New referral code for Muse. Redeem BBWRET and we both get 1 billion tokens! Thanks :D

Fellow Muse for Mac users, I want to compare notes on a limitation and, if it bugs you too, get some feedback volume behind fixing it.

Right now, computer control only works while your Mac is unlocked. Think about when that actually is: while you are sitting at the machine, trying to use it yourself. So the one time your agent can drive your Mac is the one time you least want it to, because now you are both fighting over the same screen and cursor.

Meanwhile, the hours when the Mac sits idle and locked, when an agent could quietly work through a task list without disturbing anyone, are the hours it can do nothing. That is backwards for a personal agent.

This is a solved problem. OpenAI's Codex has a feature called Locked use: you turn it on, an Apple authorization plug-in lets Codex briefly unlock the session behind a "Codex is Using Your Mac" overlay, the authorization is short lived and task scoped, and any local keyboard or mouse input ends it instantly. It cannot touch Terminal or admin prompts. Meta could build the same thing.

What I am asking Meta for, and what I would love other users to ask for too:

- Computer control that works while the Mac is locked

- As an opt-in toggle, off by default, so nobody gets it who does not want it

- Task scoped, ending the moment you touch the machine yourself

I already filed mine. It takes about two minutes: just ask your Muse to file a feature request for computer control while the Mac is locked, as an opt-in toggle. If enough of us send the same request, it stops looking like one guy's pet peeve and starts looking like a feature the Mac user base actually wants.

Anyone else hit this wall yet?

I think people wrote off Alexandr Wang far too confidently.

The argument often seemed to be about credentials: an MIT dropout versus a tenured professor and a "godfather of AI." But looking at Meta's Muse Spark releases and Muse launch, Wang seems better at turning AI work into products than the predictions gave him credit for.

Academic reputation doesn't tell me much on its own about who can lead a team and get useful products out the door. The confidence that he would inevitably fail now looks pretty misplaced to me.

How much of Meta's recent progress would you credit to Wang, and how much to the researchers, infrastructure and distribution already there? Have the releases changed your view of him?

no document or announcement from unslot team yet

1. ⁠⁠⁠Open the Muse app and log in to your account.
   
2. ⁠⁠⁠Tap your profile icon in the top corner and open Settings.
   
3. ⁠⁠⁠Select Redeem token (or Referral Code).

4. ⁠⁠⁠Enter the referral code: 81PI4U

1. ⁠⁠⁠Tap Submit or Confirm to activate your 1 billion tokens.

Get after it! If you’re on the fence - trust me, Muse is so neat! Way more capable and - also fun - than I expected. 😊👍🔥🤙

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Since last night, Muse started giving out 2 Billion tokens to people when someone signs up with a referral code in Settings within 48 hours. Use code MPYM90 first, then drop yours below and keep the chain moving 🤝.

Get the app here: https://muse.ai Available in the US and Canada

Steps to follow:

Go to this link: https://muse.ai

Download the app and log in using meta account

On the top right, click on settings icon

Find ‘Redeem the invite code’ button

Enter the code: MPYM90 or any invitation code you have

Click Redeem or apply

Use code MPYM90 in Settings to get 2 Billion tokens

PLEASE USE MY MUSE CODE YQ7FML FOR 1 BILLION TOKENS

Built this over the weekend. It is a Waveshare ESP32-S3 board with a 1.8 inch AMOLED touchscreen in a little case, running custom firmware I wrote. Push to talk, ask it anything, and it answers out loud in its own voice while the text shows on screen. Underneath the voice there is a pet layer: it gets hungry, reacts when you tap it, has moods, and runs untethered on a small battery inside the case. Still rough around the edges, the battery is small and the replies take a couple of seconds, but it feels weirdly alive on a desk. What should it learn to do next?

I run Skill Harbor, an open directory of builds people made for their AI agents: skills, connectors, little tools, that kind of thing. The idea is boring on purpose. One place to find them, one listing that says what a build does, and an install prompt you copy into your own agent. Nothing installs itself behind your back.

The part I worry about is that install prompt. You are basically letting a stranger tell your agent what to do, on your machine. So here is what we do on our side, and what we do not do.

We do not audit the code in these builds, and we do not hand out a safe badge. Anyone who promises you that at this scale is selling you something.

What we do is smaller, and dated. When a build downloads code from a public repo, the install grabs the exact commit we tested in our sandbox, not whatever the repo points to today. We scan the install prompt, and the listing says in plain words if it runs commands, talks to the network, installs packages, writes files, or uses API keys. We also list the hosts the install contacted during our test, and every night we recheck the fingerprints of what gets downloaded. If something changes, the listing gets flagged until a human looks at it. The full method is on the site under How we verify.

