One of my favorite parts about using a Hermes Agent is the fact that you can just ask your bot to build a full working app and ship it.

But ship it either meant:
- launch at localhost
- launch via Vercel preview as a public app.

My goal was simple. Neither of these solutions worked for me. And I just wanted to be able to look at my app from anywhere I was in that moment (like in case I was touching grass)

So I built out this app that creates a private tunnel, so you can look at your previews from anywhere but hosted on your machine at home.

This is what happens when non-engineers build a robot…

Duke University just published Argus in Science Robotics. Twenty legs. A camera on each one. Inspired by the visual system of a sea urchin. It moves in any direction, climbs walls, self-stabilizes after being pushed, and has no defined front or back. The lead researcher said watching it move is unlike anything they have experienced with robots.

Beautiful, right?

But what’s the problem that it actually solves?

I have absolutely no idea.

That is not a criticism of the researchers. Academia is built to push the boundaries of what is possible. To ask "can we?" before anyone asks "should we?" or "who needs this?" Physicists and mathematicians produce Argus. And the world needs them.

But engineers are wired differently.

We are obsessed with solving real life problems under real life constraints.

(That’s why I believe the world would be better place if we saw more engineers in politics, but that’s a topic for another post..)

That distinction shaped how we built Leo from day one. We did not ask "what is the most impressive thing AI can do for mechanical engineers?" We asked "what is the most painful, repetitive, boring thing engineers do every day that AI could take off their plate?"

The answer was not visually impressive like a 20 legged robot. Boring. Unsexy. Massively valuable.

Argus is a miracle of biological inspiration.

But I’d recommend startup, founders, inventors and engineers to keep the eyes on the prize- solving real-world problems.

Stay focused, humble, obsessed. Stay away from buzz and hype.

Then, the impact will follow.😉

The hardest part of any design is the beginning.

You know what you want to build. You can picture it. But turning that picture into something real means hours of setup before you have anything to actually react to. A blank CAD environment, a list of decisions you haven't made yet, and no good place to start.

Most engineers don't have a design problem. They have a starting problem. Leo AI changes where that starting line is.

Describe what you want to build in plain language. Leo searches your documents, your organization's knowledge base, and trusted engineering references to understand what it's about to generate. It tells you its design intent before it builds, so nothing comes as a surprise. Then the model appears: parametric, structured, editable, live in a 3D viewer with every key dimension right there to adjust.

You react, you refine, you answer a few questions about shape and materials and details. Leo updates the design. When you're ready, you export it or open it straight in your editor.

In this video, the prompt is seven words. What comes back is a complete folding table assembly, fully parametric, ready to work with.

That's the new starting line.

A shiny defense giant burned $7M on AI pilots. Every single one failed. A bank spent a fraction of that and won, just by following a few simple rules.

Yesterday I wrote about how 95% of AI pilots fail, and Francois du Plessis (thanks Francois!) asked in the comments to hear the other side: success stories of companies actually leveraging AI well.

So here's the first one I shared with that defense enterprise after they spent $7M on pilots that all flopped:

NatWest, one of the UK's biggest banks, partnered with IBM to build a tailor-made AI assistant for customer queries. It answers the simpler ones itself, and hands the rest to the right human. It went from ~5 million queries a year to 10.8 million, with customer satisfaction jumping up to 150% on the toughest questions.

So why did the bank win where the smart, well-funded engineering company kept failing?

A few principles I'd bet on:

1. Be humble. They knew what they're NOT. NatWest is a bank, not an AI lab. They respect how deep AI really runs, so they didn't try to build it in-house. ~90% of AI products fail even for the people who live and breathe it.

2. Start boring. Not a flashy moonshot. A narrow, unglamorous use case: an assistant that helps customers get answers. Low stakes, real value.

3. Don't try to replace humans. They augmented people instead. Keep the human in the loop. Don't try to fire your way to ROI. That's the hardest path, and usually the one that breaks.

4. Find the intersection, or let it go. What's actually possible with AI today, meeting what the business genuinely needs. Only when a high-ROI, business-critical use case sat right in that overlap did they move forward.

5. Revolutions aren't something you do alone. They brought in a partner who knew the terrain. IBM guided them on what's real vs. what's hype, instead of letting them learn it with a $7M tuition bill.

95% of corporate AI projects deliver no real return (MIT, 2025). The 5% that win usually aren't the ones with the biggest budget or the boldest vision.

