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introducing matrix. the ultimate multi-agent runtime that turns your product into actual revenue and automates 99% of what it takes to run a company. probably the most different, most future-feeling agent product you've ever seen. the harness, the interface, everything is rebuilt from scratch. think of it as the... complement to codex and claude code. they write the code. matrix does literally everything else you actually need done. plug in your codex subscription and run it for free, or byok. fully free either way. this video was edited by matrix and voiced by an agent. more real cases coming soon. matrix is in beta preview right now. full launch next week. retweet + comment and we'll send invite codes to the first 200.show more

Derek Nee

13,341 subscribers

15,361 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

37 条评论

Derek Nee 的头像

Derek Nee3 个月前

try it directly: BNPHNH HAVYPX ESHQKJ L5GYEM SWFZ8E TA9HQP QUHQWP GVEQHB P8QVYD NS2B5V XN6WXS YSQCJN C74KNL 8BFT35 VGFSBX YDYRWF

Matrix 的头像

Matrix3 个月前

😎😎😎

MAMBA 🧲 的头像

MAMBA 🧲3 个月前

5SmytRuQgRYRRnAVbUVLWVxoYyZhMmKrRKpEemtxpump they made a coin to fund your project on @Pumpfun fees goes to you

Maimu 的头像

Maimu3 个月前

There is a solana coin supporting your project Matrix! 5SmytRuQgRYRRnAVbUVLWVxoYyZhMmKrRKpEemtxpump

xWhisky 的头像

xWhisky3 个月前

This is pretty good project, are you interested in accepting donations through ur github?

Sil 的头像

Sil3 个月前

5SmytRuQgRYRRnAVbUVLWVxoYyZhMmKrRKpEemtxpump

kaize 的头像

kaize3 个月前

one of the best tools I've used recently - I've already migrated my entire content infrastructure from the Claude to Matrix to be honest, during the initial setup, something went haywire, and The Matrix identified my profile based on old posts (from a year ago), so I had to tell it that those were old posts and upload links and text to

Hybrid 🏃🏾 的头像

Hybrid 🏃🏾3 个月前

I swear there’s a new project dropping every day with some similarity to the project. I just seen dropped the previous day. I’m overwhelmed

Harper 的头像

Harper3 个月前

can we donate you money trought your Github ?

Bruce Guai 的头像

Bruce Guai3 个月前

@matrix_build 来Matrix测试，构建第一个帮你取得影响力，获得收益的 Agent 公司。在给大家抽点测试额度，名额有限！

luka 的头像

luka3 个月前

code please

Derek Nee 的头像

Derek Nee3 个月前

QZT49G try this!

ᕼ ᐱ ᖇ Ƨ ᕼ ᐱ 的头像

ᕼ ᐱ ᖇ Ƨ ᕼ ᐱ3 个月前

is there new codes? 🥺

Derek Nee 的头像

Derek Nee3 个月前

yes! QEGAP9

Husidi 的头像

Husidi3 个月前

Matrix超独特，非常实用！

Colbert 的头像

Colbert3 个月前

@matrix_build Gm need an invite

Badass 的头像

Badass3 个月前

I love to try

MrBoring 的头像

MrBoring3 个月前

👏

刺刺河豚的头像

刺刺河豚3 个月前

8ReTVJUb5E1gS67xGYjJ3xy45P3Yrux8JqhLoBvfBAGS Someone has created a tax token for you, which will provide funding for the project if you endorse it.

kierbicAI 的头像

kierbicAI3 个月前

me pls thank u

TTTVV 的头像

TTTVV3 个月前

@matrix_build 我想要

Quixoa 的头像

Quixoa3 个月前

The interesting part is whether the runtime makes ownership visible after the demo. Multi-agent systems get a lot more credible when teams can see which agent acted, what it touched, and where human review still sits in the loop.

Doctor Ponzi 的头像

Doctor Ponzi3 个月前

a bankr community coin was made to support your project, all fees go to you

Ashique 💪 的头像

Ashique 💪3 个月前

Cool

MonKMind Media🖊️ 的头像

MonKMind Media🖊️3 个月前

Matrix ⚛️

tofa 的头像

tofa3 个月前

let us fundraise the growth of this no support needed! free fees to you using @pumpfun

Felipe Andra ➔ jurishub.app 的头像

Felipe Andra ➔ jurishub.app3 个月前

I would love to try it out!

lurks 的头像

lurks3 个月前

Sent ya a DM

Türkiye In The World 的头像

Türkiye In The World3 个月前

@matrix_build Cong.

