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Introducing Kiro, an all-new agentic IDE that has a chance to transform how developers build software. Let me highlight three key innovations that make Kiro special: 1 - Kiro introduces spec-driven development, helping developers express their intent clearly through natural language specifications and architecture diagrams for complex features. This...

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Andy Jassy

3,318,734 subscribers

666,354 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 11

Фото профиля TestingCatalog News 🗞
TestingCatalog News 🗞1 год назад

Great to see Amazon being back in the game 💪

Фото профиля Mobile Scanner
Mobile Scanner1 год назад

Scan any documents, convert images into text, PDF files, etc. 👍

Фото профиля Sujit Singh
Sujit Singh1 год назад

Great to see AWS's foray into the Vibe coding arena.

Фото профиля Tyrel
Tyrel1 год назад

curious if Kiro can also set up entire AWS architectures? Would love to see that

Фото профиля Mag7 Watch
Mag7 Watch1 год назад

This looks like a big step forward for AI-powered development! Spec-driven workflows + agent hooks + adaptive UI sound like a great combo to move from quick prototypes to production-ready code faster.

Фото профиля chris / strutheo
chris / strutheo1 год назад

@ARealKiro you'll always be the real kiro to me

Фото профиля Shariq Riaz
Shariq Riaz1 год назад

interesting approach with spec-driven development 👀

Фото профиля Growth Picker
Growth Picker1 год назад

Great release!

Фото профиля Jeff Hammon
Jeff Hammon1 год назад

You realize you run THE ENTIRE company now right? Not just AWS?

Фото профиля Dorche
Dorche1 год назад

Kino

Фото профиля Sudheer Bandaru
Sudheer Bandaru1 год назад

Trying it now...

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Before software engineers even begin writing code, they have to set the stage of the entire development process. This process requires engineers to make complex tradeoffs between requirements, system design, and implementations details. Current IDEs that rely on AI features, like chat and inline coding, can help engineers get the job done quickly on small development tasks. Still, engineers spend much more time on larger projects—even after the initial code is generated—by conducting rigorous testing and creating documentation. This is where today’s AI IDEs can do more to accelerate the development lifecycle—and this is why we built Kiro. Kiro is an AI IDE that helps you go from prototype to production with spec-driven development and agent hooks. From simple to complex tasks, Kiro works alongside you to turn prompts into detailed specs, then into working code, docs, and test so what you build is exactly what you want and ready to share with your team. After a developer builds the code with Kiro, Kiro’s agent hooks help engineers solve challenging problems and automate tasks like generating documentation and unit tests. Kiro brings structure and mature engineering practices to AI coding, so you can go from concept to application while being in the driver’s seat every step of the way. Kiro is free during preview, and supports Mac, Windows, and Linux, and most popular programming languages. We're excited for you to try it out and let us know what you think ➡️

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Swami Sivasubramanian

26,037 просмотров • 7 месяцев назад

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Google AI

136,589 просмотров • 8 месяцев назад

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New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @[email protected]. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

Andrew Ng

460,630 просмотров • 3 месяцев назад

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Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 просмотров • 10 месяцев назад

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New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:

Andrew Ng

752,508 просмотров • 1 год назад

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Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Pasha Rayan

53,950 просмотров • 1 год назад

Apple announces Swift Assist for Xcode 16! "Swift Assist serves as a companion for all of a developer’s coding tasks, so they can focus on higher-level problems and solutions. It’s seamlessly integrated into Xcode, and knows the latest software development kits (SDKs) and Swift language features, so developers will always get the latest code features that blend perfectly into their projects. With Swift Assist, tasks like exploring new frameworks and experimenting with new ideas are just one request away. Swift Assist uses a powerful model that runs in the cloud — and like all Apple developer services, it is built with privacy and security in mind. Developers’ code is only used to process requests and never stored on servers, and Apple will not use it to train machine learning models." Swift Assist is coming later this year to Xcode 16.
2:36

Apple announces Swift Assist for Xcode 16! "Swift Assist serves as a companion for all of a developer’s coding tasks, so they can focus on higher-level problems and solutions. It’s seamlessly integrated into Xcode, and knows the latest software development kits (SDKs) and Swift language features, so developers will always get the latest code features that blend perfectly into their projects. With Swift Assist, tasks like exploring new frameworks and experimenting with new ideas are just one request away. Swift Assist uses a powerful model that runs in the cloud — and like all Apple developer services, it is built with privacy and security in mind. Developers’ code is only used to process requests and never stored on servers, and Apple will not use it to train machine learning models." Swift Assist is coming later this year to Xcode 16.

