Series introduction for the Pepper & Carrot AI-powered flipbook — a page-aware, spoiler-safe RAG app for a hand-drawn webcomic that runs locally on a laptop or on a five-piece free-tier cloud deploy. What it is, why it's a different problem from chat-with-PDF, and what each post in the series builds.

Applying Anthropic's Contextual Retrieval technique to personal photo libraries — where the notion of a "chunk" is a photo, not a paragraph.

An engineer's hands-on experiment connecting Claude to Xiaohongshu via MCP — and what it reveals about the future of AI-powered content creation.

A hands-on guide to training a math reasoning model with GRPO on Lambda Cloud using 2xH100 GPUs — improving Qwen2.5-Math-1.5B accuracy from ~6% to ~25% with practical implementation details.

A beginner's guide to reinforcement learning for language models — breaking down policy gradients, baselines, importance sampling, PPO, and GRPO in plain English with step-by-step examples.

Fine-tuning Qwen3-1.7B on the MATH dataset using supervised fine-tuning (SFT) on Lambda Labs, bridging the gap from local MacBook development to cloud GPU training.

A practical guide to developing ML training code on a MacBook with Apple Silicon, then seamlessly deploying to Google Colab or multi-GPU cloud instances like Lambda Labs.

The complete end-to-end journey — building a Transformer language model from scratch and training it on TinyStories, covering BPE tokenization, multi-head attention, training loop design, and text generation.

Assembling a complete training pipeline for Transformer LMs — from AdamW optimizer and learning rate scheduling to memory-efficient data loading, checkpointing, and decoding strategies.

Implementing Softmax, Log-Softmax, and Cross-Entropy from scratch in PyTorch, with key mathematical tricks for numerical stability and essential tensor operations explained.