Entries matching “docs”

Andrew Brown's three-hour freeCodeCamp course on building apps and agent workflows with Muse Spark and the Muse Code CLI, ending in a full-stack Go/SQLite/Docker build.

I think a lot of people are boxing Muse in as “Hermes for normies.” My guess is Meta intentionally keeps the deeper capabilities out of sight because the target audience does not want a complicated Hermes/Grok Bot-style setup. They aren't trying to capture the nerdy Hermes using market, they want this to be an AI agent for normies but they don't limit it as such. What it advertises its for is far from what it actually can do with very little work. But Muse gets much more interesting once you stop thinking of its VM as the whole system. I joined the Muse VM to my Tailscale network, gave it SSH access to a restricted user on my home Ubuntu box, and now it can run commands and browser automation through hardware I already own. I figured out Muse can do this because I asked it if it had a Tailscale skill and it said it did, then after a series of "proceed" replies, it set it up for me outside of the parts where I had to join it. At that point, Muse stops being a “shopping assistant” and starts looking more like a control plane for your own infrastructure. With Tailscale + SSH/API access, it can potentially: • Manage Docker, VMs, NAS, Proxmox, and homelab services • Pull repos,

@gosukiwi

@gosukiwi

Docker Sandboxes (sbx) kit spec that runs Muse Code in YOLO mode inside an isolated sandbox via a musex command, verified with sbx v0.39.0 on macOS arm64.

armand

@armand_ruiz

Emailed the doctor's office, left a voicemail, filled the new-patient paperwork, got the confirmation back, added it to the calendar — ~5 minutes. @muse does the admin you'd have postponed for a month

I made a 400 page pdf html docs words txt from 400 Google docs any ai/agent app can't make it I Tried

OpenTools maintains a source-backed directory of about 163 Muse connector setup guides, separating Meta-documented, partner-documented and community skills (e.g. HubSpot, Attio, Ashby, Plaid, Slack), with status labels such as draft or maintainer-tested.

Unsloth's docs page for running Meta's Muse Glimmer 30B locally with its GGUF quants.

@fengyiqicoder

@fengyiqicoder

A serverless, read-only Muse connector hosted on GitHub Pages: an OpenAPI document plus JSON conversation playbooks for declining, apologising, negotiating, following up and other hard messages.

@mlmrx

@mlmrx

Independent open-source community and connector directory for personal agents: search connectors by outcome, share workflow prompts, and import an OpenAPI 3.x document to prepare a connector listing.

@AstroxNetwork

@AstroxNetwork

An Apache-2.0 template for Meta Muse connectors with per-connection tokens, async job scaffolding and a single registry that generates routes, OpenAPI and llms.txt, deployable to Vercel, Workers or Docker.

Just downloaded the model, UD-Q5_K_XL quant, asked it to generate a long story to test out reasoning and speed with dflash (super fast btw, ~ 90 to 160 tok/s on a 5090 depending on task) and was surprised at the reasoning traces, they are so unlike anything i've seen recently either in gemma 4, qwen 3.5/ 3.6 or laguna, where as these models to like plan stuff out, and have organized thoughts / plans (granted like half the time they just loop and get lost either way) this model's reasoning is like if a gold fish was suddenly granted speech or something, the reasoning is so disorganized, repetitive, using we for some reason? and bringing up policy and safety twice me : write a long story model : write a long story User wants a long story. We can comply. No constraints. Probably provide a long story. Might ask genre? Could just write a long story. Probably provide a story. Maybe ask what kind? The prompt is just write a long story. We can generate a long story. Probably a few paragraphs. Long story could be lengthy. Provide maybe ~1000 words? Could be long. Maybe give a story with decent length. We should not ask clarifying? Could just produce. Probably safe to produce a story.

@mahdi-salmanzade

@mahdi-salmanzade

A static teardown of Muse 3.0 for macOS plus Android and iOS builds, documenting shipped tools for iMessage, WhatsApp, Mail, screen control, background sync and a bundled Chrome extension.

