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SimpleMind

SimpleMind: A Neural Network Implementation in JAX

The SimpleMind class is a powerful yet straightforward implementation of a neural network in JAX. It supports various activation functions, optimizers, and regularization techniques, making it versatile for different machine learning tasks. With parallel backpropagation and detailed logging, it provides an efficient and transparent framework for neural network training.

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production_transformer.py in 2026 — what the code actually is now

The 2024 article on production_transformer.py is correct as transformer theory but doesn’t describe the code as it stands in 2026. Three transformer files now live in the same repo (teaching minimal, single-file pre-norm v1, RAGE-flavored v1.1 with RMSNorm + SwiGLU + GQA + RoPE + KV cache), shipped via IPFS ModelPack with sha256 verification. Here is the operational ground truth.

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MASTERMIND aGLM with RAGE

Building a rational Autonomous General Learning Model with Retrieval Augmented Generative Engine to create a dynamic learning loop with machine.dreaming for machine.learning as a self-healing architecture. MASTERMIND uses the Autonomous General Learning Model (aGLM) enhanced by the Retrieval Augmented Generative Engine (RAGE) to create a sophisticated AI system capable of intelligent decision-making and dynamic adaptation to real-time data. This combination leverages the strengths of both components to ensure that responses are not only based on […]

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