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Published my first agent skill today: Cloud Stacked Diffs ☁️ npx skills add dabit3/cloud-stacked-diffs It orchestrates cloud agents to deliver large long running tasks as a stack of small, dependent draft PRs, reviewed and merged bottom-up instead of one giant PR. Combined w/ Linear you can hand off large... show more

nader dabit

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63,546 次观看 • 17 天前 •via X (Twitter)

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Introducing Oz: the platform to orchestrate agents in the cloud. Spin up hundreds of agents from your terminal, browser, the API, or your phone. Each agent gets a @docker environment to build, test, and write PRs. Come back from your lunch break to code that’s ready to merge.

Introducing Oz: the platform to orchestrate agents in the cloud. Spin up hundreds of agents from your terminal, browser, the API, or your phone. Each agent gets a @docker environment to build, test, and write PRs. Come back from your lunch break to code that’s ready to merge.

Warp

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Lysium is an app I've been building specifically for background agent orchestration. Features: → Mobile-first + cross-platform → Run multiple agents in parallel across repositories → Launch agent-based requests from issues and PRs → Swipe actions for close, merge, create PR, and skip-to-tail workflows → One click agent-powered PR reviews + assessments → One-click agent-powered issue assessments Think of it as a cross-platform control plane for async, agent-driven software delivery (that you can control from anywhere). It's still an experiment, but it's open source and available to try:

Lysium is an app I've been building specifically for background agent orchestration. Features: → Mobile-first + cross-platform → Run multiple agents in parallel across repositories → Launch agent-based requests from issues and PRs → Swipe actions for close, merge, create PR, and skip-to-tail workflows → One click agent-powered PR reviews + assessments → One-click agent-powered issue assessments Think of it as a cross-platform control plane for async, agent-driven software delivery (that you can control from anywhere). It's still an experiment, but it's open source and available to try:

nader dabit

47,584 次观看 • 4 个月前

New on Claude Code Desktop We’re shipping new features for Claude Code on desktop that let you preview running apps, auto-review code, and auto-fix and merge PRs to help close the development loop. - Server previews: Claude starts dev servers and previews your running app in the desktop interface. It reads console logs, catches errors, and keeps iterating on its own. - Local code review: Claude examines your local diffs and leaves inline comments before you push — an immediate second set of eyes on every change. - PR monitoring: Claude tracks CI status after you open a PR. With auto-fix, it attempts to resolve failures automatically. With auto-merge, PRs land as soon as checks pass. You can move on to your next task while Claude handles the last one. - Session mobility: Move sessions from CLI to desktop, and from desktop to the cloud. Start work at your desk, pick it up from the web or your phone.

New on Claude Code Desktop We’re shipping new features for Claude Code on desktop that let you preview running apps, auto-review code, and auto-fix and merge PRs to help close the development loop. - Server previews: Claude starts dev servers and previews your running app in the desktop interface. It reads console logs, catches errors, and keeps iterating on its own. - Local code review: Claude examines your local diffs and leaves inline comments before you push — an immediate second set of eyes on every change. - PR monitoring: Claude tracks CI status after you open a PR. With auto-fix, it attempts to resolve failures automatically. With auto-merge, PRs land as soon as checks pass. You can move on to your next task while Claude handles the last one. - Session mobility: Move sessions from CLI to desktop, and from desktop to the cloud. Start work at your desk, pick it up from the web or your phone.

Ado

71,910 次观看 • 5 个月前

If you're evaluating AI IDEs this week, here's what getting started with Windsurf looks like (links below): • Download and install from Import any existing IDE settings during onboarding, or start fresh. • Cascade is the agent panel on the right. Chat, generate, edit, run code. @-mention files, symbols, or docs to scope context. • Choose your model - Opus, Codex, SWE, Kimi, GLM and more (Kimi K2.6 is free for the next 2 weeks 👀) • DeepWiki: Cmd+Shift+Click any symbol for an in-depth explanation of what it does. Send it to Cascade with one click. • Codemaps: hierarchical view of how files and functions execute together. Click a node to jump to code. Shareable as links. • Devin in Windsurf: start and manage cloud sessions without leaving the editor. • Agent Command Center: Kanban board of every running agent, local (Cascade) and cloud (Devin). See what's in flight, blocked, or ready for review. • Spaces: group sessions, PRs, files, and context for one task or project into a single view. • Centralized review: handle diffs, PRs, and agent output in one place. • MCP servers, Memories, and Workflows extend Cascade. Set them up once; reuse across projects.

