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The next big thing is Asynchronous Coding Agents. Such Agents change our role as engineers from Conductor (directing one agent at a time) to Orchestrator (defining tasks for a "fleet" of agents working in parallel), for very well-defined tasks with human review. GitHub's Copilot Agent, as covered at #githubuniverse,... is one such example I've been trying and liking. There are a number of options also worth considering, including Jules (also great!), Codex web by OpenAI Developers, and Anthropic's Claude Code for web. Editors like Cursor are also exploring this with some very interesting UX ideas. For the more advanced, orchestrators like Claude Squad, Magnet and Conductor by (nice balance on the engineering/product spectrum) are certainly worth considering. Some caveats: Orchestration has been working well for me in small-medium sized projects: for fixing UI bugs, adding small features etc. This can be good for tackling migrations, upgrading dependencies etc. Enterprise teams are very much still figuring out their "Conductor" patterns but eventually, I do see more orchestration becoming a standard part of the engineering workflow.show more

Addy Osmani

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17,510 次观看 • 1 年前 •via X (Twitter)

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🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!

Dan Shipper 📧

145,487 次观看 • 1 年前

Introducing multi-agent orchestration in Oz, with support for Claude Code, Codex and the Warp Agent. Use /orchestrate to delegate complex tasks across a team of agents, locally or in the cloud.

Introducing multi-agent orchestration in Oz, with support for Claude Code, Codex and the Warp Agent. Use /orchestrate to delegate complex tasks across a team of agents, locally or in the cloud.

Warp

1,198,899 次观看 • 5 个月前

Introducing Helmor The open-source, local-first answer to Conductor. A more refined, faster GUI for orchestrating coding agents. No cloud. One-click import from Conductor. AI made coding faster. Helmor is about finishing the rest of the loop: orchestration, workspaces, review, testing, and merge. We believe the next generation of GUI agent orchestration should be built in the open — by the community.

Introducing Helmor The open-source, local-first answer to Conductor. A more refined, faster GUI for orchestrating coding agents. No cloud. One-click import from Conductor. AI made coding faster. Helmor is about finishing the rest of the loop: orchestration, workspaces, review, testing, and merge. We believe the next generation of GUI agent orchestration should be built in the open — by the community.

Caspian 東澔

123,298 次观看 • 6 个月前

Cursor for Design? This Claude Code UI workflow is insane And many vibe coders not aware of it: 1. Claude Code can assign parallel tasks to sub agents 2. Integrate with git worktree to create sandbox env This means you can get Claude Code generate 10 design at the same time, fork & iterate 🔁 Design is an explorative process, by leveraging sub agent + parallel task capabilities, many interesting product UX can be built; Also turned this workflow into an open-source IDE extension here:

Cursor for Design? This Claude Code UI workflow is insane And many vibe coders not aware of it: 1. Claude Code can assign parallel tasks to sub agents 2. Integrate with git worktree to create sandbox env This means you can get Claude Code generate 10 design at the same time, fork & iterate 🔁 Design is an explorative process, by leveraging sub agent + parallel task capabilities, many interesting product UX can be built; Also turned this workflow into an open-source IDE extension here:

Jason Zhou

40,635 次观看 • 1 年前

CLM is one of the most elegant ideas we've worked on together. To me, it starts with a simple interaction need: there are many cases where I just want the agent to make surgical edits to the context. For example, when the agent finishes one task and prepares for another, it should ideally be able to clean up the context for the next one, just as a human would. And there are many such cases! Instead of hard-coding such rules into the harness (I doubt that's even possible), what would a first-principles solution to this problem look like? It turns out to be shockingly simple: ask the LLM to manage the context itself! Not only can this help the agent more efficiently solve the next task, but it can also be applied recursively to many more complex cases. This unlocks new agent capabilities across a variety of challenging, long-running, and open-ended tasks. For example, equipped with CLM, the agents can run continuously for hours on challenging tasks like EdgeBench or our new software world repository optimization tasks, and achieve really good results. Check out the CLM paper for more method details and many other cool results!