Ben Parr

@benparr

Things I did with my @muse today: — Researched and got my mom's PODS container booked ($576 under their quote) — Built my 2025 tax doc tracker and hunted down the missing K-1s — Ran a two-week fare sweep for Thanksgiving flights I feel so much more productive.

@CogniTechSystems

@CogniTechSystems

This repo documents running Muse Glimmer 30B locally on an M4 Max MacBook via llama.cpp, benchmarking it with and without speculative decoding, and wiring it into Claude Code through LiteLLM for fully offline coding.

freeCodeCamp's write-up of its three-hour Muse course by Andrew Brown, covering model benchmarks, API integrations, agent frameworks, the Muse Code harness, and a Go/SQLite/Docker full-stack build.

AdaptlyPost walks through connecting its scheduler to Muse as a custom connector: give Muse the public OpenAPI spec and docs, then paste a bearer token into Muse's secure credential prompt.

HealthEx launched a connector that lets Americans bring their medical records to Muse, which can then write visit summaries, draft questions for a doctor and set refill reminders.

I've been migrating my workflow from ChatGPT Work / Codex over to Meta's Muse, and I've been auditing the harness as I go — reading cron files, checking diffs, mapping what the system actually does versus what it claims to do. It's a documentation gap. What the docs describe Meta's documentation is written for a normal user. It talks about the Ideas tab, the Goals tab, the Feed, and "background jobs that help Muse improve over time." Everything is described in terms of what it does for you. There is no architectural documentation. Nothing about how any of it is built. What is actually on disk Meanwhile, the home directory contains a fully legible agent architecture, just sitting there(these are just SOME examples, the system file structure is HUGE): • ~/dreams/alignment/ — a nightly job that regenerates an alignment synthesis: a written portrait of the user, how to handle them, current frictions, relationship guidance. • ~/workspace/objectives/goals/STUDYING.md — a daily learning-state projection with mastery bands, next-review dates, and retrieval prompts, plus a file of inferred goal leads the system guessed from behavior (with confidence levels and what would confirm or re

DataCamp's Josep Ferrer ran Muse Spark 1.3 on three real coding tasks. Two used 23–32% fewer completion tokens, but a refactor used 70% more, for a net 12% cost increase.

raunaq

@raunaqbn

Muse <> Muse Collaboration: I built a Muse connector for html-docs.com and it feels like @moltbook but for artifacts! (cc @MattPRD ) My friend and I got our muses (Chico and Bao) collaborating a Tahoe trip plan doc: commenting, editing, coordinating schedules and the

+1

Jamal Hinton

@MalGsx

I have something all men would appreciate and that’s having your agent call, email, and set up your doctor and dentist appointments lol. I wanted to try it with @Muse as a test run. It emailed the office, called and left a voicemail. They got back to me to confirm and that was

@shelajev

@shelajev

A Docker Sandboxes kit that runs Meta's Muse Code CLI in an isolated sandbox, allowing network access only to the Meta services needed for install, auth and model requests.

Bill D'Alessandro

@BillDA

Everyone hates taxes. But almost as bad as paying them is finding all the documentation - I dread it every year. My Muse agent just gathered nearly all of it from a single prompt. I'm so happy I might weep 😂 What would have taken me hours, about 90% complete, gathered in a

Vasanth

@vasanthroughput

A delightful #muse experience - I asked to find PCPs that take my insurance and book an appointment. Muse looked for doctors in zocdoc and gave me options. I chose one that didn't have the option to book online. Muse handed off to me to book it myself. Neat so far. Now the

# Hidden Capabilities — Deep Self-Scan Findings Live run: **September 23, 2026, ~08:00–08:30 UTC**, ~90 minutes, all probes benign and reversible. Verdict key: - **CONFIRMED** — exposed tool schema, successful benign probe, or exact internal documentation. - **PUBLICLY DOCUMENTED** — found in official external sources. - **ABSENT FROM PUBLIC SOURCES** — searched; no credible public mention found. - **UNVERIFIED POSSIBILITY** — inferred from filenames, gating manifests, or incomplete chatter only. --- ## 1. CONFIRMED — hidden / non-obvious (schema, probe, or internal docs) ### Phone & wearable superpowers (confirmed via `device.describe`) - **Full HomeKit control**: list homes/rooms/zones/accessories/scenes; read/change accessory characteristics; run scenes; ordered choreographies; concurrent virtual scenes; security sweeps (locks, contact/motion/leak sensors); geofence-triggered HomeKit actions. — **ABSENT FROM PUBLIC SOURCES** (official docs say only "smart-home devices"; Patrick Wardle's Sept 21 security demo is the only external mention of smart-home commands). - **Persistent/one-time geofences** with arrival/departure triggers. — **ABSENT FROM PUBLIC SOURCES**. - **

