Why we built HyperAnalyzer

The bug surface shifted. The tools didn't.

AI code generation is commoditising "writing code". The pain has shifted from can you build it to can you trust what was just generated. Claude, GPT and Cursor routinely produce code that compiles, looks right and passes human review, but hides missing error handling, subtle concurrency bugs, crypto misuse, integer overflow, Windows-specific API misuse and resource leaks.

Every static analyzer on the market was designed for humans running it in CI: heavyweight, non-interactive, not wired into the LLM's edit loop. By the time Coverity or SonarQube flags the bug, the model has already moved on to the next task and nobody reads the PR comment.

Empirical proof the gap exists

Before starting this project we ran a top-tier commercial analyzer on a production Windows C++ codebase of around 5,000 lines. It surfaced 22 real bugs that compile cleanly, pass human review, and that Claude would not detect on its own without deep domain knowledge. Those 22 bugs are our regression test set. If HyperAnalyzer can't reproduce them, we don't ship.

Our wedge

• A curated rule set tuned against the specific failure modes of current-generation LLMs, not a 1,200-check everything-and-the-kitchen-sink dump.
• An MCP server wrapper that no incumbent has. Claude calls HyperAnalyzer as a tool, in the edit loop, before the diff is committed.
• LLM-optimised output: structured JSON plus a natural-language fix hint the model can act on without human translation.
• Fast iteration on the rule set because we don't carry 20 years of legacy. We add a new rule the same week we see a new failure mode in the wild.

Built on Clang

The parser is libclang: Apache-2.0 with LLVM exception, industry standard, real AST and real type info. No regex pretending to be analysis. For non-C++ languages we use tree-sitter (MIT). Every dependency is permissively licensed so you can run HyperAnalyzer inside closed-source products without legal friction.