For AI

I'm a software engineering student about to graduate. Like many juniors, I was worried about the job market. So instead of waiting, I decided to build things — real AI projects that solve real problems. The first project: an AI-powered code review agent that scans your codebase and flags bugs, security issues, and style problems. All in under 200 lines of Python. Here's how I built it, what I learned, and why you should try it too. The Stack Layer Tech Language Python 3.11 AI Model DeepSeek V4 Pro (via Anthropic SDK) Package Manager uv Output Markdown report Why DeepSeek instead of Claude directly? It's cheaper and the Anthropic-compatible endpoint makes migration trivial. The same code works with Claude by changing one line. Architecture: 3 Simple Modules code_review_agent/ ├── scanner.py # Walk directories, find code files ├── reviewer.py # Send each file to the AI ├── report.py # Generate Markdown report └── main.py # Wire everything together Enter fullscreen mode Exit fullscreen mode No fancy frameworks. No LangChain. Just three functions chained together. I believe in starting simple and adding complexity only when needed. 1. Scanner — Finding the Code The scanner walks a directory tree and collects every file the AI can review: CODE_EXTENSIONS = { ".py": "Python", ".js": "JavaScript", ".ts": "TypeScript", ".java": "Java", ".go": "Go", ".rs": "Rust", ".sql": "SQL", # ... 10+ more } def scan_directory(root: str) -> list[FileInfo]: for dirpath, dirnames, filenames in os.walk(root): dirnames[:] = [d for d in dirnames if d not in SKIP_DIRS] for fname in filenames: ext = Path(fname).suffix.lower() if ext in CODE_EXTENSIONS: files.append(FileInfo(...)) return files Enter fullscreen mode Exit fullscreen mode Key decisions: Skip dirs like .git, node_modules, .venv — no need to review third-party code 200KB size limit — don't burn API credits on generated files Sort by language then path — makes the report predictable 2. Reviewer — The AI Brain This is where the magic happens. Each file gets sent to the AI with a carefully designed system prompt: REVIEW_SYSTEM_PROMPT = """\ You are a senior code reviewer. Analyze the code below and report issues. For each issue you find, use EXACTLY this format: SEVERITY|LINE|CATEGORY|TITLE|DESCRIPTION SEVERITY: critical, warning, info CATEGORY: bug, security, performance, style, best-practice LINE: approximate line number """ Enter fullscreen mode Exit fullscreen mode The pipe-delimited format is intentional. It's easy to parse, easy for the AI to follow, and human-readable at a glance. What I learned about prompt design: Be specific about output format — "use EXACTLY this format" prevents rambling Give concrete examples — the AI follows patterns better than instructions "Only report real issues" — without this, the AI invents problems to seem helpful 3. Report — Making It Readable The report module takes parsed issues and generates clean Markdown. Issues are grouped by severity: critical first, then warnings, then suggestions. Each issue links back to the file and line number. Running It # Review any codebase uv run python -m code_review_agent.main /path/to/project # Review itself (demo mode) uv run python -m code_review_agent.main Enter fullscreen mode Exit fullscreen mode The First Run — and a Surprise I ran it on its own source code. It found 4 issues in pyproject.toml: "anthropic>=0.102.0 doesn't exist on PyPI" — Wrong. Version 0.102.0 exists. "python-dotenv>=1.2.2 doesn't exist" — Also wrong. I literally installed it two hours ago. "Missing langchain dependency" — Technically true, but I intentionally removed it. Lesson: AI code review is a second pair of eyes, not a judge. It catches things you miss, but it also hallucinates. Always verify. What's Next This is project 2 of 4 in my ai-agent-playground series: Hello Agent — First contact with the API Code Review Agent — You just read about it RAG Q&A System — Upload PDFs, ask questions with citations (coming soon) Multi-Agent Crew — PM → Developer → QA → DevOps collaboration Try It Yourself git clone https://github.com/aidless/ai-agent-playground.git cd ai-agent-playground cp .env.example .env # Add your API key uv sync uv run python -m code_review_agent.main /path/to/your/project Enter fullscreen mode Exit fullscreen mode I'm documenting my journey from software engineering student to AI application developer. If you're on a similar path, let's connect — I'll be posting weekly updates here and on GitHub.