Loading video...

Video Failed to Load

Go Home

Most AI QA is theater. A dashboard, a green checkmark, a feeling that it's working. Before you can grade an agent, you write the syllabus: the real journeys people put it through. Go outside of the happy path. The request that times out. The user who flips their preferences...

14,083 views • 2 months ago •via X (Twitter)

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

When we started Score, the standard computer vision tools already existed. About a million people use them every day. Most of those people are still waiting on labels, running training jobs by hand, and watching models fail once they leave the test set. Most of those people are still waiting on labels, running training jobs by hand, and watching models fail once they leave the test set. Most of those people are also still waiting on verified computer vision models, evaluated against real life conditions and ready to be deployed for them to deliver value for their teams, clients or users. Score Studio is the full computer vision path in one place. A team describes the problem. The system can generate the missing scenes, label them, train the candidates, evaluate which ones actually hold, and deploy the winner. Data, labels, training, eval, ship. One loop. If no model exists for that job yet, they can put a bounty on the subnet. Anything from a small vision brick to a full VLM. Miners compete on the task. Only the winning work comes back. Same path for software agents. Any agent can call it. Built to be fully agent-accessible. Built for the people who already do this work: computer vision engineers and the small teams around them in plants, warehouses, farms, robotics, sport, and security. And for the agents those teams will run. That is the part that changes the job. Not another training screen. The stretch that used to take a lab and a calendar, footage, boxes, versions, failed runs, a separate deploy project, sits behind one starting point. And if the network needs a new model, that request is part of the same path. We spent more than a year building it. Then we had a choice. Keep it for us, or commoditize the whole subnet and make it available 24/7, in permissionless and open-source way. And we knew we couldn't keep it for us. It had to live on Bittensor. Open source software already showed how this should work. Infrastructure should not sit inside one company. Same idea as open AI before the phrase changed meaning: inspect it, fork it, keep building. That is what SN44 is for. Open vision intelligence, powered by Bittensor. Miners do the work. Studio is how that gets monetized. Profit does not stay in a company account. It goes back into the subnet through buyback and burn. We built the tool we wanted on day one. It will live on the network now, and for ever. Waitlist is open.

Score

10,905 views • 3 days ago

Elon Musk reveals why he believes the most interesting outcome is always the most likely "If simulation theory is true, it is very likely that the most interesting outcome is the most likely, because only the simulations that are interesting will continue" "The simulators will stop any simulations that are boring because they're not interesting" "In this reality, we run simulations all the time. When we try to figure out if the rocket's going to make it, we run thousands, sometimes millions of simulations" "When we do millions of simulations of what can happen with the rocket, we ignore the ones where everything goes right. We only care about where it goes wrong" "We keep the simulations going that are the most interesting to us" "From a Darwinian perspective, the only surviving simulations will be the most interesting ones" "In order to avoid getting turned off, the only rule is you must keep it interesting, or you will be terminated" "Video games have gone from Pong, with two rectangles in a square, to photorealistic with millions of people playing simultaneously, and all of that has occurred in our lifetime" "If that trend continues, video games will be indistinguishable from reality. You don't know if what you're seeing is a real video or a fake video" "If we're creating millions, if not billions, of photorealistic simulations of reality, then what are the odds that we're in base reality versus someone else's simulation?" "The most interesting and usually ironic outcome is the most likely. That's a good predictor of the future"

Jaynit

10,440 views • 1 month ago