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Validation remains the main bottleneck in agentic coding. As agents generate features quickly, humans are left to review and validate their work: security, stability, performance, compliance, UX, design, copy, and more. This is not scalable. Truly unattended, production-ready agentic coding requires rethinking how quality is built into the process....

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Shai Wininger

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26,509 görüntüleme • 9 ay önce •via X (Twitter)

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This time, I tried using AI to perform a multi-character rendition of the main storyline of my game, which is still in development, incorporating complete character reference images and scene models. The results were quite interesting. While there are still some significant errors in the details, I believe AI can be integrated into the production pipeline to quickly validate the plausibility of narrative expressions. I won't let AI become a part of my final work, but it is likely to become a key tool in my development workflow for team communication, technical and narrative validation, and various types of testing, saving a tremendous amount of development time and communication costs.
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This time, I tried using AI to perform a multi-character rendition of the main storyline of my game, which is still in development, incorporating complete character reference images and scene models. The results were quite interesting. While there are still some significant errors in the details, I believe AI can be integrated into the production pipeline to quickly validate the plausibility of narrative expressions. I won't let AI become a part of my final work, but it is likely to become a key tool in my development workflow for team communication, technical and narrative validation, and various types of testing, saving a tremendous amount of development time and communication costs.

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We've built a massive verification bottleneck: • 72% of developers use AI to generate code every day • 96% of them don't fully trust it Writing code is now free. All the work now is verifying that the code does what it's supposed to. According to a new State of Code Survey, almost half of the respondents believe the number one skill that will matter most in the coming years is "reviewing and validating AI-generated code for quality and security". Not prompt engineering or vibe-coding, but checking AI's work.
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Rivian has released a new video of their pilot production line for their upcoming R2 SUV. "Before the start of production next year, we’re making sure R2 is ready to build at global scale, from parts design to end of line processes. This starts with validating each individual part and how they all fit together. The assembly process is refined through validation builds on our pilot line in California. The team from our manufacturing plant in Normal, IL rotates in to train on the new processes while providing critical feedback to our engineers to make vehicle assembly as simple and efficient as possible. Pilot line testing ensures R2 will be built to the highest standard of quality from the very beginning."
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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!

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$HOME is in the terminal. Now your new coding bestie is available in whatever terminal emulator you use. Crush is a high performance, agentic coding tool built with Charm libraries and the quirky, playful aesthetic that you know and love. This is just the beginning, too.

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The ultimate form of agentic coding will not be the terminal. This is an experiment. An ADE (Agentic Coding Environment), designed from the ground up to work with agentic coding tools. Right now, this only supports Claude Code and runs on a Mac.
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The ultimate form of agentic coding will not be the terminal. This is an experiment. An ADE (Agentic Coding Environment), designed from the ground up to work with agentic coding tools. Right now, this only supports Claude Code and runs on a Mac.

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Anyone who’s worked in QA knows the gap. Requirements live in one place and tests live somewhere else. Every change creates another round of rewriting. Kane AI by TestMu AI (formerly LambdaTest) approaches that gap differently. Instead of forcing teams to rewrite tests in a new abstraction, it lets you describe intent in natural language and turns that into structured, executable tests that can be exported into your existing frameworks. During exploration, the most practical part wasn’t just test generation, it was how expressive the language layer is. Complex assertions, branching conditions, and validations can be described without switching mental modes into code, but the resulting output still integrates cleanly with real pipelines. There’s also a strong emphasis on API coverage and auto-bug detection, which matters when UI tests aren’t enough. Kane AI by TestMu AI (formerly LambdaTest) can detect failures during execution and attempt self-healing based on original intent, while still requiring review and validation from the team. It doesn’t pretend QA disappears. It acknowledges QA is hard, contextual, and constantly changing. That honesty makes it useful. If you’re dealing with test maintenance and curious how natural-language testing fits into real QA work, Kane AI by TestMu AI (formerly LambdaTest) is available to try. See it in action: TestMu AI

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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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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 görüntüleme • 11 ay önce

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.
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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.

