Meta 发布 816B 参数开源多模态模型 Muse Spark Open，可双 Mac Studio 本地运行

AIMeta turns Mac into an AI supercomputer

PLUS: Turn a ChatGPT answer into a tool you can use

Zach Mink

Good morning, AI enthusiasts, and welcome to our 5,678 new readers. Earlier this week, Meta caught up with OpenAI and Anthropic with its frontier model Muse Wonder. Turns out, the comeback story had one more chapter: a large open-weight release with an unlikely partner.

The company just dropped Muse Spark Open, calling it the “most capable” open-weight AI model from the U.S., and tuned it with Apple so that two Macs can work like an AI supercomputer.

In today’s AI rundown:

Meta gives the West its best open-weight model

Rowan’s Corner: My AI walks my company’s factory floor

Turn a ChatGPT answer into a tool you can use

Report: Harvard maps AI’s gender divide

LATEST DEVELOPMENTSMETA

🧠 Meta gives the West its best open-weight model

Image source: Mark Zuckerberg on X

The Rundown: Meta just released Muse Spark Open, an open-weight, 816B-parameter multimodal model built for coding and agentic work, and worked with Apple so that it could run locally on Mac Studios — a first for a model in this size class, Meta says.

The details:

Two M5 Ultra Mac Studios with 1TB combined memory can run Spark Open via Apple’s updated MLX toolkit, with Meta saying the model shipped iOS app fixes and edited video in DaVinci Resolve without the cloud.

On Artificial Analysis’ Intelligence Index, it sits at 39, ahead of the West’s open-weight models but behind China’s GLM 5.3 (45), Kimi K3 (44), and Qwen3.8 (40).

Spark Open either matches or tops open rivals on multimodal and knowledge tasks, but Chinese models still code better, with Kimi, GLM, and Qwen all ahead.

It’s the third major Western open launch this month, after Reflection AI’s Beam and Mistral’s 1T-parameter Le Chonk, whose weights arrive Oct. 27.

Why it matters: Meta built its AI rep on open Llama models before going closed. Now it’s playing both sides with Wonder at the frontier and Spark Open giving devs serious AI they can run on their hardware. China still leads the open-model race, but Meta’s return puts an American heavyweight back in contention. TOGETHER WITH HUBSPOT

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🔎 My AI walks my company’s factory floor Visit The Rundown University

Rowan: At an in-person company, the CEO can walk the factory floor. You watch how the company actually runs, catch errors in real time, spot slow handoffs, and identify processes worth automating. The best operators do it religiously.

The problem is that The Rundown is fully remote and has just over 20 people.

This week, I handed the entire prep to AI. A scheduled Claude task now runs every Friday before I wake up, and it:

Picks the random areas to inspect (randomness matters so nothing gets gamed)

Pre-gathers the samples, links, and data for each area into a report in my Notion

Checks last week’s findings against Slack to verify they actually got fixed before I move on

If you run a remote company (or any team, or honestly just your own workload), steal this. I put the exact scheduled task prompt in our Workflow Hub, ready to adapt to your tools in about 15 minutes.

It’s unglamorous plumbing, but it’s some of the highest-leverage 30 minutes of my week. AI TRAINING

🧮 Turn a ChatGPT answer into a tool you can use

The Rundown: In this guide, you’ll try ChatGPT’s new Intelligent UI by creating a workshop cost calculator. You’ll change the headcount, check the figures, and ask it to add a budget buffer.

Step-by-step:

Open ChatGPT in a web browser and start a Chat with GPT-6. Choose Medium where available and leave Pro reasoning off for this exercise

Paste the guide’s prompt to create a calculator with editable costs, budget, and attendance. Check that the starting total is $700 for 20 people

Change attendance to 30 using its controls. The total should rise to $950 and show that you are $50 over budget

Try zero attendees and check that the fixed room fee stays. Test the budget boundary too, so a useful-looking calculator does not hide a wrong result

Return to 20 people and ask for a 10% buffer. Check for a $770 final cost and $130 left, then change the headcount again to test the update

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👨🏻‍⚖️ Report: Harvard maps AI’s gender divide

The Rundown: Harvard researchers just found consistent gender gaps in how Americans use AI, get support for it, understand it, and appear in coverage of it, drawing on a survey of 5,400+ adults, browsing data, and 78K news articles.

The details:

29% of women said they’d never used AI vs. 22% of men, though browsing data showed no real gap in trying it, only in how much time users spent on it.

At work, 64% of employed women reported no AI training vs. 49% of men, with the study linking training and encouragement to higher use and confidence for both.

Men answered more AI questions correctly while women chose “not sure” more often, though more men also claimed to recognize made-up AI figures.

AI headlines named men 14.6x as often as women, a gap the researchers partly tie to male-led companies, though the gap also held among quoted experts.

Why it matters: This is one of the broadest looks yet at how AI’s gender gaps are taking shape. Access looks nearly equal today, but the real divide shows up in training, confidence, and who gets heard. As AI labs pitch superintelligence for everyone, these gaps show how far that promise still has to go.

▸ Tony used AI agents to build and refine a word puzzle

Today’s workflow comes from reader Tony Surfwell:

“I’m not a professional developer, but I built Sujido, a live word puzzle, with multiple AI agents. For months, they gave me bloat, lost context, and slop, and neither the models nor I could untangle what had been decided or why. I eventually settled on two agents: one Claude instance that plans and reviews, and another that builds. I imposed rules on both, one failure at a time.

None of these rules would surprise a pro engineering team, but nobody handed them to me. That’s the lesson I learned: agents won’t bring the discipline. You have to impose it.

When I showed the first game revision to strangers, they said that the first half was ‘boring’ and the second half was novel. It was hard news to hear about my baby, but I took the feedback and cut the game in half. That required surgery on code tangled across both halves, followed by a rebuild under the rules below.

Before the rebuild, a separate AI audit graded the codebase B−. Afterward, it graded it A−. The site shrank by 97.5%, and every serious finding was fixed and locked in by an automated test. The models didn’t change drastically, but my operating rules did.”

See the workflow on our Community Workflow Hub →

What have you fixed or built with AI lately? Share your workflow, and you could be featured right here in The Rundown.

🛠️ Trending AI Tools

🧠 Muse Spark Open - Meta’s open-weight AI for coding and long-horizon agents

🗣️ GPT-Live-1.1 - OpenAI’s new voice AI with more natural multilingual accents

🤖 Nemotron 3.5 Super VL - Nvidia’s open multimodal AI for specialized AI agents

🖼️ FLUX 3 Image Klein - BFL’s small open model for 4K image generation, editing

📰 Everything else in AI today

Amazon’s Bee launched two AI wearables starting at $74.99 that record conversations, generate summaries and to-dos, and integrate with Alexa and productivity apps.

Anthropic said it is giving six METR researchers employee-level access to assess its AI safety practices and publish findings without its approval.

Bank of America CEO Brian Moynihan told CNBC that the bank hired 1K contractors to patch software flaws exposed by Anthropic’s Mythos and other models.

OpenAI is training employees to guard against espionage, including “honeypot” schemes, as attempts to steal AI trade secrets intensify, the Wall Street Journal reported.

OpenAI also launched GPT-Live-1.1 with improved multilingual accents and upgraded ChatGPT’s Chrome extension to let users analyze open tabs, automate browser tasks, and access chat history in the sidebar.

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