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AI designs functional hardware! 🔧 Claude Opus 4.7 is leading CAD benchmarks, and AI is starting to design functional hardware autonomously. The shift happening: CAD as a tool → CAD as an autonomous system. The model didn't just sketch geometry, it generated mechanically valid designs with joints and motion... constraints. It optimized for real-world functionality, not just visual correctness like earlier LLMs. This mirrors what happened in coding last year. In software, engineers moved toward requirements, architecture, and orchestration while code became the compiled outcome of AI systems. Mechanical engineering may follow the same path: from designing parts → defining intent. This changes the role of engineering teams. Instead of manually designing every component, engineers will define constraints, validate outputs, and focus on system-level architecture. Engineers spend less time on geometry and more time on optimization and integration. AI handles the mechanical validity checks that traditionally required experienced designers. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →show more

Lukas Ziegler

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25,458 views • 3 months ago •via X (Twitter)

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Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. Flow Engineering

Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. Flow Engineering

Pari Singh

38,826 views • 1 month ago

this is a CATIA V5 extension called PYCATIA and it allows you to automate as well as create components in a purely data driven way, this is the direction that I would like to see more of from the people who want to make a professional tools for engineers in this extension you are still driving the inputs , however this is far less error prone when you have complex surface or tons of features that you have to change the fundamental problem with using pure LLM output to design mechanical components is it lacks the grounding in constraint-based parametric logic that real world CAD systems depend on. LLMs hallucinate geometry, they don’t model it. PYCATIA bridges that gap by letting you bind natural-language or data-driven inputs directly to CATIA’s kernel. this is the kind of tool that makes AI a actual engineers assistant not just spitting out STEP files that look plausible but building them from relationships references and real constraints. more tools should be built this way, coupled with existing industrial software, and building AI functionality on top of this

this is a CATIA V5 extension called PYCATIA and it allows you to automate as well as create components in a purely data driven way, this is the direction that I would like to see more of from the people who want to make a professional tools for engineers in this extension you are still driving the inputs , however this is far less error prone when you have complex surface or tons of features that you have to change the fundamental problem with using pure LLM output to design mechanical components is it lacks the grounding in constraint-based parametric logic that real world CAD systems depend on. LLMs hallucinate geometry, they don’t model it. PYCATIA bridges that gap by letting you bind natural-language or data-driven inputs directly to CATIA’s kernel. this is the kind of tool that makes AI a actual engineers assistant not just spitting out STEP files that look plausible but building them from relationships references and real constraints. more tools should be built this way, coupled with existing industrial software, and building AI functionality on top of this

Phoenix𝕏

11,243 views • 1 year ago

Andrew Ng, co-founder of Google Brain and Coursera, on the worst career advice being given about AI right now: He doesn't mince words about what he's hearing from supposed experts: "As early as earlier this year and certainly last year, there are a few people advising others to stop learning to write code because AI will automate it." His reasoning is rooted in a historical pattern most people miss: "As something becomes easier, more people should do it, not fewer. When the world moved from assembly language to COBOL, there were actually articles saying, 'Well, we now have COBOL. Programming is so easier. Looks like we don't need programmers anymore.' But the opposite happened." Andrew believes the same thing is happening now with AI-assisted coding: "As we now have AI assisted coding, a lot more people should be coding. And I think the demand for software, custom software, has no practical ceiling. So the cost of software engineering comes down, which it is, we'll just get more and more great software out in the world." But here's where the advice gets uncomfortable for experienced engineers. Andrew Ng is honest about what he's seeing on the ground: "It is true that a fresh college grad that is really on top of AI will outperform a full stack engineer with 10 years of experience that is still doing things they were back in 2022, 3 years ago before GenAI." However, there's a nuance most people miss when they hear that stereotype: "The other piece that is less well appreciated is the best engineers I know are not fresh college grads. They're actually very experienced engineers that deeply understand architecture and the conceptual framework of how to think about computers and additionally are on top of AI and on top of these AI skills."