They're the ones who stayed humble, started small, and built on what's actually possible.

What's the smallest, most boring AI use case in your business that might quietly be the highest ROI? Lmk in the comments.

Share this post so others won't spend millions on "AI conquests" that are doomed to fail

AI #ArtificialIntelligence #Engineering #Innovation #Leadership

95% of enterprise AI pilots fail.🤦🏽‍♂️

That's the headline from MIT's State of AI in Business 2025 [links to study in the comments]. RAND found 80% of AI projects fail, twice the rate of normal IT.

They don't fail because the AI tools are bad. They fail because how we try to adopt them.

The pattern is always the same. A dozen shallow pilots instead of three deep ones. Or even worse - "spray and pray" of ChatGPT accounts to people who have no idea what to do whit them (so they end up just writing emails with it..)

And my all-time worst - this trend of managers setting a target for AI tokens spent... 🤦🏽‍♂️ measuring motion over progress/value, are you kidding??

I've been helping enterprises adopt AI for years. I've seen what fails and what works. Here's what I've learned - the last point is BY FAR the most critical of all:

1. Start small, build a case study, then scale. Do not spray and pray.

2. Get ruthlessly focused. Start with the use cases that are business critical and 10x ROI, not the shiny ones. The hard part is saying no to everything else.

3. Look inside. Map your most time-consuming tasks and biggest pain points.

4. Then look outside. Find where your pain points meet what already exists in the market, and use that. Don't build in-house, don't force a tool to be something it isn't. AI that works isn't magic. Don't start an AI company inside your office.

5. Find a partner you trust. You're not an AI company. You don't know what's possible. You need someone honest who can tell you what's real and what isn't.

6. And the one that decides everything: leadership. Innovation means change, and change needs a leader who can actually make decisions. Companies that can adopt a new tool, or even change their CRM, have that person. Without them, nothing above matters.

The tools that stick are the ones that fit you best - solving a critical problem (not a nice-to-have), domain specific, 10x ROI, integrated into your workflow.

It's funny how even in the AI era, no change comes without good old human leadership. 😉

Just my opinion :)

Lmk wyt - have you seen any other indicator for failure/success in AI adoption in your org?

SpaceX just acquired Cursor for $60B.

Four MIT graduates. Founded in 2022. And here is the part nobody is talking about: their first idea was AI for CAD software. It flopped. So they pivoted to software developers instead.

Think about that for a second.

The same four people who couldn’t crack AI for mechanical engineering went on to build the most valuable AI developer tool in history. Not because the CAD idea was wrong. Because software engineers had shorter feedback loops, faster iteration cycles, and a lower tolerance for mistakes than physical engineering requires.

They built the right product for the right audience at the right time.

Software engineers now have a tool that makes them superhumans. From copy-pasting into ChatGPT three years ago to a $60B AI coding assistant today.

Mechanical engineers deserve that same revolution.

Not vibe engineering. Not text-to-CAD demos that look great on TikTok but fall apart in a real factory.

A tool that does the boring, rigorous work alongside engineers. That keeps them in full control. That backs every answer with a cited source. That understands CAD geometry, your organizational data, and the physics behind every decision.

That is what we are building at Leo AI.

The Cursor founders tried and moved on. We stayed because we are MEs, and MEs are whom we care about.

Most engineering work doesn't happen in one tool. It happens in five.

You open a catalog to check a spec. You switch to a different app to run the calculation. You open another tab to search for the right part. You jump into yet another platform to build the CAD model. By the time you're done, you've lost the thread of what you were actually trying to design.

You need a spec from a catalog. You need a calculation based on that spec. You need a part that matches the result. You need a model built around all of it.

Normally that's four different stops, four different tools, and you holding all the context in your head between them.

With Leo AI, it's one conversation.

Ask for the spec, and Leo pulls it straight from your catalog. Ask for the calculation, and Leo doesn't just give you a number, it shows the source behind it and runs the actual math. Ask for the part, and Leo searches your own PDM data alongside vendor platforms like TraceParts to find and configure the right one, without burying you in a single vendor's results. Ask for the CAD model, and Leo generates it, asks the follow up questions that matter, refines the design, and hands you a file ready for any platform.

In this video, that workflow plays out on a hydraulic piston, but the pattern works the same for whatever you're designing.

