Pika Review: A local first AI code auditor that works with Ollama/OpenAI for total privacy

Hello everyone,

I’ve spent the last few weeks building Pika Review, a CLI tool designed to shorten the feedback loop for code audits.

While cloud-integrated review bots are great, they trigger after code is pushed. I wanted to shift that intelligence to the local terminal, acting as a gatekeeper for staged and unstaged changes before they ever reach a remote repository.

Key Capabilities

Unlike traditional linters, Pika Review uses reasoning-heavy heuristics to identify structural "silent killers":

Performance Trace: Detects computational bottlenecks like $O(N^2)$ iterations and N+1 database queries that typically evade static analysis.
Security Heuristics: Scans for high-risk patterns including Path Traversal, Insecure Deserialization, and Remote Code Execution (RCE).
System Design Audits: Identifies DRY violations, fragile error handling, and memory bloat.
Privacy-First (BYOK): The tool is model-agnostic and OpenAI-API compatible. You can plug in a local instance (like Ollama) for 100% data privacy and zero compute cost.

How it Works

It generates structured Markdown reports in .pika-reports/, providing a syntax-highlighted, persistent audit trail directly in your project root.

Community Feedback Needed

I’m curious about two things from the community:

Workflow Friction: Do you find local AI auditing to be a meaningful part of your dev loop, or is the terminal already too noisy for this type of feedback?
Hallucination Mitigation: In an architectural context, how do you balance LLM reasoning with the risk of false positives?

I’d love some technical feedback on the implementation and the heuristic prompts I'm using to guide the analysis.

GitHub: https://github.com/HackX-IN/pika-review

NPM: https://www.npmjs.com/package/pika-review

Thank you for your time and any feedback you can provide!