Entries matching “images”

@danny-hines

@danny-hines

A self-hosted mini arcade cabinet on a Raspberry Pi that generates SNES-style games from a player photo and a voice prompt, using Muse Spark to judge art and Muse Image and Muse Voice Transcribe for assets and input.

We've been curious how far local models have actually come for agentic coding tasks, so we ran an experiment. Setup: • Model: Muse Glimmer (30B), packaged as a single llamafile • Agent: Hermes coding agent (connected via llamafile's local server mode, zero API keys needed) • Target: Mozilla AI's Otari gateway The Issue: We pointed Hermes at a real, reported bug in Otari (#183) where the gateway returned a vague 502 error on image requests instead of passing through the actual provider error. What the Agent Did: Hermes read the issue, navigated the repo, isolated the bug, created a branch, ran existing tests, wrote a new regression test, and opened a draft PR (#727). All of it ran locally and offline, with zero code written by hand. It's still draft PR territory rather than a merged fix, but it's a solid signal that ~30B local models are getting genuinely capable for real dev workflows, not just toy demos. Video walkthrough of the run: https://youtu.be/5GAgbT-XgHU?si=vJqEDGm9hssCO5-M Happy to answer questions about the setup, model performance, or how Hermes handled tool calling!

fal

@fal

Muse Image from @aiatmeta is live on fal. It's an agentic image model that plans your composition, uses tools, and self-corrects before you ever see a result. Complex briefs hold together instead of falling apart.

Alexandr Wang

@alexandr_wang

1/ releasing muse image today — the first image generation model from MSL. it's agentic: pairs with muse spark to reason through your prompt, search the web, and plan before it generates. people get what they meant on the first try. live now in the Meta AI app.

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Leon Lin

@LexnLin

I experimented with Muse Spark a bit more. I take my words back it seems to be quite okay. Muse Spark generated images for the website btw

Chetaslua

@chetaslua

This beautiful presentation is by Muse 1.2. It used gpt-image-v2 to create the images by itself. I told it to make a history page from Llama to Muse 1.2, and it did a great job. @alexandr_wang , you guys created magic with agentic tasks. I'm now waiting for the big model from

Red Hat AI's FP8 weight and activation quantized Muse Glimmer 30B (text and image input) for vLLM.

Matt Deitke

@mattdeitke

Excited to share Muse Spark! It's a strong natively multimodal model with many surprising properties that emerged. Here, the model is able to use Python tools to make a playable Sudoku game on the web from an image input of the board. ✨

@spora-ai

@spora-ai

Meta Muse vendor plugin for Spora agents shipping Muse Voice Transcribe speech-to-text and Muse Image generation and editing behind one Meta Model API key.

Full Value Dan

@fullvaluedan

I'm really surprised with what you can do with @Muse Most agents struggle with making decent images. For marketers, Muse automate can: - Create detailed carousels with custom images - Create skills and verification steps to learn your style - Post to social media - DM people

Simon Willison ran an 18.16GB build of Muse Glimmer locally, testing code exploration and image description. He found multi-step reasoning and tool use strong and creative generation mixed.

いにしえ@高信頼AIニュース"NeuralWire.org"運営｜Will Oldgram

@old_pgmrs_will

muse-spark-1.2-contributor で 3D都市を生成、生成コストは日本円で20円くらい 自動旋回で延々見てられる☺️ 好きな位置・カメラアングルでの画像生成もサポートしたので、別途画像生成AIの参照素材として色々使える

thehype.

@thehypedotnews

union alpha (unbiased pareto) vs deepseek v4.1 flash vs muse spark 1.3 – three paintings in three.js the setup: one four-line prompt plus the painting as an image, through @openrouter. no agent loop, no renders, no feedback – the model writes one html file blind and we open it.

LM Studio launched Muse Glimmer support, reporting it completed 83.3% of tasks on its 18-task BionicBench v0.1 versus 77.7% for Gemma 4 31B and Qwen 3.6 27B.

Ollama shipped Muse Glimmer on day one: `ollama run muse-glimmer`, plus a muse-glimmer:30b-mlx tag for Apple Silicon that Ollama says runs 1.5–1.8x faster with DFlash.

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.

