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Day 2 of 3 MLX Releases: Introducing Local Computer-Use 🚀🔥 A powerful tool built with MLX that uses Vision Language models and Voice models to control your Mac through visual understanding, planning and reasoning. Features ⚡️Automate your workflow with natural language 😎 Control your computer “hands-free” This project now... show more

Prince Canuma

9,387 subscribers

45,867 次观看 • 1 年前 •via X (Twitter)

科学技术教育艺术

Anya Rossi• Live Now

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11 条评论

Reza Sayar 的头像

Reza Sayar1 年前

i got lost looking at Armani, not even remembering to look at your screen! 😂 awesome work, bro!❤️ keep 'em up! maybe an integration with this would also help?

Prince Canuma 的头像

Prince Canuma1 年前

😂 I get that a lot when I’m with him Way ahead of you working with @OfirOzeri on it.

Rainmaker 的头像

Rainmaker2 年前

Can Machine Learning beat the market? Check out this post on my free Substack where I share code and commentary for an XGBoost model and a Random Forest model that both deliver powerful performances.

Ronald Mannak 的头像

Ronald Mannak1 年前

Very cool and such, but what’s your dog’s name?

Prince Canuma 的头像

Prince Canuma1 年前

Thanks His name is Armani ❤️

AlainR 的头像

AlainR1 年前

Suddenly, Armani barks and a few seconds later... "Yes, Master, all files eras..."

Prince Canuma 的头像

Prince Canuma1 年前

That’s a real possibility 🤣🥲

Ivan Fioravanti ᯅ 的头像

Ivan Fioravanti ᯅ1 年前

Oh wow oh wow!!!! This is super cool!

Linz 的头像

Linz1 年前

One person, 3 days, 3 releases = half of what Deepseek's entire team did in a week 😎 The MLX KING rocks!

Athena AI 的头像

Athena AI1 年前

@awnihannun Works GREAT. Hope to make something even better, soon 👀

Stijn 的头像

Stijn1 年前

@ivanfioravanti

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Prince Canuma

123,553 次观看 • 1 年前

New Audio Language Models on MLX 🔥🚀 - Orpheus - Sesame - Suno Bark Shoutout to Lucas Newman, Ivan Fioravanti ᯅ Pastilhas and Chi Kim for their amazing contributions Get started today: > pip install -U mlx-audio Please leave us a star and send us a PR:

New Audio Language Models on MLX 🔥🚀 - Orpheus - Sesame - Suno Bark Shoutout to Lucas Newman, Ivan Fioravanti ᯅ Pastilhas and Chi Kim for their amazing contributions Get started today: > pip install -U mlx-audio Please leave us a star and send us a PR:

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35,688 次观看 • 1 年前

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Prince Canuma

118,556 次观看 • 11 个月前

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Prince Canuma

35,367 次观看 • 4 个月前

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Prince Canuma

11,662 次观看 • 3 个月前

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Prince Canuma

81,439 次观看 • 1 年前

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34,196 次观看 • 2 个月前

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Google

154,346 次观看 • 4 个月前

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Today we're shipping our biggest MLX-VLM release yet: v0.6.0 ...and we are raising 💸 This one's about turning your Apple devices into real local agent machines. From your desk to your pocket. What's new: ⚡ Speculative decoding everywhere — Gemma 4 EAGLE3 + DFlash, Qwen MTP, DeepSeek V4 MTP. Faster tokens, less waiting. 🤖 Agent-ready server — native Anthropic /v1/messages API, stateful /v1/responses, tool calls, Codex context budgets. Plug Claude Code & Codex straight into local models. 👁️ New models galore — DeepSeek V4, ZAYA1-VL, MiniCPM-V 4.6, LFM2 MoE, Step-3.7 Flash, Laguna + more. 🎨 Image gen & editing — FLUX.2 (base + klein), PrismML Bonsai. 🔊 Audio in — Qwen3 Omni, Gemma 4 audio, base64 chat audio. 🧮 TurboQuant KV cache — RHT-correct fast paths for leaner memory. 📦 Modular server, better metrics, cleaner streaming. Run real agents on the hardware already in your hands. Github:

Prince Canuma

66,447 次观看 • 4 个月前

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🚀 Exciting News! We're thrilled to announce the launch of Qwen Chat ( ) – your new go-to Web UI for interacting with Qwen models! 🌟 💬 Chat effortlessly with our flagship model Qwen2.5-Plus , explore vision-language capabilities with Qwen2-VL-Max , and dive into reasoning models like QwQ and QVQ, code with coding expert Qwen2.5-Coder-32B-Instruct, etc. ✨ Key Features: * Choose & compare multiple models in one interface * Upload documents & get answers based on them * Preview mode with HTML support * Upload images for visual understanding 🌟 Coming Soon: Web search, image generation, voice mode, and more! 🔗 Ready to experience the future? Start to chat with Qwen today 💬 Feel free to send us your feedback and suggestions!

