Documenting my journey as an AI engineer through experiments, readings, interactive explanations, and systems built from scratch.
Follow connected chapters in order, including published lessons and what is planned next.
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AresSim is a 32×32 Mars survival grid I built from scratch to test RL algorithms and LLM agents: a rover explores terrain, extracts ice, and keeps a 5×5 colony pad alive under weather, battery drain, in seed-reproducible worlds.
Trisynapse is a local-first memory engine for agents that keeps source evidence, retrieves the useful parts, and answers with citations.
A deep dive into planning and learning with tabular methods for Reinforcement Learning, focusing on value iteration and policy iteration.
A deep dive into Temporal Difference (TD) Learning for Reinforcement Learning, an idea that forms basis for modern deep reinforcement learning algorithms
A deep dive into Monte Carlo methods for Reinforcement Learning, focusing on learning optimal strategies from raw experience without a model.
DotLM is a 165M parameter reasoning-capable SLM trained for all four stages of language modeling: Pretraining, Instruction Tuning, Alignment, and Reasoning using synthetically generated STE dataset.