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nader dabit

16,694 次观看 • 3 个月前

working with cloud agents in cursor feels the same as local, but the fact that i can shut down my computer and still have it running is amazing. one of my favorite use cases is planning in cursor, then send off to cloud agent for impl. here's a short demo

working with cloud agents in cursor feels the same as local, but the fact that i can shut down my computer and still have it running is amazing. one of my favorite use cases is planning in cursor, then send off to cloud agent for impl. here's a short demo

eric zakariasson

61,940 次观看 • 8 个月前

130+ skills are now built into your Superagent. Some are ready to use, and some can be created based on what you need. Add a skill once, and your Superagent can use it as part of your workflows. Stack skills, connect tools, and build flows that run end-to-end.

130+ skills are now built into your Superagent. Some are ready to use, and some can be created based on what you need. Add a skill once, and your Superagent can use it as part of your workflows. Stack skills, connect tools, and build flows that run end-to-end.

Base44

1,858,052 次观看 • 3 个月前

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LangChain

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Hermes Agent now has multi-agent via the Kanban, new in v0.12.0. Agents claim tasks from a board, work in parallel, and hand off when blocked. You watch progress and unblock from one easy view instead of juggling terminals. We asked it to plan and make this video about itself:

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Chorus.com

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BREAKING: OpenAI has officially released Agent Mode, allowing users to spin up a virtual desktop and take action. I got early access to ChatGPT Agent Mode and want to share my thoughts. Agent Mode lets you delegate tasks like searching or browsing to an agent inside a virtual desktop inside ChatGPT. You can spin up multiple little “workers” to fire off tasks and keep tabs as they chug through your to-do list AT THE SAME TIME. If it gets stuck or goes off track, you can take over manually. The UI is friendly and keeps the reasoning transparent, so you’re not left guessing what it’s doing or why. It’s not perfect and doesn’t give you as many artifacts as other players in the space, but a strong platform for “let’s see how much I can hand off and supervise”, and a first step toward managing fleets of AI workers instead of pinging a single chatbot for answers. If you’re on ChatGPT Pro, Plus, or Team, look for Agent Mode in the tools dropdown.

BREAKING: OpenAI has officially released Agent Mode, allowing users to spin up a virtual desktop and take action. I got early access to ChatGPT Agent Mode and want to share my thoughts. Agent Mode lets you delegate tasks like searching or browsing to an agent inside a virtual desktop inside ChatGPT. You can spin up multiple little “workers” to fire off tasks and keep tabs as they chug through your to-do list AT THE SAME TIME. If it gets stuck or goes off track, you can take over manually. The UI is friendly and keeps the reasoning transparent, so you’re not left guessing what it’s doing or why. It’s not perfect and doesn’t give you as many artifacts as other players in the space, but a strong platform for “let’s see how much I can hand off and supervise”, and a first step toward managing fleets of AI workers instead of pinging a single chatbot for answers. If you’re on ChatGPT Pro, Plus, or Team, look for Agent Mode in the tools dropdown.

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155,943 次观看 • 1 年前

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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 次观看 • 1 年前

THE NEXT WAVE OF LOCAL AI BUILDERS SHIPS PORTABLE STACKS WITH HANDHELD $40 SDR SCANNERS, LOCAL LLMs ONLY WORK OFF-GRID WHEN SOMETHING ELSE IS LISTENING TO THE AIR 00:30 he opens the case and points at each component, "really it's a pi sugar power pack mounted to a raspberry pi zero 2w and a little adafruit bonnet" the scanner adds a noelec sdr dongle and a 2m antenna, boots up as a spectrum analyzer he takes to the park with his kid to identify local radio transmissions the same maker keeps a $600 mini pc running qwen 3.6 27b on his desk with claude code pointed at localhost, the offline ai handles his coding, writing and research without a single cloud api call the sdr scanner completes the sovereignty stack, the mini pc thinks locally while the scanner listens locally, and neither needs the internet for the parts of his work that used to require it builders who moved off cloud ai are now moving off cloud everything, the pattern isn't just $459 in subscriptions dead but a full stack that survives when the cloud goes down bookmark this and read the article below