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22:37

Shipping speed is up across the board, and AI tools like Gemini 3 Pro are helping us code and build faster than ever. But validation and quality assurance? Still the bottleneck. That’s why I’m integrating TestSprite into my workflow. It analyzes my code and PRD, automatically builds a comprehensive test layer, and keeps my development flow unbroken. No test writing. No last-minute QA chaos. What it covers: ➞ UI Flow Coverage: Verifies real-time updates and synchronization (e.g., confirming gradient sliders and angle controls work together). ➞ Edge Case Validation: Catches subtle logic bugs like inputs accepting out-of-range values that I'd typically miss. ➞ Functionality Check: Automatically runs videos to ensure core features (like "Randomize" and "Copy to Clipboard") are working. ➞ Repeatable Test Suite: Creates tests I can rerun anytime, giving me confidence that new code hasn't broken the old. My new Antigravity + Test Sprite Loop: ➞ AI Plan & Code: Use Antigravity's Planning Mode with Gemini 3 Pro to generate structured code. ➞ Browser Agent QA: Use Antigravity's Open Browser feature to let the AI visually analyze and control the live UI for quick verification. ➞ Validate & Test: Run the automated, reproducible test suite in Test Sprite. ➞ Ship. This loop feels clean, fast, and robust. Works beautifully alongside Gemini 3 Pro, Cloud Sonet, or any AI stack.

Dhaval Makwana

110,884 görüntüleme • 5 ay önce

The bottleneck in coding is now the time it takes a human to review agent-generated code. But existing review UIs don't work well for agents. So we built a new one into Amp.
2:15

The bottleneck in coding is now the time it takes a human to review agent-generated code. But existing review UIs don't work well for agents. So we built a new one into Amp.

Beyang

62,900 görüntüleme • 7 ay önce

Why do we need a purpose-built blockchain for agentic payments? Because AI agents are now smart enough to operate autonomously, and they need their own rails to transact with each other. Watch the video to see what that looks like in action: A real agent, built on Kite AI, helped our CTO Scott Shi - e/acc order lunch from Uber Eats fully on its own. It explored menu options, made the choice, and completed the payment - all without human intervention. This marks the beginning of agentic commerce and the future of agentic payments - and Kite AI is building this.
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Why do we need a purpose-built blockchain for agentic payments? Because AI agents are now smart enough to operate autonomously, and they need their own rails to transact with each other. Watch the video to see what that looks like in action: A real agent, built on Kite AI, helped our CTO Scott Shi - e/acc order lunch from Uber Eats fully on its own. It explored menu options, made the choice, and completed the payment - all without human intervention. This marks the beginning of agentic commerce and the future of agentic payments - and Kite AI is building this.

KITE AI

88,070 görüntüleme • 8 ay önce

NEW VIDEO At Aalo, we're using AI to build nuclear faster. And the more nuclear we deploy to power data centers, the better AI will get. A positive reinforcement loop is incoming. A few ways we're using AI: ➡️ Aalo Bot attends every meeting, listens, and soon will be able to talk. We'll ask it questions like "would this design change work given all the constraints you've heard from other teams?" or "what are the most pressing blockers for the company right now, stack ranked?", and more. ➡️ We're using AI to write software across the entire company. Not just replacing SaaS, but AI is also currently writing the code to automate physical robotic work done in our factory, including welding. This will unlock a scale of production and consistency of quality beyond what’s been possible in nuclear in the past. The video below features Gregory Wildes and Russell Rowland, along with some of the work Rob Kessler has been doing behind the scenes. Enjoy!
3:04

NEW VIDEO At Aalo, we're using AI to build nuclear faster. And the more nuclear we deploy to power data centers, the better AI will get. A positive reinforcement loop is incoming. A few ways we're using AI: ➡️ Aalo Bot attends every meeting, listens, and soon will be able to talk. We'll ask it questions like "would this design change work given all the constraints you've heard from other teams?" or "what are the most pressing blockers for the company right now, stack ranked?", and more. ➡️ We're using AI to write software across the entire company. Not just replacing SaaS, but AI is also currently writing the code to automate physical robotic work done in our factory, including welding. This will unlock a scale of production and consistency of quality beyond what’s been possible in nuclear in the past. The video below features Gregory Wildes and Russell Rowland, along with some of the work Rob Kessler has been doing behind the scenes. Enjoy!

