Writing about developer products.

I have spent two years watching agents fail in production. Here is what I keep seeing and what the field is starting to do about it.

Most AI agent taxonomies are either too academic or too vague to be useful. Here is the classification I use when I need to decide what kind of agent to build.

Agent memory spans working state, episodic history, retrieval, and consistency controls. The right architecture depends on the agent's task and risk.

Context windows are not memory. Here is what every production AI agent engineer needs to understand about token budgets, overflow handling, and how short-term and long-term memory actually work together.

Understand the fundamental differences between RAG and memory systems for LLM applications, when to use each, and how to combine them in production.

Voice AI agents live or die by how they manage memory across a real-time streaming pipeline. Text chatbots solve memory with RAG. Voice agents need something different.

A deep dive into how HyperAgents retain context across interactions, layer memory architectures, and handle session continuity in production.

A deep dive into the memory architecture of DeerFlow: layered context passing, session state files, sub-agent isolation, and how it compares to Letta, AutoGen, and CrewAI.

A practical guide to understanding how Claude Code retains context across sessions, uses project files, and manages long-term memory for coding tasks.

Context is a per-request reasoning budget. Memory is persistent state retrieved into a later request. Long input capacity does not make them interchangeable.

A senior engineer's breakdown of what memory management for LLMs actually looks like in production: eviction strategies, KV cache management, importance-weighted retention, and why your agent keeps forgetting things.

Most engineering documentation fails for the same reasons. Here is what actually moves the needle.

Real numbers, real pricing, and concrete strategies for keeping your LLM spend predictable.

A systems-level guide to tracing latency across endpointing, transcription, model inference, speech synthesis, and transport.

A practical guide to RAGAs, recall, precision, and the metrics that separate production RAG systems from prototypes.

Hard-won lessons from running AI agents in production: the error patterns that actually break systems, and the patterns that fix them.

A research-led comparison of KV-cache eviction strategies, including their accuracy risks and implementation trade-offs.

A comparison of PGlite and SQLite-vec for browser-based vector search, including indexing, memory, and deployment trade-offs.

A workflow comparison of Claude Code and Gemini CLI for repository refactoring, verification, and human review.

I analyze the structural correlation between documentation quality and engineering throughput, using DORA metrics and Accelerate research to quantify the ROI of technical writing.