Andrew Ng, co-founder of Google Brain and Coursera, on the worst career advice being given about AI right now: He doesn't mince words about what he's hearing from supposed experts: "As early as earlier this year and certainly last year, there are a few people advising others to stop learning to write code because AI will automate it." His reasoning is rooted in a historical pattern most people miss: "As something becomes easier, more people should do it, not fewer. When the world moved from assembly language to COBOL, there were actually articles saying, 'Well, we now have COBOL. Programming is so easier. Looks like we don't need programmers anymore.' But the opposite happened." Andrew believes the same thing is happening now with AI-assisted coding: "As we now have AI assisted coding, a lot more people should be coding. And I think the demand for software, custom software, has no practical ceiling. So the cost of software engineering comes down, which it is, we'll just get more and more great software out in the world." But here's where the advice gets uncomfortable for experienced engineers. Andrew Ng is honest about what he's seeing on the ground: "It is true that a fresh college grad that is really on top of AI will outperform a full stack engineer with 10 years of experience that is still doing things they were back in 2022, 3 years ago before GenAI." However, there's a nuance most people miss when they hear that stereotype: "The other piece that is less well appreciated is the best engineers I know are not fresh college grads. They're actually very experienced engineers that deeply understand architecture and the conceptual framework of how to think about computers and additionally are on top of AI and on top of these AI skills."

Big Brain AI

211,530 views • 1 month ago

For a long time I've been teaching a self-extending design system to generate aerospace hardware from pure physics no CAD, just code and constraints. Tonight I hooked my CEM up to Fusion 360 via MCP after a design and it rebuilt it! Real feature trees a machinist can open, edit, and cut toolpaths from. The system that designs in code can now speak the language of every shop floor on Earth. So to answer if you can transfer your LEAP 71 work to CAD. Yes, you can.

For a long time I've been teaching a self-extending design system to generate aerospace hardware from pure physics no CAD, just code and constraints. Tonight I hooked my CEM up to Fusion 360 via MCP after a design and it rebuilt it! Real feature trees a machinist can open, edit, and cut toolpaths from. The system that designs in code can now speak the language of every shop floor on Earth. So to answer if you can transfer your LEAP 71 work to CAD. Yes, you can.

Dylan Small

14,519 views • 9 days ago

Wow! AI ASSISTED GARAGE MANUFACTURING IS ABOUT TO EXPLODE! CAD Drawings From Just A Picture! MIT just released something profound for creators and engineers alike. Picture this. You take a photo of an object, upload it, and an AI delivers a fully parametric CAD model, complete with editable code and construction history. This is open source GenCAD, from MIT's Decode Lab. It uses autoregressive transformers and diffusion models, trained on hundreds of thousands of images and CAD files. Input a 2D photo or sketch. Output valid CadQuery Python code that beats models like GPT-4.5 in accuracy. Why does this matter? It speeds up reverse engineering, prototyping, and part searches in vast databases. No more hours spent modeling from scratch. Field repairs, custom designs, education, all transformed. It even retrieves similar parts from libraries of thousands. For industries like manufacturing and aerospace, it cuts costs and boosts innovation. Hobbyists gain pro tools without the steep curve. I am testing it now on random objects and can not believe how much of a super power this is. I can start dozens of companies just on this AI model. This open-source gem is here: The future of building stuff arrives in a snapshot.

Wow! AI ASSISTED GARAGE MANUFACTURING IS ABOUT TO EXPLODE! CAD Drawings From Just A Picture! MIT just released something profound for creators and engineers alike. Picture this. You take a photo of an object, upload it, and an AI delivers a fully parametric CAD model, complete with editable code and construction history. This is open source GenCAD, from MIT's Decode Lab. It uses autoregressive transformers and diffusion models, trained on hundreds of thousands of images and CAD files. Input a 2D photo or sketch. Output valid CadQuery Python code that beats models like GPT-4.5 in accuracy. Why does this matter? It speeds up reverse engineering, prototyping, and part searches in vast databases. No more hours spent modeling from scratch. Field repairs, custom designs, education, all transformed. It even retrieves similar parts from libraries of thousands. For industries like manufacturing and aerospace, it cuts costs and boosts innovation. Hobbyists gain pro tools without the steep curve. I am testing it now on random objects and can not believe how much of a super power this is. I can start dozens of companies just on this AI model. This open-source gem is here: The future of building stuff arrives in a snapshot.

