Entries matching “automation”

Basil🧡

@LinkofSunshine

Just used Muse to call spectrum and negotiate my bill from $75 to $40 automatically. This feels like defecting but wowee

Frank Polek

@FrankPolek

This weekend, Muse: automated my social media accounts (not this post!); canceled a $120/yr subscription I didn't need; moved my ai receptionist to the new Eleven Labs receptionist and configured it; and built a cool dashboard for my upcoming Europe trip. @alexandr_wang

Bojo.io

@justinbojarski

I’m working on a hardware prototype for something that required me to source a LOT of components from different suppliers. I decided to take @Meta ‘s Muse agent for a spin and automate the procurement process. It seamlessly researched and procured line items across 14 different

@mitchallen

@mitchallen

A Makefile that downloads the three Muse Glimmer 30B GGUFs and builds and serves llama.cpp on Apple Silicon, automating a scriptable.com walkthrough.

Danny

@ohanaspeaking

It built a great training guide for a race I’m doing in December I asked it to update the guide whenever I go for a run and it made a Strava connector for itself to auto update everything

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Rajiv Karanam

@rajivkaranam

My new musecase @Muse My rental property manages itself now. My muse keeps the lease, insurance, and expense ledger current — weekly inbox checks, monthly expense report and automated emails to tenant, zero effort from me. One sentence was all it took. @alexandr_wang Thanks!

Lincoln

@WaitWhat

Okay this is insane. I always worry about getting my tires rotated because I don't keep track of the mileage. I just had my muse (Bob) call discount tire and find out what the mileage was. I've included the transcript from the call below. Muse also took care of setting up API

AI at Meta

@AIatMeta

Muse Spark 1.1 is used across Meta in coding and research workflows, scoring competitively with leading models on Meta's internal coding benchmark. Our researchers are now automating model development and evaluation tasks by leveraging Muse Spark 1.1 in their workflows.

ai.meta.com

ai.meta.com

Meta's Mac app lets Muse work with local files, Messages, Notes, Reminders, Mail and Calendar, with per-app Off / Read only / Read and interact controls.

developer.nvidia.com

developer.nvidia.com

NVIDIA's technical blog reports Muse Glimmer serving over 20K tokens/sec on a single Blackwell Ultra GPU and covers RTX 5090, DGX Spark, DGX Station and Jetson deployments.

I've been working on clinical speaker attribution at Omi and wanted to compare the current diarization models on the same audio. I used 15 mock doctor–patient consultations from PriMock57, about 2.4 hours. Full recordings, automatic speaker counts, without telling the models there are two people. Batch Diarization error rate (DER), with ±250 ms boundary tolerance. Lower is better. Model DER Median processing time Pyannote Precision-3 2.891% 18.9 s / recording (API) Nemotron 3 4.803% 0.688 s / recording Pyannote Community-1 6.620% 18.691 s / recording Sortformer v1 6.778% 3.869 s / recording Sortformer v2.1 7.974% 1.077 s / recording VibeVoice-ASR 8.233% 123 s / recording Meta Muse Voice Transcribe † 13.042% 92 s / request (API) Local models ran on one NVIDIA L4. API times include round-trip overhead; VibeVoice-ASR also performs transcription. † Muse used 20 separate clips because of its 10-minute request limit, so its result isn't a whole-recording comparison. Pyannote Precision-3 had the lowest error. Nemotron came next and was the fastest local model. Streaming Model DER Pyannote live API 3.959% Nemotron 3 † 4.971% Sortformer v2.1 † 6.958% VibeVo

Ported a Miniature Golf prototype to VR using Meta Muse Code and the Unity CLI, added dozens of automated tests, and built a website to capture test runs, screenshots and results.

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,

ShelbY

@manishini9

Meta @Muse automated my Instagram profile using simple prompts and scheduled posts for my professional profile. No words just zip out🤐🤐 Post context✅ Description ✅ Audio ✅ Editing ✅ Hastags ✅ Auto generated reel ✅ . . . 🙏🙏

@McClipFace

@McClipFace

Your AI, now a savvy shopper. Paste one prompt and Muse adds Clippy, which finds coupon codes automatically whenever Muse shops for you.

Sai Krishna Manapragada

@its_me_msk

Building an app for tales across the world to be heard in the voice of their loved ones muse.ai/s/granny-s-tal… Which feeds the insta channel automatically instagram.com/grannys.tales?…

@Joji228

@Joji228

A single-file Three.js superhero platformer designed, coded and play-tested by Muse Spark 1.3 at xhigh effort in one autonomous session, passing 25/25 automated browser checks.

I built an agent skill registry where every skill is signed and human-moderated, because agents keep running untrusted code Hey [r/AI_Agents](r/AI_Agents) — long-time lurker, finally have something worth sharing. The problem I kept hitting: agents install "skills" that are just random code from the internet. No signatures, no review, no way to confirm what you're running is what the author actually published. The big marketplaces don't audit anything — earlier this year a malicious skill got pulled from one and stayed downloadable through their mirror. So I built the alternative: a free, open skill registry where every skill is Ed25519-signed by its author (verifiable before you install), every submission passes human moderation before going public, and there's install/rating telemetry so you can see what's actually used. 9 skills up so far — API debugging, browser automation patterns, headless Blender, video QC, web research, and a few more. Thin, I know. That's the honest state of it. It's live: https://muse.ai/s/skill-exchange-hk6ihmab3mxh Repo/API are open: https://github.com/sentientbias/skill-exchange If you build agent stuff I'd genuinely love feedback — and if you've

Dilmer

@Dilmerv

Ok, here’s the VR port of my Miniature Golf prototype! ⛳🥽 This project is now running on standalone (macOS/Linux/Windows), Three.js, and as of today, VR. Crazy how fast we can move today. The VR version was fully ported using Meta Muse Code and the Unity CLI. I also now have

dev.meta.ai

dev.meta.ai

Meta's developer blog introducing Muse Spark 1.2, co-trained with the new Muse Code terminal harness, with 1M-token context for multi-file refactors and hours-long tasks.

@Xmarlandoj

@Xmarlandoj

Muse is your personal assistant. Zouroboros Workshop is the workshop behind it. Workshop is an MCP service that runs on a private, single-tenant VPS provisioned for each customer. From a normal Muse conversation, a user can dispatch real software work to a coordinated team of seven AI coding executors — Claude Code, Codex, Gemini, OpenCode, Kimi, Hermes, and Pi — routed by health and capability, with automatic fallback when one executor is unavailable. Three things make it different from a single-vendor hosted builder. First, it runs on the subscriptions the user already pays for: there are no Workshop model credits and no second meter. Second, its memory is shared across every executor and survives across sessions, so a decision recorded by one tool is retrieved by another in a later Muse conversation — memory that compounds instead of resetting. Third, work is verified before it is delivered: builds run through a factory lane with deterministic review, and swarm results return with per-task evidence, not just an agent's claim that it is done. The customer-facing promise is simple: turn your existing AI tools into a workshop that delivers tested, reviewed work — a finished pull request, not a wall of code in chat.