M1

79,615 просмотров • 2 лет назад

🚨 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!
44:33

🚨 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 просмотров • 1 год назад

🌎Making Game Development Simple with the World Engine The World Engine is revolutionizing how game engines work in many ways. If you create a game, it's a lot of single work that you're doing, and then you're pushing it to other people, and then they work with it. But with the World Engine, you build together in real time, like in a shared sandbox. It’s faster, easy to use, runs on JavaScript/TypeScript, and includes AI tools that help both developers and non-coders create games easily. 🖥 Watch the video for the full breakdown!
1:19

🌎Making Game Development Simple with the World Engine The World Engine is revolutionizing how game engines work in many ways. If you create a game, it's a lot of single work that you're doing, and then you're pushing it to other people, and then they work with it. But with the World Engine, you build together in real time, like in a shared sandbox. It’s faster, easy to use, runs on JavaScript/TypeScript, and includes AI tools that help both developers and non-coders create games easily. 🖥 Watch the video for the full breakdown!

NeuralAI

24,349 просмотров • 1 год назад

How can players make a big jump next year?!?! For High School and College players… they all put in work with their coaches. Most of them have a trainer they see to put in work. So what is the separator? 1. Are you working on the right things with your coaches that are going to help you be a better player working your team and overall 2. HOW MUCH TIME DO YOU PUT IN THE GYM ON YOUR OWN. This is a HUGE SEPARATOR because this time isn’t dependent on anyone and is unlimited in how much you do. Almost every player I’ve worked with that has made a BIG JUMP has put in extra time on their own. It’s not only because of the work, but it also helps players understand their game better and gives them a bigger investment in their development. As a player you should assess that part of your development today!
0:49

How can players make a big jump next year?!?! For High School and College players… they all put in work with their coaches. Most of them have a trainer they see to put in work. So what is the separator? 1. Are you working on the right things with your coaches that are going to help you be a better player working your team and overall 2. HOW MUCH TIME DO YOU PUT IN THE GYM ON YOUR OWN. This is a HUGE SEPARATOR because this time isn’t dependent on anyone and is unlimited in how much you do. Almost every player I’ve worked with that has made a BIG JUMP has put in extra time on their own. It’s not only because of the work, but it also helps players understand their game better and gives them a bigger investment in their development. As a player you should assess that part of your development today!

Steve Dagostino

33,613 просмотров • 2 лет назад

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:
4:01

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:

DeepLearning.AI

23,255 просмотров • 1 год назад

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.
2:14

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

Aaron Levie

24,618 просмотров • 3 месяцев назад

🚀 Today, we are thrilled to share that Amazon Q is now generally available. ◼Amazon Q is the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. It eliminates tedious work for developers and employees across organizations. ◼Q helps to test, debug and write code, and has the highest reported code acceptance rates in the industry, for assistants that perform multi-line code suggestions. BT Group recently reported they accepted 37% of Q’s code suggestions and National Australia Bank reported a 50% acceptance rate. ◼Amazon Q Developer Agents can autonomously perform a range of tasks—everything from implementing features, documenting, and refactoring code, to performing software upgrades. Developers can ask Amazon Q to implement an application feature, and the agent will analyze their existing application code and generate a step-by-step implementation plan. ◼Amazon Q's capabilities extend beyond coding. It also allows employees to easily get insight from their company's internal data that is spread across multiple documents, systems, and applications. Q connects all these siloed sources and can answer questions, provide summaries, analyze trends, and generate content. ◼We're also introducing Q Apps, a powerful new way for anyone to create generative AI apps based on their organization's data—no prior coding experience required. Simply describe the app you need in natural language, and Q Apps will build it for you. This unlocks endless possibilities for teams to automate workflows and daily tasks. Customers across industries are using Amazon Q to transform the way they work. I'm incredibly excited to see what Q can do for you.
1:29

🚀 Today, we are thrilled to share that Amazon Q is now generally available. ◼Amazon Q is the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. It eliminates tedious work for developers and employees across organizations. ◼Q helps to test, debug and write code, and has the highest reported code acceptance rates in the industry, for assistants that perform multi-line code suggestions. BT Group recently reported they accepted 37% of Q’s code suggestions and National Australia Bank reported a 50% acceptance rate. ◼Amazon Q Developer Agents can autonomously perform a range of tasks—everything from implementing features, documenting, and refactoring code, to performing software upgrades. Developers can ask Amazon Q to implement an application feature, and the agent will analyze their existing application code and generate a step-by-step implementation plan. ◼Amazon Q's capabilities extend beyond coding. It also allows employees to easily get insight from their company's internal data that is spread across multiple documents, systems, and applications. Q connects all these siloed sources and can answer questions, provide summaries, analyze trends, and generate content. ◼We're also introducing Q Apps, a powerful new way for anyone to create generative AI apps based on their organization's data—no prior coding experience required. Simply describe the app you need in natural language, and Q Apps will build it for you. This unlocks endless possibilities for teams to automate workflows and daily tasks. Customers across industries are using Amazon Q to transform the way they work. I'm incredibly excited to see what Q can do for you.