Guoguo Lei 的头像

Guoguo Lei3 个月前

is there new codes? thanks

Sanjeev 的头像

Sanjeev3 个月前

Exciting , and even more exciting if it would become open source

Ritesh Ranjan 的头像

Ritesh Ranjan3 个月前

invite code, plz

Hervé Dumoulin 的头像

Hervé Dumoulin3 个月前

@matrix_build I hope I might receive a code to explore your latest product achievement.

MISFITSッ的头像

MISFITSッ3 个月前

Gib

Jason 的头像

Jason3 个月前

Love to try it

Husidi 的头像

Husidi3 个月前

已转，希望可以获得一个邀请码，体验最新的agent

I_am_eth 的头像

I_am_eth3 个月前

id like one please. 🙏

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rody

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what if you can run an entire 0-person company — without the grind of running a team? matrix is the runtime that makes it possible. in last week’s limited beta, our users created tens of thousands of new 0-person companies and started real businesses in matrix. today, matrix is open to everyone. launch yours ↓

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MAVERICK X

791,268 次观看 • 3 年前

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Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

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We made an MCP for your phone. Your laptop is just half of your life, and the other half is in your phone. Now your agent gets the mobile screen too. No connectors, no complex setup, it has access to everything that lives on your phone, all the logged-in apps, your notes, your calls, you name it. It sees the screen, taps it, types on it, from Claude Code, Codex, or any MCP client. Live now at $20 in credits, $20 more for the first 100 who connect a phone and run a task. Comment "discord" if you want the invite to the builder Discord, I'll DM it to you.

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43,993 次观看 • 1 个月前

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We spent two years building an AI that takes one goal all the way to the inbox. That required inventing a new email language. One that renders perfectly in Gmail, Apple Mail, dark mode, even Outlook 2003. Introducing Migma AI It designs one campaign and adapts it for different audiences and languages. You approve it, and it sends. It also sends from your own domain, learns from every send, and connects to Cursor, Claude Code or Codex so your agent can wire up your app's emails. Migma Pro is free for 7 days and 50% off forever for first 200 supporters. Repost and comment "Migma" and we'll send the access over. Live today ↓

Adam Lababidi

183,957 次观看 • 2 个月前

You wanted shadcn — so we built the heinous infra to make it work. Introducing Agent Arena, starting with the best: Claude Code by Anthropic Codex 5 by OpenAI Cursor Agent by Cursor Devin by Cognition Gemini CLI by Google DeepMind Grok Code Fast 1 by xAI 1/ Your prompt launches a containerized agent inside a fresh GitHub repo 2/ The agent can install packages, make tool calls, and write multi-file code 3/ The repo is deployed to Vercel *by the agent*, clonable to your heart's desire

You wanted shadcn — so we built the heinous infra to make it work. Introducing Agent Arena, starting with the best: Claude Code by Anthropic Codex 5 by OpenAI Cursor Agent by Cursor Devin by Cognition Gemini CLI by Google DeepMind Grok Code Fast 1 by xAI 1/ Your prompt launches a containerized agent inside a fresh GitHub repo 2/ The agent can install packages, make tool calls, and write multi-file code 3/ The repo is deployed to Vercel by the agent, clonable to your heart's desire

Grace Li

169,090 次观看 • 11 个月前

Introducing the first ever video marketing AI agent Overlap is an autonomous agent that creates viral clips from any video and posts them to your socials The best way to get your content seen by the people who matter Try it out for free today (link below)

Introducing the first ever video marketing AI agent Overlap is an autonomous agent that creates viral clips from any video and posts them to your socials The best way to get your content seen by the people who matter Try it out for free today (link below)