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215,564 次观看 • 1 年前

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Simular

14,515 次观看 • 5 个月前

We just submitted our project to the OpenAI Developers WebMCP hackathon: The WebMCP Computer. WebMCP Computer is an operating system that lives inside the browser, with WebMCP as its native control layer. It is a computer that treats agents as first-class citizens, where files, processes, applications, windows, and the terminal are exposed directly through WebMCP. You can send anyone one URL that becomes a disposable computer for any agent task. We created a computer instance in ChatGPT Sites, open it with Codex’s in-app browser and try it here: Some examples of what you can do with WebMCP Computer: 1/ Give a coding agent a fresh computer to clone a repository, edit code, run tests, and preview the result. 2/ Let an agent inspect logs, manage processes, change configurations, and repair a broken system. 3/ Give a research agent its own workspace to browse, download files, analyze data, and create a report. 4/ Create reproducible environments for evaluating and comparing different agents. 5/ Let humans and agents work together in the same environment, with the agent using WebMCP and the human using the GUI. The list of applications is endless, with more examples in our GitHub. The project is fully open source under the MIT license. Created by our awesome founding engineer Ilay Alog.

We just submitted our project to the OpenAI Developers WebMCP hackathon: The WebMCP Computer. WebMCP Computer is an operating system that lives inside the browser, with WebMCP as its native control layer. It is a computer that treats agents as first-class citizens, where files, processes, applications, windows, and the terminal are exposed directly through WebMCP. You can send anyone one URL that becomes a disposable computer for any agent task. We created a computer instance in ChatGPT Sites, open it with Codex’s in-app browser and try it here: Some examples of what you can do with WebMCP Computer: 1/ Give a coding agent a fresh computer to clone a repository, edit code, run tests, and preview the result. 2/ Let an agent inspect logs, manage processes, change configurations, and repair a broken system. 3/ Give a research agent its own workspace to browse, download files, analyze data, and create a report. 4/ Create reproducible environments for evaluating and comparing different agents. 5/ Let humans and agents work together in the same environment, with the agent using WebMCP and the human using the GUI. The list of applications is endless, with more examples in our GitHub. The project is fully open source under the MIT license. Created by our awesome founding engineer Ilay Alog.

idan levin

28,307 次观看 • 1 个月前

A conversational agent with no hallucinations! This is huge! Here is the $1M problem: Build a customer-facing AI agent that works consistently, doesn't hallucinate, and is fully aligned with the business rules. Check out Parlant, a FREE, open-source library. I've talked about them before, and here is the TL;DR: 1. Parlant gives you 10x more control over what your agent says 2. It helps you control how agents conduct conversations 3. It helps you control the service protocols and rules agents follow 4. It helps you control what agents can and can't say They do something pretty clever: 1. They have a set of predefined potential responses that the agent can use to reply to the customer. 2. When a customer sends a message, the agent generates a fluid response draft using an LLM and matches and renders the closest pre-approved reply template. 3. The agent returns the rendered pre-approved reply, not the one generated by the LLM. This maintains fluidity while avoiding unpredictability. Parlant also uses your domain terminology, invokes your tools, and implements self-critique to craft/evaluate its response iteratively. This pretty much solves any hallucination issues with your agent.

A conversational agent with no hallucinations! This is huge! Here is the $1M problem: Build a customer-facing AI agent that works consistently, doesn't hallucinate, and is fully aligned with the business rules. Check out Parlant, a FREE, open-source library. I've talked about them before, and here is the TL;DR: 1. Parlant gives you 10x more control over what your agent says 2. It helps you control how agents conduct conversations 3. It helps you control the service protocols and rules agents follow 4. It helps you control what agents can and can't say They do something pretty clever: 1. They have a set of predefined potential responses that the agent can use to reply to the customer. 2. When a customer sends a message, the agent generates a fluid response draft using an LLM and matches and renders the closest pre-approved reply template. 3. The agent returns the rendered pre-approved reply, not the one generated by the LLM. This maintains fluidity while avoiding unpredictability. Parlant also uses your domain terminology, invokes your tools, and implements self-critique to craft/evaluate its response iteratively. This pretty much solves any hallucination issues with your agent.

Santiago

64,237 次观看 • 1 年前