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🚀 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.show more

Adam Selipsky

52,692 subscribers

121,755 просмотров • 2 лет назад •via X (Twitter)

Наука и технологии Новости и политика

Anya Rossi• Live Now

Private livecam show

Комментарии: 9

Фото профиля Rahul Pathak

Rahul Pathak2 лет назад

Super excited about this launch!

Фото профиля Gautam Goswami 🎯

Gautam Goswami 🎯2 лет назад

Looking forward to try out Q Apps.

Фото профиля h

h2 лет назад

Please fix s3 buckets 4xx DDos concerns issues

Фото профиля Sarbjeet Johal

Sarbjeet Johal2 лет назад

Coding is very important as well but currently, model training, RAG, model discover and acquisition, model extending, model mixing has taken the center stage. Please highlight the Q’s capabilities in that area.

Фото профиля Cassandra

Cassandra2 лет назад

Awesome

Фото профиля Jeff Hammon

Jeff Hammon2 лет назад

'generally'. My god is it, or isn't it?

Фото профиля ScottFive

ScottFive2 лет назад

@Scobleizer "generally available" - what does that even mean? Like, is it available or not? lol

Фото профиля Don Cordite

Don Cordite2 лет назад

@awscloud I got a demo the other day where one of your AI capabilities was asked “what is an LLM” and it couldn’t answer. All of the customers secretly lol’d.

Фото профиля Daniel Hull

Daniel Hull2 лет назад

@BlackBerry Jarvis and Blackberry QNX IVY are just two tech acronyms that come to mind.

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14,445 просмотров • 5 месяцев назад

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Santiago

41,738 просмотров • 1 год назад

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Aaron Levie

91,863 просмотров • 11 месяцев назад

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Andrew Ng

81,048 просмотров • 1 год назад

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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 ➡️

Swami Sivasubramanian

154,343 просмотров • 1 год назад

Our first Generative AI short course in JavaScript! GitHub recently reported that JavaScript is again the world’s most popular programming language. To support web developers exploring and developing with generative AI, we just launched a new short course in JavaScript taught by Jacob Lee, founding engineer at . In Build LLM Apps with LangChain.js you’ll learn elements common in AI development, including: (i) Using data loaders to pull data from common sources such as PDFs, websites, and databases (ii) Prompts, which are used to provide the LLM context (iii) Modules to support RAG such as text splitters and integrations with vector stores (iv) Working with different models to write applications that are not vendor-specific (v) Parsers, which extract and format the output for your downstream code to process You’ll also build with the LangChain Expression Language, which lets you easily compose sequences (also called chains) of modules to perform complex tasks using LLMs. Putting all this together, you’ll also work on a conversational question-answering LLM application capable of using external data as context. Please sign up here:

Our first Generative AI short course in JavaScript! GitHub recently reported that JavaScript is again the world’s most popular programming language. To support web developers exploring and developing with generative AI, we just launched a new short course in JavaScript taught by Jacob Lee, founding engineer at . In Build LLM Apps with LangChain.js you’ll learn elements common in AI development, including: (i) Using data loaders to pull data from common sources such as PDFs, websites, and databases (ii) Prompts, which are used to provide the LLM context (iii) Modules to support RAG such as text splitters and integrations with vector stores (iv) Working with different models to write applications that are not vendor-specific (v) Parsers, which extract and format the output for your downstream code to process You’ll also build with the LangChain Expression Language, which lets you easily compose sequences (also called chains) of modules to perform complex tasks using LLMs. Putting all this together, you’ll also work on a conversational question-answering LLM application capable of using external data as context. Please sign up here:

Andrew Ng

284,320 просмотров • 2 лет назад

It's not every day I can share news that will completely transform your workday 🚀 Over the last couple of years, we’ve seen how AI can transform our personal lives, but this hasn’t been replicated at work—yet. That's all changing today with Amazon Quick Suite, a new agentic AI experience invented at Amazon and tested by tens of thousands of our employees. Amazon Quick Suite is your AI teammate that helps you cut through the noise of repetitive tasks and fragmented information. It can pull data from your documents (in SharePoint, Google Drive, OneDrive, etc.), emails (like Outlook), messages (like Slack), enterprise apps (like Salesforce and ServiceNow), data warehouses (like Redshift, Snowflake, Databricks, and many more) and the web so you can get work done faster. With support for MCP and partners like Workato and Zapier, you can connect data from 1000s of apps with Quick. There’s a lot more to Quick than I can cover in this post alone—my latest blog has all the details. Give Quick a spin today and see why teams at Amazon are loving this! ➡️

It's not every day I can share news that will completely transform your workday 🚀 Over the last couple of years, we’ve seen how AI can transform our personal lives, but this hasn’t been replicated at work—yet. That's all changing today with Amazon Quick Suite, a new agentic AI experience invented at Amazon and tested by tens of thousands of our employees. Amazon Quick Suite is your AI teammate that helps you cut through the noise of repetitive tasks and fragmented information. It can pull data from your documents (in SharePoint, Google Drive, OneDrive, etc.), emails (like Outlook), messages (like Slack), enterprise apps (like Salesforce and ServiceNow), data warehouses (like Redshift, Snowflake, Databricks, and many more) and the web so you can get work done faster. With support for MCP and partners like Workato and Zapier, you can connect data from 1000s of apps with Quick. There’s a lot more to Quick than I can cover in this post alone—my latest blog has all the details. Give Quick a spin today and see why teams at Amazon are loving this! ➡️

