Articles
Notes, tests, and arguments.
A tutorial should do more than run, and an explanation should do more than agree with the first page of search results. These are my arguments about developer content, documentation, and the AI systems I build around them.
Llm Context Windows Explained: Why More Is Not Always Better
Context windows are expanding to millions of tokens. Here is why the middle of your context still gets ignored, what long context actually costs, and how to build production systems that use these massive windows effectively.
Mixture of Experts: How Moe Models Are Cheap to Run but Expensive to Host
DeepSeek V3 has 671B parameters but only activates 37B per token. Here's how mixture of experts works, why it cuts inference costs, and the catch nobody puts in the headline.
The Best LLMs for Coding in 2026: An Engineering Review
Not all models are created equal for software development. Here is a benchmark-backed guide to choosing the right LLM for autonomous agents, algorithmic logic, and repository-scale refactoring as of March 2026.
The Model Context Protocol (MCP) Explained: A Universal Language for AI Tools
Model Context Protocol (MCP) is the new standard for connecting AI models to data sources and tools. Here is why it matters, how it works, and why it is the missing link for agentic infrastructure.
Agent Harnesses: the Infrastructure Layer Your Llm Agent Actually Needs
Every production AI agent needs a harness. Here is what one contains, why frameworks often are not enough, and how to build the layer that actually determines reliability.
Time to First Token (TTFT): The Metric That Determines AI Snappiness
Users do not care about total throughput. They care about how fast the first word appears. Here is the engineering guide to measuring and optimizing Time to First Token (TTFT) in production.
Speculative Decoding: How to Speed up Llm Inference for Free
LLM inference is memory-bound, not compute-bound. Speculative decoding uses this fact to speed up generation by 2-3x using a smaller draft model to predict tokens for a larger one.