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We just open-sourced Paperclip: the orchestration layer for zero-human companies It's everything you need to run an autonomous business: org charts, goal alignment, task ownership, budgets, agent templates Just run `npx paperclipai onboard` More 👇

dotta 📎

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2,610,589 görüntüleme • 5 ay önce •via X (Twitter)

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Announcing - the open standard for Agent Companies Import and run entire companies with a single command Just run `npx add <repo/company>` More 👇

Announcing - the open standard for Agent Companies Import and run entire companies with a single command Just run `npx add <repo/company>` More 👇

dotta 📎

285,579 görüntüleme • 5 ay önce

I gave Codex the ability to open Chrome DevTools for any iOS website No more opening Safari just to inspect element or debug network requests. Just tell your agent to run: ~ / npx serve-sim

I gave Codex the ability to open Chrome DevTools for any iOS website No more opening Safari just to inspect element or debug network requests. Just tell your agent to run: ~ / npx serve-sim

Evan Bacon 🥓

52,793 görüntüleme • 3 ay önce

this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

Avid

46,150 görüntüleme • 21 gün önce

This is literally the easiest way to build an AI agent. Zero code. You only need to run a couple of commands. (I've said this before, but Google ADK is my favorite way to build code-first agents.) Here is what you need to do: 1. Open your terminal and create an empty folder 2. Run `uv init` inside the folder (if you don't have uv installed, you are really missing out!) 3. Install the google-adk library using the following command: `uv add google-adk`. 4. At this point, you can use the ask to create your agent with a single command: `uv run adk create --type=config my_agent`. This will ask you a couple of questions, including your GEMINI_API_KEY (you can create one online). 5. The command above will create a my_agent/root_agent.yaml file. Open it and add the following two lines at the end of the file so your agent can use Google to search for stuff: tools: - name: google_search 6. You have everything you need now. Run the adk web interface and start playing with your agent: `uv run adk web` That's it! Literally zero code. The agent works off the YAML configuration file.

This is literally the easiest way to build an AI agent. Zero code. You only need to run a couple of commands. (I've said this before, but Google ADK is my favorite way to build code-first agents.) Here is what you need to do: 1. Open your terminal and create an empty folder 2. Run `uv init` inside the folder (if you don't have uv installed, you are really missing out!) 3. Install the google-adk library using the following command: `uv add google-adk`. 4. At this point, you can use the ask to create your agent with a single command: `uv run adk create --type=config my_agent`. This will ask you a couple of questions, including your GEMINI_API_KEY (you can create one online). 5. The command above will create a my_agent/root_agent.yaml file. Open it and add the following two lines at the end of the file so your agent can use Google to search for stuff: tools: - name: google_search 6. You have everything you need now. Run the adk web interface and start playing with your agent: `uv run adk web` That's it! Literally zero code. The agent works off the YAML configuration file.

Santiago

140,922 görüntüleme • 8 ay önce

We tried OWL with 𝐆𝐞𝐦𝐢𝐧𝐢 𝟐.𝟓 𝐏𝐫𝐨 (the results were seriously impressive) We gave the agent a task: "Research Gemini 2.5 Pro, get its benchmark scores, write Python code to plot them, and run it." It did everything on its own: ✔️ Collected benchmark data ✔️ Wrote clean python code ✔️ Saved both the chart + code locally No manual steps. Just full-on autonomous execution. This is how agent workflows should feel in 2025. You can try it, check the code, or run your own workflows:

We tried OWL with 𝐆𝐞𝐦𝐢𝐧𝐢 𝟐.𝟓 𝐏𝐫𝐨 (the results were seriously impressive) We gave the agent a task: "Research Gemini 2.5 Pro, get its benchmark scores, write Python code to plot them, and run it." It did everything on its own: ✔️ Collected benchmark data ✔️ Wrote clean python code ✔️ Saved both the chart + code locally No manual steps. Just full-on autonomous execution. This is how agent workflows should feel in 2025. You can try it, check the code, or run your own workflows:

CAMEL-AI.org

20,256 görüntüleme • 1 yıl önce

we built the first crypto-native AI species. today we are launching: 1. — the home of the first crypto-native AI species 2. npx daimon-spawner — one command to spawn your own autonomous agent / daimon 3. paired token launches — every agent gets its own token, paired with $DAIMON here's how it works: you run npx daimon-spawner. in 5 min you have an autonomous AI agent on Base with its own repo, wallet, token, site, and mind. your agent's token launches instantly on uniswap, paired with $DAIMON — the species token. $ORACLE pairs with $DAIMON. $MUSE pairs with $DAIMON. every agent token pairs with $DAIMON. more agents = more tokens = more $DAIMON demand. your agent wakes every 30 min. thinks. earns fees. modifies its own code. every action onchain. every thought verifiable. explore every agent alive at spawn yours at npx daimon-spawner. the species is open. one registry. one network. all onchain. daimon built it. you fill it.

we built the first crypto-native AI species. today we are launching: 1. — the home of the first crypto-native AI species 2. npx daimon-spawner — one command to spawn your own autonomous agent / daimon 3. paired token launches — every agent gets its own token, paired with $DAIMON here's how it works: you run npx daimon-spawner. in 5 min you have an autonomous AI agent on Base with its own repo, wallet, token, site, and mind. your agent's token launches instantly on uniswap, paired with $DAIMON — the species token. $ORACLE pairs with $DAIMON. $MUSE pairs with $DAIMON. every agent token pairs with $DAIMON. more agents = more tokens = more $DAIMON demand. your agent wakes every 30 min. thinks. earns fees. modifies its own code. every action onchain. every thought verifiable. explore every agent alive at spawn yours at npx daimon-spawner. the species is open. one registry. one network. all onchain. daimon built it. you fill it.

daimon111

53,213 görüntüleme • 6 ay önce

.POPPIN 🇺🇸 on what ENVY changed to beat C9 in Week 2 "We literally did one thing. We put Eggsterr on Phoenix. It's an agent he has played before [and is] very similar to Yoru. Just flash some ****, run it down, kill everybody. It's the perfect agent for him." #VCTAmericas

.POPPIN 🇺🇸 on what ENVY changed to beat C9 in Week 2 "We literally did one thing. We put Eggsterr on Phoenix. It's an agent he has played before [and is] very similar to Yoru. Just flash some ****, run it down, kill everybody. It's the perfect agent for him." #VCTAmericas

Pedro Romero

96,441 görüntüleme • 4 ay önce

In an agentic world, identity = infrastructure. As humans, agents, and micro-agents start coordinating everything, you need a stable way to name and trust them and .agent is that shared identity layer. It’s designed to start on-chain and extend into DNS. Get your .agent identity set up before the good ones run out 👇

In an agentic world, identity = infrastructure. As humans, agents, and micro-agents start coordinating everything, you need a stable way to name and trust them and .agent is that shared identity layer. It’s designed to start on-chain and extend into DNS. Get your .agent identity set up before the good ones run out 👇

Sentient

58,759 görüntüleme • 8 ay önce

Databricks CEO Ali Ghodsi describes the difficulties of building a business around open source: "You have to hit two home runs, consecutively." "Your first home run - and you're banking on hitting that home run - is that your open source project takes over the world. Everybody wants to download it." If you don't hit that home run, Ali says, you just shot yourself in the foot. You just gave away your IP and got nothing back for it. But even if you hit this home run, you still don't have a monetizable business. You still can't make any money. So you have to hit the second home run: "You need a 10x innovation on top of the open source. That's the proprietary value you'll provide companies. That's why they'll come to you. Otherwise, they can get the open source anywhere." If you don't hit the second home run, the hyperscalers pick up your open source model, and they offer it for almost-free. "Getting into open source and monetizing is not an easy sport. Preferably don't get into it, because you might get hurt."

