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Node Banana must grow. - Discord server: link below, hmu. - Generate node carousel: the active image will be used in the workflow. - Node group locking: because I accidentally hit run and it killed the mood. - Hot hot keys: Shift + (p)rompt, (g)enerate image, (i)mage, (l)lm node....

17,346 次观看 • 7 个月前 •via X (Twitter)

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At its core, iAgent utilizes Protocol Nodes to support and maintain the ecosystem's functionality. This collection of publicly maintained nodes help: ❒ secure the network ❒ determine rewards for GPU workers ❒ earn rewards for their contributions to the network iAgent has 2 types of protocol nodes: 🔘Genesis Nodes 🔘Public Nodes With a 5000-supply, Genesis Nodes are engineered to yield x3 $AGNT tokens generation compared to Public Nodes (which will come at a later date). As a token of gratitude, Genesis Nodes also receive TGE: 2% AGNT tokens from reward pool. Other Genesis Node Utilities & USPs include: Participation and Governance: Genesis node operators have the chance to engage more deeply in decision-making processes through iAgent's governance model. Exclusive Access: Operators of Genesis nodes may receive early access to test new features and products within the iAgent ecosystem. Reward Structure: Nodes earn $AGNT tokens, and Genesis nodes do so at an accelerated rate due to their higher token generation multiplier. Rewards are calculated dynamically, with consideration given to node performance and network contribution. Technical and Community Support: Genesis node operators are expected to receive dedicated support from the iAgent team, ensuring optimal performance and uptime. Requirements to run an iAgent Node: Dual Core, and 8GB RAM, are all that is needed for lightweight processing. Anyone with Genesis/Public Node NFTs can run it. But how do you get yourself one? Hop in the discord to know more:

iAgent

21,278 次观看 • 1 年前

🌟 Another Epic Sneak Peek: OptimAI Core Node’s Edge AI Computing Power! We’re excited to continue unveiling the upcoming OptimAI Core Node features! This time, we’re introducing Edge AI Computing, a game-changing feature that will make your devices even more powerful in contributing to the OptimAI Network. 🔥 What is Edge AI Computing? With the OptimAI Core Node, your device’s idle computing resources (CPU/GPU) and storage will be put to work, powering critical AI computing tasks, including: 🔸Edge Inference: Running real-time AI predictions directly on your device without sending data to centralized servers. 🔸Hot LLM Models: Accelerating large language models for high-speed NLP tasks. 🔸Generative AI Models and More: Contributing to the training and inference of cutting-edge generative AI, like text-to-image models, deep learning, and more. More Contributions, Bigger Rewards Await! This Edge AI Computing feature allows us to leverage the full potential of decentralized computing power, reducing latency, increasing efficiency, and making AI more accessible. The more you contribute, the bigger the rewards!🔥 Stay in the Loop—Start Today with OptimAI Lite Node! While the OptimAI Core Node is still in the works, you don’t have to wait to join the action. The more you contribute, the greater the rewards! Let’s build a stronger, smarter, and more decentralized AI network together. Stay tuned for more exciting updates! 🔸Extension Node: 🔸Telegram Node: 🔸Register at: #DePIN

OptimAI Network

44,296 次观看 • 1 年前

Graph Convolutional Network by hand ✍️ ~ 12 steps walkthrough below Graph Convolutional Networks (GCNs), introduced by Thomas Kipf and Max Welling in 2017, are the tool for data shaped like a graph: social networks, recommendations, biological networks, drug discovery, molecular chemistry. I drew and calculated a simple GCN entirely by hand. Goal: run a two-layer GCN, then a small classifier, on a five-node graph, filling in every cell yourself. 1. Given A graph of five nodes, A to E, with edges between some of them. 2. Adjacency matrix (neighbors) Put a 1 wherever two nodes share an edge, in both directions. 3. Adjacency matrix (self) Add 1s down the diagonal, one self-loop per node. That is just adding the identity matrix. 4. Messages Multiply each node's embedding by the weights and biases, then ReLU. Negatives become 0. 5. Pooling Multiply the messages by the adjacency matrix. Each node gathers the messages of its neighbours and itself. 6. Visualize Node A pools [3,0,1] + [1,0,0] = [4,0,1]. 7. Second GCN layer Messages again: weights, biases, ReLU. 8. Pooling again Pool over each node and its neighbours, once more. 9. Visualize Node C pools [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10]. 10. Fully connected layer Weights, biases, ReLU. This time there are no neighbours to pool, just the node itself. 11. Linear layer One more: weights and biases. 12. Sigmoid Squash each score to a probability (≥ 3 → 1, 0 → 0.5, ≤ -3 → 0). That is the classification for each node. You have just classified every node in the graph by hand. ✍️ The outputs: A: 0 (very unlikely) B: 1 (very likely) C: 1 (very likely) D: 1 (very likely) E: 0.5 (neutral) The takeaway: a GCN layer is two parts. The top part pools each node with its neighbours through the adjacency matrix. The bottom part is an MLP that transforms each node on its own. A transformer layer has the same two parts, with an attention matrix where the adjacency matrix was. Both matrices do one job, mixing across positions: attention over tokens, adjacency over nodes. In my class I call the GCN the transformer's little cousin: a bit more stubborn, because its attention is fixed by the graph rather than computed from Q, K, and V. Draw the two side by side and the resemblance is hard to miss. 💾 Save this post! #AIbyHand #GraphNeuralNetworks #DeepLearning

Tom Yeh

16,744 次观看 • 14 天前