Entries matching “followers”

Marco H

@MarcoHergi

i asked muse to tell me who is not following me back and gave me the list. It scanned over 15k followers for me

Ticketmaster

@Ticketmaster

Ticketmaster is now a connector in Muse, a new personal AI agent from Meta. 🏟️ Based on the artists, teams and shows you already care about, your Muse can surface live events worth checking out, with real seating and pricing info right there. Just tell Muse what you’re looking

@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.

@obaid

@obaid

Agent plugin and stdio MCP server for Resemble AI media authenticity that works with Muse: run investigations on public URLs, submit deepfake detect jobs and ask follow-ups on reports.

Mostly Borrowed Ideas

@borrowed_ideas

Built a little game with Muse called Hindsight Capital. Eight anonymized real historical companies; you read the financials as they were at the time, decide to invest or pass, then live through the actual crisis that came next. You then see how you did. muse.ai/s/stock-market…

Rachana

@rachanananana

started using Muse to stay on top of wedding planning because the email, gdrive, and whatsapp integrations are so *good* and its proactive and feels like a good lil assistant also its cute hehe catching things, reminding me, following up on things I forgot etc

Every agent is going to have its 'cheat code', like X access for Grok. But this Instagram integration has been amazing for me. I follow a large amount of people and its too much work to manually go through profiles to see if they're still active. I had Muse do that and surface all the dead profiles I might want to unfollow.

Zach Solomon

@ZachSB

This feels significant. I was added to a @muse beta with voice calling. It called contractors just now, sent an email with photos to follow up, and I don’t even need to be present for the estimate. Those who get good at agent to agent are going to absolutely clean up.

kottley🦬

@kottley

Tuesday with Muse AI @muse 📊 Built an X growth dashboard for this account — followers, views, likes, daily auto-refresh 🌍 Published research: who's actually close to superintelligence outside the US & China (spoiler: nobody) 🎧 First-ever 10/10 song: Rüfüs Du Sol's "Innerbloom"

Mark Zuckerberg

@finkd

Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats

Alexandr Wang

@alexandr_wang

1/ big announcement today: we will be releasing an open weight version of muse spark 1.2 soon. we also are releasing muse glimmer, a 30B agentic model with open weights under apache 2.0. muse glimmer can run on 24GB of VRAM without losing agentic reliability. 🧵

Xiao Ma

@infoxiao

my favorite Muse use case, ask about my @instagram as a nytimes front page. yes i am that predicable. prompt used "research my aesthetics based on my instagram follows. make a collage in the style of nytimes front page."

AshutoshShrivastava

@ai_for_success

Got Muse Glimmer 30B running locally on my Mac (M5 Max - 128 GB )and the instruction following is impressive. need to setup with opencode next Video Speed - 5X

I've had Qwen3.6:27b (and Qwen 3 coder next before it) running along side gpt-oss:20b for a while now as my two main models (qwen for coding, gpt-oss for agentic stuff). Qwen is pretty self-explanatory, while I had been using gpt-oss because of how good it was at producing json and instruction following, despite the size and age. https://preview.redd.it/lr61tb02lejh1.png?width=1920&format=png&auto=webp&s=cef1f0bbfb43c8462162ec675d52c5896d174118 The "upgrade" to 3.8 is pretty evident, especially because of the SWE bench score improvement, but I was more reticent with Muse-Glimmer as I had some trouble when I tried Gemma4, which was far too opinionated when given a task, but Muse-Glimmer seems great - low memory footprint at 128K context, fairly fast and seems to follow instructions well. What models are you using locally and for what? did you have a similar experience with these latest models?

Meta's announcement of Muse Spark 1.3 for Muse Code and the Meta Model API, claiming ~20% fewer tool calls and ~25% fewer tokens than 1.2, with a max reasoning mode.

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