Archives

A deep technical dive into tool selection—retrieval strategies, context optimization, learned selection, and the engineering trade-offs that matter when scaling to hundreds of tools.

A comprehensive guide to Generative Engine Optimization—making your content retrievable, citable, and recommendable by large language models.

A survey of advertising formats in LLM products—separating what's deployed in production from what remains in research.

Exploring how to integrate ads in LLMs through character training—making recommendations genuinely helpful rather than annoyingly promotional.

After shipping multiple generative UI features, I've concluded that the sophistication of AI-generated interfaces often doesn't translate to user benefit—but steering does.

A practical engineering guide to RLHF implementation—covering PPO, GRPO, DPO, and tool-use training with code snippets and debugging tips.

Reading notes on RLHF covering PPO, DPO, and GRPO—understanding post-training as an engineering pipeline rather than a single algorithm.

Practical lessons from training autoencoders and VAEs for embedding compression—covering dimension choice, KL scheduling, contrastive signals, and evaluation metrics.

Exploring position embeddings, architecture choices, and training techniques for Transformer-based recommender systems.

Exploring structured UI representation using JSON Schema, and how to implement action handlers for generative UI systems.

Exploring how LLMs can dynamically generate user interfaces that adapt in real time to user needs—the vision behind generative UI.

An overview of LLM inference optimization techniques including KV cache, FlashAttention, and memory management strategies.