Brian Roemmele

121,948 views • 4 months ago

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 ➡️

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 views • 1 year ago

You say it. A robot builds it. 🗣️ MIT researchers just showed a system where spoken language turns directly into physical objects. Say “I want a simple stool,” and a robotic arm designs and assembles it in minutes. 🪑 The pipeline is wild but elegant: speech → language model → 3D generative design → voxelized structure → robotic assembly. No CAD, no programming, no manufacturing expertise needed. Unlike 3D printing, this is fast, modular, and reversible. Objects are built from reusable parts, meaning they can be disassembled and turned into something else later. This feels like an early glimpse of “physical AI” becoming an interface: humans describe intent, machines handle geometry, planning, and fabrication. Read more about the paper here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

You say it. A robot builds it. 🗣️ MIT researchers just showed a system where spoken language turns directly into physical objects. Say “I want a simple stool,” and a robotic arm designs and assembles it in minutes. 🪑 The pipeline is wild but elegant: speech → language model → 3D generative design → voxelized structure → robotic assembly. No CAD, no programming, no manufacturing expertise needed. Unlike 3D printing, this is fast, modular, and reversible. Objects are built from reusable parts, meaning they can be disassembled and turned into something else later. This feels like an early glimpse of “physical AI” becoming an interface: humans describe intent, machines handle geometry, planning, and fabrication. Read more about the paper here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

32,926 views • 7 months ago

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.

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

82,737 views • 4 months ago

I just designed a website with Claude Fable 5. It launched today. And honestly, the lines between AI generated designs and human designs is blurring fast! I run a design agency. AI generated slop makes me want to close it fast! Fable didn't do any of that. Real hierarchy. Intentional whitespace. Restraint. The kind of decisions you usually only see from designers who've shipped real products. No model has come close to this before. Not one. The benchmarks back it up. 80.3% on SWE-bench Pro, where Opus 4.8 scored 69.2%. But the design output impressed me more than any number. Here's the part worth reading twice though. Anthropic's own announcement says writing code stopped being the slow part a while ago, and that Fable pushes the bottleneck toward verification and review. Same is now true for design. → Producing a beautiful screen: getting solved → Knowing which screen to produce, and why: not solved → Designing for trust, edge cases, real users with real money: not solved → Verifying it holds up in production: not solved For designers and developers: production will no longer be the moat. Your taste and Judgment will be. Fable just made that very obvious. Check it out here: #AI #ProductDesign #SaaS #ClaudeFable5 #DesignCraft

I just designed a website with Claude Fable 5. It launched today. And honestly, the lines between AI generated designs and human designs is blurring fast! I run a design agency. AI generated slop makes me want to close it fast! Fable didn't do any of that. Real hierarchy. Intentional whitespace. Restraint. The kind of decisions you usually only see from designers who've shipped real products. No model has come close to this before. Not one. The benchmarks back it up. 80.3% on SWE-bench Pro, where Opus 4.8 scored 69.2%. But the design output impressed me more than any number. Here's the part worth reading twice though. Anthropic's own announcement says writing code stopped being the slow part a while ago, and that Fable pushes the bottleneck toward verification and review. Same is now true for design. → Producing a beautiful screen: getting solved → Knowing which screen to produce, and why: not solved → Designing for trust, edge cases, real users with real money: not solved → Verifying it holds up in production: not solved For designers and developers: production will no longer be the moat. Your taste and Judgment will be. Fable just made that very obvious. Check it out here: #AI #ProductDesign #SaaS #ClaudeFable5 #DesignCraft

Asaad Mahmood - The Small Square

62,121 views • 1 month ago

$Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)$

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)

Ihtesham Ali

96,980 views • 27 days ago

xAI is hiring across multiple roles 🚀 currently building the world's best coding model and training it on a MILLION H100 equivalents The future of Grok Code is being written in real-time Exponential takeoff is here xAI is hiring: → AI training experts → Elite low-level software engineers → Systems design specialists If you're obsessed with code, this is where you need to be Join xAI now

xAI is hiring across multiple roles 🚀 currently building the world's best coding model and training it on a MILLION H100 equivalents The future of Grok Code is being written in real-time Exponential takeoff is here xAI is hiring: → AI training experts → Elite low-level software engineers → Systems design specialists If you're obsessed with code, this is where you need to be Join xAI now