CLM is one of the most elegant ideas we've worked on together. To me, it starts with a simple interaction need: there are many cases where I just want the agent to make surgical edits to the context. For example, when the agent finishes one task and prepares for another, it should ideally be able to clean up the context for the next one, just as a human would. And there are many such cases! Instead of hard-coding such rules into the harness (I doubt that's even possible), what would a first-principles solution to this problem look like? It turns out to be shockingly simple: ask the LLM to manage the context itself! Not only can this help the agent more efficiently solve the next task, but it can also be applied recursively to many more complex cases. This unlocks new agent capabilities across a variety of challenging, long-running, and open-ended tasks. For example, equipped with CLM, the agents can run continuously for hours on challenging tasks like EdgeBench or our new software world repository optimization tasks, and achieve really good results. Check out the CLM paper for more method details and many other cool results!

Shannon Shen

15,797 次观看 • 1 个月前

Google Brain founder, Andrew Ng: “In future, 100% of tasks will be done by AI agents. 80% of my tasks are already done by Claude & GrokBot agents. Prompting is gone.” in 25-minute talk, he explains how the future of AI Engineering will look like - and how to prepare for it. worth more than a $500 AI engineering course. watch it today, then read the full breakdown on building agentic teams with GrokBot in the article below.

Google Brain founder, Andrew Ng: “In future, 100% of tasks will be done by AI agents. 80% of my tasks are already done by Claude & GrokBot agents. Prompting is gone.” in 25-minute talk, he explains how the future of AI Engineering will look like - and how to prepare for it. worth more than a $500 AI engineering course. watch it today, then read the full breakdown on building agentic teams with GrokBot in the article below.

Movez

10,356 次观看 • 1 个月前

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

elvis

104,372 次观看 • 5 个月前

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Malte Ubl

125,326 次观看 • 10 个月前

🧲 Meet Magnet — the AI workspace for agentic coding. Build software with Claude Code, Cursor, Codex & co. → Write a ticket → Magnet assembles context → Fires to Claude Code, Deep Research, and other agent sessions in parallel Git worktree sandboxes → PRs appear → Use agents to help you spec, research, and decompose tasks Let your agents cook!

🧲 Meet Magnet — the AI workspace for agentic coding. Build software with Claude Code, Cursor, Codex & co. → Write a ticket → Magnet assembles context → Fires to Claude Code, Deep Research, and other agent sessions in parallel Git worktree sandboxes → PRs appear → Use agents to help you spec, research, and decompose tasks Let your agents cook!

Nicolae Rusan

16,743 次观看 • 1 年前

Meet Agent Bake-Off: blind side by side tests for coding agents like Claude Code, Cursor , Amp , Factory , Codex, & more. We've been building the largest real world benchmarks for coding agents, and we're excited share an early preview of our open community tooling.

Meet Agent Bake-Off: blind side by side tests for coding agents like Claude Code, Cursor , Amp , Factory , Codex, & more. We've been building the largest real world benchmarks for coding agents, and we're excited share an early preview of our open community tooling.

brexton

18,309 次观看 • 1 年前

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

Om Patel

159,976 次观看 • 7 个月前

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,570 次观看 • 9 个月前

UI Skills are now easier for agents. Introducing UI Skills CLI. ask Codex, Cursor, Claude Code, or any agent to use the CLI, like: "use `npx ui-skills start` to fix the motion in this dialog" it finds the best design-engineering skill for the job and uses it as context

UI Skills are now easier for agents. Introducing UI Skills CLI. ask Codex, Cursor, Claude Code, or any agent to use the CLI, like: "use `npx ui-skills start` to fix the motion in this dialog" it finds the best design-engineering skill for the job and uses it as context

Ibelick

62,979 次观看 • 5 个月前

GLM 5.4/ GLM 5.5 on the way... ZCode just got what might be its biggest update yet with the new Workflow mode, and it feels like a completely different experience! I asked it to orchestrate 10 subagents at once and now I can actually watch each one working independently, see how the tasks are progressing and follow the entire workflow in real time. What I love the most is the interface, everything is so well organized and being able to see exactly what each agent is doing makes the whole experience much better, especially when you're working on complicated projects with multiple agents. To give you an idea of how Dynamic Workflows works, instead of having an AI agent coordinate everything through chat, you now have code that coordinates the agents directly, which makes managing complex tasks much more structured. And when you combine this with Goal Mode, Execution Modes, support for multiple models and GLM's long context window, ZCode is becoming one of the most advanced tools I've tried in 2026 for large development projects that require multiple agents working together!

