NN Visual is an interactive web-based neural network visualizer. It lets users configure hidden layers, select datasets such as auto_mpg, iris, xor, or mnist, and train a network while observing forward and backpropagation step by step. The tool displays training loss, mean absolute error, weight distributions, and node activation brightness. Written explanations accompany the visualizations to teach concepts like gradient descent, weights, biases, and activation functions without requiring code.
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NASGym is a proof-of-concept OpenAI Gym environment for Neural Architecture Search. It implements reinforcement learning steps for NAS and encodes networks using the Neural Structure Code from BlockQNN. A network is modeled as a list of NSCs, an action adds a layer, and the reward is accuracy after early-stop training. CIFAR-10 is the default dataset. The code and documentation may contain minor bugs.
Rebyte is an execution layer for enterprise agents. It runs agents in the cloud with durable execution, tools such as browsers, files, and compute, and policy controls over models, skills, MCP servers, and network access. Execution traces record model steps, tool calls, errors, tokens, and cost. The Agent API is compatible with the official OpenAI client and is described as model agnostic.