X Freeze

22,976 views • 5 months ago

AI instead of UI The last few years radically changed how we think about digital products & product design process in general. What we are witnessing right now is a transition where AI is no longer just a feature; it is becoming the infrastructure of interaction. For decades, UI design was rooted in the concept of “Happy Path,” a series of static, linear screens (routes from A to B) designed to funnel users toward a goal. This “one-size-fits-all” approach assumes that all users have the same mental model, which is far from the truth. The rise of AI tools like ChatGPT and Gemini is showing that we are moving into an era of Generative Interfaces. Instead of a designer pre-determining every button and menu, the interface is synthesized in real-time based on user actions. Back in 2019, Gleb Kuznetsov and I were working on the concept of morphing Interface, the idea of a UI that adapts its structure based on user intent. The great thing about this UI is that it was highly dynamic: different users saw different content/contextual actions based on their behavior. This concept was crazy in 2019, but it is highly relevant to the modern state of the product design process. And I strongly believe that it represents the future of UI design. We will move beyond the “AI chatbox" crutch Yes, many products today treat AI as a sidecar (think of Copilots or chatbots pinned to the side of a traditional dashboard), but this is a transitional phase. Why? Because adding a chat window to a 20-year-old software layout is like putting a jet engine on a horse-drawn carriage. That's why the future isn't "AI as an add-on"; it is AI as the Operating System. AI will power generative interfaces that are rooted in anticipatory design: UI won’t wait for a command; it will surface tools based on the user's current environmental context and historical behavior. This evolution changes the very nature of product design, and we will move away from designing pages/screens and toward designing systems of logic.

AI instead of UI The last few years radically changed how we think about digital products & product design process in general. What we are witnessing right now is a transition where AI is no longer just a feature; it is becoming the infrastructure of interaction. For decades, UI design was rooted in the concept of “Happy Path,” a series of static, linear screens (routes from A to B) designed to funnel users toward a goal. This “one-size-fits-all” approach assumes that all users have the same mental model, which is far from the truth. The rise of AI tools like ChatGPT and Gemini is showing that we are moving into an era of Generative Interfaces. Instead of a designer pre-determining every button and menu, the interface is synthesized in real-time based on user actions. Back in 2019, Gleb Kuznetsov and I were working on the concept of morphing Interface, the idea of a UI that adapts its structure based on user intent. The great thing about this UI is that it was highly dynamic: different users saw different content/contextual actions based on their behavior. This concept was crazy in 2019, but it is highly relevant to the modern state of the product design process. And I strongly believe that it represents the future of UI design. We will move beyond the “AI chatbox" crutch Yes, many products today treat AI as a sidecar (think of Copilots or chatbots pinned to the side of a traditional dashboard), but this is a transitional phase. Why? Because adding a chat window to a 20-year-old software layout is like putting a jet engine on a horse-drawn carriage. That's why the future isn't "AI as an add-on"; it is AI as the Operating System. AI will power generative interfaces that are rooted in anticipatory design: UI won’t wait for a command; it will surface tools based on the user's current environmental context and historical behavior. This evolution changes the very nature of product design, and we will move away from designing pages/screens and toward designing systems of logic.

Nick Babich

53,320 views • 5 months ago

KYRALL TURNS "I NEED THIS PART" INTO SECONDS, WITHOUT OPENING CAD you describe the part. the AI generates it. no SolidWorks. no Fusion. no hours of setup. why this matters to you: modeling a part in CAD is slow and technical. sketch, constrain, extrude, fillet — for every bracket, housing or fixture. and simple one-off parts eat the same ceremony every time. for an engineer, that bottleneck stalls the whole project. Kyrall skips it: → prompt → 3D part ready → exports to STEP and STL, the formats you already use → for when you need the geometry now, not in three days it doesn't compete with CAD suites on millimeter control. it competes on getting you to a usable part first. idea → part → keep building. link below :)

