Hardik Kabaria

@VinciPhysics enabled users to perform 30K simulations in production. Scale isn’t a benchmark. It’s the constraint a foundation model for physics has to survive. No fine-tuning, one foundation model for physics for everyone in industry.

Same model working out-of-the-box across all types of parts, physical scales and industry be it semiconductor or robotics. End to end automation. 33M degrees of freedom problem solved in 20 seconds of inference. No fine tuning, no customization. Continuous physics intelligence.

Honored Vinci won the ISIG Startup Competition. The real signal from ISIG: the hardest problems in AI infrastructure are now physical. Power. Cooling. Heat. Manufacturability. The last wave of AI worked on words. The next great intelligence will help build matter. That's what we are shipping at getvinci.ai

@tbpn @EclipseVentures This is right. The enduring companies are usually the ones solving constraints the market cannot simply work around. The physics problems we are working on at Vinci are not edge cases they sit at the core of how modern hardware gets designed, built, and trusted.

.@eclipseventures Founder & CEO Lior Susan says the reason SpaceX will have a successful IPO is because they operate in a category defined by difficulty. "The reason SpaceX can have that type of an IPO is because they solve something that is really, really hard. So it's really, really hard for the second person to solve that same problem as well." "I think the reason you'll see a correction in the SaaS world is because you had a lot of companies where entry is easy, but the TAM is not too big and as a result, the public markets correct." "I believe we're going to see a lot of companies in the semiconductor, space, AI infrastructure, and data center space go public in the next 18 months or so."

.@AnneliesGamble has been a backer of Vinci since day 0 and has one of the most rigorous takes I’ve seen on AI for physics. Getting reliable behavior in engineering environments is hard. But that is also what makes the opportunity to build enduring simulation infrastructure so compelling.

AI will generate more hardware designs. Physics will determine what ships. We’ve now extended Vinci from thermal to full thermo-mechanical simulation (stress + warpage) at manufacturing resolution. Already benchmarked across real semiconductor workflows — from HBM and advanced packaging to fabless and PCB environments. Deterministic run after run. Deploys without per-customer tuning or custom code. The foundation model now extends across coupled thermo-mechanical behavior. As hardware design accelerates, validation can’t remain human-bounded. Physics has to become infrastructure. That’s the layer we’re building. More details: getvinci.ai Press release: getvinci.ai/news/vinci-shi…

We are backed by @EclipseVc, @khoslaventures, and xora.vc Learn more about us at getvinci.ai

And, we are just getting started; things are heating up! We’re hiring, so DM if you are interested in this field and joining our mission.

Today, we announced that we are deploying a foundation model for physics for hardware design– giving a single engineer the ability to explore more materials, design variations, and configurations in a matter of a day compared to what an entire team would test in weeks. “Foundation model” means we deploy a pre-trained general model for a physics that applies across many problem classes, materials, geometries, and industries without retraining per customer or per use case. We're shipping our software in industrial/enterprise environments. Our customers’ data stays private. What this unlocks: - ~10³× faster than traditional physics solvers - Deterministic - Accurate results for the underlying partial differential equation-governed phenomena - Multi-scale - resolving single-digit nm level features on centimeter scale domains– spanning 7 orders of magnitude between the smallest feature size and the domain size. Same model. Same software. Because the laws of physics are universal. Now onto some cool viz! Here is the video showcasing the product

@Auzaiffe Pretty awesome stuff!

The source code for the Longest edge bisection, tetrahedral based, volume representation for faster path tracing project is online available on GitHub and is under MIT license. Feel free to grab it and play with it! github.com/AnisB/leb3D

@zhhol141234 Nice work! 1536^3 is impressive. Ping me , would love to chat further 1:1

🔥 Sparc3D—Next-Gen Ultra-Res 3D Shape Generation • Mesh detail up to 1536³ resolution ✅ • Watertight, 3D-print-ready output ✅ Live demo 👉huggingface.co/spaces/ilcve21… 🏠 Homepage: lizhihao6.github.io/Sparc3D/ 📄 Paper: arxiv.org/abs/2505.14521

@keenanisalive @Caltech @CarnegieMellon We are hiring a computational geometry software engineer. Would love to connect with Mark

One of the absolute best students I've ever had (star @Caltech grad and @CarnegieMellon PhD in Computer Science) just had faculty interviews canceled because universities are on a hiring freeze, due to uncertainty about funding. Not a great move for technological supremacy💀💻🇺🇸

Interested in learning about the recent software advances on Topological Deep Learning (TDL)? #TopoX (github.com/pyt-team) is going to Stanford Graph Learning workshop next week. If you are in the Bay Area come and join us on the 5th of Nov! @PyT_Team_

@githubstatus still seeing this

@VFSGlobal the best thing that can happen to someone is they don't have to ever deal with you. Make someone's day and get out of the business. Wishing you lots of luck!

@rishabhkaul Paradise Biryani Pointe in Santa Clara

Ok SF - who serves the spiciest biryani in town? I am assuming it will ofcourse be of the Andhra variety. I mean the hotter going out than coming in types. Comparable to Meghana (in BLR)