(iterate think thoughts): Putting Code Under a Microscope: Wavelet-Based Context for LLMs

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June 2, 2026

Putting Code Under a Microscope: Wavelet-Based Context for LLMs

Every developer who has tried an AI coding tool is familiar with the problem of watching the model fumble with the codebase to find relevant sections to edit. Since it's not possible to load an entire codebase into the context for large projects, it greps through a few files to give it some context, and guesses what to do next. But code has a hierarchical structure with layers and boundaries. Functions sit inside classes. Classes live in files. Files make up modules. One 400-line file can contain six different conceptual areas, each with its own distinct purpose.<br>When a human developer reads the code, we leverage the structure when we try to understand it. We examine the way files and classes are organized, and try to find relevant logic based on that. Wouldn't it be nice if the agent could, like you, zoom in and out of the code so that it could look at the big picture, then jump directly to the exact function it needs without having to open the entire file.<br>That’s what WaveScope does. It’s an MCP server that uses wavelet transforms to give LLMs a multi-resolution view of the codebase. Imagine something like progressive image loading, but for source files. Even though newer models can handle large contexts on paper, what happens in practice is that they start losing focus. The more context the agent has the harder it is to figure out what it actually needs to work on, and what to prioritize leading to context rot.<br>Currently, there are two main ways of dealing with the problem. Grep-based search finds exact matches but ignores structure since pattern matching on individual lines can’t tell you where a class boundary is or where an error handling region starts. Embedding-based RAG is another approach which understands semantic meaning but loses position and structure. Neither gives the model a real sense of the architecture of the code.<br>Code Has a Rhythm<br>Open a file in any language like Clojure, TypeScript, Rust, or Go and you’ll see repeating common structures throughout the code. Imports sit at the top. Class and function definitions pop up at regular intervals. Indentation also has its own distinct pattern in every language. Comment blocks and blank lines are pauses in between. What if there was some way to extract these patterns, and create a structure similar to an AST without actually having to know the syntax of the language.<br>Luckily for us, wavelets were made for processing exactly this sort of signal by decomposing it into multiple scales at once. The transform gives you all the fine details along with the large-scale patterns at the same time. This family of algorithms is very versatile and has been used in many different fields. Seismologists use them to spot earthquakes, doctors sharpen MRI scans with them, and audio engineers separate basslines from vocals. Code structure, it turns out, is just another kind of signal that can be decomposed in a similar fashion.<br>So how does that actually work? Once each line has a score, the file can be treated as a 1D signal representing a sequence of numbers that rises and falls with the density of the structure. The Ricker wavelet is a little template shaped like a bump with a dip on each side. You slide it across the signal one position at a time and, at every position, measure how well the signal underneath matches that shape. A strong match means there's an elevated region sitting between two quieter regions which suggests a structural boundary. The output is a coefficient at every line scored on how much it looks like a boundary.<br>The trick for encoding different resolutions lies in the width of the template. You can slide the same shape stretched to many widths, each representing a different scale. A narrow wavelet only matches when the elevated region is a line or two wide, so it fires on small, sharp features. A wide one ignores line-level noise and responds to larger regions elevated relative to their surroundings, firing for big structures. So, the next trick is to run multiple widths at once to see boundaries which light up across a band of widths rather than just one. Features consistent across different scales tend to be genuine structural edges that we care about.<br>Since code structure nests at different sizes, each size shows up at the width that fits it. We can see a concrete example of how this works by running WaveScope on its own src/context.ts. A single line such as a lone import statement, or export class FileContext { declaration stands out over its quieter neighbors making it a sharp one-line spike that the fine, narrow wavelets lock onto. Its coefficient peaks at scale 1 or 2 and fades away at wider scales. At the other extreme is the long keyword-dense cascade inside the inferLabel method, which has a wide run of consecutive if...