Ask ten AI developers what tools they use, and you'll probably get ten different answers.
The AI ecosystem is evolving so quickly that it's easy to believe you need every new framework, model, and application to stay productive.
I don't think that's true.
Over the past year, I've experimented with dozens of AI tools while building products, writing technical content, managing prompt libraries, and developing AI workflows. Along the way, my stack has become surprisingly simple.
It's not built around the "best" tools.
It's built around the tools that work well together.
Here's the AI stack I rely on in 2026 and, more importantly, why each tool has earned its place.
1. ChatGPT: My Primary Thinking Partner
ChatGPT is where most of my work begins.
Not because it can do everything, but because it helps me think faster.
I use it for:
- Brainstorming ideas
- Structuring articles
- Reviewing technical concepts
- Exploring architectural trade-offs
- Refining prompts
- Research assistance
I rarely expect the first response to be perfect.
Instead, I treat it like collaborating with a knowledgeable teammate who accelerates my thinking.
2. Cursor: My AI-Powered Development Environment
When it's time to write code, I move into Cursor.
Its strength isn't just code generation.
It's understanding the context of an entire project.
Whether I'm building a FastAPI backend, integrating APIs, or refactoring an existing codebase, having AI directly inside the editor removes a huge amount of friction.
The less I switch between applications, the more productive I become.
In fact, one of the biggest lessons I've learned is that adding more AI tools doesn't automatically improve productivity. Sometimes it has the opposite effect. I explored this idea in The Hidden Cost of Using Too Many AI Tools, where I explain why a smaller, well-integrated stack often outperforms a collection of disconnected applications.
3. GitHub: The Source of Truth
Every project eventually ends up in GitHub.
Not just source code.
I also version:
- Prompt templates
- Documentation
- API specifications
- Experiment notes
- Workflow diagrams
Treating AI assets like software assets has made collaboration and maintenance much easier.
GitHub isn't simply where my code lives.
It's where my AI knowledge evolves.
If you're building AI applications, I also recommend exploring several open-source repositories that have significantly improved my own workflow. I shared my favorites in 7 GitHub Repositories I Recommend to Every AI Builder.
4. MCP: Connecting Everything Together
The biggest change in my stack this year hasn't been a new language model.
It's been adopting Model Context Protocol (MCP).
Instead of manually copying information between applications, MCP allows AI systems to interact with repositories, documentation, databases, and external services through standardized interfaces.
Rather than thinking about individual AI tools, I now think about connected workflows.
That shift has been far more valuable than upgrading from one model to another.
If you're curious about practical implementations, I recently shared 5 MCP Servers That Changed How I Build AI Workflows, covering the MCP servers that have had the greatest impact on my projects.
5. A Structured Prompt Library
One of the least exciting parts of my stack is also one of the most valuable.
A structured prompt library.
Every useful prompt eventually becomes a reusable asset.
Instead of leaving prompts buried inside chat history, I organize them into categorized libraries that can be searched, refined, and reused across projects.
This has dramatically reduced duplicated work and improved consistency.
I shared the complete system in How I Organize 10,000+ Prompts Across Projects, including the principles I use to manage large prompt collections effectively.
6. FastAPI: The Backbone of My AI Applications
Whenever I need to expose AI capabilities through APIs, FastAPI is my preferred framework.
It provides:
- Excellent performance
- Automatic API documentation
- Type validation
- Clean architecture
- Easy integration with AI services
Most AI applications eventually need reliable APIs.
FastAPI has consistently delivered that reliability.
7. Simple Workflows Over Complex Architectures
One trend I've intentionally avoided is building overly complicated AI systems.
- Multiple agents.
- Multiple orchestration frameworks.
- Multiple reasoning layers.
- Sometimes they're necessary.
- Often they aren't.
I've found that a simple workflow that's easy to understand, maintain, and debug usually delivers more value than an impressive architecture that's difficult to operate.
Technology should reduce complexity.
Not introduce it.
My AI Stack Isn't Fixed
One thing people often ask is:
"What's the best AI stack?"
There isn't one.
My stack changes every few months.
Tools evolve.
Models improve.
New standards emerge.
What doesn't change is the philosophy behind it.
Every tool has to earn its place.
If it creates unnecessary complexity, overlaps with another tool, or doesn't improve my workflow, it doesn't stay.
The Principle That Guides Every Tool Choice
Over the past year, I've realized something important.
Successful AI builders don't optimize for the number of tools they use.
They optimize for the quality of the system they build.
That's also true at the organizational level.
Before investing in new AI platforms, businesses should first understand whether their processes are actually ready for AI adoption. I discussed this in AI Process Assessment: 9 Signs Your Business Is Ready for AI, which outlines practical indicators that help organizations evaluate their readiness before implementing AI.
Because the best AI stack isn't the one with the most tools.
It's the one that helps you deliver consistent results with the least unnecessary complexity.
Author Profile: Jaideep Parashar
Founder & Director, ReThynk AI
Six Sigma Black Belt | Lean Expert | AI Strategist | Researcher | Author | Keynote Speaker
Connect with Author: LinkedIn Profile
Articles Reference:
https://dev.to/jaideepparashar/the-hidden-cost-of-using-too-many-ai-tools-poo
https://dev.to/jaideepparashar/7-github-repositories-i-recommend-to-every-ai-builder-4hl4
https://dev.to/jaideepparashar/5-mcp-servers-that-changed-how-i-build-ai-workflows-16j6
https://dev.to/jaideepparashar/how-i-organize-10000-prompts-across-projects-2g30
https://rethynkai.com/ai-process-assessment-business-ready-for-ai/
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