📚 Learning, 👨🏻💻 Building, 📝 Documenting...
Hi! I'm Duncan Leung and I currently lead Frontend Engineering at Airvet.
In a previous career I worked overseas in Beijing as a Product Manager at a skunkworks studio building digital experience projects for Ford Motor (China) and consulted on UX projects for Starwood Hotels. During this time I also mentored startups at Chinaccelerator and ran startup events and hackathons in Shanghai.
👨🏻💻 I'm a self taught software developer, and this is a place for me to jot down some notes. I mostly cover Javascript, Typescript, React, and software development.
Claude Code's /compact command compresses your conversation to free context, but it silently drops external state mutations, abandoned approaches, and behavioral constraints. I built a precompact/postcompact checkpoint system that writes ground truth to disk before compaction and cross-checks it afterward — including live reconciliation against git, CI, and external systems. Here's the design, the research behind it, and the failure modes it prevents.
Every Claude Code session produces decisions, patterns, and context that vanish when the terminal closes. I built a daemon that watches for session endings, extracts the knowledge, distills it with Haiku, and compiles it into an Obsidian wiki with Sonnet — turning every coding session into searchable institutional memory for about $20/month.
Claude Code has a dozen ways to research a question — from a single MCP call to a 98-agent adversarial pipeline. This guide maps every research tool by cost, agent count, and use case so you pick the right one the first time. Includes the full source of a custom deep-research-plus workflow that adds a Perplexity search lane to Claude Code's built-in deep-research pipeline.
LLM context rot isn't a bug. It's a mathematical property of how the attention mechanism works. Three interacting failure modes (softmax's sum-to-one constraint, RoPE's distance decay, and causal masking's primacy bias) combine to produce the U-shaped attention curve where models attend well to the beginning and end of context but lose the middle. Understanding the mechanism changes how you structure prompts and manage context.
LLM context windows have a marketed size and a useful size, and they're not the same number. Research shows quality degrades gradually based on absolute token count, not fill percentage, and a 1M-token model at 40% full is far more degraded than a 200K model at 40%. Here's the research behind the Smart Zone, the Warn Zone, and the Dumb Zone, plus a Claude Code statusline that tracks which zone you're in and how deep.
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