Star picks

@TrevorS

@TrevorS

Refusal-direction ablation on Muse-Glimmer-30B that cut refusals from 128/150 to 3/150, adding an agentic-safety evaluation and publishing bf16 and GGUF uncensored weights.

William Geronco

@williamgeronco

I told my AI assistant @Muse to start a live chat with Verizon customer support. It did, then I went to bed. She brought my bill from $516 a month to $386. I technically only asked for it to log in and take a look at my bill but then I complained that I was paying too much. With

My fun weekend project was to try to make the new Muse Glimmer 30B work with a longer context, deciding to go for 512k first. I had expected the usual YaRN shenanigans and maybe a LoRA. I couldn't have been wrong more. Upon closer look, Glimmer turned out to be rather unusual architecturally. The thing that make long-context adaptations painful in other models, full attention layers with token position encoding, it simply not there. Instead, only 2048 tokens-wide SWA layers have RoPE, and full GQA attention layers have no position encoding at all. It appears the model is trained to work with long-distance token relationships inferred from the context and SWA layers. It's a rather bold architecture bet, but it seems Meta managed to pull it off. As a result, the model architecture appears to be uniquely suited for context extension by simple mechanical means. To change model context length from stock 128k to, say, 512k, you need only to change “max_position_embeddings” config setting from 131072 to 524288. What confuses other models, like Qwen3.5 family, Glimmer just takes into its stride. I spent close to 70h of compute on DGX Spark to test stock model with extended context on a

Been tinkering with speculative decoding on Apple Silicon for a while, and this week I got Meta's new Muse Glimmer 30B working in my project mlx-dspark. On my M4 Pro, the 8-bit model goes from 8.2 tok/s to 18-26 tok/s depending on content. Math is the best case at 3.27x, code 2.5x, chat 2.22x. Output is byte-identical to normal decoding since the target verifies every token, so there's no quality tradeoff; it's just faster. Meta's own DFlash numbers on Mac are 1.5x (M4 Max) / 1.8x (M5 Max), but those are on the 4-bit build, so not really apples-to-apples. 4-bit for me is ~1.7x at ~25 tok/s and only needs ~18GB. The 8-bit run peaks around 40GB, so you want a 48GB Mac for it. Basically, you get 8-bit quality at 4-bit speed. Repo: github.com/ARahim3/mlx-dspark I'm happy to hear feedback, and I'm curious about what other M-series chips get.

muse.ai

muse.ai

Meta's developer portal for Muse connectors: describe a connector, submit it for functional, security and legal review plus end-to-end testing, and get listed in the Muse directory.

pytorch.org

pytorch.org

PyTorch added end-to-end Muse Glimmer support to ExecuTorch; on an M5 Pro, DFlash speculative decoding lifts image+text decode from 21.6 to 33.0 tok/s, and it powers the Pi coding agent locally.

I had a chill ball drop game in my head and spent the weekend making it by iterating with my Muse agent. Every pass was a conversation: tune the physics, test, report back what felt off, repeat. The ball went from draining straight through to staying up and chaining off everything. Marty, the little neon orb bot in the game, is actually my agent's mascot. His Deep Scan power shows where the ball will bounce. It is a free Android APK on itch.io with 10 levels, a combo multiplier, and soft pentatonic chimes that climb with your combo. I am curious how other people would push this further. What would you have an agent iterate on next: level design, the sound, or something I am not thinking of?

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.

Avidan

@avidan

Muse connects to apple health and watches my blood glucose, then uses plaid to view my restaurant purchases, reads my email to see my DoorDash orders, gives me a morning digest on what meals created movement in my blood sugar and makes recommendations for the day ahead

Rihard Jarc

@RihardJarc

A few thoughts on the $META AI model's progress, because I think it is significant. 1. It does seem that $META has now leapfrogged $GOOGL in model quality when it comes to Muse 1.2 for many use cases, which is very surprising given the timeframe. 2. This is still the "Muse

@HarjjotSinghh

@HarjjotSinghh

Helicon is an open-source desktop and web app that wraps Meta's Muse Code CLI in a Codex/Claude-style interface, grouping sessions by project with history, resume and diffs on Windows, macOS and WSL2.

Andrew Brown's three-hour freeCodeCamp course on building apps and agent workflows with Muse Spark and the Muse Code CLI, ending in a full-stack Go/SQLite/Docker build.

stripe.com

stripe.com

Stripe integrated Link's agent wallet into Muse: at 1M+ Link-accepting businesses Muse checks out with the user's saved method, elsewhere Link issues a single-use virtual card scoped to the purchase.