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You really can just do things! Use *any* Hugging Face space as a MCP server along with your Local Models! 🔥 Here in we use Qwen 3 30B A3B with ggml llama.cpp and Hugging Face tiny agents to create images via FLUX powered by ZeroGPU ⚡ It's quite a...

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Vaibhav (VB) Srivastav

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39,113 görüntüleme • 1 yıl önce •via X (Twitter)

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What’s the cost of mistakes in your contracts? If you work with contracts day-to-day, it’s time to automate. Track every detail, streamline workflows ... ✨ Make managing contracts as easy as a few clicks. Visit our new website & book your demo today!

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@ggml_org @huggingface This setup is wild! Qwen 3 30B + llama.cpp + FLUX for local model automation? Brilliant hack for video workflows. 🔥

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🚨 Founders: free, private, 100% local AI is finally here I downloaded Qwen 3.6 and ran it on my MacBook inside Strawberry browser. I couldn't believe it. We now have 100% free and private AI that is on par with the best models from OpenAI, Anthropic and Google. (see video!) The CTO of Hugging Face called this "the second revolution of AI." He's right. If you are a building an AI product, you HAVE to start thinking about how you can leverage open source models in your product. It's only a matter of time until local models are mainstream and can run on most people's devices. LLMs are commoditizing. The human brain only requires a AAA battery's worth of energy to run. If biology figured out efficient intelligence, software can too. Maybe we won't need artificial super intelligence and thousands of data centres to do most economically valuable work. Maybe there's a future where AI agents just run on your laptop, fine-tuned to you, fully owned by you, costing only what your energy bill costs. In this future, it's all about building the most delightful, intuitive experience. We've been building for exactly this moment. Stay tuned.

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LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's @younesbelkada and Marc Sun, you'll: - Learn how to quantize nearly any open source model - Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library - Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here:
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LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's @younesbelkada and Marc Sun, you'll: - Learn how to quantize nearly any open source model - Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library - Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here:

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288,316 görüntüleme • 2 yıl önce

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Introducing Clips - 100% free, open source, agent-native alternative to Loom Unlike Loom, agent's can fully understand Clips just from a URL. Every Clip comes with APIs and metadata for agents to explore their contents. Agents can "see and hear" anything in a Clip - not just transcripts, but everything visually in the video at any timestamp. Easily share bug reports, feedback, analyses, or anything else in a way that you can easily pass to agents to use to improve products, reports, or more. Also unlike Loom, you own the software, so no one can jack up prices on you suddenly like Loom did to us. Clips is made to be customized. The built-in agent can customize its own code, so you can personalize the app to your needs and workflows. This, in my opinion, is the future of software. Open-source, forkable, customizable with agents, to make your own personal version of anything. You can also import Looms just from a URL and upload videos as well. I got so sick of telling people "don't send me feedback as looms, I can't pass those to agents, I need text and images" that I had to just solve this once and for all. There's a free hosted version you can use too, or fork and self host yourself. Will link to both in the replies.

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Andrew Ng

127,724 görüntüleme • 1 yıl önce

Cursor gets Perplexity API search tool while being FULLY AUTO CONTROLLED by Claude Computer use. So cursor performs web research! cursorrules and system prompt in comment Tool is just a function in tools .py file and you can add more tools of any kind as long as you update the cursorrules This was tricky because we are restricting comp use to certain coordinates for upper and lower input boxes. Luckily "run command" button also appears at the lower input box and its coordinates are fixed so we can use that to out advantage! You can download the source code by becoming a patron. link in comment essentially comp use is restricted to a limited action space and only 3 coordinates it can use. this way we are trying to ensure it doesn't do things we don't want and also make less mistakes(although mistakes do happen still both from comp use and from cursor although not much -------- If you are interested in learning how to use Cursor more in depth, I have a 28 chapter 1000x Cursor Course building full apps from scratch with 19 hours of content. link is in my bio if you are interested inspired partially by Stijn and Ryan O'Donnell (stijn mentioned in DM)
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Cursor gets Perplexity API search tool while being FULLY AUTO CONTROLLED by Claude Computer use. So cursor performs web research! cursorrules and system prompt in comment Tool is just a function in tools .py file and you can add more tools of any kind as long as you update the cursorrules This was tricky because we are restricting comp use to certain coordinates for upper and lower input boxes. Luckily "run command" button also appears at the lower input box and its coordinates are fixed so we can use that to out advantage! You can download the source code by becoming a patron. link in comment essentially comp use is restricted to a limited action space and only 3 coordinates it can use. this way we are trying to ensure it doesn't do things we don't want and also make less mistakes(although mistakes do happen still both from comp use and from cursor although not much -------- If you are interested in learning how to use Cursor more in depth, I have a 28 chapter 1000x Cursor Course building full apps from scratch with 19 hours of content. link is in my bio if you are interested inspired partially by Stijn and Ryan O'Donnell (stijn mentioned in DM)