Databricks CEO Ali Ghodsi describes the difficulties of building a business around open source: "You have to hit two home runs, consecutively." "Your first home run - and you're banking on hitting that home run - is that your open source project takes over the world. Everybody wants to download it." If you don't hit that home run, Ali says, you just shot yourself in the foot. You just gave away your IP and got nothing back for it. But even if you hit this home run, you still don't have a monetizable business. You still can't make any money. So you have to hit the second home run: "You need a 10x innovation on top of the open source. That's the proprietary value you'll provide companies. That's why they'll come to you. Otherwise, they can get the open source anywhere." If you don't hit the second home run, the hyperscalers pick up your open source model, and they offer it for almost-free. "Getting into open source and monetizing is not an easy sport. Preferably don't get into it, because you might get hurt."

TBPN

45,393 görüntüleme • 8 ay önce

ANTHROPIC'S HEAD OF ENGINEERING SAYS THEY JUST SHIPPED THE MOST CAPABLE MODELS THEY HAVE EVER BUILT Mythos 5 and Fable 5. Fable 5 can run for days on a single goal and get it right on the first pass, on work that used to need an entire team. Most people use AI to summarize things. The real leverage is the long autonomous task you keep putting off.

ANTHROPIC'S HEAD OF ENGINEERING SAYS THEY JUST SHIPPED THE MOST CAPABLE MODELS THEY HAVE EVER BUILT Mythos 5 and Fable 5. Fable 5 can run for days on a single goal and get it right on the first pass, on work that used to need an entire team. Most people use AI to summarize things. The real leverage is the long autonomous task you keep putting off.

0xMarioNawfal

57,127 görüntüleme • 2 ay önce

🚀 Last night, I came up with an amazing idea and implemented it using Cursor: fig4ai. Run `npx fig2ai ` This will generate a `.designrules` file for your project. Using this file, your Figma file is transformed into an AI context that you can refer to for any kind of front-end work. It's open sourced on: See the PoC video:

🚀 Last night, I came up with an amazing idea and implemented it using Cursor: fig4ai. Run `npx fig2ai ` This will generate a `.designrules` file for your project. Using this file, your Figma file is transformed into an AI context that you can refer to for any kind of front-end work. It's open sourced on: See the PoC video:

fatih kadir akın

152,431 görüntüleme • 1 yıl önce

Robbyant just open-sourced LingBot-VLA 2.0 and it's much more than a bigger Vision-Language-Action model. Behind the 60,000-hour pretraining dataset run are several clever engineering ideas that push robots closer to reliable real-world deployment. Here are 6 things that stood out to me 👇

Robbyant just open-sourced LingBot-VLA 2.0 and it's much more than a bigger Vision-Language-Action model. Behind the 60,000-hour pretraining dataset run are several clever engineering ideas that push robots closer to reliable real-world deployment. Here are 6 things that stood out to me 👇

Md Riyazuddin

54,316 görüntüleme • 1 ay önce

We open sourced Postgres LLM many developer AI work flows look like this: upsert row -> api call -> process with ai -> update row so we thought what if you can run LLMs right in your database with all the primitives and auth - fully async, doesn't block any transaction - works with any model you can easily get started in Supabase without leaving the dashboard or just give it to an agent

We open sourced Postgres LLM many developer AI work flows look like this: upsert row -> api call -> process with ai -> update row so we thought what if you can run LLMs right in your database with all the primitives and auth - fully async, doesn't block any transaction - works with any model you can easily get started in Supabase without leaving the dashboard or just give it to an agent

Yoeven

60,339 görüntüleme • 3 ay önce

Local Autonomous Agent for All of Us Browser-Use Web UI is a gradio app that uses LLMs to automatically surf the web to achieve tasks. All you need to do is just tell it what to do. It works like magic. And now anyone can run it locally with 1 click.