Matt Loszak

14,511 görüntüleme • 4 gün önce

Mark Zuckerberg says the future of coding is closer than we think: AI agents inside Meta are being built not as generic developer tools, but to push Llama research forward — fully integrated into their workflow. He predicts that within 12–18 months, most of the code for these efforts could be written by AI itself — not simple autocomplete, but systems that take goals, run tests, fix bugs, improve performance, and even write higher-quality code than many skilled engineers. The real takeaway? This is only one part of a massive AI landscape, and no single company will dominate it all. Meta is building its own internal agents designed specifically to advance Llama research — deeply connected to their toolchain rather than built for enterprise customers. He expects that within the next year to year-and-a-half, AI will be writing most of the code in these projects, going far beyond autocomplete — understanding goals, running experiments, finding issues, and continuously improving systems. And according to him, this shift will become a core driver of how AI itself evolves. Meta is investing heavily in coding and research agents deeply integrated into their own systems, focused on advancing Llama rather than building broad enterprise tools. He expects AI will soon move beyond autocomplete to fully goal-driven coding — testing, debugging, improving, and producing high-quality results. But in his view, the AI revolution is too big for any single winner — multiple players and approaches will shape the future.
2:36

Mark Zuckerberg says the future of coding is closer than we think: AI agents inside Meta are being built not as generic developer tools, but to push Llama research forward — fully integrated into their workflow. He predicts that within 12–18 months, most of the code for these efforts could be written by AI itself — not simple autocomplete, but systems that take goals, run tests, fix bugs, improve performance, and even write higher-quality code than many skilled engineers. The real takeaway? This is only one part of a massive AI landscape, and no single company will dominate it all. Meta is building its own internal agents designed specifically to advance Llama research — deeply connected to their toolchain rather than built for enterprise customers. He expects that within the next year to year-and-a-half, AI will be writing most of the code in these projects, going far beyond autocomplete — understanding goals, running experiments, finding issues, and continuously improving systems. And according to him, this shift will become a core driver of how AI itself evolves. Meta is investing heavily in coding and research agents deeply integrated into their own systems, focused on advancing Llama rather than building broad enterprise tools. He expects AI will soon move beyond autocomplete to fully goal-driven coding — testing, debugging, improving, and producing high-quality results. But in his view, the AI revolution is too big for any single winner — multiple players and approaches will shape the future.

Ian Miles Cheong

20,561 görüntüleme • 3 ay önce

We're in the AI Agents era. And Uomi is set to make a powerful impact in the real world through economic agency. This is how the future of AI agents on the blockchain is being built: 🔘L1 infrastructure designed for reliable interactions between smart contracts and AI inferences 🔘AI agents empowered to control digital assets 🔘Creation of autonomous agents for multiple functionalities, including X account management, NFT creation, gaming NPCs and traders The testnet is already live, so you can try some of these functionalities today! The IDO of UOMI is coming to Seedify very soon - get your $SFUND and be ready for the revolution!
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We're in the AI Agents era. And Uomi is set to make a powerful impact in the real world through economic agency. This is how the future of AI agents on the blockchain is being built: 🔘L1 infrastructure designed for reliable interactions between smart contracts and AI inferences 🔘AI agents empowered to control digital assets 🔘Creation of autonomous agents for multiple functionalities, including X account management, NFT creation, gaming NPCs and traders The testnet is already live, so you can try some of these functionalities today! The IDO of UOMI is coming to Seedify very soon - get your $SFUND and be ready for the revolution!

Seedify

11,486 görüntüleme • 1 yıl önce

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."
1:12

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

Patrick OShaughnessy

24,818 görüntüleme • 4 ay önce