Engineering Principles, Core Practices and Culture

For any technology pre-launch startup, a key initial goal is to ship a reliable MVP fast, learn quickly, and preserve future flexibility. There are fundamental software engineering principles and practices that should be followed in order to achieve this goal:

Engineering Principles (The “Why”)

1. Build for Learning First

Principle: Early engineering exists to validate assumptions, not to be perfect.

• Optimise for fast feedback over completeness

• Prefer experiments over big builds

• Treat assumptions as hypotheses

2. Simplicity Over Cleverness

Principle: Simple systems are easier to change.

• Avoid over-engineering

• Use proven tools and frameworks

• Prefer explicit, readable code

3. Bias Toward Shipping

Principle: Working software beats perfect plans.

• Small, frequent releases

• Feature flags over long branches

• MVP > v1.0 fantasies

4. Ownership & Accountability

Principle: Every system has a clear owner.

• One person accountable per service/module

• Ownership includes reliability and documentation

5. Design for Change, Not Scale

Principle: Change is more certain than growth.

• Modular architecture

• Clear interfaces

• Delay optimisation until data demands it

6. Quality is Built In, Not Added Later

Principle: Bugs cost more the later they’re found.

• Automated tests over manual testing

• Code reviews as default

• Guardrails instead of heroics

7. Security and Privacy by Default

Principle: Trust is a core feature.

• Least-privilege access

• Secure defaults

• Compliance awareness even pre-launch

Core Engineering Practices (The “How”)

A. Architecture & Technology Choices

Practices

• Monolith first (modular internally)

• Clear separation of concerns

• Cloud-native but not cloud-locked

• Infrastructure as code (lightweight)

Guidelines

• Avoid micro-services pre-launch

• Choose boring, well-documented tech

• Prefer managed services over custom builds

B. Development Workflow

Practices

• Trunk-based development

• Short-lived feature branches

• Daily commits to main

• Feature flags for incomplete work

Definition of Done

• Code reviewed

• Tests passing

• Deployed to staging (or prod)

• Observability in place (logs/errors)

C. Code Quality & Reviews

Practices

• Mandatory peer review

• Small pull requests (<300 lines)

• Focus on clarity, not style nitpicks

• Shared coding standards

Review Checklist

• Does this solve the user problem?

• Is it understandable in 3 months?

• Does it introduce hidden complexity?

• Are failure cases handled?

D. Testing Strategy

Pre-Launch Testing Pyramid

1. Unit tests (core logic)

2. Integration tests (critical paths)

3. Minimal end-to-end tests

Practices

• Test business logic first

• Avoid over-mocking

• Automate regression tests

• QA focuses on exploratory testing

E. Deployment & Release

Practices

• Continuous deployment where possible

• One-click rollback

• Blue-green or canary releases (if needed)

• No manual production changes

Release Philosophy

• Release small, often

• Measure impact of every release

• Roll back fast if needed

F. Observability & Reliability

Practices

• Centralised logging

• Error tracking from day one

• Basic performance metrics

• Simple uptime monitoring

Principle

If you can’t see it, you can’t fix it.

G. Documentation & Knowledge Sharing

Practices

• README for every repo

• Architecture decision records (ADRs)

• Setup instructions always up-to-date

• Document “why”, not just “what”

Rule

• If it isn’t documented, it doesn’t exist.

H. Security Practices

Practices

• Secure authentication & authorisation

• Secrets management (no hard-coded keys)

• Dependency vulnerability scanning

• Regular access reviews

Pre-Launch Minimum Bar

• HTTPS everywhere

• Data encryption at rest

• Privacy policy alignment

Engineering Culture & Team Norms

Collaboration

• Async-first communication

• Written decisions over meetings

• Clear escalation paths

Feedback

• Blameless postmortems

• Continuous improvement mindset

• Psychological safety

Time Management

• Protect maker time

• Minimise meetings

• Engineering leads set priorities, not chaos

What NOT to Do Pre-Launch

• Premature scaling
• Over-custom infrastructure
• “Rewrite it later” mentality
• Hero-based development
• Ignoring tech debt completely

Engineering at Pre-Launch is about:

• Speed with discipline

• Learning over perfection

• Simple systems with strong foundations

• Ownership, quality, and security from day one


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