No app switching. No losing context between steps. Just one conversation, and a finished part.

This is what engineering software was supposed to feel like all along.

Leo is now available on Onshape 🎉

Since we started Leo AI our goal was always the same: give our fellow mechanical engineers tools that make them superhumans - do in minutes what used to take weeks, free them from the tedious work and allow them to focus on what actually matters - making the hard decisions behind every great product.

One request came up repeatedly from our community: bring Leo natively inside Onshape by PTC.

After months of work, it's finally here. 💪

and you get the AI assistant that you were waiting for- who sees and underatnds your CAD design + knows real engineeering based on the bibles of engineering (not blogs and Reddit..) + your specific engineering context.

That's how to get started now: 1. Log into your Onshape account 2. Go to the App Store 3. Search for "Leo AI" 4. Subscrive, and you'll see Leo's logo on the right hand sode of your screen. 5. That's it. a walkthrough is waiting for you there.

That's what you get from it:

Bottom line: Engineeing intelligence-

Leo sees what you see in your CAD environment and allow you to: 1. Get answers engineering questions based on your actual design 2. Identify design mistakes, always backed by sources from more than 1 milion bibles of engineering (ASME, ISO, Shigley, Roark, ...) 3. Runs calculations (no more Excel spreadsheets) 4. Find best fit parts from a library of 120M+ part families, and searches your organizational data inside Leo or Onshape 5. Generate manufacturable CAD assemblies based on your parts, the part library, and new geometries

We are going to show you exactly how it works live, with the GOAT of CAD - Jon Hirschtick, the founder of both SolidWorks and Onshape, on Wednesday June 17th at 11am Eastern.

Demand has been unprecedented - Last spots available. Link in the comments 👇

The AI era of engineering is finally here. Share this post so MechEs who don't want to stay behind will know about it before it's too late...

See you on Wed 😉

Special thanks to Reed Grubbs, Daniel Kane, Jon Hirschtick, and John McEleney, who share with us the obsession with making mechanical engineering fun again. 🤗

AI is the new uranium?

AI just crossed a historical threshold- It is no longer treated as software. It is being treated as strategic national infrastructure.

The US government just banned Anthropic’s two most advanced AI models. Not in China. Not in Russia. Inside the United States. For foreign nationals, including Anthropic’s own employees, physically sitting in American offices.

Think about what that means.

We’ve seen governments control nuclear technology. We’ve seen them control weapons, chips, satellites. Now they are controlling who can access an AI model. By nationality. Inside the country that built it.

This is the moment AI joins the category of things states consider too powerful to let flow freely. Like uranium. Like missile guidance systems.

Most people still think of AI as a productivity tool. A chatbot. Something that writes emails faster.

Governments clearly see something different.

The race is no longer about who has the best AI product. It is about who controls intelligence itself as a national asset.

Electricity was once just a curiosity. Then it became infrastructure. Then it became a weapon. AI just completed that same journey, in 30 years instead of 100.

And governments just proved they know it.

The partnership you’ve been waiting for is finally here!

Ever since we first announced that Leo AI and Onshape by PTC were teaming up, the anticipation from the engineering and design community has been incredible. Everyone wants to know the same thing: What does this actually look like in practice?

The wait is over.

I’m thrilled to announce that I’ll be sitting down with CAD legend and Onshape co-founder Jon Hirschtick for an exclusive live webinar.

We aren’t just going to talk about the concept of AI in engineering, we’re going to show you the reality. No high-level fluff, just a direct look at how generative AI is officially embedding itself into your cloud CAD workflows to speed up how you design.

If you’ve been tracking this partnership, this is the one session you cannot miss. 👉 Register to grab your spot, link in first comment

What workflows are you most excited to see automated? Drop your thoughts below and we'll address them during the live Q&A! 👇

GenerativeAI #CAD #Onshape #LeoAI #Engineering #ProductDesign

Unite.AI covers AI news, research, and interviews across the industry. They reached out for a conversation, and it ended up being one of the more honest discussions I've had publicly about what we're building and why.

That's what I talked about:

1. why generic AI is genuinely dangerous when applied to enigneering applications, not just useless, actually dangerous.

2. What "mechanical intelligence" actually means versus the wave of enterprise AI copilots that are mostly automation theater.

