I gave the TestDrive Car a flying-car upgrade with @Kling_ai The result starts with the little car on the street, lifts it into the air, then follows it flying between skyscrapers. The camera movement and lighting give it the feel of a miniature car commercial. What I liked most was how Kling turned a static mascot into a moving scene with a clear sequence of actions. The flight feels cinematic, even if the physics lean more toward playful sci-fi than realism. I’d like to push the motion further and see how much control I can get over more complex actions. A fun way to bring a character to life.

I tried using @OpenBot_ to build a simple Bittensor subnet research checklist. The setup was straightforward enough, but when I sent the task, I hit this error: “Authentication required: provider authentication required.” My internet connection was working, so this wasn't the connection issue I initially suspected. I couldn't complete the task, which makes the provider setup an important part of the experience. I'd like to see clearer guidance on which models work without authentication and how to connect them. The agent workflow looks promising, but I couldn't properly evaluate the output because I got stuck before execution.

AI models can be confidently wrong, and that’s a problem when real money is on the line. Imagine an insurance AI assessing a car damage claim. If a subtly altered image tricks the model, a fraudulent claim could slip through. @perturbaix, Bittensor Subnet 26, is built to find these weaknesses before attackers exploit them. Miners discover inputs that fool image models, while validators verify the attacks. Image-model testing is live today, with text, LLM and multimodal attacks on the roadmap. But how do you find a weakness before it becomes a costly mistake? That’s the story behind THE WITNESS. 🎬 Step into the investigation. See how Perturb fights AI with AI. @perturbaix #PerturbVideo > Angle 0

Energy is becoming one of the constraints behind AI compute. But the problem isn’t always a lack of energy. Sometimes it’s energy in the wrong place, at the wrong time, or without an efficient way to turn it into something valuable. @green_compute_ is approaching this differently through Bittensor Subnet 110. The idea is simple: Excess clean energy can power GPUs → GPUs provide real compute → commercial demand pays for that compute → more demand creates a reason to deploy more infrastructure. That creates an interesting flywheel. Instead of treating energy, compute and demand as separate markets, SN110 is trying to connect them through one incentive system. The recent ~$7M in permanent GPU deployments is interesting for that reason. The bigger question is how much compute this model can keep pulling into Bittensor as real demand grows.

Bittensor is getting harder to navigate. There are hundreds of subnets, each with different narratives, performance and opportunities. @CrucibleLabs makes that easier to explore from the wallet itself. You can: > browse subnets by category > see top subnets by emission > track gainers and losers > view subnet-level performance > swap TAO and subnet tokens > stake and manage positions So instead of switching between different tools to understand what’s happening across Bittensor, you can explore the network from one place. It’s less like a wallet for Bittensor and more like a dashboard for navigating it