This is exactly why the next 18 months of vertical AI are going to be bananas. The promise of vertical AI, as opposed to vertical SaaS, is to build software that understands a job well enough to actually do the work — not just give a person better tools to do it. There are 3 trends that have created a perfect storm for vertical AI: 1. AI software is capable of getting jobs done in more magical ways than ever before. 2. The first place that magic has landed is in AI software development itself, so there's a mad rush to build for the builders. 3. This makes for a frenzied loop: build AI tools to solve problems in the current AI tooling → uncover new problems with AI tooling → build different tools to solve the new problems... Every day a new (and better, and faster) way of building emerges. But it also introduces a new set of problems that require a new set of products. Infra and tooling right now are on a runaway treadmill that just keeps going up in speed. The side effect is an insane tailwind of constant stack upgrades for building vertical AI. It's a generational opportunity to literally invent solutions for problems that haven't been solved in 100+ years. A factory floor today has the same tribal-knowledge problem it had in 1926: the person who knows why the line really runs is about to retire, and nobody wrote it down. We are now finally getting to the point where we can take on massive real world problems and build solutions that get those jobs done in a magical way.

Procedure generation in Squint really is wild. Our AI watched this video of me using our office Nespresso machine — with no narration or documentation — and created a detailed procedure in minutes. This is just coffee, you should see what it can do with a forklift or a thermoformer. Check it out here: procedures.squint.ai/request

The @SquintAI engineering team cooked! This isn’t some gimmick OCR feature. Our team filtered through the forest of models and tactics to build a best-in-class pipeline for transforming legacy documentation into digital work instructions. Squint can read a PDF. Detect the intent of the sheet/diagram/chicken scratch. Interpret it as a procedure. Convert it into Squint data structures for work instructions, tasks, Copilot responses… It’s so good, I’m willing to take challenging docs live on X and try them. Send me your most complex PDF and we’ll give it a spin. Or, try it yourself. It’s available to anyone. Request access here: procedures.squint.ai/request

Sounds like a lot but 400k Kit Kats is probably 10% of a factory’s daily volume Here’s my back of the napkin based on my adventures: 5 lines, 1000 bars a minute per line, 18 hours of uptime => 5.4M Kit Kats per day per factory 🤯 @KITKAT let me know if I’m close here

Sometimes the weather doesn't cooperate with load-in, and your (local) designer needs to set up the booth last minute. But...they get to ride on the forklift. @Generis_Mfg #GenerisAMS #MfgUS26

@MenloVentures just mapped where $37B in enterprise AI spend is going, and @SquintAI is on the board. The industrial world needs AI tools that actually make a difference for operators, and that’s exactly what we’re delivering at Squint.

The narrative around manufacturing automation has long been framed as a zero-sum game: either humans do the work, or machines do. But this isn’t just outdated, it’s holding us back. I feel pretty strongly about this, so I wrote about it for @FastCompany 👇 fastcompany.com/91389767/the…