THE NEXT WAVE OF LOCAL AI BUILDERS SHIPS PORTABLE STACKS WITH HANDHELD $40 SDR SCANNERS, LOCAL LLMs ONLY WORK OFF-GRID WHEN SOMETHING ELSE IS LISTENING TO THE AIR 00:30 he opens the case and points at each component, "really it's a pi sugar power pack mounted to a raspberry pi zero 2w and a little adafruit bonnet" the scanner adds a noelec sdr dongle and a 2m antenna, boots up as a spectrum analyzer he takes to the park with his kid to identify local radio transmissions the same maker keeps a $600 mini pc running qwen 3.6 27b on his desk with claude code pointed at localhost, the offline ai handles his coding, writing and research without a single cloud api call the sdr scanner completes the sovereignty stack, the mini pc thinks locally while the scanner listens locally, and neither needs the internet for the parts of his work that used to require it builders who moved off cloud ai are now moving off cloud everything, the pattern isn't just $459 in subscriptions dead but a full stack that survives when the cloud goes down bookmark this and read the article below

starmex

40,403 次观看 • 17 天前

Start building for an agent-first world. If you have a product, you need to start offering skills for Claude, Codex, Cursor, and any other agents. Your skills should specify: • How to navigate and use your product • Best practices the agent must follow • Detailed instructions on how to accomplish things • Anti-patterns to avoid Redis is one of the most popular in-memory data stores in the world, and they just released their agent skills. It takes one second to install, and it will turn your agent into a Senior Redis Engineer: $ npx skills add redis/agent-skills In the attached video, I show you how to install it as a plugin in Claude Code and some of its benefits. This is the easiest way to "teach" models what they don't know and keep their knowledge up to date. If you ask me, skills is literally one of the most brilliant ideas that Anthropic has put out there. If you use Redis, their skill is a must-have. If you don't, this skill will show you how to build and structure yours. Here is what their skill teaches your agent: 1. Current patterns for common use cases: caching, rate limiting, session management, vector search, semantic caching, pub/sub, streams. 2. Which data structure to use and when: hashes vs. JSON vs. sorted sets vs. vector sets. 3. Anti-patterns to avoid: no KEYS in loops, no unbounded key growth, no large values that amplify every operation. 4. Production-aware defaults: connection pooling, pipelining, cluster compatibility, error handling that doesn't silently swallow failures.

Start building for an agent-first world. If you have a product, you need to start offering skills for Claude, Codex, Cursor, and any other agents. Your skills should specify: • How to navigate and use your product • Best practices the agent must follow • Detailed instructions on how to accomplish things • Anti-patterns to avoid Redis is one of the most popular in-memory data stores in the world, and they just released their agent skills. It takes one second to install, and it will turn your agent into a Senior Redis Engineer: $ npx skills add redis/agent-skills In the attached video, I show you how to install it as a plugin in Claude Code and some of its benefits. This is the easiest way to "teach" models what they don't know and keep their knowledge up to date. If you ask me, skills is literally one of the most brilliant ideas that Anthropic has put out there. If you use Redis, their skill is a must-have. If you don't, this skill will show you how to build and structure yours. Here is what their skill teaches your agent: 1. Current patterns for common use cases: caching, rate limiting, session management, vector search, semantic caching, pub/sub, streams. 2. Which data structure to use and when: hashes vs. JSON vs. sorted sets vs. vector sets. 3. Anti-patterns to avoid: no KEYS in loops, no unbounded key growth, no large values that amplify every operation. 4. Production-aware defaults: connection pooling, pipelining, cluster compatibility, error handling that doesn't silently swallow failures.

