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title: Meta's new model wants "deep access" to your personal...
description: A satirical, heavily sarcastic rundown of Meta's release of Muse Glimmer, a 30B parameter agentic model distilled from the closed Muse Spark model and released...
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og:description: A satirical, heavily sarcastic rundown of Meta's release of Muse Glimmer, a 30B parameter agentic model distilled from the closed Muse Spark model and released...
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# Meta's new model wants "deep access" to your personal life...

**[Fireship](https://daily.dev/sources/fireship)** · 5 min read · 6 upvotes · 0 comments

## Summary

A satirical, heavily sarcastic rundown of Meta's release of Muse Glimmer, a 30B parameter agentic model distilled from the closed Muse Spark model and released under Apache 2.0. The video mocks Meta's history since Llama 4's poor reception, Zuck's spending spree on Scale AI and researcher poaching, and the formation of Meta Superintelligence Labs. It covers technical details: logit distillation from Muse Spark, 4-bit quantization shrinking memory from 55GB to under 20GB, and speculative decoding with a small draft model called Dlash giving a 3x speedup on an RTX 5090. Benchmarks reportedly show it beating Gemma 4 and matching Qwen 3.6, with a 28% prompt injection success rate versus 40% for Qwen. The piece questions the sincerity of Meta's open-source pivot given Zuck's manifesto calling for AI power decentralization while his company faces fines and controversy, and mentions upcoming open weights for Muse Spark 1.2.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=G55HSGpuh1M>

## Questions this post answers

### What is Meta's Muse Glimmer model and how big is it?

Muse Glimmer is a 30 billion parameter agentic model released by Meta under the Apache 2.0 license, distilled directly from Meta's larger closed model Muse Spark using logit distillation. It is small enough to run on consumer GPUs after being quantized to about 4 bits, reducing memory requirements from over 55GB at full precision to under 20GB.

_Developers tracking new open-weight model releases can follow updates like this one on daily.dev._

### How did Meta shrink Muse Glimmer to run on consumer GPUs?

Meta used quantization to compress the model weights to roughly 4 bits, cutting memory needs from over 55GB to under 20GB, then applied speculative decoding where a small draft model called Dlash generates blocks of tokens that the larger model verifies in a single pass, yielding a reported 3x speedup on an RTX 5090.

_Anyone evaluating inference optimization techniques can keep up with methods like these via daily.dev._

### How does Muse Glimmer's prompt injection resistance compare to Qwen 3.6?

Prompt injection attacks succeeded against Muse Glimmer 28% of the time, compared to a 40% success rate against Qwen 3.6, a figure Meta presented as a relative win despite still leaving over a quarter of attacks succeeding.

_Developers weighing model security trade-offs can track benchmark comparisons like this on daily.dev._

## Similar posts on daily.dev

- [Meta’s Muse Spark: a smaller, faster AI model for broad app deployment](https://daily.dev/posts/meta-s-muse-spark-a-smaller-faster-ai-model-for-broad-app-deployment-rxhf7mbsh) · InfoWorld · 2 upvotes · 0 comments

---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#llama](https://daily.dev/tags/llama)

[View this post on daily.dev](https://daily.dev/posts/meta-s-new-model-wants-deep-access-to-your-personal-life--kkmusj5a1)

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