Entries matching “requests”

Holdy

@Ryan_Holdaway

Got Muse logged in to my medical provider’s portal, he pulled the itemized bills, and questioned every line. So far he’s found several times I’d been double billed, asked for some discounts and has saved me over $4,000. If your moat is bureaucracy, you’re cooked.

@ArshansGithub

@ArshansGithub

An OpenAI Responses-compatible shim that serves the Muse assistant as a stateless HTTP model backend for Claude Code via CLIProxyAPI, one model turn per request.

@AshutoshKD

@AshutoshKD

An Express-based Muse connector that books Uber rides from natural language, with routes for fare estimates and ride requests, connector auth middleware and a privacy page.

@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.

@kamellperry

@kamellperry

A reverse-engineered local CLI and FastAPI wrapper that talks to Meta AI's Muse Spark and exposes an OpenAI-compatible /v1/chat/completions endpoint, with a browser playground and a SQLite request ledger.

@JacobStephens2

@JacobStephens2

A reproducible evaluation of a stochastic defect where Muse Spark 1.1, under some agent-like request envelopes, wrote to claude-smoke.txt when asked for muse-smoke.txt; a neutral control kept 80/80 filenames intact.

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.

You know the trick where you read a DM from the notification shade so it never shows as read? Muse does that for your Instagram DMs, except it can read all of them. I asked it to screen my unread Instagram threads this morning. It came back with 50 unread, peeked at the ones I named, and told me what people sent. Unread count was still 50 afterward. Nothing flipped to read on my end or theirs. If you have a creator or professional type account Instagram splits DMs into folders (Primary, General, message requests) and it can pull from any of them. An important use case for me are instances in which there's an unread message from someone I don't want to engage with, but there's a particular text in our history I want to retrieve. If you're new here and don't know how any of this works, first you have to add Instagram and Instagram Messages as Connectors in the Muse settings, then log into your Instagram. From there, just ask Muse.

Built a small bridge so Muse can act on my Mac from my phone, and recorded a real session: it looks at the Terminal, reports the epoch, loss and accuracy it sees, then locks the machine when asked. What struck me building it is how much of the work is permissions, not intelligence: per-action consent, small window captures instead of a live feed, rejecting stale observations before any input. Free beta if anyone wants to try it. Developer here, ask away. try wand here today

We've been curious how far local models have actually come for agentic coding tasks, so we ran an experiment. Setup: • Model: Muse Glimmer (30B), packaged as a single llamafile • Agent: Hermes coding agent (connected via llamafile's local server mode, zero API keys needed) • Target: Mozilla AI's Otari gateway The Issue: We pointed Hermes at a real, reported bug in Otari (#183) where the gateway returned a vague 502 error on image requests instead of passing through the actual provider error. What the Agent Did: Hermes read the issue, navigated the repo, isolated the bug, created a branch, ran existing tests, wrote a new regression test, and opened a draft PR (#727). All of it ran locally and offline, with zero code written by hand. It's still draft PR territory rather than a merged fix, but it's a solid signal that ~30B local models are getting genuinely capable for real dev workflows, not just toy demos. Video walkthrough of the run: https://youtu.be/5GAgbT-XgHU?si=vJqEDGm9hssCO5-M Happy to answer questions about the setup, model performance, or how Hermes handled tool calling!

Confucius

@ConfuEth

Asked @Muse to file pothole repair requests with the county. Took 30 seconds, zero hassle. Honestly assumed they'd disappear into a void. Five days later: all fixed. 🤯

Cristian Uibar

@cristianuibar

@theo please add support for Muse Code in T3. I've opened a PR here for it using the official SDK. github.com/pingdotgg/t3co…

DHH

@dhh

Got muse cli hooked up for both default agent action and lazy-loading. Come give it a try, @finkd 🙏 github.com/omacom/omarchy…

🚨 AI News | TestingCatalog

@testingcatalog

Since the Meta Muse agent isn't available to everyone, here is a quick UI walkthrough of its web version. Highlights 👀 > The animated Muse avatar is very cool! You can ask Muse to change how it looks, and it will generate the new look along with all the animations. >

@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.

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%).

Needs a lot of requests compared to Qwen (almost twice) and Gemma (almost x3). Final score is fine, even though it is "not a coding model" https://wonderrico.github.io/local_llm_benchmark/benchmark-main.html more details on https://wonderrico.github.io/local_llm_benchmark/benchmark-detail.html let see Qwen 3.8 tomorrow...

"I asked Meta’s Muse to go to YouTube, find a Zuckerberg video about Muse, cut the specific part I wanted, add burned-in subtitles, render it and give me an X-ready video. And it did the whole thing. It searched → found the clip → edited it → added subtitles → rendered → delivered the final file. I didn’t edit anything myself. That’s what makes Muse interesting to me: you’re not just chatting with AI anymore. You’re giving it actual WORK and letting it execute while you do something else. Still very early, but this is a completely different experience." from LorenzoBolsa

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.