Adam Selipsky

121,755 просмотров • 2 лет назад

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!
3:40

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,224,481 просмотров • 1 год назад

"What is an AI Agent and why do they matter?" An agent is a program that autonomously completes tasks or makes decisions based on data. What do I mean by autonomous? The agent understands task intent, can plan steps to solve the problem, decide and execute and actions and adapt to the environment. Consider how many of us use AI chat interfaces today. You might ask ChatGPT to write an article from start to finish and get a one-shot response. You probably need to do some work to iterate on it yourself. An agentic version is more nuanced - it might write an outline, decide if research is needed, write a draft, evaluate if it needs work and revise itself. Unlike traditional AI models that simply respond to queries, agents are designed to be autonomous and proactive. Think of them as assistants that can not only understand what you need but also take initiative to accomplish tasks by using various tools and making decisions along the way. For example, an AI agent might help a marketing team by not just analyzing campaign data, but actively monitoring performance, adjusting budget allocations, and even drafting social media posts based on real-time engagement metrics. The significance of AI agents lies in their potential to transform how we work. In customer service, agents can handle complex inquiries by accessing multiple databases, processing payments, and updating records - all while maintaining natural conversations with customers. In software development, they can assist programmers by not just suggesting code but actively debugging issues, writing test cases, and even refactoring entire codebases. This level of autonomy and capability represents a fundamental shift from AI as a tool to AI as a collaborative partner. While there remain many unknowns, I'm excited about the potential for agents and we're thinking about how they can help users and developers on the web over in Chrome. The key to success will likely be finding the right balance between human oversight and agent autonomy, ensuring that these powerful tools enhance rather than diminish the human element in business operations.
3:43

"What is an AI Agent and why do they matter?" An agent is a program that autonomously completes tasks or makes decisions based on data. What do I mean by autonomous? The agent understands task intent, can plan steps to solve the problem, decide and execute and actions and adapt to the environment. Consider how many of us use AI chat interfaces today. You might ask ChatGPT to write an article from start to finish and get a one-shot response. You probably need to do some work to iterate on it yourself. An agentic version is more nuanced - it might write an outline, decide if research is needed, write a draft, evaluate if it needs work and revise itself. Unlike traditional AI models that simply respond to queries, agents are designed to be autonomous and proactive. Think of them as assistants that can not only understand what you need but also take initiative to accomplish tasks by using various tools and making decisions along the way. For example, an AI agent might help a marketing team by not just analyzing campaign data, but actively monitoring performance, adjusting budget allocations, and even drafting social media posts based on real-time engagement metrics. The significance of AI agents lies in their potential to transform how we work. In customer service, agents can handle complex inquiries by accessing multiple databases, processing payments, and updating records - all while maintaining natural conversations with customers. In software development, they can assist programmers by not just suggesting code but actively debugging issues, writing test cases, and even refactoring entire codebases. This level of autonomy and capability represents a fundamental shift from AI as a tool to AI as a collaborative partner. While there remain many unknowns, I'm excited about the potential for agents and we're thinking about how they can help users and developers on the web over in Chrome. The key to success will likely be finding the right balance between human oversight and agent autonomy, ensuring that these powerful tools enhance rather than diminish the human element in business operations.

Addy Osmani

30,412 просмотров • 1 год назад

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.
10:43

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Bret Taylor

1,060,880 просмотров • 4 месяцев назад

AI can’t yet one-shot an entire product—but with the rise of vibe coding, supercharged developers are getting close. Part intuition and experimentation, part validation, it's the new frontier of software development. YC's Tom Blomfield (Tom Blomfield) has spent the last month building side projects with tools like Claude Code, Windsurf, and Aqua, seeing just how far you can push modern LLMs. From writing full-stack apps to debugging with a single copy paste of an error message, AI is becoming a legit collaborator in the dev process. With tips from YC founders in the Spring batch, this is a playbook for anyone who wants to get the most out of vibe coding and build faster.
0:45

AI can’t yet one-shot an entire product—but with the rise of vibe coding, supercharged developers are getting close. Part intuition and experimentation, part validation, it's the new frontier of software development. YC's Tom Blomfield (Tom Blomfield) has spent the last month building side projects with tools like Claude Code, Windsurf, and Aqua, seeing just how far you can push modern LLMs. From writing full-stack apps to debugging with a single copy paste of an error message, AI is becoming a legit collaborator in the dev process. With tips from YC founders in the Spring batch, this is a playbook for anyone who wants to get the most out of vibe coding and build faster.

Y Combinator

151,433 просмотров • 1 год назад