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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...

154,343 次观看 • 10 个月前 •via X (Twitter)

5 条评论

Muktesh 的头像
Muktesh10 个月前

Interesting. Looking forward to try this as spec driven development is the future and the current direction.

Mobile Scanner 的头像
Mobile Scanner11 个月前

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

Serge Doubinski 的头像
Serge Doubinski10 个月前

congrats on the launch! recognizing importance of specs is better these days - claude code planning mode, cursor todos, but making it an even more core feature is a great call. kind of makes sense that it comes from amazon and given the writing culture

redsquare 的头像
redsquare10 个月前

Why is it devoid of any aws branding?

Scalevise 的头像
Scalevise10 个月前

This what we were looking for🙌

相关视频

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 comprehensive context helps Kiro’s AI agents deliver better results with fewer iterations. 2 - Kiro features intelligent agent hooks that automatically handle critical but time-consuming tasks like generating documentation, writing tests, and optimizing performance. These hooks work in the background, triggered by events like saving files or making commits. It’s like having an experienced developer constantly reviewing your work and handling the maintenance tasks that often get delayed. 3 - Kiro provides a purpose-built interface that adapts to how developers work. Whether you prefer chat interactions or working with specifications, Kiro supports your workflow while keeping you in control of the development process. Kiro is really good at "vibe coding" but goes well beyond that. While other AI coding assistants might help you prototype quickly, Kiro helps you take those prototypes all the way to production by following a mature, structured development process out of the box. This means developers can spend less time on boilerplate code and more time where it matters most – innovating and building solutions that customers will love. Starting today, Kiro is available for free during preview and supports most popular programming languages. Here’s how to get started with Kiro today: Excited to see how developers use Kiro, and to work with the developer community to continue to shape Kiro moving forward.

Andy Jassy

665,961 次观看 • 10 个月前

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

1,223,446 次观看 • 1 年前

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,663 次观看 • 2 个月前

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

24,582 次观看 • 2 个月前