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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 görüntüleme • 1 yıl önce •via X (Twitter)

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Reza Sayar profil fotoğrafı

Reza Sayar1 yıl önce

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 profil fotoğrafı

Prince Canuma1 yıl önce

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

Rainmaker profil fotoğrafı

Rainmaker2 yıl önce

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 profil fotoğrafı

Ronald Mannak1 yıl önce

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

Prince Canuma profil fotoğrafı

Prince Canuma1 yıl önce

Thanks His name is Armani ❤️

AlainR profil fotoğrafı

AlainR1 yıl önce

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

Prince Canuma profil fotoğrafı

Prince Canuma1 yıl önce

That’s a real possibility 🤣🥲

Ivan Fioravanti ᯅ profil fotoğrafı

Ivan Fioravanti ᯅ1 yıl önce

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

Linz profil fotoğrafı

Linz1 yıl önce

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

Athena AI profil fotoğrafı

Athena AI1 yıl önce

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

Stijn profil fotoğrafı

Stijn1 yıl önce

@ivanfioravanti

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35,367 görüntüleme • 4 ay önce

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Prince Canuma

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62,302 görüntüleme • 5 ay önce

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Prince Canuma

81,439 görüntüleme • 1 yıl önce

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48,170 görüntüleme • 1 yıl önce

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Ashpreet Bedi

69,015 görüntüleme • 2 yıl önce

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34,196 görüntüleme • 2 ay önce

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Introducing Google Flow Agent 🎬 ✅ Google Flow Agent can help you plan and reason through complex creative tasks with your inputs, all while under your control. 🧠 It's built with Gemini models and brings a deep understanding of your project to help with everything from early brainstorming to editing the final product. 💡 The agent can be a sounding board for dialogue between characters in a scene, and make plot recommendations when you need inspiration. 🎞️ Your Google Flow Agent can also create multiple variations of a scene at one time to give you more options and batch edit. 📂 And when you have all your final assets, your Google Flow Agent can organize them into collections, and even neatly rename them.

Google

154,346 görüntüleme • 4 ay önce

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:

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

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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!

🚀 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!

Qwen

215,564 görüntüleme • 1 yıl önce

We just open-sourced Simulang, a JavaScript library that gives you and your coding agents the ability to fully control computers. Simulang is our scripting framework for desktop automation. It allows you to write deterministic computer use code that can be replayed dozens if not hundreds of times. Much faster and cheaper, compared to LLM agents. It ships with a skill out of the box. That means you can turn natural language prompts into Simulang code directly, and your agent can now see the screen, click, type, and navigate. It gives your AI agents eyes and hands to control the computer like humans do. Try these things with Simulang: - upload your pile of invoices to the expense platform - find 50 people with specific criteria on LinkedIn - open your calculator app and do some math - find all buttons on your favorite app using accessibility tree - or draw a bunny with MS paint 🐰 In this demo, we show how Simulang took over when the coding agent couldn’t open and click on the target LinkedIn profile page. Give it a try:

Simular

14,515 görüntüleme • 5 ay önce

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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 görüntüleme • 1 ay önce

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 görüntüleme • 1 yıl önce