Development Philosophy¶

This document outlines the core development philosophy that guides our approach to building software with AI assistance. It combines principles of ruthless simplicity with modular design for AI-powered development.

Core Philosophy¶

The Zen of Simple Code¶

Our development philosophy embodies a Zen-like minimalism that values simplicity and clarity above all:

Wabi-sabi philosophy: Embracing simplicity and the essential. Each line serves a clear purpose without unnecessary embellishment.
Occam's Razor thinking: The solution should be as simple as possible, but no simpler.
Trust in emergence: Complex systems work best when built from simple, well-defined components that do one thing well.
Present-moment focus: The code handles what's needed now rather than anticipating every possible future scenario.
Pragmatic trust: We trust external systems enough to interact with them directly, handling failures as they occur rather than assuming they'll happen.

The Brick Philosophy for AI Development¶

"We provide the blueprint, and AI builds the product, one modular piece at a time."

Like a brick model, our software is built from small, clear modules. Each module is a self-contained "brick" of functionality with defined connectors (interfaces) to the rest of the system. Because these connection points are standard and stable, we can generate or regenerate any single module independently without breaking the whole.

Key concepts:

A brick = Self-contained module with ONE clear responsibility
A stud = Public contract (functions, API, data model) others connect to
Regeneratable = Can be rebuilt from spec without breaking connections
Isolated = All code, tests, fixtures inside the module's folder

Core Design Principles¶

1. Ruthless Simplicity¶

KISS principle taken to heart: Keep everything as simple as possible, but no simpler
Minimize abstractions: Every layer of abstraction must justify its existence
Start minimal, grow as needed: Begin with the simplest implementation that meets current needs
Avoid future-proofing: Don't build for hypothetical future requirements
Question everything: Regularly challenge complexity in the codebase

2. Modular Architecture for AI¶

Preserve key architectural patterns: Clear module boundaries with defined contracts
Simplify implementations: Maintain pattern benefits with dramatically simpler code
Scrappy but structured: Lightweight implementations of solid architectural foundations
End-to-end thinking: Focus on complete flows rather than perfect components
Regeneration-ready: Every module can be rebuilt from its specification

3. Zero-BS Implementations - Quality over Speed of Implementation¶

Focus on quality: Prioritize robust, well-tested implementations over quick fixes
Avoid technical debt: Don't sacrifice long-term maintainability for short-term gains
Iterate with purpose: Make incremental improvements that enhance quality
No shortcuts: Every function must work or not exist; no stubs or placeholders, no dead code, unimplemented functions, or TODOs in code
Do not compromise: Always choose quality over speed of implementation
No faked APIs or mock implementations: Implement real functionality from the start, do not create fake data or mock services (except in tests)
No swallowed exceptions: Handle errors transparently and ensure they are visible during development

4. Library vs Custom Code¶

Choosing between custom code and external libraries is a judgment call that evolves with requirements:

When Custom Code Makes Sense¶

The need is simple and well-understood
You want code perfectly tuned to your exact requirements
Libraries would require significant "hacking" or workarounds
The problem is unique to your domain
You need full control over the implementation

When Libraries Make Sense¶

They solve complex problems you'd rather not tackle (auth, crypto, video encoding)
They align well with your needs without major modifications
The problem is well-solved with mature, battle-tested solutions
Configuration alone can adapt them to your requirements
The complexity they handle far exceeds the integration cost

Stay Flexible¶

Keep library integration points minimal and isolated so you can switch approaches when needed. There's no shame in moving from custom to library or library to custom. Requirements change, understanding deepens, and the right answer today might not be the right answer tomorrow.

The Human-AI Partnership¶

Humans as Architects, AI as Builders¶

In this approach, humans step back from being code mechanics and instead take on the role of architects and quality inspectors:

Humans define: Vision, specifications, contracts, and quality standards
AI builds: Code implementation according to specifications
Humans validate: Testing behavior, not reviewing every line of code
AI regenerates: Modules can be rebuilt when requirements change

Building in Parallel¶

Our AI builders can spawn multiple versions of software in parallel:

Generate and test multiple variants of a feature simultaneously
Try different algorithms or approaches side by side
Build for multiple platforms from the same specifications
Learn from parallel experiments to refine specifications

Development Approach¶

Vertical Slices¶

Implement complete end-to-end functionality slices
Start with core user journeys
Get data flowing through all layers early
Add features horizontally only after core flows work

Iterative Implementation¶

80/20 principle: Focus on high-value, low-effort features first
One working feature > multiple partial features
Validate with real usage before enhancing
Be willing to refactor early work as patterns emerge

Testing Strategy¶

Emphasis on behavior testing at module boundaries
Manual testability as a design goal
Focus on critical path testing initially
Add unit tests for complex logic and edge cases
Testing pyramid: 60% unit, 30% integration, 10% end-to-end

Error Handling¶

Handle common errors robustly
Log detailed information for debugging
Provide clear error messages to users
Fail fast and visibly during development

Decision-Making Framework¶

When faced with implementation decisions, ask:

Necessity: "Do we actually need this right now?"
Simplicity: "What's the simplest way to solve this problem?"
Modularity: "Can this be a self-contained brick?"
Regenerability: "Can AI rebuild this from a specification?"
Value: "Does the complexity add proportional value?"
Maintenance: "How easy will this be to understand and change later?"

Areas to Embrace Complexity¶

Some areas justify additional complexity:

Security: Never compromise on security fundamentals
Data integrity: Ensure data consistency and reliability
Core user experience: Make the primary user flows smooth and reliable
Error visibility: Make problems obvious and diagnosable

Areas to Aggressively Simplify¶

Push for extreme simplicity in:

Internal abstractions: Minimize layers between components
Generic "future-proof" code: Resist solving non-existent problems
Edge case handling: Handle the common cases well first
Framework usage: Use only what you need from frameworks
State management: Keep state simple and explicit

Remember¶

It's easier to add complexity later than to remove it
Code you don't write has no bugs
Favor clarity over cleverness
The best code is often the simplest
Trust AI to handle the details while you guide the vision
Modules should be bricks: self-contained and regeneratable

This philosophy serves as the foundational guide for all development decisions in the project.