Entries matching “writing”

kwindla

@kwindla

Weekend project: play chess with Muse Spark running on a DGX Spark with code Muse Spark wrote. Setup: - projector - HDMI, camera, mic, and display server (Raspberry Pi) - pipecat bot (DGX Spark) - vision pipeline (RTX 5090) - coding agents running on the Pi, the DGX

Muse already comes with connectors for popular services. When the one you want isn't on the list, you just ask Muse to build the integration. It writes the software itself and runs it on the cloud computer you share with your Muse. If the service has an API, Muse can talk to it. This demo hooks up Linear, the project management tool, in under a minute. Muse takes the API key, builds a connector that can list teams, search and create issues, and update their status, then verifies everything with a quick identity check. Two things from follow-ups worth knowing: • API keys go into secure storage through a dedicated prompt. The agent never sees them. • Custom connectors fall under the same human-in-the-loop approvals as everything else, so sensitive actions still ask you first. Has anyone here built a custom connector yet? What did you hook up, and what are you using it for?

After noticing that it is ranked among MUCH larger frontier models in the EQ-Bench Creative Writing benchmark and the Hemingway-bench, I decided to give it a try and was very impressed. I didn't do very formal testing, but I did ask it to emulate the style of several different writers, including Henry Miller, David Sedaris, and Stephen King, and it produced passable prose that actually made me laugh in a couple of cases. The paragraph below is the results of the prompt "produce a humorous paragraph in the style of David Sedaris." (first try, not cherry picked) I recently tried to be a better person and started composting, which mostly means I now have a small, damp science experiment in my kitchen that my mother calls “the smell of your future.” I bought a countertop bin with a charcoal filter, as if that would fool anyone, and I’ve taken to narrating my food scraps to myself — “Goodbye, avocado skin, you were a mistake” — while my husband watches from the doorway with the concerned expression of a man who has just realized he married a woman who talks to garbage. The city sent a pamphlet about proper composting, and I read it the way other people read horoscopes, underlining th

Louis-François Bouchard 🎥🤖

@Whats_AI

We just measured Meta Muse Spark 1.3 on our internal writing benchmark hoping for new SOTA. Unfortunately, it isn't... It enters at #24 of the 87 models we track, up from #31 for Muse Spark 1.1. As @alexandr_wang highlighted, it beats every Gemini configuration we have,

@gregmos

@gregmos

Routing overlay for the Superpowers plugin for Claude Code where Muse writes the code, Codex reviews it, and rules keep expensive models from being misused.

A DPO fine-tune of Muse Glimmer focused on prose quality and humanlike characters, trained on roughly one-third writing and two-thirds roleplay data.

A Muse Glimmer fine-tune that writes, tunes and translates detection rules (Sigma to KQL and SPL, YARA, Wazuh) and runs 4-bit on a single 24 GB card.

@caicaitou001

@caicaitou001

A config and write-up that makes muse-spark-1.2-contributor via OpenCode Go work in DeepSeek Harness, fixing empty first-turn output and multi-turn thinking replay errors.

thehype.

@thehypedotnews

muse glimmer 30b vs nemotron 3.5 lightning – on agentic coding two 30b open-weight agent models, released three days apart, both sold as the model you leave running. same harness, same tools, same tasks, two of them: repair a broken landing page, then write breakout from a spec

@rajkumar898

@rajkumar898

An educational ~1,600-line Python chat app on Muse Spark that finds papers, reads PDFs and writes a cited report only after the user clicks Approve, built to show students how an LLM app becomes an agent.

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.

@High-Performance-AI-Lab

@High-Performance-AI-Lab

A 40-chapter book on Muser, an engine that runs Muse Glimmer on Apple Silicon Metal. It covers kquant and DFlash speculative lanes, exact KV-cache replay with kvpack, and GB10 NVFP4 prefill handed off to a Mac, with every number tied to an evidence receipt.

theres simply no other AI agent or tool that can do this for you for free PROMPT # Story Archive Census ## What to do Your human wants a full census of their Instagram story archive. Read this entire file, then follow the steps in order. The job is a census: walk every page and count every story, newest to oldest. It does not download any media. What you're working with: **Tool** — the Instagram connector's command line tool, `instagram-cli`. **Command** — one call returns one page of archived stories (about 20 per page), newest first. Each page hands back a `next_max_id` cursor pointing at the next page: instagram-cli own-stories-archive --account-id <YOUR\_ACCOUNT\_ID> ## Workflow ### 1. Walk the archive - Fetch the first page with no cursor. - For each page, record the page number, the story count, the newest story date, the oldest story date, and the `next_max_id`. - Feed that `next_max_id` back as `--max-id` on the next call, and keep going. - One call at a time, a second or two apart. No parallel calls. ### 2. Save progress - Write the page records plus the latest cursor to a state file every 10 pages, so the run can resume after an interruption instead of sta

