If you Moneyball your startup idea, you end up selling data
If you follow your emotional startup idea, you end up building a personal assistant
If you build something that people want, you end up forward deploying AI into car dealerships in middle America
One of the things I’ve been testing at @webuildscore. Can’t say much yet, but you can see the difference.
On top, the boxes jump around and miss half of what happens between frames. Below, they just stay locked on.
It’s a small thing until you’ve watched the choppy version for a few weeks. More soon.
normal cv engineers are building another toyota
my delusional team is building a new tesla
beyond the car analogy, the real challenge is not to build another bounding box machine
it is to automate physical businesses
"company brain" startups that construct information from internal messages and documents when they realize 90% of information only exist in peoples heads: 😭😭😵💫😵💫
China's Unitree just dropped H2-X Kung Fu Legend Guardian.
Dents car doors. 4 meter drops. Spinning kicks + flips. Even lifts front of a car.
We are cooked 💀
computer vision companies are dying from long use-case → model → deploy feedback loops.
they run out of runway before they can even find the right use case.
meanwhile CV is being commoditised in real time on sn44.
every niche will be covered.
if you run a computer vision company, the miners are coming for you or are here to help depending on how you want to approach this.
“just use a foundation model” they said.
so we benchmarked 10 of them against Manako on a real forecourt damage clip.
same full 1280x720 frames, same class prompts (person, car, fuel dispenser, fuel nozzle), no crops, no zoom.
the hard class is the nozzle/hose. small, thin, occluded, and the thing that actually gets damaged.
- Grounding DINO labelled a bin as a fuel nozzle.
- Florence-2 labelled the canopy pole as one.
- OWL-ViT, YOLOE and YOLO-World got the dispenser at best, never the nozzle.
- RF-DETR doesn’t have the class.
- Gemma 4 E2B described the scene, boxed the pump, and returned no incident time.
the only open model that localised the nozzle was full SAM3. 3.45 GB, 0.2-1 FPS on a Jetson Orin Nano.
Manako (3 specialists - 29.5 MB), 3-6 FPS on the same Jetson, tracked the nozzle at 5 FPS and fired a live equipment_damage event on the hose drop at 10.3s.
117x smaller than the only model that could keep up.
open-vocab models are great at the classes the internet photographs a lot.
operations live in the long tail:
nozzles, hoses, cabinets, the small parts that break.
that’s where specialised detectors win, and it’s why we built Manako the way we did.