Resources & Downloads
Guides, whitepapers, checklists, and templates — curated by our engineers and consultants to help you make better technology decisions.
The CTO's Guide to AI-Driven Software Development
A 40-page practical handbook covering AI tooling, team adoption, governance, quality gates, and real ROI measurement — written for engineering leaders.
What's Inside
- Chapter 1: Why AI in development is a methodology shift, not just tooling
- Chapter 2: Evaluating and selecting AI coding assistants
- Chapter 3: Building AI-aware code review and QA workflows
- Chapter 4: Measuring productivity, quality, and cycle-time gains
- Chapter 5: Governance, IP, and security considerations
- Appendix: Toolkit checklist & team rollout template
Building Secure Multi-Tenant SaaS on AWS
Architecture patterns, IAM policies, VPC design, and encryption strategies for multi-tenant B2B SaaS products.
React Performance Optimisation Playbook
Code splitting, lazy loading, memoisation, virtual lists, and Lighthouse audit walkthroughs for production React apps.
API Security Audit Checklist — 50 Points
A comprehensive review checklist covering authentication, rate limiting, input validation, error handling, and logging.
Sprint Planning & Estimation Template
A Notion-compatible sprint planning template with story point tracking, velocity charts, and retrospective sections.
Microservices Migration Roadmap for Monolith Applications
Strangler fig pattern, domain boundary identification, database decomposition, and rollback strategies.
From 0 to 50K Orders/Day — E-Commerce Scale Story
Technical deep-dive on architecture, caching strategy, search infrastructure, and delivery tracking for a D2C scale-up.
Flutter vs React Native — Decision Framework 2025
Side-by-side comparison across performance, DX, ecosystem, cost, and real project outcomes with a decision matrix.
Production Deployment Checklist — 60 Points
CI/CD gates, rollback procedures, health checks, monitoring alerts, and database migration safety — before every deploy.
AI/ML Integration Patterns for Non-Data-Science Teams
Pre-trained models, API-based AI services, prompt engineering basics, and responsible AI governance for engineering teams.
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