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Forest Station · Learning Trail 4 Active Waypoints

Topics being studied deeply enough to turn into systems, notes, and experiments.

Trailhead

The roadmap tracks topics worth studying deeply enough to turn into usable systems, notes, or experiments.

Current learning route across reinforcement learning, performance engineering, graph systems, and agents.

  1. Waypoint 01
    In Progress

    Applied Reinforcement Learning

    Reading

    Reinforcement Learning (Sutton & Barto), Deep Learning (Goodfellow et al.)

    Building With

    MarkdownPythonObsidianPyTorchNumPy

    Exploring

    RL algorithms • Policy gradients

  2. Waypoint 02
    In Progress

    High-Performance Systems

    Building With

    CUDAPolarsTritonParallelizationTensorRT

    Exploring

    Distributed systems • GPU acceleration

    Operating Principles

    • Prototype fast in Python, then optimize the critical path deliberately.
    • Use hardware acceleration where latency matters enough to justify complexity.
    • Keep operational visibility alongside performance work.
  3. Waypoint 03
    In Progress

    Graph Intelligence

    Reading

    Graph Representation Learning (Hamilton)

    Building With

    PyTorch GeometricNeo4jNetworkXC++

    Exploring

    Knowledge graphs • GNNs

  4. Waypoint 04
    In Progress

    Autonomous Agents

    Reading

    Generative Agents (Park et al.)

    Building With

    CodexClaudeTanStack AI

    Exploring

    Multi-agent systems • Emergent behavior