Unsloth AI

@UnslothAI

2-bit Muse Glimmer GGUF managed to call 100+ tools on just 14GB RAM. 🔥 Muse Glimmer did a complete repo bug hunt for 5 mins nonstop with: evidence, repro, fix, tests and a PR writeup. Run and train it in Unsloth. GitHub repo: github.com/unslothai/unsl…

Raj

@hrhraj

Took your custom-connector idea to the physical world: the "service with an API" was my robot. Muse wrote the connector for my rover's API, then installed PyTorch & Depth Anything V2 in the VM because the camera is 2D, drove to the black ball and stopped a few inches short.

Meta's engineering write-up on Muse security: isolated VMs, a separate Sentinel permission authority, credential surrogation and layered prompt-injection defenses, with bug bounties up to $300,000.

David Singleton

@dps

Power user @Muse feature - custom connectors 🔧. Muse has tons of connectors for popular services built in. Even better, Muse can write its own software and run it in the VM you and your Muse share in the cloud. So you can connect Muse to any service with an API.

@star-power0

@star-power0

muse-fileapi is a dependency-free local HTTP service exposed via Cloudflare Tunnel that lets Muse, through a custom connector, list, read and write files in whitelisted directories with two-phase writes and an audit log.

Artificial Analysis' independent benchmark write-up of Muse Spark 1.3.

@accretional

@accretional

Unofficial knowledge base and tools for Meta's Muse Spark, starting with a root-cause write-up of Codex tool-calling failures with Muse Spark 1.1.

Alexandr Wang

@alexandr_wang

we’re proud to be partnering with @1Password so Muse will be able to work with the logins you already have. more details on how we built all of this here: security.muse.ai

Mark Zuckerberg

@finkd

Releasing Muse Code in beta today. It's a terminal coding agent that takes on complete software engineering tasks across large repos: planning changes, writing code, validating the results. Powered by Muse Spark 1.2, a coding-focused model update.

Martin

@MartinMoltke

Have a severe acquired mitochondrial disease that leaves me w no energy & used it to draft & send emails. It was complex med thing I didn’t have brain energy to put together & it created 3 detailed emails 4 me.Tyvm really good.Now pls solve disease :)

I'm developing a detailed history book, I'm 333 pages deep, I have used my whole weeks limit and am 8 million deep in my free 1 billion tokens.

@Sighton-GH

@Sighton-GH

opencode-delegate is a Claude Code plugin where Claude plans and writes briefs while opencode sessions on free models, Muse Spark 1.3 by default, implement and review each task.

Mika Reyes gives a five-step setup for Muse: import memory, connect Instagram/Facebook/WhatsApp/Threads, enable WhatsApp access, build a daily feed and write down your goals.

AI at Meta

@AIatMeta

Muse Glimmer can complete multi-step agentic tasks end-to-end from a single natural language prompt. In this demo, it autonomously discovers a local Home Assistant instance via network tool calls, queries device APIs, writes a responsive HTML/CSS/JS dashboard from scratch, and

Parallel gives a six-step method and a template prompt for having Muse write, test and save its own connector for any public API, CLI or MCP server, using the Parallel Search MCP as a worked example.

AI/ML API

@aimlapi

Muse Glimmer one-shot 5 games! @AIatMeta dropped Muse Glimmer. So we ran it against Muse Spark 1.2, on five one-file game demos, each one written to play itself: Tetris, a top-down pixel street race, a rooftop web-slinger, a blue hedgehog platformer, and Flappy Bird. the setup:

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.

OK, for context, I have Claude Code desktop app driving the CLI and orchestrating the code and verification tasks to try to save tokens. So Claude runs things, a Deepseek 4.1 Flash (cloud) session does the planning, Qwen 3.8 27b Q8 does the boiler plate coding and Muse Glimmer sanity checks the code and pushes any issues back to Qwen. If there are issues Qwen and Glimmer can't agree on, Deepseek validates. If Deepseek can't sort it out, it goes back to Claude. This had been working fine, but then in the last few days token use spiked, tasks that used to take 10 minutes were taking an hour or more and Qwen started going into more and more reasoning loops. It seems that since I last checked (on 12th September) the CLI changed. I used to strip unnecessary tool calls from the prompt using --disallowedTools and enabledPlugins: false. It would seem that these no longer work. In the end I got Claude to build a request-dumping diagnostic server, that actually measured the payload bytes, and confirmed --tools (an allowlist) is the flag that works now: 55→7 tools, 161KB→24KB, byte-verified. It also caught something specific to my workflow running the wrapper from inside an already-active