Entries matching “documents”

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.

@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.

@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.

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.

# 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**. - **

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

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.

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

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

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.

@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.

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.