When running local AI workloads and large language models (LLMs), raw compute (FLOPS) is often secondary to unified memory bandwidth and capacity. Without sufficient VRAM or unified memory, running 70B+ parameter models at full precision becomes practically impossible without severe quantization, complex multi-GPU setups, or cloud dependencies. I recently read an excellent analysis by Kunal […]
Every time an AI coding agent starts a new session, it performs costly file searches and full-file reads («grep-and-read archaeology») to understand your codebase architecture. This process wastes thousands of context tokens on structure discovery alone. The Solution: Local Codebase Indexing & Retrieval Instead of forcing the model to re-read files from scratch, pre-index your […]
When AI agents run terminal commands (npm test, git log, docker build, or pytest), the console outputs hundreds of lines of noise, progress bars, and redundant stack traces directly into the model’s context window. This noise quickly consumes thousands of input tokens and triggers context rot. What is RTK? RTK (Rust Token Killer) is an […]
Every developer working with AI coding agents encounters the same issue: as conversation history grows, reasoning quality degrades and answers become unreliable. This decline is known as Context Rot. Context engineering isn’t about fitting more into the context window—it’s about ruthlessly controlling what stays in it. What is Context Engineering? Context Engineering is the practice […]
AI agents in the terminal are powerful, but they aren’t bulletproof. Here is how I recovered lost changes using VS Code’s built-in tools. I have been using Claude Code directly in my terminal for several months as part of my daily Full Stack development workflow. It has significantly boosted my speed when refactoring code, writing […]