Arena.ai

@arena

Exciting news: Meta’s Muse Image just claimed #2 in the Image Arena! Muse Image from @AIatMeta now ranks second only to OpenAI's GPT Image 2, outperforming Nano Banana, Grok Imagine, MAI Image, and many other leading image models. It holds #2 across the board: Text-to-Image,

@kevintsai1202

@kevintsai1202

MCP server that gives Claude Code, Claude Desktop, Cursor and other MCP clients image generation and editing powered by Meta's Muse image model, published to npm.

Meta for Developers

@MetaforDevs

Muse Image is now available on Meta Model API and priced for production volumes at $0.01/image. It’s an agentic image model that reasons before it renders. Each call searches the web to refine the output through iterative passes and evaluates the prompt for precise elements

@HawgAuto

@HawgAuto

Glimmer HD Vision is an OpenAI-compatible proxy that keeps images within Muse Glimmer 30B's 4,096 visual-token limit by sending a 4K image as one overview plus four overlapping detail tiles, with an OCR/layout mode.

llama.cpp imatrix quantizations of Muse Glimmer 30B with image support via mmproj and MTP/DFlash notes.

Jon ONeill

@HouseHackerJon

I’ve built this but for @Muse A community styles Creator studio Prompt and images available musecharacters.jononeill.dev Use the tag #MuseCharacterStudio And I’ll add yours to our gallery! @MattPRD have you seen any cool ones you want added? Or has @alexandr_wang ?

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Design Arena

@DesignArena

BREAKING: Muse Spark 1.2 by @AIatMeta takes 1st for Video-to-Website with an Elo rating of 1279 and impressive scores across all of our multimodal code categories. Muse Spark 1.2 also takes 2nd for Image-to-HTML with an Elo rating of 1252 and 3rd for Image-to-Frontend with Elo

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Leon

@leontologia

Everyone’s talking about ways to save money with Muse, but they’re missing a big one I made my girlfriend a Muse Wardrobe “For You” page of new looks using clothes she (already!) owns Every day, Muse imagines a dozen new outfits for her to swipe on Fellas, give it a try

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@nicedreamzapp

@nicedreamzapp

An architecture port adding the muse_glimmer model class (vision tower, language model, projector and image processor) to mlx-vlm, so any Muse Glimmer checkpoint runs multimodally on Apple Silicon.

Trevor Phillippi

@trevorphillippi

For the icons, we discussed it at length in a sidechat and my Muse made me the initial list, helped with prompts, and the approach for expansion. We rendered them right there in chat with Muse Image, and he made me a catalog Artifact to manage them and add notes for re-runs.

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@alpharomercoma

@alpharomercoma

A reproducible setup serving Muse Glimmer 30B on one AMD MI300X with a custom 11 GB vLLM 0.28.0 ROCm image, exposed as an authenticated OpenAI-compatible endpoint and driven from the pi CLI.

D I G I

@digicodermon

Connected @muse with my @NotionHQ meal planner. Now I can take a picture of my grocery receipt have each purchase registered to an ingredient in my pantry, Updating my pantry stock, tracking per item cost, and suggests meals based on what I actually have in stock vs my recipes.

Vercel added Muse Spark 1.3 (meta/muse-spark-1.3) to AI Gateway on launch day, with 1M-token context, text/image/PDF input, and both standard and contributor pricing tiers.

The vLLM recipe page for Muse Glimmer has this for speculative decoding: --speculative-config '{"method": "dflash", "model": "meta-models/Muse-Glimmer-30B-assistant", "num_speculative_tokens": 15}' This errors out on the current vllm/vllm-openai:muse-glimmer image, and each fix reveals the next error. Six separate issues in total, all in the DFlash path. The base model runs fine without the spec config. The source for the image isn't public yet (the recipe says "code will be released soon"), so I pulled the image layers through the registry API and read the code to figure out what was going on. Also checked tensor names by range-requesting the safetensors headers off HF instead of downloading the weights. What I found: • The drafter's config declares MuseGlimmerAssistantModel, which is in vLLM's registry. But the dflash code renames it to DFlashMuseGlimmerAssistantModel before the registry lookup, and that name isn't registered. Dies in config validation. • vLLM maps the drafter's config to Qwen3Config (there's a comment calling it "Qwen3-shaped"). The muse JSON omits vocab_size and use_sliding_window, so Qwen3Config fills in its own defaults: vocab becomes 151936 (the model i