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49,245 görüntüleme • 1 yıl önce

New short course: Open Source Models with Hugging Face 🤗, taught by Maria Khalusova, Marc Sun, and Younes Belkada! Hugging Face has been a game changer by letting you quickly grab any of hundreds of thousands of already-trained open source models to assemble into new applications. This course teaches you best practices for building this way, including how to search and choose among models. You’ll learn to use the Transformers library and walk through multiple models for text, audio, and image processing, including zero-shot image segmentation, zero-shot audio classification, and speech recognition. You'll also learn to use multimodal models for visual question answering, image search, and image captioning. Finally, you’ll learn how to demo what you build locally, on the cloud, or via an API using Gradio and Hugging Face Spaces. You can sign up here:
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3. Agent factory: Foundry is the complete app platform for building apps and agents. We are adding support for more models from Grok, Hugging Face, Meta, Mistral, and more. Plus: Agentic retrieval in Azure AI Search, Foundry Agent Service, integration with Copilot Studio, and more. And we are ensuring the tools you already use for identity, management, and security will now all extend to agents too. The NFL has already used Foundry, along with our data stack, to run its recent Combine.

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Open source/weight models are often used in regulated industries like Health Care or Financial Services, where they handle personally identifiable data, and can't send it to proprietary LLM providers. We recently chatted to Vaibhav (VB) Srivastav about the partnership VS Visual Studio Code and Hugging Face inference providers have to let you use open weight models directly in your IDE! Something that surprised me was just how fast inference providers like Cerebras make it to generate code! In this episode we made a journaling CLI tool using Qwen! I personally love open source and hope more of these models will become small enough to run locally!
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Open source/weight models are often used in regulated industries like Health Care or Financial Services, where they handle personally identifiable data, and can't send it to proprietary LLM providers. We recently chatted to Vaibhav (VB) Srivastav about the partnership VS Visual Studio Code and Hugging Face inference providers have to let you use open weight models directly in your IDE! Something that surprised me was just how fast inference providers like Cerebras make it to generate code! In this episode we made a journaling CLI tool using Qwen! I personally love open source and hope more of these models will become small enough to run locally!

Marlene Mhangami

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Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.
4:10

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Santiago

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I'm now building Executor full time as a startup! The state of tool calling is a mess: - Everyone is using different agents - You have no idea what actions are going to run - Destructive actions are treated the same as normal Executor lets you set up everything once & use it with any agent. You're able to control what tools can be called, require approval on destructive actions, and invite your team to all use them. Under the hood, it's built on codemode to let you add thousands of tools with 0 context bloat. I've been using it for a month so far and it has completely changed how I interact with agents. There's so much to be built here, immediately next up on the roadmap is workspaces, skills, workflows, and generative ui. Everything is being built open source over at there's a lot more coming on an SDK to embed it into your own agents, a self hostable cloud app, and plugins There's an early beta version over at It's very early on - if you hit any bugs let me know and I'll get them fixed up, excited to share more as I build it!
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I'm now building Executor full time as a startup! The state of tool calling is a mess: - Everyone is using different agents - You have no idea what actions are going to run - Destructive actions are treated the same as normal Executor lets you set up everything once & use it with any agent. You're able to control what tools can be called, require approval on destructive actions, and invite your team to all use them. Under the hood, it's built on codemode to let you add thousands of tools with 0 context bloat. I've been using it for a month so far and it has completely changed how I interact with agents. There's so much to be built here, immediately next up on the roadmap is workspaces, skills, workflows, and generative ui. Everything is being built open source over at there's a lot more coming on an SDK to embed it into your own agents, a self hostable cloud app, and plugins There's an early beta version over at It's very early on - if you hit any bugs let me know and I'll get them fixed up, excited to share more as I build it!

