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About AI Do Now

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The Journey

AI Do Now started as a simple idea: document my learning journey building a complex multi-tenant SaaS platform from scratch. Too often, technical content shows the polished end result without the messy middle - the false starts, the refactors, the “oh no, that was the wrong approach” moments. I wanted to share the real journey, including the mistakes.

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What I’m Building

I’m working on a personal project: a multi-tenant SaaS platform built in Rust with event sourcing, DynamoDB, and AI-assisted development workflows. The platform explores:

• Event sourcing for audit and compliance
• Multi-tenancy with capsule-level isolation
• Macro-driven development for consistency
• Four-level testing for event-driven systems
• AI collaboration using multi-agent workflows

This site documents that journey - the architecture decisions, the patterns discovered, the mistakes made, and the lessons learned.

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My Approach

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1. Decision-Driven Documentation

Every significant choice gets an Architecture Decision Record (ADR):

• What was the context?
• What options did I consider?
• What did I decide and why?
• What were the consequences?

I share these decisions here, with the rationale and trade-offs clearly explained.

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2. Test Everything

I follow a four-level testing strategy:

• Level 1: Unit tests (domain logic)
• Level 2: Repository integration tests (DynamoDB with LocalStack)
• Level 3: Event flow tests (DynamoDB Streams → EventBridge → SQS)
• Level 4: End-to-end workflow tests

Every pattern I share has been tested at all relevant levels.

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3. Share the Failures

Most valuable lessons come from mistakes:

• Optimizing too early (wasted 2 weeks)
• Wrong DynamoDB schema design (3 rewrites)
• Over-engineering abstractions (deleted 1000+ lines)

I document these failures alongside the successes.

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4. AI as a Force Multiplier

I use AI agents to accelerate development:

• Evaluator Agent (Opus) for architecture planning
• Builder Agent (Sonnet) for implementation
• Verifier Agent (Sonnet) for quality gates
• Specialist Agents (CISO, Auditor) for reviews

But AI is a tool, not a replacement for thinking. I share how I collaborate with AI effectively.

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Content Philosophy

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What You’ll Find Here

✅ Real patterns from real projects - Not academic exercises ✅ Honest mistakes and pivots - Learning from what didn’t work ✅ Decision rationale - Why I chose A over B ✅ Code examples - Pseudocode and generic patterns (not proprietary code) ✅ Trade-off analysis - No silver bullets, only choices

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What You Won’t Find

❌ Clickbait titles (“This ONE TRICK…”) ❌ Affiliate marketing for tools ❌ Outdated content (I update or deprecate old articles) ❌ Proprietary code from employers ❌ Content that hasn’t been tested in practice

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Why “AI Do Now”?

The name combines:

• Code - What we write
• -matic - Systematic, automated, methodical

It reflects my belief that good software is built systematically, not haphazardly. Automation, testing, and clear decision-making processes matter.

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Background

I’m a software engineer who’s worked across:

• Multi-tenant SaaS platforms
• Event-driven architectures
• Cloud infrastructure (AWS)
• Rust and TypeScript ecosystems

I care deeply about:

• Architecture that scales technically and organizationally
• Testing that catches bugs before production
• Documentation that helps the next engineer (including future me)
• First principles thinking over cargo-culting patterns

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My Stack

For the project I’m documenting here: Backend:

• Rust (for type safety and performance)
• AWS DynamoDB (event store and data persistence)
• AWS EventBridge (event routing)
• AWS SQS (consumer queues)

Development:

• Architecture Decision Records (ADRs)
• Four-level testing (LocalStack for integration)
• AI-assisted workflows (Claude Code)
• Git worktrees for parallel work

Content:

• Mintlify (this site)
• YouTube Shorts (60-second tips)
• LinkedIn (professional discussions)

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Get in Touch

I love discussing architecture patterns, event sourcing, and building scalable systems.

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Disclaimer

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