FinWiz AI Minimalism

Core Principle: AI agents are tools, not the alpha and the omega. Use Python for deterministic, rule-based tasks.

Decision Framework

Use AI Agents ONLY For

✅ Analysis requiring reasoning - Interpreting complex financial data, identifying patterns ✅ Synthesis of complex information - Combining multiple data sources with judgment ✅ Generating insights from unstructured data - Analyzing news, sentiment, qualitative factors ✅ Natural language understanding - Parsing complex text, extracting meaning ✅ Creative content generation - Writing analysis narratives, explanations

Use Python (NOT AI) For

❌ HTML generation - Use Jinja2 templates ❌ Data consolidation - Use Python functions ❌ File I/O operations - Use standard Python file operations ❌ Calculations and formulas - Use Python math/numpy ❌ Data validation - Use Pydantic models ❌ Template rendering - Use Jinja2 ❌ Data transformation - Use pandas/Python ❌ Deterministic logic - Use if/else, loops, functions

Cost-Benefit Analysis

Example: Generating 100 HTML reports

Savings: $5-10 per 100 reports, 500x faster, 100% reliable

Evaluation Checklist

Before creating an AI task, ask:

• Is this task deterministic? (same input = same output)
• Can this be expressed as a template?
• Is this just data transformation?
• Is this a calculation or validation?
• Can a junior developer implement this in Python?

If you answered YES to any question, use Python, not AI.

Implementation Examples

❌ WRONG: Using AI for HTML Generation

@task
def generate_html_report(self) -> Task:
    return Task(
        description="Generate HTML report from JSON data",
        agent=self.reporter(),  # AI agent
        # WRONG: Wasting LLM calls on template rendering
    )

✅ CORRECT: Using Python Template

def generate_html_report(json_data: dict) -> str:
    """Generate HTML report using Jinja2 template."""
    template = jinja_env.get_template('report.html')
    return template.render(data=json_data)
    # CORRECT: Fast, cheap, testable

❌ WRONG: Using AI for Data Consolidation

@task
def consolidate_reports(self) -> Task:
    return Task(
        description="Read all crew reports and consolidate them",
        agent=self.aggregator(),  # AI agent
        # WRONG: Wasting LLM calls on file reading
    )

✅ CORRECT: Using Python Function

def consolidate_reports(file_paths: list[str]) -> ConsolidatedReport:
    """Consolidate crew reports using Python."""
    reports = []
    for path in file_paths:
        with open(path) as f:
            report = CrewReport.model_validate_json(f.read())
            reports.append(report)
    return ConsolidatedReport(reports=reports)
    # CORRECT: Fast, cheap, testable

Benefits of Python Over AI

1. Cost: Free vs. LLM API costs
2. Speed: Milliseconds vs. seconds
3. Reliability: Deterministic vs. probabilistic
4. Testability: Unit tests vs. prompt testing
5. Maintainability: Code review vs. prompt engineering
6. Debugging: Stack traces vs. LLM output inspection

Implementation Guidelines

HTML Templates (Jinja2)

• Create professional templates with light/dark mode
• Accept JSON data as input
• Include CSS for responsive design
• Make templates maintainable by developers

Data Processing (Python)

• Use Pydantic for validation
• Use pandas for data transformation
• Write unit tests for all functions
• Keep functions pure (no side effects)

File Operations (Python)

• Use pathlib for path handling
• Use context managers for file I/O
• Validate data with Pydantic after reading
• Handle errors gracefully

Remember

Be strong, not lazy. Save the project, not your energy.

Evaluate every AI task critically. If Python can do it, use Python.

When in doubt, choose Python for:

• Template rendering
• Data transformation
• File operations
• Calculations
• Validation
• Deterministic logic

Reserve AI for:

• Complex reasoning
• Natural language tasks
• Creative content
• Unstructured data analysis