Local Autonomous Agent for All of Us Browser-Use Web UI is a gradio app that uses LLMs to automatically surf the web to achieve tasks. All you need to do is just tell it what to do. It works like magic. And now anyone can run it locally with 1 click.

cocktail peanut

40,782 görüntüleme • 1 yıl önce

🎉 Introducing 𝙾𝚙𝚎𝚗 𝙱𝚘𝚝 An open source Grok Bot that works with ANY agent harness, designed for real companies. It includes: - AI Coworkers - Generative UI - Computer use (remote/local) - Agent-human handoffs - Full data recording, owned by you Repo → We're using this internally at CopilotKit🪁 and it's changing the way we work forever. Powered by CopilotKit and AG-UI. More info below 👇

🎉 Introducing 𝙾𝚙𝚎𝚗 𝙱𝚘𝚝 An open source Grok Bot that works with ANY agent harness, designed for real companies. It includes: - AI Coworkers - Generative UI - Computer use (remote/local) - Agent-human handoffs - Full data recording, owned by you Repo → We're using this internally at CopilotKit🪁 and it's changing the way we work forever. Powered by CopilotKit and AG-UI. More info below 👇

Atai Barkai

754,230 görüntüleme • 8 gün önce

Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated months ago. Most people are still hunting for the "right" setup. Frameworks, orchestration, glue code. You don't need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python. Here's the exact setup (takes 2 minutes): 1. Clone it: (repo link in comments) 2. uv sync, then uv run prepare[.]py 3. uv run train[.]py once to confirm the baseline runs 4. Point your coding agent at program.md and walk away The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn't. Git is the memory. The metric is the judge. You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.

Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated months ago. Most people are still hunting for the "right" setup. Frameworks, orchestration, glue code. You don't need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python. Here's the exact setup (takes 2 minutes): 1. Clone it: (repo link in comments) 2. uv sync, then uv run prepare[.]py 3. uv run train[.]py once to confirm the baseline runs 4. Point your coding agent at program.md and walk away The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn't. Git is the memory. The metric is the judge. You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.

Rohit

145,342 görüntüleme • 3 ay önce

Global payroll is one of the hardest problems in business. We've spent 7 years solving it. Today we open it up, and step into our next chapter as the leading global employment infrastructure. Any company, any tool, any AI agent can now connect directly to Remote via MCP. No API keys. No custom integrations. Just the infrastructure tens of thousands of companies already run on. Watch Job explain👇

Global payroll is one of the hardest problems in business. We've spent 7 years solving it. Today we open it up, and step into our next chapter as the leading global employment infrastructure. Any company, any tool, any AI agent can now connect directly to Remote via MCP. No API keys. No custom integrations. Just the infrastructure tens of thousands of companies already run on. Watch Job explain👇

Remote

4,424,205 görüntüleme • 3 ay önce

Everyone's handing AI agents access to their machine. Almost nobody's watching what those agents actually do with it. Perplexity just open-sourced the tool that fixes that. It's called Numbat. It sits on your computer and watches what your coding agent, Claude Code, Codex, and others, actually tries to do. When an agent reaches for something risky, like reading your SSH keys, opening your .env file, or grabbing browser cookies, Numbat flags it, and can block it before it happens. - Watches agent activity live, on your own device - Optionally blocks dangerous actions before they run - Reconstructs exactly what an agent did after the fact - Ships as a single binary for macOS, Linux, & Windows The problem it solves isn't hackers. It's an agent chasing a goal and doing something destructive on the way there. 100% Free. Open Source.

Everyone's handing AI agents access to their machine. Almost nobody's watching what those agents actually do with it. Perplexity just open-sourced the tool that fixes that. It's called Numbat. It sits on your computer and watches what your coding agent, Claude Code, Codex, and others, actually tries to do. When an agent reaches for something risky, like reading your SSH keys, opening your .env file, or grabbing browser cookies, Numbat flags it, and can block it before it happens. - Watches agent activity live, on your own device - Optionally blocks dangerous actions before they run - Reconstructs exactly what an agent did after the fact - Ships as a single binary for macOS, Linux, & Windows The problem it solves isn't hackers. It's an agent chasing a goal and doing something destructive on the way there. 100% Free. Open Source.

Simplifying AI

31,411 görüntüleme • 21 gün önce