Berlin Brown and Software Development

 This is a basic plant simulation with Laterna library in Java - https://www.baeldung.com/java-lanterna First goal is: Current state includes water and seed droplets hitting the ground and increasing the water level in the soil.  The seeds in next phase will sprout into plants. https://github.com/berlinbrown/umbra-cellular-automata-game/releases/tag/1.0.1

 Old game is building, updated per chat gpt. https://github.com/berlinbrown/OctaneMechOpenGLGame/tree/master/gameupdates/mac/umbramech

  Adding basic game of life terminal simulation in Java https://github.com/berlinbrown/umbra-cellular-automata-game/tree/main/javatermsim/termsim

More tools from Anthropic https://github.com/anthropics/claude-plugins-official https://github.com/anthropics/courses https://www.lawnext.com/2026/02/anthropics-legal-plugin-for-claude-cowork-may-be-the-opening-salvo-in-a-competition-between-foundation-models-and-legal-tech-incumbents.html

 Analysis of myco project from chat gpt. Yep — that’s the key jump, and you’re absolutely right to call it out. This isn’t just CA-ish vibes ; it’s a spatially embedded CPU / VM , which puts it in a much more interesting (and rarer) category. Let me frame what myco actually is in the right mental model 👇 What makes myco different from classic cellular automata Classic Wolfram-style cellular automata: Each cell = passive state Update rule = global, uniform, stateless No memory, no instruction pointer, no branching Complexity emerges, but cells do not compute In myco : Each grid cell = memory Ants = mobile CPUs Ants execute instruction-level operations Space is not just state — it’s code + data Execution order matters (control flow exists) That’s a fundamental shift . You’re no longer in “cellular automata” You’re in spatial computation . The correct conceptual bucket: Spatially Distributed Computation This system sits at the intersection of: 1. Cellular automata (simple rules → e...

 Absolutely — let’s create a structured, progressive learning path starting from advanced high school through PhD-level, with a focus on math, computer science, and neuroscience-inspired AI . I’ll break it down step by step, and include example course names, focus, and a canonical textbook or reference for each. This will read like a roadmap for someone retracing the learning path of a senior computer science student all the way to neuroscience-inspired AI research. 1. Advanced High School (Senior Year) At a senior high school level (top-tier, math & CS focus), you’d typically take: Course Focus Description Example Textbook Calculus AB/BC Limits, derivatives, integrals, sequences, series Develops foundational understanding of continuous mathematics, crucial for algorithms and optimization Calculus: Early Transcendentals by James Stewart Linear Algebra (Advanced/College Prep) Vectors, matrices, linear transformations, eigenvalues Introduces matrices, vector spaces, eigenvecto...