A super smart scientist asked me last week: what's so hard about reshoring general advanced manufacturing (short of TSMC frontier precision chemistry)? Isn't the issue just labor cost? Nope. A toy example to illustrate: We're trying to build a lot of drone motors in America right now, and falling short. It's not a labor cost issue. The work instruction says: this magnet, this adhesive, cure at this temperature, wipe the rotor first. Any competent team can build the station and follow it. But in week 3, motors start failing vibration testing. Some of them, mostly night shift. Also, weirdly, whenever the careful new hire is on station. The adhesive cures differently with humidity, and the dehumidifier cycles differently at night. The new hire wipes the rotor generously, and that leaves a solvent film the adhesive doesn't like very much. Someone who has run a motor line before can figure this out in 10m. Everyone else -- struggle city. The "fix" is two lines in the work instructions: run the dehumidifier continuously, one solvent wipe per rotor. But those lines aren't in the work instruction, and they're not in anyone's! At best the fix becomes a corrective-action report in one line's files, linked to nothing and never read. A motor line rapidly may go from 5 to 40 stations, each with a dozen of these painful stories on the weekly as it grows. "What Shenzhen knows" is not written down in a book whose covers can be sliced off and ingested into a model, it's a few million humidity stories stored in people, and a pace of knowledge-rich adjustment that keeps cost down and yield high. The only way to get it back is work with people who have brought up lines, to reshore and grow the ecosystem of people who have this knowledge, and to capture that knowledge as we build. Lots of people have clever ideas about the data capture/API piece, but we think the business of manufacturing may need equal portions pain tolerance, customer commitment, and cleverness on talent. America needs to solve the other half of reshoring.

Breaking news: things are going well. $FIG Q2 2026 results: → $370M in revenue → 48% YoY revenue growth rate, the 3rd straight quarter of accelerated growth → 136% Net Dollar Retention Rate

Excited to share that BBVA launched Sierra's first long-running Horizon agent in just 30 days — from initial discussion to production. It’s now serving BBVA customers in both Spain and Argentina.  Deploying AI at this pace is truly remarkable for an organization operating at the scale of BBVA and a testament to the leadership of @bravoacin and the bank’s amazing technology teams. Customers come first is a core BBVA value. With Horizon agents, they can build deeper relationships that help grow their business. @claybavor and I are so grateful for our partnership.

Come join Embed v7! Embed is a small group of exceptional founders working at the frontier of AI. Past participants have included @cognition , @ListenLabs, Quadrillion, @Intelligence_ai, @ReflectionAI_ and more. Participants include a mix of consumer, enterprise, hardware and foundation-labs founders. The early v7 class has a lot of robotics / hardware folks, but we’re interested in anyone pushing the frontier forward. I'm a big believer that founders have better ideas than I do, but one idea I’ve been noodling on recently is what happens when creating software becomes as easy as recording video. Do we end up w/ TikTok-like platforms and a new software creator class? Does software become more content-like?

Our entire experience of reality is mediated by electrical activity in the brain. I’m inspired by BCI becoming increasingly, backable engineering, pulled forward by AI: models we interact with, translation architectures, and predictors of how perturbations reshape experience.

Today, we are releasing Inkling-Small. Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available. Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.

Drop #3# is here: 2 new types of robots are now LIVE online. Those are real robots. 1. Scientist robot: A robot that does real chemistry experiments. 2. Bomb-Defusing robot: A robot on a Special Ops mission to defuse a bomb. Try them out for yourself at robots dot online.

What’s in @saranormous's bag? The ultimate robotics brain. 🧠 NVIDIA Jetson AGX Thor, packing up to 2,070 FP4 TFLOPS of AI compute and 128 GB of memory. Power next generation humanoids and autonomous systems with real-time reasoning and server class performance, built for the lab, factory, and field. Learn more 👉

We started Onyx two years ago with the mission to control advanced AI. Today, we’re announcing our $113M Series B led by @BessemerVP, and partnering with Cyberstarts, @TCVTech, @FirstMarkCap, @conviction, Vintage, QuantumLight and G Squared. As we enter the age of superintelligence, our mission becomes more critical. We are just getting started! @amitkarp @morgangerlak @spdholakia @HZinshtein @giliraanan @saranormous @DavidWaltcher @iren_reznikov @SpencerMcLeodVC

Palo Alto secured traffic at the application layer when business software moved to the web. CrowdStrike shipped a cloud-native sensor when work left the perimeter and the laptop became the frontline. Wiz built a graph-first CNAPP when cloud adoption accelerated. The largest companies in cyber get created during secular platform shifts. Each one of those shifts changed how we work. This time, it's also changed who's doing the work: agents. To do so, they inherit the same permissions and access as the humans they act for, which makes them a highly privileged and capable threat vector operating at superhuman speed. @maximbarkogan and @gil_elbaz_ saw that coming before anyone, and founded @onyx_security to build the category defining control plane to govern and secure agents. Security tools were built to watch deterministic software and human-led workflows. They can't govern a non-deterministic model chaining tool calls against inputs nobody vetted in advance. Onyx's bet is that only AI can: proprietary models sitting in the runtime path, inspecting every agentic action and blocking or redirecting it before it reaches a downstream system with warp speed and accuracy. Excited to be backing Maxim, Gil, and the Onyx team in their $113M Series B, alongside our friends at @BessemerVP, @Cyberstarts1, @conviction, and @TCVTech. cc @sullivan @amitkarp @saranormous @HZinshtein