Overlap

142,111 次观看 • 1 年前

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 次观看 • 10 个月前

Another insane Jev use case! Jev makes it incredibly cheap to evaluate and classify agent runs at scale. And finally, someone open-sourced a self-improving memory layer that can put that capability to work across agent harnesses. It turns your agent sessions into a compounding knowledge layer, where every successful run can make future agents smarter across: - Codex - Claude Code - Cursor - OpenCode and 20+ more Beacon by Asymptote Labs continuously builds a shared history across your agent harnesses and uses Jev to identify the runs worth learning from. It then turns the best workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐) Most agent runs are messy. They contain exploration, failed commands, dead ends, and one-off fixes that should never become permanent memory. So Beacon preserves the full session history, while Jev helps decide what should be promoted, reviewed, or discarded. The recording below shows this in action. Beacon found 579 sessions across 5 coding-agent harnesses and normalized them into one consistent history. From there, Jev surfaces the lessons worth keeping and makes them available across your agent stack. - A pattern learned in Cursor can carry into OpenCode. - A lesson from Claude Code can improve the next Codex run. Every successful run adds to the shared knowledge layer, making future agents smarter. If you want to dive deeper into Jev, I also wrote a breakdown of how it works. The article is quoted below.

Another insane Jev use case! Jev makes it incredibly cheap to evaluate and classify agent runs at scale. And finally, someone open-sourced a self-improving memory layer that can put that capability to work across agent harnesses. It turns your agent sessions into a compounding knowledge layer, where every successful run can make future agents smarter across: - Codex - Claude Code - Cursor - OpenCode and 20+ more Beacon by Asymptote Labs continuously builds a shared history across your agent harnesses and uses Jev to identify the runs worth learning from. It then turns the best workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐) Most agent runs are messy. They contain exploration, failed commands, dead ends, and one-off fixes that should never become permanent memory. So Beacon preserves the full session history, while Jev helps decide what should be promoted, reviewed, or discarded. The recording below shows this in action. Beacon found 579 sessions across 5 coding-agent harnesses and normalized them into one consistent history. From there, Jev surfaces the lessons worth keeping and makes them available across your agent stack. - A pattern learned in Cursor can carry into OpenCode. - A lesson from Claude Code can improve the next Codex run. Every successful run adds to the shared knowledge layer, making future agents smarter. If you want to dive deeper into Jev, I also wrote a breakdown of how it works. The article is quoted below.

Akshay 🚀

118,955 次观看 • 7 天前

My new favorite way to build with agents is to organize them into a factory. This is the 4-step workflow: 1) Station. - A custom order gets its own station, so the order does not interfere with the rest of production. - In software, this station is a branch or a work tree. 2) Assembly line. - The agent writes code, changes files, and adds structure. 3) Quality control. - Does it turn on? - Does it fit? - Does it break under pressure? - In software, you run tests, open a preview, and read the logs and screenshots. 4) Shipping. - The product goes out the door. - You merge, deploy the PR, and write release notes. If a product fails quality control, you send feedback to the product team. Then you do the loop again. Write each step of your agent workflow as a factory job.

My new favorite way to build with agents is to organize them into a factory. This is the 4-step workflow: 1) Station. - A custom order gets its own station, so the order does not interfere with the rest of production. - In software, this station is a branch or a work tree. 2) Assembly line. - The agent writes code, changes files, and adds structure. 3) Quality control. - Does it turn on? - Does it fit? - Does it break under pressure? - In software, you run tests, open a preview, and read the logs and screenshots. 4) Shipping. - The product goes out the door. - You merge, deploy the PR, and write release notes. If a product fails quality control, you send feedback to the product team. Then you do the loop again. Write each step of your agent workflow as a factory job.

The Startup Ideas Podcast (SIP) 🧃

13,017 次观看 • 14 天前

New open source project: Easel, a macOS workspace for AI-assisted product design and frontend iteration powered by the Codex SDK. Think of it as "Claude Design for Codex." It brings together a local project library, design-system setup, Codex chat, and a live web preview with a point-and-click inspector. Set up your design system however you like. Import one from Figma , bring your own, or have the agent generate it as a design.md. Then use it to build prototypes and slide decks. Codex-first, with Claude and other AI providers and local models planned for future releases. OpenAI Developers

New open source project: Easel, a macOS workspace for AI-assisted product design and frontend iteration powered by the Codex SDK. Think of it as "Claude Design for Codex." It brings together a local project library, design-system setup, Codex chat, and a live web preview with a point-and-click inspector. Set up your design system however you like. Import one from Figma , bring your own, or have the agent generate it as a design.md. Then use it to build prototypes and slide decks. Codex-first, with Claude and other AI providers and local models planned for future releases. OpenAI Developers

James Rochabrun

61,608 次观看 • 3 个月前