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Instinct shows how far LLM computer use has come but it’s still awfully slow 😴 and no one wants to share payment (or address) credentials with a 3p. 😱 Here’s a short demo of my agent, Concierge using Shopify’s UCP tools and our Shop identity tokens to directly checkout... show more

Gil

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19,956 views • 19 days ago •via X (Twitter)

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I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

Johnny

27,179 views • 1 year ago

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Andrew Ng

170,541 views • 1 year ago

Agents will become a common way people shop. So today we are releasing 3 tools to make adding commerce to those agents trivial: - Checkout Kit: embed commerce widgets and checkout(!) directly into your agent and chat. This is already being used by Microsoft’s Microsoft Copilot. - Shopify Catalog: low latency global product search across millions of merchants - Universal Cart: shop from any store, anywhere, in one cart With these, you can build seamless embedded commerce experiences quickly, starting with all the best products by the best merchants in the world instead of having to sign them all up individually. No need to build a complex new checkout, or dealing with regulatory marketplace rules. Commerce just plugs in and feels seamless in conversations. We believe commerce should be everywhere people are, so we’re making that as easy as possible for the AI age.

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tobi lutke

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Thumbnail process! Had to speed things up to fit the Twitter video limit. Still a lot to learn but really happy with how far I’ve come If you're a creator and would like thumbnails like this, my DMs are open. Let’s work together 😊

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Adonai

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How to build a standout brand in 2026 ✨ I’m working on a YouTube video where I show my full process of building a brand identity from scratch - how I use Midjourney to create an awesome style, what tools I use, and the coolest part, how to build your own tools with AI and vibe coding to create unique textures, effects, and animations no one else has. If this sounds like something you’d watch, share it and drop a kind word below. 👇

Alexandr.

72,335 views • 5 months ago

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)

elvis

15,295 views • 2 months ago

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PalmPay Nigeria

22,796 views • 1 year ago

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

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eric zakariasson

61,966 views • 10 months ago

wanted to share our first public demo of Aligned wallet as a service. our engineering team has been doing an incredible job. really proud of how fast we’re building this. this is a key product for us. it’s how we help partners like Sovra – The Identity Stack onboard millions of users in a seamless and secure way. still work to do, but we’re on the right path. here’s the demo: 1. sign in with google 2. create a wallet with touch id passkey saved to icloud 3. send usdt on chain signed with your fingerprint 4. open on a new device 5. send another tx immediately how we’re building it: - 2 of 2 multisig where user passkey and server co sign every transaction - erc 4337 account abstraction - auth via google + webauthn passkeys. - passkeys sync across devices via icloud keychain or google password manager - server guardian recovery with timelock + mfa still under development: censorship resistance allowing users to retrieve their encrypted server key independently using their device key + mfa so no one can block access to their wallet we will be sharing more demos in the future and a full blog post explaining the architecture and design decisions.

wanted to share our first public demo of Aligned wallet as a service. our engineering team has been doing an incredible job. really proud of how fast we’re building this. this is a key product for us. it’s how we help partners like Sovra – The Identity Stack onboard millions of users in a seamless and secure way. still work to do, but we’re on the right path. here’s the demo: 1. sign in with google 2. create a wallet with touch id passkey saved to icloud 3. send usdt on chain signed with your fingerprint 4. open on a new device 5. send another tx immediately how we’re building it: - 2 of 2 multisig where user passkey and server co sign every transaction - erc 4337 account abstraction - auth via google + webauthn passkeys. - passkeys sync across devices via icloud keychain or google password manager - server guardian recovery with timelock + mfa still under development: censorship resistance allowing users to retrieve their encrypted server key independently using their device key + mfa so no one can block access to their wallet we will be sharing more demos in the future and a full blog post explaining the architecture and design decisions.

RJ 🟩

55,481 views • 6 months ago

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

Andrew Ng

297,131 views • 2 years ago

We are excited to share a research preview of our generative agent. The agent is being trained to solve the hardest tasks in 3D and beyond, using only keyboard and mouse actions. Join the waitlist: Our agent app runs on Windows or Mac, either locally or with one-click setup for a Windows VM. It’s still early days, but this paves the way for production-level workflows for the first time ever. Blog:

We are excited to share a research preview of our generative agent. The agent is being trained to solve the hardest tasks in 3D and beyond, using only keyboard and mouse actions. Join the waitlist: Our agent app runs on Windows or Mac, either locally or with one-click setup for a Windows VM. It’s still early days, but this paves the way for production-level workflows for the first time ever. Blog:

Common Sense Machines

155,199 views • 1 year ago

You can't pretend you care about security if you are still sharing API tokens in .env files or sharing SSH keys. This might be good enough to provide simple access to a resource, but it's a horrible way to do security when it really matters. Look into "Identity-Based Access". The state of the art today is Identity-Based Access. With traditional security, you have to present a key to get access to the resource (an API key, a password, or an SSH key). These are long-lived keys you need to keep secret somehow. If somebody steals your key, they become you and get the same access you had. With Identity-Based Access, you need to prove who you are instead. If you are a human, you can prove this with a passkey, SSO with Google or GitHub, or Multi-Factor Authentication. If you are a computer, you can use AWS IAM, Kubernetes, a cloud service account, or any other trusted environment. Once the system verifies your identity, it checks your permissions and gives you a short-lived credential. Prove identity → Permissions → Short-lived credential → Access The difference here is huge, because those credentials are: • Short-lived • Tied to a specific identity • Scoped to specific permissions • Automatically expired • Much easier to audit In practice, implementing this is as simple as integrating with Teleport, which will handle everything for you. Here is a full explanation of how Teleport works and how you can start using it: Thanks to the Teleport team for partnering with me on this post.

