.“One of the most unexpected outcomes of working with Dirac: it’s accelerated our sales.” — Matt Grimm, Cofounder & COO, @AndurilTech Anduril is now pitching Dirac directly to the Navy & Air Force as part of how modern systems get designed, manufactured, and fielded faster. With Dirac, manufacturing software stops being internal tooling and becomes a strategic advantage. @FilArons @mttgrmm @tbpn

Robotics founders need to burn thoughtfully. Venture funding in Robotics has grown by over 400% over the last 2 years. Actual installations of robotics is up <25% YoY. Robotics is not AI. The diffusion will be slower for a while and your underlying metrics will need to catch up to the valuation at some point.

To build the future of manufacturing, you need a coalition of partners that are not constrained by the past. We are proud to partner with @DiracInc to bring AI model-based process design into our mission to build complex metal structures 10x faster.

It’s really simple. Our Robots and AI vs their Robots and AI. If we can catch up on robotics/software , and can stay ahead on AI, there is zero reason manufacturing won’t come back, or initiate here. At Cost Plus Drugs, the only thing holding us back from making generic drugs cheaper in Dallas than we can buy from Asia, are the fees we have to pay.

I've spent years in China building robots. I can tell you this playbook won't work: Pre-seed $: Invent the ultimate actuator. Seed $$: Figure out mass production. The hard part isn't inventing a beautiful topology. It's process engineering. The real delta between the US and China is the ability to execute the enormous amount of physical x mental work required to iterate thousands of times on product designs and manufacturing processes. Partly because much of the tacit knowledge / management systems has left the US, but also because of the sheer human labor required. This isn't something you can solve by buying traditional automation lines either. It requires: 1. Ideation driven by human intuition. Walking up and down the line, talking to assembly workers, and developing hypotheses about what and where improvements can be made. Raw human hours and attention spent understanding a specific manufacturing process. 2. Large batches of experiments across multiple variables. Coordinating experiments that can easily involve 50+ people, issuing instructions, and tracking the execution and results of each experiment. 3. Purpose-built test design to identify key metrics. For example, a change in gear hobbing profile might require accelerated aging tests on a dyno, while a change in bearing tolerance might require NVH testing. 4. Data + human intuition-driven analysis. Interpreting test results and deciding whether to implement a process change or run the next set of experiments. 5. Strict process control. Reissuing work instructions to the line and ensuring every process is followed to the letter. The best manufacturers in Asia, from Foxconn to TSMC, have built their competitive advantage on systematically turning thousands of physical experiments into better manufacturing processes. Their moat isn't just production scale. It's the organizational machinery that makes manufacturing learn and improve faster than anyone else. I don't think most people in SF clock this. Say you're manufacturing actuator gears in-house in the US. After you've made your first article, will you hire a team of 10 process engineers to coordinate the 50 technicians running concurrent experiments with designs, processes, and materials? To understand wear patterns across every pin in a cycloidal drive after 10,000 hours? To reduce backlash from 15 arcmin to 5, then to 1? Just to keep pace with China's progress over the next few years? Do you have the skill set and management systems in place to do this? That's why our approach isn't just to automate manufacturing. It's to automate the process of learning how to manufacture better. Automate simulation and design optimization. Automate assembly and physical testing. Automate the design and execution of process experiments. Feed real-world experimental data back into simulation to continuously improve sim-to-real accuracy. The goal is to turn what currently takes 50 engineers coordinating thousands of experiments into a closed-loop system that continuously learns and improves. Then the same robotic line mass-manufactures the product. PS: Coming back to actuator design, many actuator designs are already commoditized in China. The project in the video is open source, and CNC machining cost in China is just $27 (watch the video). I hope people understand this reality. I'll link more examples in the replies.

Wtf

I am starting to think in anti-scale. if something can be done by anyone using Claude, it’s probably zero alpha. instead, what’s an approach that is too much effort for everyone else? do that

My feed is getting flooded with vibe coded CAD apps. I don't want another vibe coded CAD app, I just want solidworks to work on my linux laptop. My take; AI doesn't understand CAD workflows, maybe someday it will, but there's a reason Fusion and SW have the workflows they have.