Soft Computers
The AI Revolution in Development
In 2024, we made a decision: embrace AI coding assistants across our entire development team. A year in, the results are clear to us:
- Faster delivery, especially on routine and boilerplate-heavy work
- Fewer bugs reaching production, because human review now focuses on logic
- Faster onboarding, since new developers can question the codebase in plain language
But getting here required establishing clear guidelines. Here's our framework.
The AI Assistants We Use
GitHub Copilot
Our primary coding companion:
- Inline code suggestions
- Chat for explanations
- CLI for terminal commands
- Best for: Day-to-day coding
Claude (Anthropic)
Our architecture partner:
- Complex system design
- Code review and refactoring
- Documentation generation
- Best for: Senior developer tasks
ChatGPT (OpenAI)
Our research assistant:
- Technology comparisons
- Learning new frameworks
- Debugging complex issues
- Best for: Exploration and learning
Our AI Coding Framework
Rule 1: AI Writes, Humans Review
Every line of AI-generated code is reviewed by a human developer. No exceptions.
Why? AI assistants:
- Hallucinate non-existent APIs
- Generate deprecated patterns
- Miss security vulnerabilities
- Don't understand business context
Rule 2: Security-First Prompting
We never share in prompts:
- API keys, credentials, secrets
- Customer data (even samples)
- Internal security architecture
- Proprietary algorithms
If you need help with sensitive code, use sanitized examples.
Rule 3: AI for Acceleration, Not Replacement
We use AI to accelerate skilled developers, not replace junior ones.
Good AI prompts:
"Write a TypeScript function that validates email
addresses using RFC 5322 regex, handles edge cases,
and returns a typed result object."Bad AI prompts:
"Write my whole authentication system"Rule 4: Test AI Output More Rigorously
AI-generated code requires additional testing:
- Unit tests for edge cases
- Security scanning (Snyk, CodeQL)
- Performance benchmarks
- Integration testing
Productivity Gains by Task
Here's where AI helps most:
| Task | Time Saved | Quality Impact |
|---|---|---|
| Boilerplate code | Major | Neutral |
| Unit tests | Major | Positive |
| Documentation | Major | Positive |
| Bug fixes | Moderate | Positive |
| Architecture | Minor | Mixed |
| UI components | Moderate | Neutral |
Security Considerations
Code Leakage Risk
Anything you share with AI may be stored:
- GitHub Copilot: Can opt out of training
- Claude/ChatGPT: Check data retention policies
- Solution: Self-hosted models for sensitive projects
Dependency Vulnerabilities
AI often suggests outdated or vulnerable packages:
- Always check package versions
- Run security audits after AI sessions
- Maintain an approved dependency list
Injection Attacks
AI may generate code vulnerable to:
- SQL injection
- XSS
- Command injection
- Solution: Mandatory security scanning in CI/CD
The Future of AI in Development
By 2027, we predict:
- AI will handle much of the routine coding
- Developers become "code architects"
- Security scanning will be AI-native
- Testing will be largely automated
Getting Started
Want to implement AI-assisted development at your organization? We offer:
- AI adoption consulting
- Team training programs
- Security framework implementation
- Custom toolchain setup
Talk to us about how AI can accelerate your development.