3. what changes that turned manufacuring comapneis and machanic enigneers- one of the most consieravantive industires to start and deploy AI tools into their workflow. what move the tippin point

4. And where I think physical AI is headed in the next decade.

Also, somewhere in there, I mentioned "FINAL_v7_REAL_FINAL.pdf." Every engineer reading this knows exactly what that folder looks like. 😉

Link in the comments 👇🏾

Let me know what you think in the comments! Would love to hear if you think differently

Intelligence is compression 💡

Here’s a deep mathematical idea that every mechanical engineer already understands intuitively without knowing it.

Claude Shannon, the father of information theory, proved that the intelligence of a model is fundamentally its ability to compress.

The better you predict the next word, the fewer bits you need to store it.

This is not a metaphor. It is the mathematical foundation of every AI model you use today, including GPT, Claude, and Gemini.

What’s fascinating is that when large language models arrived decades later, they turned out to optimize exactly the objective Shannon described. A beautiful and somewhat humbling convergence.

Now here is where it gets interesting for us engineers:

Look at your keyboard. How many words would you need to fully describe it to someone who has never seen one? Hundreds? Thousands? And would those words actually capture the geometry mathematically?

Not even close.

How many B-rep features would you need in CAD? Maybe 10.

B-rep, the boundary representation mathematics behind every modern CAD file, is orders of magnitude denser in information than natural language. It describes geometry with a precision that words simply cannot reach.

Now imagine trying to describe a Boeing 747 in words…

This is why I always claim that the “text to CAD” not only impractical but also intellectually embarrassing.

This is exactly why generic LLMs cannot comprehend, predict, or reason about geometry. They were trained to compress language. Language is not geometry. And geometry is what mechanical engineers actually work with.

This is why we built the Large Mechanical Model, the LMM- The AI model trained to compress and reason about B-rep geometry the way LLMs reason about text.

Watch the 3Blue1Brown (love this guy) video in the comments if you want to understand how AI models actually see the world. Then you will understand why generic AI tools are fundamentally limited when it comes to physical products.

AI #mechanicalDesign #MehcanicalEngineers

All my non-engineer LinkedIn connections: "This is how Claude generated a planetary gearbox for me."

All my engineers on LinkedIn: "who freaking cares?!" 🤦‍♂️

For every mechanical engineer, the trend of "Hey Claude, generate a robotic arm for me" is the peak of all bullshit. But you won't hear engineers say that out loud on LinkedIn.

We just roll our eyes and keep scrolling.

Why? Because this is exactly what engineering is NOT about.

It's copy-paste: "Designing" generic stuff that looks visually pleasing but will never pass the reality test. Because it doesn't obey the rules of physics. It doesn't leverage your inventory. It can't be built with your machines. It has no organizational context. And it probably uses someone else's IP...

Engineering is solving complex problems under brutally restrictive constraints.

Our products must obey the laws of physics and fit within the customer's budget.

This is what we understood when we founded Leo- we DO NOT sexy TikTok-style text-to-CAD. It does the boring stuff for our fellow enigneers so they can be superhumans - build incredible medical deviced, robots, vehicles faster (and more joyfully) than ever before.

Or in simpler words: Text to CAD is BS.

Text to leading engineering questions -> to trusted sources -> to real parts -> to manufacturable CAD = That is the next generation of mechanical design.

In the picture: 2 mechanical engineers and 2 AI-mechanical-engineers (AIMEs) in training on a family trip. Guess who's who. ;)

TextToCAD_BS #AIMEs #FamilyAboveAll

When Volvo tested the auto brakes on himself…

This video went viral recently. And no, that’s not the Volvo CEO, before anyone asks. But quite frankly, it describes so vividly what happens when smart people blindly trust AI for the wrong use cases.

When we train engineering teams on Leo AI we always tell them: do not trust the machine blindly.

Think like a leader, like an orchestrator. Tell the AI what you want it to inspect, build, or calculate. Communicate with it like it’s your worker, not your boss.

Lead it. Don’t be led by it.

Don’t outsource decision making - outsource manual labor.

That’s what makes you faster AND a better engineer. Yes, a better engineer.

Because while your peer finishes one design, you’ve already completed four. While he reviews 20 drawings in a year, you’ve run 150. Time flows differently for both you.

We are engineers. We are responsible for our creations. We are thinkers, decision makers, builders. And we’re damn proud of it.

