The Day I Tried to Build My Own “Cursor”

A few months ago, I thought: “If Cursor is basically RAG + LLM + code, I can build something similar for my workflow.” I opened VS Code, connected the Claude API, and sent the entire repository as context.

The result was a slow-motion disaster. The model received 50,000 tokens of unstructured code, hallucinated about functions that didn’t exist, suggested edits on wrong lines, and delivered diffs that broke the project in three different places.

In that moment I understood what I’ve been exploring on this blog in other contexts: the magic isn’t in the model — it’s in the engineering around it. Cursor isn’t “just a GPT wrapper.” It’s one of the most sophisticated pieces of software engineering in the 2026 AI ecosystem. And understanding how it works taught me more about AI architecture than any academic paper.

What Cursor Really Is (And Isn’t)

Cursor is a complete IDE — a VS Code fork maintained by Anysphere. It’s not a plugin. It’s not a chatbot with file access. It’s a development environment where AI is the primary interface, and the text editor is secondary.

Cursor 3, launched April 2, 2026, took this to the extreme: the Agents Window became the primary interface. The /multitask command distributes tasks across up to 8 simultaneous sub-agents, each working on an isolated branch via Git worktrees. Refactoring 4 modules at once? Possible.

For comparison, Claude Code operates in the terminal (not an IDE) but fits 25,000-30,000 lines of code in a single prompt thanks to Opus 4.6’s 1M token window. No chunking, no retrieval, no manually selecting files. Complementary approaches: Cursor for daily editing, Claude Code for long autonomous work.

But what interests me — and the reason for this post — is the internal architecture that makes it work. Because it’s replicable. And the principles are universal.

Pillar 1: Intelligent Indexing with AST Parsers

In traditional RAG systems, documents are chunked by token count or paragraphs. If you do this with source code, you’ll cut a function in half, destroying the logic the AI needs to understand.

The solution is chunking using AST (Abstract Syntax Tree) with tools like Tree-sitter — the same parser that Zed (the IDE built by the creators of Atom and Tree-sitter) uses natively.

How it works: Tree-sitter maps the code’s grammatical structure, letting the system split files exactly at the real boundaries of functions, methods, and classes. Each block saved in the vector database maintains a perfect logical unit.

BuildMVPFast described Cursor’s implementation as “surprisingly sophisticated for how invisible it is” — chunking happens in the background, without developer intervention, and is significantly smarter than token-based chunking.

This connects directly to what I wrote in the Chunking post: FloTorch showed recursive 512-token chunking beat semantic for general text. But for code, AST chunking is categorically superior — because it respects the syntactic structure that defines meaning.

Pillar 2: Efficient Updates via File Hashes

Repositories change every second. Re-indexing the entire project with each change is unfeasible. The elegant solution: File Hashes — digital signatures of files.

The RAG pipeline monitors hashes continuously. When a file changes (different hash), only that file gets re-indexed. The rest of the repository stays intact in the vector database. This transforms indexing from O(n) — proportional to repo size — to O(delta) — proportional only to changes. In 100,000+ file repositories, the difference is between “wait 10 minutes” and “instant.”

Pillar 3: Dynamic Context Selection and Re-ranking

When the developer asks a question, the assistant must decide what goes in the context window — and order matters enormously.

Re-ranking. Chunks retrieved by the vector database pass through a re-ranking model (cross-encoder) that evaluates and scores the exact relevance of each snippet to the user’s query. This filters false positives that pure vector search doesn’t catch.

Hierarchical prioritization. The context window is filled following a strict hierarchy:

Maximum priority: currently open file — the developer’s immediate focus. Always included first.

High priority: recent edits — change history that contextualizes the current moment.

Normal priority: RAG chunks — pieces from other repository files that connect the dots. Selected by semantic relevance + re-ranking.

This hierarchy mirrors how a human developer thinks: “I’m looking at this file, I just changed that, and I need to know about that other part.” Cursor doesn’t read the entire repository — it reads what matters now.

Pillar 4: Full File Rewrites (The Most Important Secret)

This is the architectural insight that surprised me most — and explains why my homemade attempt failed so spectacularly.

The intuitive approach to applying changes is generating a diff (indicating which lines to remove and add). But diffs have a nearly 40% failure rate with LLMs, because models are notoriously bad at counting and tracking exact line numbers. “Insert at line 47” — but the model miscounts and inserts at 45, breaking the logic.

The ideal architecture uses Full File Rewrites. Instead of edit instructions, the AI generates the entire updated file. Line-counting error rate: 0%. The cost? Generating 2,000 lines when only 5 changed seems wasteful.

The speed solution: speculative edits. The system uses the original file as a Draft Token. The model processes the file in parallel, generating in real time only the lines that actually changed — maintaining the 0% counting error guarantee while drastically accelerating the process. The developer sees the edit appear almost instantly.

Pillar 5: Multi-Model Orchestration

Cursor doesn’t use one giant model for everything. It distributes work across specialized agents — the same harness engineering principle Stanford proved:

Frontier model (large): macro solution planning, complex logic, architectural decisions. Claude Opus or GPT-5.

Edit model (medium): applying changes with speed. Optimized for latency, not deep reasoning. Sonnet or equivalent.

Scoring model (small): ranking relevant code chunks. Lightweight fine-tuned model, sub-second latency.

Each model does what it does best. The large one thinks. The medium one executes. The small one filters. It’s orchestration — not omniscience.

How This Connects to Everything I’ve Written

These five pillars are the concrete application of principles I’ve explored in previous posts:

AST Chunking = semantic chunking for code (Chunking RAG post). File Hashes = pipeline efficiency (Context Window and costs post). Re-ranking and prioritization = context engineering (Beyond the Prompt post). Full rewrites = harness > model (Harness Engineering post). Multi-models = orchestration > single model (Claude Code 98% post).

Cursor is the production proof that these ideas work together. Not theory — 5M+ developers using it daily.

Conclusion: The Magic Has an Address

Building a high-level development assistant requires respecting programming’s peculiarities. By swapping blind text splitting for structural parsers, abandoning diffs for speculative rewrites, and distributing work across specialized models, you create a tool that’s robust, reliable, and extremely fast.

Cursor’s “magic” isn’t magic. It’s engineering. And now you know the address.

Share if this clarified the architecture:

I tried sending the entire repo to the API. Cursor does AST chunking, file hashing, re-ranking, speculative rewriting, and multi-model orchestration. The difference? 500,000 lines of engineering that no prompt can replace.

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