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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 просмотров • 1 год назад •via X (Twitter)

Наука и технологии Образование Искусство

Anya Rossi• Live Now

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Комментарии: 11

Фото профиля Reza Sayar

Reza Sayar1 год назад

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?

Фото профиля Prince Canuma

Prince Canuma1 год назад

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

Фото профиля Rainmaker

Rainmaker2 лет назад

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.

Фото профиля Ronald Mannak

Ronald Mannak1 год назад

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

Фото профиля Prince Canuma

Prince Canuma1 год назад

Thanks His name is Armani ❤️

Фото профиля AlainR

AlainR1 год назад

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

Фото профиля Prince Canuma

Prince Canuma1 год назад

That’s a real possibility 🤣🥲

Фото профиля Ivan Fioravanti ᯅ

Ivan Fioravanti ᯅ1 год назад

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

Фото профиля Linz

Linz1 год назад

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

Фото профиля Athena AI

Athena AI1 год назад

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

Фото профиля Stijn

Stijn1 год назад

@ivanfioravanti

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Google

154,346 просмотров • 4 месяцев назад

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Qwen

215,564 просмотров • 1 год назад

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 просмотров • 5 месяцев назад

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idan levin

28,307 просмотров • 1 месяц назад

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 просмотров • 1 год назад