GLM 5.4/ GLM 5.5 on the way... ZCode just got what might be its biggest update yet with the new Workflow mode, and it feels like a completely different experience! I asked it to orchestrate 10 subagents at once and now I can actually watch each one working independently, see how the tasks are progressing and follow the entire workflow in real time. What I love the most is the interface, everything is so well organized and being able to see exactly what each agent is doing makes the whole experience much better, especially when you're working on complicated projects with multiple agents. To give you an idea of how Dynamic Workflows works, instead of having an AI agent coordinate everything through chat, you now have code that coordinates the agents directly, which makes managing complex tasks much more structured. And when you combine this with Goal Mode, Execution Modes, support for multiple models and GLM's long context window, ZCode is becoming one of the most advanced tools I've tried in 2026 for large development projects that require multiple agents working together!

Iam_

30,417 次观看 • 24 天前

AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.

AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.

CivAI

18,451 次观看 • 11 个月前

Temporal CEO Samar Birwadker says GitHub was designed for humans, but is now at the mercy of agents. "Agents are starting to get more and more long-lived, more asynchronous, and more mission-critical." "For example, if you pick coding agents alone, is something like GitHub was designed for humans collaborate and create [pull requests] right now." "Now a lot of code is being generated by these agents, and they're the ones creating these PRs essentially, and even code review process is being automated by other agents." "So now this entire system backend, which is being built for human consumption, is now at the mercy of these agents kind of using it for their own coordination and collaboration."

Temporal CEO Samar Birwadker says GitHub was designed for humans, but is now at the mercy of agents. "Agents are starting to get more and more long-lived, more asynchronous, and more mission-critical." "For example, if you pick coding agents alone, is something like GitHub was designed for humans collaborate and create [pull requests] right now." "Now a lot of code is being generated by these agents, and they're the ones creating these PRs essentially, and even code review process is being automated by other agents." "So now this entire system backend, which is being built for human consumption, is now at the mercy of these agents kind of using it for their own coordination and collaboration."

MTS

26,902 次观看 • 6 个月前

new codex app first impressions! - pros: reliably coding in swift 🤍, a nice UI for kicking off concurrent threads/alerts, clearly there is a lot to unpack across automations/skills - asks: would love to see more killer use cases for automations, perhaps broken down by product type (iOS, web, electron); had some wrangling on chat context & a feature I expected to be more straightforward overall: I am bought in that a clean/beautiful UI for coordinating many agents (some with long running tasks) is powerful; I sense there's a lot more to unlock with time. I still want the ability to quickly switch between codex and claude code, as they seem to excel in different (+ complementary) dev tasks

new codex app first impressions! - pros: reliably coding in swift 🤍, a nice UI for kicking off concurrent threads/alerts, clearly there is a lot to unpack across automations/skills - asks: would love to see more killer use cases for automations, perhaps broken down by product type (iOS, web, electron); had some wrangling on chat context & a feature I expected to be more straightforward overall: I am bought in that a clean/beautiful UI for coordinating many agents (some with long running tasks) is powerful; I sense there's a lot more to unlock with time. I still want the ability to quickly switch between codex and claude code, as they seem to excel in different (+ complementary) dev tasks

