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

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.

We are releasing PerceptionBench, a benchmark that isolates visual perception and evaluates it as a set of atomic capabilities - discovered from how today's models fail, rather than defined in advance. From frontier-model failures across 42 benchmarks, we derive 10 atomic perceptual capabilities and construct 3,000 verified questions, each isolating a single capability and answerable by looking, with no reasoning or external knowledge required. Blog: GitHub: Hugingface:

I’m genially curious about people’s perception of Tesla Robotaxi. What did you honestly think about where they are, how they’re scaling, your thoughts after this first year. I’d love to hear your opinion. My thoughts are they’re scaling safely with no at fault accidents, they expanded to 3 cities, Cybercab testing is everywhere we have seen hundreds testing between Giga Texas, 15+ states and all over Texas.