If you have been following decentralized AI you have probably seen @PerceptronNTWK. The project builds a network where humans not platforms earn by running simple nodes that share idle bandwidth from browsers or phones. These nodes create authentic viewpoints across more than 150 countries. The network now holds over 810000 nodes with around 115000 active daily and roughly 10 TB of bandwidth each day. Passive nodes gather diverse public web signals while data questing lets users handle targeted tasks like labeling for extra pay. Everything supplies live rich datasets to AI teams at lower cost and a $10M AI Data Fund supports developers. Rewards come in $PERC and early active contributors build reputation for better chances. It already runs with paying clients and turns everyday users into the quiet backbone of fresh trustworthy data for AI models.

Interactive web visualisation for a compact multi-layer perceptron trained on the MNIST handwritten digit dataset. Draw a digit, watch activations propagate through the network in 3D, and inspect real-time prediction probabilities. github/DFin/Neural-Network-Visualisation

Internet video is now robot training data! 🛜 @perceptroninc, the startup from the ex-Meta team behind the Chameleon multimodal models, just released Isaac 0.5, an open-source embodied foundation model. The same weights answer questions about a video, point to objects, track them over time, report task progress, and generate the robot's actions. What it learns from perception directly shapes what it does. To make a big model run fast enough for control, they built on something called Null Experts: each token can pick how many experts it activates, so the model spends more compute on hard parts of a scene and coasts through the easy ones. → 36B-parameter sparse model, trained across 35+ robot systems and 3T multimodal tokens → 62.6 on ScreenSpot-Pro grounding vs 54.8 for the strongest Qwen3-VL run → Does it at ~8.5× lower inference cost, 26.9 TFLOP per request against 228.4 → On a chess-manipulation task, one epoch on a single episode cut action loss 10.5× Perception and action sharing one brain, with compute that flexes to the task, that's a genuinely different shape for a robot model. And it's fully open, weights, training code, inference via LeRobot. Here's the blog: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

The entire core of a neural network on four cards. Neuron, forward pass, activations, backprop. Learn these four and you understand how every model from a perceptron to a transformer predicts and learns.

Projects Fundraising on RootData (July 27 - Aug 2) Last week, 10 blockchain companies/projects received funding. 💎 @axisrobotics — Physical AI infrastructure, raised $12M in seed round by @hack_vc, @NomadCapital_io, @PiCoreTeam Ventures & others. 💎 @PerceptronNTWK — Decentralized AI data economy, raised $6.5M in strategic round by @PolygonVentures, @QCPgroup, @SeliniCapital & others. 💎 @Beezie — Digital Claw Machine for Collectors, raised $4M led by Psalion VC. 💎 Birdai — On-chain infrastructure, raised $4M in seed round by @CastleIslandVC, @metalayervc & @deptvc.

Perception: •⁠ ⁠This is the craziest founder reveal ever! •⁠ ⁠This is the most insane velvet-rope launch strategy ever! Reality: •⁠ ⁠Some people just prefer quietly building vs. tweeting •⁠ ⁠Compute is really scarce right now! Have you not been reading Twitter?

AI perceptions in the US in 2026 reveal cautious optimism mixed with concerns about ethics, bias, and societal impact — a complex reality shaping our future. — @meisshaily #ArtificialIntelligence# #AI# #Tech# #techNews# #Technology#