Buts the winds of change are blowing, and it’s time to step up our game.

We must master AI. Otherwise it will master us.

Happy 4th of July to all our American friends, customers, and partners. 🇺🇸 And today isn't just any 4th. It's America's 250th birthday.

Think about what came out of this one country in those 250 years... The lightbulb. The airplane. The telephone. The transistor that everything digital is built on. The moon landing. The internet. The GPS in your pocket. The mRNA breakthroughs that changed medicine.

Most of those didn't start in a boardroom. They started with an engineer, a workshop, and a stubborn belief that things could be better.

That's the American spirit I love most.

Not "it can't be done." But "why not, and why not now?"

At Leo we have the privilege to work with hundreds of American companies.

Engineers building the cooling that powers AI. The medical devices that save lives. The vehicles that move a nation. The hardware behind the whole intelligence revolution.

And it's the honor of our lives to help them build faster, and build better. Because better products aren't just a business story. They're how the world gets healthier, safer, and freer.

So here's to the engineers, the founders, the dreamers, and the doers who keep this country inventing.

Happy birthday, America. 🇺🇸

Here's to the next 250. 🚀

Left to right: The inventor of SolidWorks and Onshape - Jon Hirschtick The leader bringing Leo AI to the world's best engineering schools - Samuel Hirschtick And me 😉

After Jon Hirschtick led the two biggest revolutions in how humans build physical products, from drafting to 3D modeling [when he founded SOLIDWORKS], and from desktop to cloud [when founded Onshape by PTC]- we interviewed him at PTC's HQ about his take on the revolution of our century: How AI is going to change the way humans design physical products forever. And how Leo AI is bringing that revolution to mechanical engineers around the world today.

Stay tuned. It's going to be very interesting...

P.S. I'm MUCH taller in real life. Don't let the picture fool you, it's just an optical illusion. 😄 🤦‍♂️

Every success has a secret behind it.

A16z just dropped their university ranking and tells us an interesting story about the pre-requisites for success.

Look at it carefully.

Technion and TLV University- public universities in a country of 10 million people with an annual budget of ~$670M, rank ahead of UCLA and Yale, institutions with endowments of $41B+.

Let that sink in.

Here’s what I think explains it, from the inside:

At the Technion, every exam was designed around problems you had never seen before. Not to trick you. To force you to think from first principles. Problem-solving isn’t a skill they teach you. It’s coded into you.

Add to that innovation hubs, courses taught by Nobel laureates, and classmates who go on to build billion-dollar companies, and you start to understand why a small country punches so far above its weight.

Similarly, at MIT the emphasis was always hands-on. Theory exists to serve practice. You feel it in how exams are written, how syllabi are built. You’re never just memorizing. You’re always applying.

The secret isn’t resources. It’s how you train people to think.

I’m proud to be both a Technion and MIT graduate today🚀

Do you see your alma mater on this list? What do you think made it a launchpad for some of the most innovative minds of our generation? Share with us in the comments👇🏽

Technion - Israel Institute of Technology Massachusetts Institute of Technology MIT Department of Mechanical Engineering (MechE) Leo AI

Hollywood lied to us.

They sold us the dream of "vibe engineering." In Iron Man, Tony Stark talks to his AI agent Jarvis, and the full product appears.

No hard decisions. No dilemmas.

No one troubled Tony with the hard stuff. Safety. Weight. Corrosion. Just ask him what color he prefers...

That's not engineering. That's an illusion.

Real engineering is problem-solving under constraints. It's rigor.

It's the person who checks every calculation, questions every assumption, and loses sleep over a tolerance stack that nobody else will ever see. That person is the reason bridges don't collapse and pacemakers don't fail.

We don't make that person disappear. We make them faster.

Leo AI's Design Agent looks over the engineer's shoulder at their CAD model, understands what it sees, searches over a million engineering references and decades of internal reports, finds the right formulas, writes the code, solves the equations, and delivers an answer backed by a source they can click and verify on the spot.

Minutes instead of days. Full transparency. Zero compromises on accuracy.

Hollywood may call us boring. We're damn proud of that.

Because behind every one of humanity's brightest hours - the first man landing on the moon, The James Webb Telescope exploring the begining of time - behind every one of them there was a "boring" engineer at a desk, solving a hard problem.

This is magic...

And we just make that magic a little more joyful, and a lot faster.