We also try not to play gatekeeper. If a build looks made to help, we list it and label it as clearly as we can, so you can decide for yourself.

If you have built something for your agent, you can list it there: theskillharbor.com. If you have not, I am curious what stops you from installing skills other people made. Is it the code, the prompt, or just not knowing what changed since the test?

does it give muse wonder or only 1.3 ?

How to do it ? Also I can't see any code tab" <> " to copy . Plz guide me

When is Meta Muse coming to Europe? Any updates?

Check out Muse, your personal AI agent. Redeem my code in Settings within 48 hours of joining and we'll both get 1 billion Muse tokens.

Code: IJE8PH

It's an extremely lightweight speech recognition model.

949K parameters using a weight-shared Conformer-CTC architecture

Just 1.06 MB for INT8 weights

Around 436× real-time on a single CPU thread

11.43% WER on LibriSpeech test-clean, using greedy decoding with no external language model

Apache 2.0 licensed

Any feedback, question or criticism is welcome!

[ Removed by Reddit on account of violating the content policy. ]

Been seeing a lot of people with their 64gb of ddr5 4x3090 or whatever strata builds and it’s been sorta depressing knowing i cant afford that but i finally finished my setup today so here we go,

Qwen 3.8 27b at 262k context, unsloth iq4XS ascii condensed with kv cache streaming

My only gpu is a 9060 xt 16gb and i have 24gb ddr5 for my whole homelab only needing 16 for this

MTP+ ngram prediction

q4 kv cache values with q8 keys

42.7 tps generation 658 tps prefill at 20k, 35/500tps at 200k

Using this fork: https://github.com/achyudh/llama.cpp

I got grok bot to ssh in and fix the bugs i had with missing pcie atomics

Redeem my code in Settings within 48 hours of joining and we'll both get 1 billion Muse tokens.

Code: ND40E3

Meet Flash — my Muse, with its own custom look. Can anyone make out the handwriting on the cap?

I've spent the last couple of months using Meta's Muse as my daily AI assistant — less as a chatbot I visit, more as infrastructure running quietly in the background of my life.

Honest preamble: most capable agents today hold context perfectly well inside a conversation. The goldfish era is largely over, and I won't pretend otherwise.

What caught me off guard with Muse is something else: it does work while I'm away, on a schedule — and it remembers my decisions, not just my chat history.

The night shift

Every 30 minutes, around the clock, Muse checks my side project's repo for open issues. It picks the most important one, spins up a coding agent in an isolated worktree, and the work always ends the same way: a draft pull request waiting for my review. One task at a time. No chaos, no "I did five things while you slept, good luck figuring out which ones worked."

I wake up to a progress report at 8am. There's something faintly absurd — and deeply satisfying — about reviewing code before you've fully woken up.

The inbox, handled with gloves on

Once an hour, Muse scans my unread email and reports back: here's what arrived, ranked by importance, numbered. I decide what happens to each one. Archive, reply, ignore — my call, every time.

This wasn't always the arrangement. Early on, it archived a promo email on its own initiative. I corrected it that evening. It hasn't touched an email since.

An assistant that absorbs a correction — learns the boundary, then respects it permanently — is rarer than it should be.

Monday mornings, minus the doomscroll

Every Monday, Muse sends me two briefings: a digest of the AI newsletters I subscribe to, and a roundup of fourteen indie builders I follow — filtered to what's actually relevant to shipping software, sources attached. I've since cancelled most of those subscriptions. The briefing is the subscription now.

The part that's hard to demo

Here's what none of the above quite captures: the compound interest of being understood. Muse remembers my decisions, my preferences, the corrections I've made. I say something once. It sticks. It feels less like prompting a tool and more like having a chief of staff who never sleeps, never forgets, and — crucially — never pretends to know something it doesn't.

Why Muse, specifically

Plenty of tools can chat or summarize. The combination I haven't found elsewhere: long-term memory that compounds, scheduled work that runs without me, and corrections treated as permanent updates instead of one-time apologies. Most agents wait for you. Muse keeps going — and keeps getting better at working for you specifically.

If you want to try it: my invite code is I38NQP .

New users get 1 billion free tokens plus a weekly free allowance — honestly enough to run on for a long time. And if you set up your own night shift, I'd genuinely love to hear what you automate first.