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IBM dropped CUGA, open-source enterprise agent to automate boring tasks 🔥 > given workspace files, it writes and executes code to accomplish any task 🤯 > comes with a ton of tools built for enterprise tasks, supports MCPs > plug in your favorite LLM 👏 here's a small demo... show more

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Florence-2, the new vision foundation model by Microsoft, can now run 100% locally in your browser on WebGPU, thanks to Transformers.js! 🤗🤯 It supports tasks like image captioning, optical character recognition, object detection, and many more! 😍 WOW! Demo (+ source code) 👇

Florence-2, the new vision foundation model by Microsoft, can now run 100% locally in your browser on WebGPU, thanks to Transformers.js! 🤗🤯 It supports tasks like image captioning, optical character recognition, object detection, and many more! 😍 WOW! Demo (+ source code) 👇

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🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

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18,369 views • 2 years ago

using tools, Cursor Agent have the capability to reason about when it should - read & write code - search your codebase - call MCP servers - run terminal commands an example where it connects to a postgres database, runs a query and writes to file

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eric zakariasson

34,917 views • 1 year ago

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

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203,595 views • 7 months ago

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124,382 views • 1 year ago

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Hassan

27,086 views • 10 months ago

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.

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Daniel San

57,240 views • 4 months ago

Task Master CLI will makes Cursor 10x better at building projects. I created a full video demo of how it works. For those that don't want to watch. Here's what happens: 1️⃣ Install npm i task-master-ai CLI 2️⃣ Give it a description of your project. It will generate a PRD 3️⃣ It will then convert the PRD into tasks (10 detailed .txt files and a .json that acts as a local db to task track status) 4️⃣ Ask it to do a task. It will do the task and mark it off as done. Each task can also be broken down into small tasks using "expand" command. Following these steps it will build out an entire project. This is the future of dev and how tools like Manus work. But you can run it locally and follow step by step to make sure you're happy with what's happening. Seems a fitting to be posting about Task Master the day before Passover starts. Good job by Eyal Toledano who built this project.

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Elie Steinbock

36,636 views • 1 year ago

Want to reduce your Claude Code bill? Claude Code uses smaller models like Haiku for simple tasks. Instead of sending every small request to a paid API, run those tasks locally with Jan and keep heavier work in the cloud. One setup. Lower API usage. More control. Get the latest version at

Want to reduce your Claude Code bill? Claude Code uses smaller models like Haiku for simple tasks. Instead of sending every small request to a paid API, run those tasks locally with Jan and keep heavier work in the cloud. One setup. Lower API usage. More control. Get the latest version at

👋 Jan

13,168 views • 5 months ago

More people need to understand that Claude Code is a general intelligence that can do stuff on your computer. To help non-coders understand its power, I'm building Folio, an app where you share a folder with Claude and it can work on your files directly. Here's a quick demo:

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218,956 views • 7 months ago

The open-source community is INSANE 🤯 Claude Code routines dropped literal hours ago, and this guy's already shipped a clone. Meet Multica Autopilot: Now you can run routines entirely locally using whatever agent you want (Opencode, Codex, Hermes, OpenClaw etc). repo in 🧵↓

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136,098 views • 3 months ago

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Itamar Friedman

139,228 views • 2 years ago

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Andrew Ng

124,823 views • 5 months ago

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Fetch.ai

14,280 views • 1 year ago

I created a demo of a bioautomation system that uses LLMs, Opentrons, and lua to create a dynamic programming environment for cloud labs or robot/human clusters. It can reason about its own code based off of lab measurements. Most importantly, I actually fucking implemented it, and it is open source. Took about 4 days for this rough draft, and it is very much a draft. The user inputs their task, the system creates code, and then executes it. The code defines control flow from data generated in the lab. Not only can it create code, but it can reason about things that could have gone wrong, run analysis using an internal sandbox, and then create new code based off of that analysis for execution. Timestamps: 0:00 - intro and code generation 3:04 - homebrewed replacement for Opentrons API for running all this code 5:26 - dynamic control flow using data 6:10 - LLM reasoning about a biological protocol and fixing it 10:15 - rant on the future of cloud labs and bioautomation I made this as a demo for how I think we should be thinking about building and scaling biology. I believe we can encode the tacit knowledge of a laboratory into the knowledge of an LLM, that we can do reinforcement learning off of results it creates, and that we must do that by leveraging a sufficient quantity of unique, useful, verifiable protocols. That doesn't come from just doing drug screens - it comes from doing basic everyday experiments and doing them well. Through the elimination of tacit knowledge necessary to physically operate a lab + proper batching + models writing code, I think we can make building biotechnology 10x-100x cheaper and easier than it is nowadays.

I created a demo of a bioautomation system that uses LLMs, Opentrons, and lua to create a dynamic programming environment for cloud labs or robot/human clusters. It can reason about its own code based off of lab measurements. Most importantly, I actually fucking implemented it, and it is open source. Took about 4 days for this rough draft, and it is very much a draft. The user inputs their task, the system creates code, and then executes it. The code defines control flow from data generated in the lab. Not only can it create code, but it can reason about things that could have gone wrong, run analysis using an internal sandbox, and then create new code based off of that analysis for execution. Timestamps: 0:00 - intro and code generation 3:04 - homebrewed replacement for Opentrons API for running all this code 5:26 - dynamic control flow using data 6:10 - LLM reasoning about a biological protocol and fixing it 10:15 - rant on the future of cloud labs and bioautomation I made this as a demo for how I think we should be thinking about building and scaling biology. I believe we can encode the tacit knowledge of a laboratory into the knowledge of an LLM, that we can do reinforcement learning off of results it creates, and that we must do that by leveraging a sufficient quantity of unique, useful, verifiable protocols. That doesn't come from just doing drug screens - it comes from doing basic everyday experiments and doing them well. Through the elimination of tacit knowledge necessary to physically operate a lab + proper batching + models writing code, I think we can make building biotechnology 10x-100x cheaper and easier than it is nowadays.

Keoni Gandall

21,403 views • 1 year ago