Shawn Scully

unpopular opinion: ai generated designs are not slop. or even remotely close to slop. ai raises the design floor so dramatically now that dismissing it as such is really no diff than when mathematicians got irked when ai solved decades old math problems. if you’ve studied interaction design to any extent you obviously know that so so much of it is patterns, conventions, & adapting to established behaviors. geee, imagine what new technology is amazing at this? you should maximize its utilization.

big labs "selling ai to enterprise" need 100 million more FDEs to own the market i've talked to 40+ huge public company ceos/execs, still zero examples of labs showing a single example of how they can use ai a companies founders led sales will solve this, then labs will copy

OpenAI's James Sun and Ari Weinstein on the AI adoption gap between San Francisco and the average American business, and why agentic computer use is the way to bridge it: "Living in San Francisco, we see a lot of thinking, 'Wow, the labs can do everything,' and, 'Why doesn't everybody just install Codex and just have it do stuff?'" "But for the average American and the average small to medium business owner or the average enterprise, even understanding what the tools are capable is [hurdle] one, connecting tools in a way that makes sense is two, deploying it is three, servicing is four." "The really cool thing about computer use as a technology is the universality of it. It works with every application. It's unlike any other thing that we've built where the developer has to do some work to adopt it, someone has to build a connector or MCP server." "If there are complex workflows that require a lot of disparate software, computer use is a great way to join them, complete the entire end-to-end workflow, and then building it in a format and UI that feels good to hold for those customers feels still like a really, really big opportunity that I think is still open." @JamesZmSun @AriX

AI robot dentists are already a reality in China. Precision dental work without a human dentist. via @ViralRushX #AIDentist RT!

Don't forget FEA and CFD. They're fucked too. And multiphysics. Once the robots get mass produced enough, labor itself.

6.1 Terra is here! It is good at a lot of things, but we are especially excited for this new era of computer use.

text-to-cad with gpt 6.1 terra is very very very fast soon you will be able to CAD at the speed of thought [[ free and open source ]] [[ works with all coding agents ]]

big news: the anthropic s-1 doesn't say "or it might kill / unemploy everyone" in the first paragraph! a win for humanity

We’ve raised a $2.5M pre-seed to fix hardware hiring. The most reliable way to know if an engineer can do the job is to watch them work. In hardware, that’s been almost impossible to do at scale. Colare runs simulation-based assessments that show how candidates actually work, in the tools they’d use on the job, before anyone books an interview. We assess across: - Mechanical (CAD, DFM, FEA) - Electrical (PCB, schematics) - Systems - Manufacturing, & more. Candidates solve problems from first principles. Hiring teams see that across their whole pool, so engineers only interview people who’ve already cleared the bar. We're already working with everyone from seed-stage startups to multi-billion-dollar aerospace manufacturers. If you’re hiring hardware engineers, let’s talk: colare.com/

Today we’re announcing a $600M Series C, co-led by @CapitalG and @emergencecap, to bring fully autonomous machines to more job sites across the U.S. We want autonomous equipment to become beautifully unremarkable, as ordinary on a job site as GPS or grade control.

Talked to multiple founders yesterday who have been building their products over the last 3 years. Started manual code. They feel behind and slower than companies that started this year with all code written by agents. I have never seen such an order of magnitude difference.

The biggest bottleneck in AI for biology is still the wet lab. AI can generate more ideas than our labs can test. Closing that gap is how we turn better models into faster discovery. Today, we’re introducing X-Automate at Xaira: bringing agentic AI and physical robotics into the lab. 🧵

Today we’re announcing a new collaboration with @nvidia to bring AI to industrial engineering: - faster simulation - open benchmarks and supporting datasets for physics AI - evaluations for agentic AI in CAE and EDA More here: mistral.ai/news/mistral-nvid…

Turning a simulation idea into a working application takes a lot of work. Frontier AI agents are making it easier. 👏 Developers are combining models like OpenAI GPT-6 Astra with NVIDIA Omniverse libraries to prepare scenes, connect simulation tools, test physical behavior, and guide improvements. From humanoid simulators and driving test environments to digital twins and a browser-based space station, see how NVIDIA developers are bringing their ideas to life. Read our blog 👉 nvda.ws/3UAsXIb

there is no moat

lane

30min to generate and run this aero sim using rotating bodies in a new solver our team’s cooking up

Portrait mode launched 10 years ago today. I think it resonated with people because it didn’t feel digital. It felt analog. And this was likely because the inspiration behind its technique went all the way back to oil painting. Sometimes pure math isn’t enough; you need to combine it with insights from the liberal arts. If you recall Portrait mode shipped a month after the iPhone did. This was because this insight about oil painting took a while to figure out. Initially, Portrait mode was seen as a giant math problem. You’d do disparity calculations on the two sensors and output a depth map. The problem was this approach feels and looks digital; it feels fake. In areas without texture, it’s impossible to do this calculation. In areas with ultra fine detail, a lower resolution depth map leads to terrible transitions and a full resolution depth map takes forever to compute. One solution would be to reduce the amount of bokeh so the issues are not as obvious. But then you lose the whole point of the feature, which was to make it look like you had a large, wide-open lens. The solution to this conundrum—the tradeoff between artifacts and the amount of blur— was quite elegant, but it took a while to stumble across. There’s a saying in painting that if you get the eyes right, nothing else matters. Inspired by this principle from oil painting, the idea was to make sure we never got the eyes wrong. We brought in a machine learning block to find the eyes, and draw a mask layer that protected them, then paired that with the disparity depth map. Counterintuitively, the solution was not to decrease the amount of blur, it was to increase it. The additional blur that we could add once the eyes were protected allowed us to hide the artifacts that were create by bad depth calculation. We let the ears go soft, let the nose go a bit soft. This is that wide open look that people loved, even if people didn’t realize that the ears going soft was why. I was thinking about this recently with regard to AI. We’ll be able to do more amazing things… we just need to study the masters who came before us.

Break every problem down to first principles. The old ways no longer serve what the future needs. @MachinaLabs_ founder @EdwardMehr and @ErikPalitsch break down what @SpaceX taught them and how those lessons apply to the hard problems we’re solving today.

JUST IN: Tractian raised $130M at a $1.4B valuation to build sensors that warn factories before their machines break down. Its sensors attach to industrial equipment, tracking vibration, ultrasound, and other signals to detect problems early. -AI identifies faults and recommends fixes -Trained on a massive industrial failure dataset -Used in 2,000+ facilities worldwide -Customers include Coca-Cola, Whirlpool, and Eli Lilly -Also monitors data centers and critical infrastructure Unplanned downtime costs the world’s 500 largest industrial companies an estimated $1.4 trillion annually.