Standard AI coding assistants feed entire files into large context windows and hit hallucination walls. By feeding exact Tree-sitter AST syntax scopes into frontier reasoning models, Kernax achieved 69.3% first-attempt diff accuracy across 16 programming languages on the standardized Aider Polyglot benchmark suite.
The Diff Application Paradox
In autonomous programming, generating intelligent code is only half the battle. Deterministically applying code edits without corrupting imports, indentation, or surrounding method signatures is where traditional LLM wrappers collapse.
Kernax bypasses line-based fuzzy matching entirely. By constructing in-memory Tree-sitter AST graphs on developer machines, our editing engine isolates the target syntax node and performs mathematical tree splicing. The result: zero hallucinations and sub-second diff application.