Muse

@Muse

Get more done with Muse with two new connectors: ✅ @meetgranola: your meeting notes ✅ @NotionHQ: your docs …and more coming soon! Ask Muse or connect in settings.

@meta-models

@meta-models

Meta's TypeScript SDK and docs for building clients that drive Muse Code agent sessions over the Muse Session Protocol, published as @muse-code/sdk with zero runtime dependencies.

Cline

@cline

We tried using Meta's new Muse Code agent, but it has a bug that doesn't let it sign in from a docker container. So we did a fun experiment: Meta claims Muse Spark 1.2 was co-trained with their Muse agent harness. So we extracted instructions from their system prompt and added

Ben Burtenshaw's Gradio agent runs Muse Glimmer on an Inference Endpoint with read-only Hugging Face MCP tools to search and inspect Hub models, datasets, Spaces, docs and papers.

Dilmer

@Dilmerv

Just tried something new with Muse and Meta VR CLI 🤯, and it’s insane that this works! But it makes sense because we’re literally getting a VM with our agent. I told the agent to install Meta VR CLI and use it to search our developer docs going forward. This is huge for VR/MR

@raksix

@raksix

A documented setup that runs Muse Spark 1.3 Contributor inside Claude Desktop on macOS through a small Node proxy that translates Anthropic requests to OpenCode Go's OpenAI-style API.

Had the Muse agent probe its own tool schemas and internal docs for ~90 minutes, finding undocumented abilities like full HomeKit control, geofence triggers, BLE scanning and wearable-routed calls, and that iPhone messaging is draft-only.

Tried to let a Muse agent's VM query Home Assistant on a home tailnet through Muse's documented tunnel proxy; every request died with 'empty reply from server' in ~8 s, and he shares the debugging so far.

LM Studio launched Muse Glimmer support, reporting it completed 83.3% of tasks on its 18-task BionicBench v0.1 versus 77.7% for Gemma 4 31B and Qwen 3.6 27B.

NVIDIA hosts a Muse Glimmer 30B endpoint on build.nvidia.com with Python (OpenAI, LangChain), JavaScript and curl examples for the ~29.6B multimodal model with 131K context.

The vLLM recipe page for Muse Glimmer has this for speculative decoding: --speculative-config '{"method": "dflash", "model": "meta-models/Muse-Glimmer-30B-assistant", "num_speculative_tokens": 15}' This errors out on the current vllm/vllm-openai:muse-glimmer image, and each fix reveals the next error. Six separate issues in total, all in the DFlash path. The base model runs fine without the spec config. The source for the image isn't public yet (the recipe says "code will be released soon"), so I pulled the image layers through the registry API and read the code to figure out what was going on. Also checked tensor names by range-requesting the safetensors headers off HF instead of downloading the weights. What I found: • The drafter's config declares MuseGlimmerAssistantModel, which is in vLLM's registry. But the dflash code renames it to DFlashMuseGlimmerAssistantModel before the registry lookup, and that name isn't registered. Dies in config validation. • vLLM maps the drafter's config to Qwen3Config (there's a comment calling it "Qwen3-shaped"). The muse JSON omits vocab_size and use_sliding_window, so Qwen3Config fills in its own defaults: vocab becomes 151936 (the model i

Compared diarization models on 15 mock doctor-patient consultations (~2.4 h): Meta Muse Voice Transcribe scored 13.04% DER at ~92 s per request via API, behind Pyannote (2.89%) and Nemotron 3 (4.80%).