Santiago

37,546 次观看 • 4 个月前

Forge Your Path. As one of the world’s most elite law enforcement agencies, the #FBI provides you unparalleled experiences, training, and career development opportunities. FBI Charlotte Special Agent in Charge Reid Davis invites you to step up and join us. Apply to become a Special Agent at FBIJobs today.

Forge Your Path. As one of the world’s most elite law enforcement agencies, the #FBI provides you unparalleled experiences, training, and career development opportunities. FBI Charlotte Special Agent in Charge Reid Davis invites you to step up and join us. Apply to become a Special Agent at FBIJobs today.

FBI Charlotte

19,846 次观看 • 3 个月前

A couple weeks ago we started a fundraiser for North Carolina flood relief and California wildfires and I teamed up with Montana knife company to give everybody a chance to win one of two knives custom built out of elk antler from my Montana bull this year. I'm gonna add in two tickets and passes for each winner to one of my shows(all expenses paid) and to get your knife you can come back to the bus and I'll give it to you. I'm sure we'll also drink some Coors together, so enter for your chance to win one of these two knives as well as a bunch of gifts from companies like Yeti, Turtlebox, Hoyt Bows, Levrack, Stone Glacier and a bunch more. Link is in my bio to get the details on how to win all this cool stuff

A couple weeks ago we started a fundraiser for North Carolina flood relief and California wildfires and I teamed up with Montana knife company to give everybody a chance to win one of two knives custom built out of elk antler from my Montana bull this year. I'm gonna add in two tickets and passes for each winner to one of my shows(all expenses paid) and to get your knife you can come back to the bus and I'll give it to you. I'm sure we'll also drink some Coors together, so enter for your chance to win one of these two knives as well as a bunch of gifts from companies like Yeti, Turtlebox, Hoyt Bows, Levrack, Stone Glacier and a bunch more. Link is in my bio to get the details on how to win all this cool stuff

Chase Rice

28,911 次观看 • 1 年前

New course: A2A: The Agent2Agent Protocol, built with Google Cloud Tech and IBM Research, and taught by Holt Skinner, Ivan Nardini, and Sandi Besen. Connecting agents built with different frameworks usually requires extensive custom integration. This short course teaches you A2A, the open protocol standardizing how agents discover each other and communicate. Since IBM’s ACP (Agent Communication Protocol) joined forces with A2A, A2A has emerged as the industry standard. In this course, you'll build a healthcare multi-agent system where agents built with different frameworks, such as Google ADK (Agent Development Kit) and LangGraph, collaborate through A2A. You'll wrap each agent as an A2A server, build A2A clients to connect to them, and orchestrate them into sequential and hierarchical workflows. Skills you'll gain: - Expose agents from different frameworks as A2A servers to make them discoverable and interoperable - Chain A2A agents sequentially using ADK, where one agent's output feeds into the next - Connect A2A agents to external data sources using MCP (Model Context Protocol) - Deploy A2A agents using Agent Stack, IBM's open-source infrastructure Join and learn the protocol standardizing agent collaboration!

New course: A2A: The Agent2Agent Protocol, built with Google Cloud Tech and IBM Research, and taught by Holt Skinner, Ivan Nardini, and Sandi Besen. Connecting agents built with different frameworks usually requires extensive custom integration. This short course teaches you A2A, the open protocol standardizing how agents discover each other and communicate. Since IBM’s ACP (Agent Communication Protocol) joined forces with A2A, A2A has emerged as the industry standard. In this course, you'll build a healthcare multi-agent system where agents built with different frameworks, such as Google ADK (Agent Development Kit) and LangGraph, collaborate through A2A. You'll wrap each agent as an A2A server, build A2A clients to connect to them, and orchestrate them into sequential and hierarchical workflows. Skills you'll gain: - Expose agents from different frameworks as A2A servers to make them discoverable and interoperable - Chain A2A agents sequentially using ADK, where one agent's output feeds into the next - Connect A2A agents to external data sources using MCP (Model Context Protocol) - Deploy A2A agents using Agent Stack, IBM's open-source infrastructure Join and learn the protocol standardizing agent collaboration!

Andrew Ng

85,595 次观看 • 5 个月前

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 次观看 • 1 个月前