ashe

15,974 次观看 • 9 个月前

this is pure f*cking treasure 10 GitHub repos with 165K+ combined stars to build your own AI agent team that works 24/7 most people still use one AI chat for everything. these repos turn Claude into a whole team: one agent researches, one codes, one reviews, and a lead runs them all one resource per step, from your first 3 agents to a full swarm running Claude and Codex together (1/10) CrewAI the easiest way to assemble a team of agents: give them roles and tasks, then hit kickoff ▸ (2/10) LangGraph connect agents into a graph with checkpoints, so if something fails it resumes instead of starting over ▸ (3/10) wshobson/agents ready-made agents for Claude Code (backend, frontend, security, tests, devops) plus an orchestrator to run them ▸ (4/10) oh-my-team 12 specialist agents for Claude Code that split the work and merge the results ▸ (5/10) agent-swarm your own self-hosted AI company: one lead agent hires workers and runs them 24/7 ▸ (6/10) Open-Agents spawns many Claude agents in parallel, so 6 tasks run at once instead of one after another ▸ (7/10) swarms an enterprise-grade framework for when your team grows from 5 agents to 50 ▸ (8/10) Microsoft Agent Framework Microsoft's new framework, the AutoGen successor, for workflows, agents and runtime in one place ▸ (9/10) CLI Agent Orchestrator runs Claude, Codex and other CLIs side by side as one team, with a supervisor watching all of them ▸ (10/10) ensemble launches Codex and Claude together, so they build and ship as one ▸ the loop: assemble → plug in → run → scale → orchestrate pick one, click, let it run, and while everyone else is still typing prompts, your agents are already shipping ↓

this is pure f*cking treasure 10 GitHub repos with 165K+ combined stars to build your own AI agent team that works 24/7 most people still use one AI chat for everything. these repos turn Claude into a whole team: one agent researches, one codes, one reviews, and a lead runs them all one resource per step, from your first 3 agents to a full swarm running Claude and Codex together (1/10) CrewAI the easiest way to assemble a team of agents: give them roles and tasks, then hit kickoff ▸ (2/10) LangGraph connect agents into a graph with checkpoints, so if something fails it resumes instead of starting over ▸ (3/10) wshobson/agents ready-made agents for Claude Code (backend, frontend, security, tests, devops) plus an orchestrator to run them ▸ (4/10) oh-my-team 12 specialist agents for Claude Code that split the work and merge the results ▸ (5/10) agent-swarm your own self-hosted AI company: one lead agent hires workers and runs them 24/7 ▸ (6/10) Open-Agents spawns many Claude agents in parallel, so 6 tasks run at once instead of one after another ▸ (7/10) swarms an enterprise-grade framework for when your team grows from 5 agents to 50 ▸ (8/10) Microsoft Agent Framework Microsoft's new framework, the AutoGen successor, for workflows, agents and runtime in one place ▸ (9/10) CLI Agent Orchestrator runs Claude, Codex and other CLIs side by side as one team, with a supervisor watching all of them ▸ (10/10) ensemble launches Codex and Claude together, so they build and ship as one ▸ the loop: assemble → plug in → run → scale → orchestrate pick one, click, let it run, and while everyone else is still typing prompts, your agents are already shipping ↓

Noisy

15,433 次观看 • 28 天前

Lead of Claude Code: "100% of our code at Anthropic is now shipped by Claude. Over 30% of it is written using /loops. Loop engineering is the future of coding. Loops are as big a shift as move from source coding to agents" in a 1-hour podcast, a Claude Code lead engineer shares the internal setup for building the #1 coding tool. It’s the most useful breakdown of how the Anthropic team uses Claude internally that I’ve seen on YouTube. Worth more than a $500 vibe-coding course.

Lead of Claude Code: "100% of our code at Anthropic is now shipped by Claude. Over 30% of it is written using /loops. Loop engineering is the future of coding. Loops are as big a shift as move from source coding to agents" in a 1-hour podcast, a Claude Code lead engineer shares the internal setup for building the #1 coding tool. It’s the most useful breakdown of how the Anthropic team uses Claude internally that I’ve seen on YouTube. Worth more than a $500 vibe-coding course.

Codez

37,770 次观看 • 4 个月前

Just open sourced Monocode - a desktop UI for your coding agents. Works with your existing Claude Code, Codex, Cursor, and OpenCode harnesses. Tabs can be sessions, a lightweight editor or a terminal. Currently only supports macOS Apple Silicon. Still very early so expect bugs. Feedback and small PRs are welcome :)

Just open sourced Monocode - a desktop UI for your coding agents. Works with your existing Claude Code, Codex, Cursor, and OpenCode harnesses. Tabs can be sessions, a lightweight editor or a terminal. Currently only supports macOS Apple Silicon. Still very early so expect bugs. Feedback and small PRs are welcome :)

Nikolay Petkov

69,113 次观看 • 2 个月前