You can't pretend you care about security if you are still sharing API tokens in .env files or sharing SSH keys. This might be good enough to provide simple access to a resource, but it's a horrible way to do security when it really matters. Look into "Identity-Based Access". The state of the art today is Identity-Based Access. With traditional security, you have to present a key to get access to the resource (an API key, a password, or an SSH key). These are long-lived keys you need to keep secret somehow. If somebody steals your key, they become you and get the same access you had. With Identity-Based Access, you need to prove who you are instead. If you are a human, you can prove this with a passkey, SSO with Google or GitHub, or Multi-Factor Authentication. If you are a computer, you can use AWS IAM, Kubernetes, a cloud service account, or any other trusted environment. Once the system verifies your identity, it checks your permissions and gives you a short-lived credential. Prove identity → Permissions → Short-lived credential → Access The difference here is huge, because those credentials are: • Short-lived • Tied to a specific identity • Scoped to specific permissions • Automatically expired • Much easier to audit In practice, implementing this is as simple as integrating with Teleport, which will handle everything for you. Here is a full explanation of how Teleport works and how you can start using it: Thanks to the Teleport team for partnering with me on this post.

Santiago

20,971 views • 18 days ago

Shopify’s latest Edition has SO MANY new features for ecommerce operators, here's the highlights: - native A/B testing on theme and checkout - Shopify is building the data layer for your products to be sold with AI - Shop Campaigns can now be run in ChatGPT - in-dashboard monitoring of how and when products show up in agentic search - Markets got a visualization redesign... and this is just scratching the surface. See all the updates, in my profile.

Oren John

28,986 views • 2 months ago

Announcing my new course: Agentic AI! Building AI agents is one of the most in-demand skills in the job market. This course, available now at teaches you how. You'll learn to implement four key agentic design patterns: - Reflection, in which an agent examines its own output and figures out how to improve it - Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc. - Planning, where you'll use an LLM to decide how to break down a task into sub-tasks for execution, and - Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task You'll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns. But here's what I think is the most important part of this course: Having worked with many teams on AI agents, I've found that the single biggest predictor of whether someone executes well is their ability to drive a disciplined process for evals and error analysis. In this course, you'll learn how to do this, so you can efficiently home in on which components to improve in a complex agentic workflow. Instead of guessing what to work on, you'll let evals data guide you. This will put you significantly ahead of the game compared to the vast majority of teams building agents. Together, we'll build a deep research agent that searches, synthesizes, and reports, using all of these agentic design patterns and best practices. This self-paced course is taught in a vendor neutral way, using raw Python - without hiding details in a framework. You'll see how each step works, and learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps. Come join me, and let's build some agentic AI systems! Sign up to get started:

Announcing my new course: Agentic AI! Building AI agents is one of the most in-demand skills in the job market. This course, available now at teaches you how. You'll learn to implement four key agentic design patterns: - Reflection, in which an agent examines its own output and figures out how to improve it - Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc. - Planning, where you'll use an LLM to decide how to break down a task into sub-tasks for execution, and - Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task You'll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns. But here's what I think is the most important part of this course: Having worked with many teams on AI agents, I've found that the single biggest predictor of whether someone executes well is their ability to drive a disciplined process for evals and error analysis. In this course, you'll learn how to do this, so you can efficiently home in on which components to improve in a complex agentic workflow. Instead of guessing what to work on, you'll let evals data guide you. This will put you significantly ahead of the game compared to the vast majority of teams building agents. Together, we'll build a deep research agent that searches, synthesizes, and reports, using all of these agentic design patterns and best practices. This self-paced course is taught in a vendor neutral way, using raw Python - without hiding details in a framework. You'll see how each step works, and learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps. Come join me, and let's build some agentic AI systems! Sign up to get started:

Andrew Ng

889,257 views • 11 months ago

$ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.$

ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.

Yarchi

462,259 views • 2 months ago

New short course: DSPy: Build and Optimize Agentic Apps DSPy is a powerful open-source framework for automatically tuning prompts for GenAI applications. In this course, you'll learn to use DSPy, together with MLflow. This is built in partnership with Databricks and taught by Chen Qian, co-lead of the DSPy framework. Many AI builders spend hours hand-tuning prompts. When given a set of evals, DSPy automates this process. It’s especially useful for optimizing prompts, including few-shot prompts, in complex agentic AI workflows. Further, if you switch an application to a newer LLM, performance can degrade if your prompts were optimized to the previous model. DSPy automatically optimizes the entire system for the new LLM as well, using just a few evaluation examples. This course teaches DSPy works, and best practices for using it. You’ll write programs using DSPy’s signature-based programming model, debug them with MLflow tracing -- to gain visibility into how different parts of a pipeline, as well as how the overall system, are performing -- and automatically improve their accuracy with DSPy Optimizer. Please sign up here:

New short course: DSPy: Build and Optimize Agentic Apps DSPy is a powerful open-source framework for automatically tuning prompts for GenAI applications. In this course, you'll learn to use DSPy, together with MLflow. This is built in partnership with Databricks and taught by Chen Qian, co-lead of the DSPy framework. Many AI builders spend hours hand-tuning prompts. When given a set of evals, DSPy automates this process. It’s especially useful for optimizing prompts, including few-shot prompts, in complex agentic AI workflows. Further, if you switch an application to a newer LLM, performance can degrade if your prompts were optimized to the previous model. DSPy automatically optimizes the entire system for the new LLM as well, using just a few evaluation examples. This course teaches DSPy works, and best practices for using it. You’ll write programs using DSPy’s signature-based programming model, debug them with MLflow tracing -- to gain visibility into how different parts of a pipeline, as well as how the overall system, are performing -- and automatically improve their accuracy with DSPy Optimizer. Please sign up here:

Andrew Ng

181,996 views • 1 year ago