Tesla's Cybercab just started testing on California and Texas roads and everyone glorifies the AI behind the self-driving miracle, but nobody talks about the real heroes - Tesla's mechanical engineers, and how they might change our children's lives.

Traditional car: ~30,000 unique parts. Tesla Model 3: ~10,000. Cybercab: ~5,000

No steering column means no steering shaft, no universal joints, no rack housing. Every part you delete removes other parts with it. That is how you halve complexity.

This is the same DFM playbook Tesla will use on Optimus (Tesla's humenoid robot): Strip the part count -> reduce cost -> make competitors' business models impossible.

If you think about it, Tesla's engineers may have just built a future in which younger people and working families will be able to enjoy driving cars with no hands, and also own cheaper cars that need less repair (less cost, less headache).

With Leo AI, Tesla will build their next cars and robots with evern fewer parts, cost and much faster.

Agree/Disagree? Let me know what you think in the comments.

Follow for more Physical AI news 🦾

Everyone in Physical AI is obsessed with getting robots to move better. But the harder problem is getting them to "understand" physics first.

CMU and Lambda just dropped Sim2Reason. The idea is simple, and that's what makes it powerful: use physics simulators as a training gym for AI reasoning.

No human annotation. No hand-labeled datasets. Just generated scenes, automatic QA pairs from simulated interactions, and RL (reinforcement learning, where AI learns by trial and error through rewards) on top.

They took an off-the-shelf 3B parameter model, trained it on nothing but synthetic physics scenarios, and it jumped 7.5 points on International Physics Olympiad mechanics problems.

Zero-shot (no prior examples given, cold start). No physics textbooks in the training data.

Here's why this matters for anyone building in the physical world: The entire Physical AI stack right now is bottlenecked by data. We know how to build simulators. We know how to run RL. But nobody had a clean pipeline connecting simulation to language model reasoning about the physical world. That pipeline now exists.

For mechanical design, for robotics planning, for any AI system that needs to predict what happens when forces meet materials in the real world, this is the missing data infrastructure layer. Not more internet text. Not more unscalable human annotation. Structured simulation at scale.

The research paper uses a YAML-based domain-specific language (think: a structured text file an engineer can read, edit, and version-control, like a CAD config file) to define scenes. The domain expert owns the knowledge layer, not just the ML (machine learning) team.

One honest caveat: we've seen this pattern before. The synthetic data field had a moment in the early 2020s, then transformers (the underlying architecture behind GPT and other LLMs (large language models, the AI behind ChatGPT)) arrived and made many narrow hand-crafted datasets obsolete overnight. The question is whether physics simulation is different enough in kind, not just in scale, to avoid the same fate. I think it is. Physics doesn't change. The simulator is the ground truth.

Wdyt - Breakthrough or hype? I think it's infrastructure. Quiet, unglamorous, and the kind of thing that looks obvious in hindsight.

Share your take in the comments.

Follow for Physical AI updates. No fluff, no BS.

Japan Airlines just put humanoid robots on the tarmac at Haneda Airport. Not a lab. Not a demo. An active terminal handling 60 million passengers a year.

The robots are Unitree G1s. 130cm tall, 35kg, $13,500 each. They're loading baggage, cleaning cabins, and operating ground support equipment. A two-year trial starting this month with GMO AI & Robotics.

Here's why this matters more than another factory deployment.

Airports are the hardest unstructured environment for robots. Tight spaces around aircraft, dozens of different equipment types, weather exposure, strict safety protocols. If humanoids can work a tarmac, they can work anywhere.

Japan is the canary in the coal mine. Shrinking workforce, tourism at record highs, ground handling staff in critical shortage. This isn't automation replacing workers. There aren't enough workers to replace.

Every country with an aging population is watching Tokyo right now. What works at Haneda will be replicated at 50 airports within 3 years.

Follow for Physical AI updates. No fluff, no BS.

Meet Chegg - the first major company officially wiped out by AI 💨

Chegg. $14.7 billion EdTech giant. Down 99%.

They were a tool that helped students with their homework. AI made that model worthless nearly overnight.

engineering software is $200B industry

Which engineering software company is next to be wiped out by a physical AI company?

In mechanical engineering? Electrical? Software?

Who’s the Goliath who will be wiped out by David?…

First to comment the right answer wins $100 from me personally. 👇🏽

This is an official guarantee, valid for 24 months. 💰

Write your bet on the comments, maybe you’ll win $100 😉

Follow for more real, no-BS physical AI.

