My AI Development Stack: A Strategic Approach to Building Products

The Philosophy: Specialization Over Generalization

In my daily workflow, I don't believe in using a single AI for everything. Instead, I've built an orchestrated stack where each tool serves a specific purpose, creating a synergistic ecosystem that dramatically amplifies productivity. Some days I use 4-5 different AI models, and this diversity is precisely what gives me a competitive edge.

The Stack Breakdown

1. Gemini: The Context Guardian

Gemini excels at staying aligned with your initial objectives. Here's why it's become my go-to for strategic planning:

Primary use case: Preventing over-engineering and scope creep.

When you're building a product and explicitly state you want to avoid over-engineering, Gemini acts as your architectural conscience. If you start drifting from your MVP goals or creating unnecessarily complex solutions, it pulls you back with a gentle reminder: "Hold on, you said that wasn't the objective."

Key strength: Context retention and alignment. Once you establish a context, Gemini follows it religiously without blindly agreeing with everything you propose. This critical thinking while maintaining alignment is invaluable.

2. Notebook LM + Gemini: The Research Powerhouse

I integrate Gemini with Notebook LM to create a powerful research workflow:

The process is straightforward: conduct comprehensive internet research, aggregate large volumes of articles, blogs, and documentation related to a specific context, then bring it back to Gemini. The integration with Notebook allows me to absorb content efficiently while maintaining accuracy through RAG (Retrieval-Augmented Generation) systems that always stay aligned with the notebook's content.

3. Perplexity: Real-Time Intelligence

Perplexity is exceptional because every response is grounded in live internet research with cited sources. Unlike models trained on data from 2024 or earlier, Perplexity provides 100% current information at the time of your query.

What I use it for:

• Accessing up-to-date documentation
• Attaching repositories for code review
• Debating whether logic follows best practices and current documentation
• Debugging critical issues
• Creating workspaces with extensive context windows

Model flexibility: I can switch between Gemini 3, Claude 4.5, Grok 4, and others depending on the task.

4. GitHub Copilot + Claude Opus 4.5: The Code Quality Engine

Here's where things get serious. Code quality is non-negotiable, even for MVPs. Your users need a product that actually works without breaking.

Why Claude Opus 4.5? Simply put, it's one of the best coding models available. But I don't use Anthropic's CLI directly.

Why GitHub Copilot? It serves as my universal interface to multiple models:

• Claude Opus (my primary choice)
• Gemini 3.0 Pro
• Grok
• OpenAI Codex
• And many others

GitHub Copilot continuously improves its integration capabilities, adding better features for working with these models. While Anthropic's CLI ecosystem is excellent, Copilot gives me the flexibility to use agents and skills across different models seamlessly.

5. Token Optimization: The Hidden Multiplier

I use several tools and techniques that allow me to use expensive LLM models extensively without hitting rate limits constantly. This requires technical knowledge, and I've documented these approaches in my blog (tools for economizing tokens).

Understanding how to efficiently save tokens means I can work with premium models much longer without interruptions.

6. ChatGPT (Yes, Really): The Surprise Performer

I'll be honest: for a long time, I didn't use ChatGPT for coding. I always found OpenAI's models subpar for code generation. But they surprised me.

The game-changer: gpt-5.2-codex high

With some clever workarounds using specific tools and extensive hook instructions (many instructions), I managed to get this model to work on a complex task for over an hour, implementing it from start to finish autonomously.

The secret? I studied Anthropic's documentation extensively, particularly focusing on RLHF (Reinforcement Learning from Human Feedback). Understanding RLHF gives you a solid foundation for creating better instructions and agents when working with LLMs.

7. MimuMagical.com: Solving the Full-Stack Designer Problem

For design, I use a tool called 🔗mimumagical.com - and full disclosure, I built it.

Why I created it: I discovered I could create excellent designs with AI and thought, "Why not build a product since this solved my biggest full-stack problem?"

Current workflow:

1. Create page designs in MimuMagical
2. Copy the generated code
3. Ask Claude to implement it in my product

This AI-orchestrated approach to design has been incredibly valuable, and the app simply facilitates access to this workflow. I'm dedicating time to optimize it further.

Key Takeaways

1. Specialization beats generalization: Don't use one AI for everything. Each model has strengths.

2. Understand the fundamentals: Study RLHF, prompt engineering, and token optimization. This knowledge compounds.

3. Build your own tools: If you identify a consistent problem in your workflow, consider building a solution.

4. Context management is crucial: Whether using RAG with Notebook LM or maintaining alignment with Gemini, how you manage context determines output quality.

5. Clean architecture always matters: Even with AI assistance, maintain professional standards. MVPs should be sustainable, not technical debt.

The Bottom Line

This orchestrated approach to AI-assisted development isn't about using the latest shiny tool. It's about understanding each model's strengths, building efficient workflows, and maintaining architectural integrity while maximizing productivity.

The future of development isn't human vs. AI or human replaced by AI. It's human orchestrating AI tools strategically to build better products faster.