Swami Sivasubramanian

11,011 просмотров • 10 месяцев назад

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

Andrew Ng

139,873 просмотров • 1 год назад

We've all experienced how consumer AI can help in our personal lives, but that same experience hasn't been available at work. Most enterprise data is siloed, consumer AI can't access your work tools, and many orgs won't allow it due to security concerns. We built Amazon Quick Suite to solve those problems. It’s an agentic AI app that acts as your AI teammate at work – connecting to your business data and apps, and letting you interact with AI agents that can find insights, conduct research, and even take actions across your systems – with the security and privacy enterprises need. Quick works across your existing ecosystem, from emails to Salesforce to Slack to AWS services. You can create custom agents to draft content in your personal style, use Quick Research for in-depth analysis, or set up Quick Flow to automate tasks. And most importantly, your interactions never train the underlying models. We've been testing with Amazon employees and select customers and early results show just how transformational Quick is… cutting analysis time from months to days, and turning week-long tasks into minutes. This is an exciting moment and we're just getting started. Quick is available now, so have at it!

We've all experienced how consumer AI can help in our personal lives, but that same experience hasn't been available at work. Most enterprise data is siloed, consumer AI can't access your work tools, and many orgs won't allow it due to security concerns. We built Amazon Quick Suite to solve those problems. It’s an agentic AI app that acts as your AI teammate at work – connecting to your business data and apps, and letting you interact with AI agents that can find insights, conduct research, and even take actions across your systems – with the security and privacy enterprises need. Quick works across your existing ecosystem, from emails to Salesforce to Slack to AWS services. You can create custom agents to draft content in your personal style, use Quick Research for in-depth analysis, or set up Quick Flow to automate tasks. And most importantly, your interactions never train the underlying models. We've been testing with Amazon employees and select customers and early results show just how transformational Quick is… cutting analysis time from months to days, and turning week-long tasks into minutes. This is an exciting moment and we're just getting started. Quick is available now, so have at it!

Andy Jassy

49,241 просмотров • 10 месяцев назад

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 просмотров • 4 месяцев назад

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Santiago

64,425 просмотров • 8 месяцев назад

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

DeepLearning.AI

32,513 просмотров • 1 год назад

Excited to share that I’ve joined Anomaly as founding designer. I’m thrilled to work with Jay dax Frank Wang and Adam — a team stacked with talent! I’ve always been fascinated by challenges that influence the way people work. Today, AI is reshaping software development at a pace we’ve never seen. From solo developers to global enterprises, the way we write code has shifted dramatically in the past year, and will continue to change in the years ahead. This is an opportunity for me to help shape these experiences for everyone, regardless of frameworks, IDEs, or tools. I want to explore how they’re adopted and how they can have the greatest impact. The team is hard at work on OpenCode — the open source AI coding agent for the terminal. With over 23K stars on GitHub, it’s already changing how thousands of developers write code every day. Follow OpenCode as we go beyond the terminal and design and build world-class AI developer experiences ✌️

Excited to share that I’ve joined Anomaly as founding designer. I’m thrilled to work with Jay dax Frank Wang and Adam — a team stacked with talent! I’ve always been fascinated by challenges that influence the way people work. Today, AI is reshaping software development at a pace we’ve never seen. From solo developers to global enterprises, the way we write code has shifted dramatically in the past year, and will continue to change in the years ahead. This is an opportunity for me to help shape these experiences for everyone, regardless of frameworks, IDEs, or tools. I want to explore how they’re adopted and how they can have the greatest impact. The team is hard at work on OpenCode — the open source AI coding agent for the terminal. With over 23K stars on GitHub, it’s already changing how thousands of developers write code every day. Follow OpenCode as we go beyond the terminal and design and build world-class AI developer experiences ✌️

David Hill

172,131 просмотров • 10 месяцев назад

Many people still remember when ManusAI was released. It was astonishing to see what AI agents are already capable of today, and its in-depth research was impressive. But what else can ManusAI actually do? Unlike traditional chatbots, Manus can independently plan, execute, and optimize complex, multi-step tasks from start to finish—without any ongoing user intervention. In short: Manus AI is an autonomous AI agent that can independently research, plan, analyze, design, and program—without you having to manually accompany every step. For example: • Create market analyses and generate structured reports from online data • Evaluate Excel or Google Sheets and derive data-driven recommendations • Automatically design websites, fill them with content, and publish them online • Write applications, emails, or social media posts in the desired style • Write Python code, test it, and use it to solve technical tasks Below is a brief overview of the user interface. Prompt used: Create a presentation with 5 slides on the state of the AI industry in 2025. Focus: investments, areas of application, risks, regulation, outlook, everything in english

Many people still remember when ManusAI was released. It was astonishing to see what AI agents are already capable of today, and its in-depth research was impressive. But what else can ManusAI actually do? Unlike traditional chatbots, Manus can independently plan, execute, and optimize complex, multi-step tasks from start to finish—without any ongoing user intervention. In short: Manus AI is an autonomous AI agent that can independently research, plan, analyze, design, and program—without you having to manually accompany every step. For example: • Create market analyses and generate structured reports from online data • Evaluate Excel or Google Sheets and derive data-driven recommendations • Automatically design websites, fill them with content, and publish them online • Write applications, emails, or social media posts in the desired style • Write Python code, test it, and use it to solve technical tasks Below is a brief overview of the user interface. Prompt used: Create a presentation with 5 slides on the state of the AI industry in 2025. Focus: investments, areas of application, risks, regulation, outlook, everything in english

Chubby♨️

51,219 просмотров • 1 год назад