code can do with approval modes, stage-by-stage shell-command review, scoped trust, and an OS-enforced sandbox.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Large Language Model (LLM) · AI Agent
Link last checked: 2026-09-24
Rewind a conversation
Reopen an earlier Muse Code message in a new conversation branch without changing the original session or workspace files.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Large Language Model (LLM) · AI Agent
Link last checked: 2026-09-24
Coordinate sessions with messages
Name Muse Code sessions and send local messages between them for handoffs, review requests, and status updates.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: AI Agent · Workflow Orchestration · Multi-agent Orchestration
Link last checked: 2026-09-24
Run multi-agent workflows
Use Muse Code workflows to coordinate parallel agents, monitor their progress, and save repeatable multi-agent tasks.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: AI Agent · Workflow Orchestration · Multi-agent Orchestration
Link last checked: 2026-09-24
Muse Glimmer
Open-source multimodal model distilled from Muse Spark, built for local and edge deployment.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Large Language Model (LLM) · AI Agent
Link last checked: 2026-09-24
Deploy with ExecuTorch
Export Muse Glimmer ahead of time and serve it on CUDA or Apple silicon with vision, tool calling, and DFlash speculative decoding.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Model Deployment Options · LLM Inference Optimization · Model Quantization
Link last checked: 2026-09-24
Fine-tuning
Fine-tune Muse Glimmer with LoRA, QLoRA, or full-parameter supervised training for your domain.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Fine-tuning · Reinforcement Learning · Model Quantization
Link last checked: 2026-09-24
Deploy with llama.cpp
Run Muse Glimmer locally with llama.cpp for CPU, mixed, and GPU inference.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Meta's own implementation and claims. Recheck preview products, pricing and API versions before use.
Related concepts: Model Deployment Options · LLM Inference Optimization · Model Quantization
Link last checked: 2026-09-24