# Linkedin Posts October 2026 Code Review Assistant

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In mid‑October 2026 I announced a capstone project from my MCP course: Eder Ignatowicz’s AI‑powered Code Review Assistant, an open‑source Python FastMCP server that reviews code diffs, flags bugs and security issues, measures complexity, suggests refactorings, and summarizes changes.

- **Purpose** – Review code diffs for bugs and security issues, analyze quality and complexity, generate refactoring suggestions, and produce concise change summaries.
- **Design decision** – Use **Pydantic** for LLM output validation to ensure responses match expected schemas.
- **Design decision** – Store review guidelines in **MCP resources** so checklists are reusable across workflows.
- **Design decision** – Employ **workflow patterns** (sequential, parallel, conditional) to orchestrate static analysis, LLM review, and optional refactoring steps.
- **Design decision** – Include a **human‑in‑the‑loop** UI where developers accept or reject each finding.
- **Design decision** – Adopt a **layered architecture** separating the FastMCP API layer from business logic.
- **DevOps practices** – Ruff linting, pre‑commit hooks, GitHub Actions CI, and Docker containerization enforce code quality and provide reproducible environments.

The open‑source repository is https://lnkd.in/drsDyJi2, and a free seven‑lesson starter guide is available at https://lnkd.in/dGbNfAHY.

- **Result** – CI passes all linting, type‑checking, and unit tests on each push.
- **Reason** – Improves testability and maintainability.
- **Reason** – Balances automation with developer judgment.
- **Reason** – Supports complex pipelines while keeping each step independent.
- **Reason** – Centralizes criteria and enables easy updates.
- **Reason** – Reduces downstream errors caused by malformed LLM outputs.
- **Tech stack** – Python, FastMCP, Pydantic, Ruff, pre‑commit hooks, GitHub Actions CI, Docker.

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