KYRALL TURNS "I NEED THIS PART" INTO SECONDS, WITHOUT OPENING CAD you describe the part. the AI generates it. no SolidWorks. no Fusion. no hours of setup. why this matters to you: modeling a part in CAD is slow and technical. sketch, constrain, extrude, fillet — for every bracket, housing or fixture. and simple one-off parts eat the same ceremony every time. for an engineer, that bottleneck stalls the whole project. Kyrall skips it: → prompt → 3D part ready → exports to STEP and STL, the formats you already use → for when you need the geometry now, not in three days it doesn't compete with CAD suites on millimeter control. it competes on getting you to a usable part first. idea → part → keep building. link below :)

marcus

31,238 views • 12 days ago

How a Chief Design Officer (yours truly) uses AI to design like an IC. This demo is for any designers that haven't yet embraced AI in their workflow. I’m one of two designers working on this plugin. I manage an organization of about 40 designers of various flavors, but I still keep myself in the trenches whenever possible. Lynt is one of these efforts. The examples I share are intentionally very simple to demonstrate how any designer at any level can leverage artificial intelligence to amplify and accelerate their human intelligence. AI is revolutionizing how we work as designers, yet I think of it more like a renaissance; a return to designing in consideration of the medium in which our designs are rendered, much like we did years ago when I first started. Arbitrarily, something like 80% of what we do as designers (in the act of designing) is exploration and iteration. For the past decade or more so much of that has happened in Figma and previously Sketch. These are great tools. But as a result of designing exclusively in a static environment, many have lost the ability to connect ideation with execution. Handholding your work from start to finish eventually gave way to handing off your work, and we never looked back. Artificial intelligence flips all of this on its head, positioning you (the designer) to be the one who first turns your design into code, iterate and iterate again, and experience using your work as users will, literally from day one of your design process. All the while freeing our engineering colleagues to focus on the work that only they can do, while designing alongside them.

How a Chief Design Officer (yours truly) uses AI to design like an IC. This demo is for any designers that haven't yet embraced AI in their workflow. I’m one of two designers working on this plugin. I manage an organization of about 40 designers of various flavors, but I still keep myself in the trenches whenever possible. Lynt is one of these efforts. The examples I share are intentionally very simple to demonstrate how any designer at any level can leverage artificial intelligence to amplify and accelerate their human intelligence. AI is revolutionizing how we work as designers, yet I think of it more like a renaissance; a return to designing in consideration of the medium in which our designs are rendered, much like we did years ago when I first started. Arbitrarily, something like 80% of what we do as designers (in the act of designing) is exploration and iteration. For the past decade or more so much of that has happened in Figma and previously Sketch. These are great tools. But as a result of designing exclusively in a static environment, many have lost the ability to connect ideation with execution. Handholding your work from start to finish eventually gave way to handing off your work, and we never looked back. Artificial intelligence flips all of this on its head, positioning you (the designer) to be the one who first turns your design into code, iterate and iterate again, and experience using your work as users will, literally from day one of your design process. All the while freeing our engineering colleagues to focus on the work that only they can do, while designing alongside them.

Cameron Moll

57,536 views • 3 months ago

QACE Dynamics | Qognix Rule Engine Qognix is built on a library of modular AI blocks that can be reused across many applications. Each block has NLP built in, so it understands natural instructions and converts them into intents, rules, and actions. The blocks are organized as tiles for specific domains such as surveillance, navigation, manipulation, or voice, which makes it simple to assemble intelligence for any robot. Every block is simulation ready, so engineers can validate workflows inside the dApp or in Gazebo before deployment. Once tested, the same blocks run seamlessly on hardware through QARC or directly on the robot. This approach turns robotics AI into a system of reusable and composable parts. From perception to decisions, Qognix makes intent driven autonomy practical and scalable

QACE Dynamics | Qognix Rule Engine Qognix is built on a library of modular AI blocks that can be reused across many applications. Each block has NLP built in, so it understands natural instructions and converts them into intents, rules, and actions. The blocks are organized as tiles for specific domains such as surveillance, navigation, manipulation, or voice, which makes it simple to assemble intelligence for any robot. Every block is simulation ready, so engineers can validate workflows inside the dApp or in Gazebo before deployment. Once tested, the same blocks run seamlessly on hardware through QARC or directly on the robot. This approach turns robotics AI into a system of reusable and composable parts. From perception to decisions, Qognix makes intent driven autonomy practical and scalable

Qace Dynamics

36,922 views • 9 months ago

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,960,360 views • 1 month ago