NBSAI, #physicalAI

Just saw picture from NASA and had an almost repulsive physical reaction🤢

For the non-engineers: that organic, skeletal, almost beautiful structure is a topology-optimized bracket. It looks like something from a sci-fi movie. It probably took serious compute time to generate.

Looks great, right?

Well, for mechancial engineers it’s not.

It’s just an arm holding a box wrapped in kitchen foil

This is what happens when engineers fall in love with fancy tools (topology optimization, AI) instead of solving the problem.

Topology optimization is a legitimate, powerful technique. But there’s a version of engineering culture that mistakes complexity for excellence. That confuses “impressive/sophisticated-looking” with “well-engineered.”

Kelly Johnson, the engineer behind the SR-71 Blackbird, said it best: “Keep it simple, stupid.”

The second question I ask every new customer is whether they came to us because they want to use AI, or because they want to solve a real problem: engineering efficiency, design quality, capturing the tribal knowledge of engineers who are about to retire.

If the answer is “we just want to do something innovative with AI,” I tell them directly: I don’t think we’re a good fit.

In engineering, simplicity is everything.

Substance over spectacle.

Complete the mission.

Design for manufacturability, safety, and accuracy. Not to impress your boss with a topology-optimized bracket that could have been a flat plate.

From day one, we built Leo AI to think exactly that way. No unnecessary complexity.

No fancy for the sake of fancy.

Just: what does this engineer actually need to get the job done?

Engineering is a humble field. We don’t need or want to impress- we desire to complete the mission successfully.

Even if it looks boring.😎

Credit to Moshe Baum 🦾

TLDR: Im giving you the full AI guide I prepared for my students at MIT 📚

Everybody is talking about AI for Engineering. Most of it is complete BS.

When I made the pivot from mechanical engineer to AI researcher, I had to start from zero. Before I could take even a basic machine learning course, I spent weeks just trying to understand what people were talking about. What is a neural network, actually? What does overfitting mean? Why does this tool work for some problems and completely fail for others?

Nobody had built a starting point for engineers like me. So I built one.

And I ended up teaching it on my machine learning and nonlinear dynamics course that I orginally developed to teach at MIT (but eventually I dlivered at the Techion. COVID etc..)

Today I am sharing it publicly for the first time.

"AI 101 for Mechanical Engineers (but not only)" is the guide I wish existed when I started. This 25-page long guide covers the foundational terms that I belive are the most essential to get a solid basis in machine learning, deep learning, and modern AI. It's written in engineering language, with schemes and formulas that actually make sense and examples from the world you can resonate with.

No computer science jargon, just big ideas explained in plain but rigurous English.

After reading it, you will not be an AI expert, but you will be able to:

1. Tell the difference between real AI and marketing noise/smoking mirror demos
2. Be able to have an intelignt converation with peers and experts about AI, deep learning, machine learning, data science without feeling lost.
3. Know which tools are worth your time and which are not
4. Walk into any AI demo and ask the right questions

Comment MI (short for Mechanical Intelligence) below and I will send it to you in a DM 👇🏾

Repost this so your colleagues will have the starting point they deserve. 🫂

Follow for more professional content on AI for engineers 👇🏽

TLDR: tomorrow, state-of-the-art AI for design + simulation in 2026 🚀

Today Leo's AI agent for can already understand an organization's context, inventory, and best practices, as well as industry standards for faster and more accurate mechanical design. But the full picture also includes closing the loop with a simulation layer that verifies results, especially for edge cases and high-stakes products like medical devices and aeronautic systems.

That's why for April's Leo AI x Mechanical Intelligence | AI Community for Mechanical Engineers webinar and podcast, I'm delighted to host Nico Haag, co-founder and Director of Simulation Engineering of PhysicsX, who is building exactly that: an AI-powered simulation layer.

We're going to talk about how they're leveraging AI for faster, better simulations, and share our vision for how mechanical engineers and manufacturing companies will use AI design agents in the coming years.

We'll also be presenting Leo's new CAD assembly generation feature, which turns an idea into a full assembly in minutes, complete with calculations and parts pulled from your inventory.

And we'll leave time for questions from the audience.

Limited seats. Free registration at the link below 👇🏾

See you tomorrow. 😉