Rhys

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This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.
1:06

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

Aaron Levie

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Sequoia partner Pat Grady says software is shifting from apps that demand attention to agents that work quietly in the background. This shift will change what moats will look like, and will be especially hard for incumbents to deal with. "It's two very different business paradigms," he says. "In an era of apps, you want a lot of surface area with your customers, you want them to spend a lot of time in your product." "In an era of agents, things can just be running passively in the background. The amount of surface area you have with your customers, the amount of time they might spend in your product might be de minimis. So the nature of the moat that you [will need to build] is different." "I think we'll see a lot of companies in the coming years able to live in the software world and have some of the workflows people are accustomed to. And then separately they can deploy these passive agents that kind of function as coworkers who just come back to you when things are done."
2:02

Sequoia partner Pat Grady says software is shifting from apps that demand attention to agents that work quietly in the background. This shift will change what moats will look like, and will be especially hard for incumbents to deal with. "It's two very different business paradigms," he says. "In an era of apps, you want a lot of surface area with your customers, you want them to spend a lot of time in your product." "In an era of agents, things can just be running passively in the background. The amount of surface area you have with your customers, the amount of time they might spend in your product might be de minimis. So the nature of the moat that you [will need to build] is different." "I think we'll see a lot of companies in the coming years able to live in the software world and have some of the workflows people are accustomed to. And then separately they can deploy these passive agents that kind of function as coworkers who just come back to you when things are done."

TBPN

58,374 görüntüleme • 7 ay önce

.Public co-CEO Leif Abraham says AI agents can now automatically "buy the dip" and automate "any type of workflow in your brokerage account." "There's a lot of people who understand that you could generate income using covered calls on your portfolio." "High-level they understand, but now to actually go find the right contracts and see what the income potential would be—that's what starts to break down." "With agents, you can literally just be like, 'Look at my portfolio. Show me if I could do $5,000/month in income potential on it." "It scans your portfolio and shows you opportunities for covered calls and the income potential behind it. Then you can just activate it, and it runs as a workflow."
1:30

.Public co-CEO Leif Abraham says AI agents can now automatically "buy the dip" and automate "any type of workflow in your brokerage account." "There's a lot of people who understand that you could generate income using covered calls on your portfolio." "High-level they understand, but now to actually go find the right contracts and see what the income potential would be—that's what starts to break down." "With agents, you can literally just be like, 'Look at my portfolio. Show me if I could do $5,000/month in income potential on it." "It scans your portfolio and shows you opportunities for covered calls and the income potential behind it. Then you can just activate it, and it runs as a workflow."

sourcery

21,368 görüntüleme • 4 ay önce

nvidia allows you to use many llm models on their website. to use it programatically you can either create a phone verified nvidia nim account (heavily limited, often slow and NOT AVAILABLE IN TURKEY) or my wrapper app which bypasses this measures. > how it works? - it spawns and invisible safari webview of nvidia page - it solves the captcha using js (because page uses really soft version of hCaptcha) - because it's easy, it can create a pool of tokens - then spawns a local openai-anthropic compatible proxy server on port 2289 so you can use it with any tool, locally.
0:20

nvidia allows you to use many llm models on their website. to use it programatically you can either create a phone verified nvidia nim account (heavily limited, often slow and NOT AVAILABLE IN TURKEY) or my wrapper app which bypasses this measures. > how it works? - it spawns and invisible safari webview of nvidia page - it solves the captcha using js (because page uses really soft version of hCaptcha) - because it's easy, it can create a pool of tokens - then spawns a local openai-anthropic compatible proxy server on port 2289 so you can use it with any tool, locally.

Batuhan Katırcı

54,548 görüntüleme • 4 ay önce

ELON MUSK: “The way to expand compute is really in space. There’s a lot of room in space. And if you look at the size of Earth relative to the Sun or relative to the Solar System, you realize just how tiny it is. We’re very, very tiny. We only receive about half a billionth of the Sun’s energy. So you can really think of Earth as being like a tiny dust mote in a vast darkness. So the way to expand compute without, you know, using up all the land on Earth is to do so in space. And then you can do it without using space for power and water on Earth; you can just do it in space. So I think we’ll probably be launching our first AI satellites next year, and then we’ll be able to do that, I think, at large scale in about two years.”
1:08

ELON MUSK: “The way to expand compute is really in space. There’s a lot of room in space. And if you look at the size of Earth relative to the Sun or relative to the Solar System, you realize just how tiny it is. We’re very, very tiny. We only receive about half a billionth of the Sun’s energy. So you can really think of Earth as being like a tiny dust mote in a vast darkness. So the way to expand compute without, you know, using up all the land on Earth is to do so in space. And then you can do it without using space for power and water on Earth; you can just do it in space. So I think we’ll probably be launching our first AI satellites next year, and then we’ll be able to do that, I think, at large scale in about two years.”

DogeDesigner

65,218 görüntüleme • 1 ay önce