$0.003/sec changes the economics of running real-time AI video. I’ve been testing Vivix W1, and that number is hard to ignore. The comparison shows visual quality on par with the models alongside W1. About a second of generation time per second of footage, too. But W1 isn’t just another video generator. It’s built for real-time interactive video, where the model keeps generating as the experience moves forward. Every second of that experience adds to the generation bill. At $0.003/sec, you can actually start thinking about a story that keeps going as viewers make choices. @VivixLabs_HQ already supports real-time generation through the API. The browser playground currently uses offline generation, with real-time browser tools coming soon. Continuous video is one thing. Giving viewers meaningful control is another. If I change my mind halfway through a story, how much can it actually change with me? That’s the interesting challenge. The next plot twist could be yours 👇 platform.vivix.ai/vivix-w1-m…

okay, this could make figuring out daily workouts so much easier

Wait, the fueling advice comes with it too? That's a nice bonus.

Now that's the move. Turn the data people already have into decisions they can actually use.

Plot twist: your wearable becomes useful for something beyond reminding you to stand up

Bro, your body doesn’t perform the same every day 😂 So why follow the same training plan? Outcurve adjusts your training and fueling based on how you actually show up.

finally bro, some innovation beyond another watch tracking your steps.

The fact that all these data layers are publicly accessible is honestly wild. Convert to PDF

Star the repo if you run agents: github.com/superlinked/sie

A GPU bills the same per hour whether your agent is calling it or not. That adds up fast when an agent leans on several models. Embeddings, a reranker, OCR, entity extraction, a safety check, and an LLM often end up on separate servers, and a server usually keeps its GPU memory reserved while it waits for traffic. SIE, the Superlinked Inference Engine, serves all of them from one cluster in your own cloud. It loads models on demand and evicts the least recently used ones, so GPU memory goes to the models in use, not to every model at once. For production, it brings a load-balancing gateway, Helm charts, KEDA autoscaling that scales to zero, and Grafana dashboards. It serves 100+ models and is open source under Apache 2.0. Try it locally with pip install “sie-server[local]” and sie-server serve. Star the repo if you run agents: github.com/superlinked/sie

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AI creators sharing tools is one thing. Building something together could open up much bigger opportunities.

This could make maintaining consistent metrics so much less painful

Honestly, keeping analytics in Git feels like something data teams should've had ages ago.

Your analytics stack belongs in version control too. Querio puts your analytics context, metrics and permissions in a Git repo you own. Connect your agent. Make changes in a PR. Keep analytics consistent across your stack. Finally, analytics that can fit into the way developers already work.