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

Md Riyazuddin

31,199 views • 1 month ago

Anthropic released Claude Design TODAY and it's now accessible at I spent the last hour giving it a first look, and shared my thoughts and results in the video below. This is a BIG drop. This is a new design surface from Anthropic, and it changes what "AI design" means. Short version: Claude can now design. Not "describe a design." Not "generate an image of a design." Actual production work — prototypes, wireframes, high-fidelity mocks, slide decks, landing pages — editable, on-brand, and ready to hand off. Here's what stood out on first look: → Real design surfaces Prototypes, wireframes, hi-fi, and slide decks — each with templates and proper structure, not just pretty screenshots. → Comment-based edits Leave a comment on any element and Claude revises it. This is the Figma-style review loop, with the designer replaced by a model that works at 3am. → Brand design systems You can feed it your system — colors, type, components — and it actually respects it. On-brand output, not generic AI slop. → Export anywhere PDF, PowerPoint, Canva, standalone HTML. Plus a built-in handoff straight to Claude Code for engineers to implement. → Import from real tools Figma, GitHub, and captured web elements come in as inputs. Your existing work is the starting line, not the discard pile. → Collaboration Share links for view / comment / edit — the exact tier system teams already expect. What I tested on Opus 4.7: • A 5-slide deck generated from a single screenshot. Claude asked clarifying questions BEFORE generating and shipped speaker notes by default. • A landing page build. Solid first pass, real components, real layout logic. • Multiple chats running concurrently. You can parallelize design work across threads like a small team. Why this matters: PMs, founders, marketers, and non-engineers can now create designs that engineers can actually ship with production-ready output and a claude code handoff built in. The gap between "I have an idea" and "here's a working prototype with my brand applied" just collapsed to minutes. Full walkthrough, live demos, exports, and honest takes on where it breaks below. P.S. • This is an Anthropic Labs product — NOT GA yet. • Claude Design is currently webapp only (no API), and does not yet support the Analytics API, Compliance API, or cost/usage reporting. • Availability: – Default ON for Pro / Max / Team – Default OFF for Enterprise Enterprise admins can toggle it on via RBAC in console (comes with a ~$20/user initial credit).

JJ Englert

32,445 views • 3 months ago

Robots aren’t just the future — they’re already reshaping our world. At IIT Madras, the new in Robotics, offered under the Department of Engineering Design, is building the brains behind tomorrow’s intelligent machines. From motion planning and control to AI/ML and hands-on labs, this two-year program blends cutting-edge research with real-world industry relevance — powered by a strong robotics faculty team and a legacy of interdisciplinary innovation. Rooted in the success of the Inter-Disciplinary Dual Degree (IDDD) Robotics program, this two-year program brings together expert faculty members and a powerful blend of theory and hands-on learning. From robot design and analysis to motion planning, control systems, and AI/ML, students dive deep into the science and engineering behind next-generation robotic systems. Add immersive lab work and cutting-edge projects, and you have a curriculum that’s as practical as it is pioneering. Manufacturing, healthcare, logistics, and defence — robotics is reshaping them all. This program is about building the minds that will lead to that transformation. If the future runs on robots, it starts here. For more details visit: IIT Madras

Robots aren’t just the future — they’re already reshaping our world. At IIT Madras, the new in Robotics, offered under the Department of Engineering Design, is building the brains behind tomorrow’s intelligent machines. From motion planning and control to AI/ML and hands-on labs, this two-year program blends cutting-edge research with real-world industry relevance — powered by a strong robotics faculty team and a legacy of interdisciplinary innovation. Rooted in the success of the Inter-Disciplinary Dual Degree (IDDD) Robotics program, this two-year program brings together expert faculty members and a powerful blend of theory and hands-on learning. From robot design and analysis to motion planning, control systems, and AI/ML, students dive deep into the science and engineering behind next-generation robotic systems. Add immersive lab work and cutting-edge projects, and you have a curriculum that’s as practical as it is pioneering. Manufacturing, healthcare, logistics, and defence — robotics is reshaping them all. This program is about building the minds that will lead to that transformation. If the future runs on robots, it starts here. For more details visit: IIT Madras

IIT Madras

22,421 views • 3 months ago

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05

Dan Shipper 📧

35,342 views • 9 months ago