I've shipped five AI products in the last two years. TAWK, a macOS voice to text app. MissionOS, an OKR platform that manages 44 projects and 200+ tasks across Mindvalley. A Support Intelligence dashboard that surfaces patterns from thousands of customer tickets. A program management system that orchestrates 100+ digital education programs. And TwoSpreads, a product still in its early days.
I built all of them while running growth at Mindvalley as Managing Director. I did it mostly solo, without a team of engineers or a six month roadmap, in the gaps of an already full calendar. That is possible mainly because of the stack I use, which is chosen for one thing: releasing quickly without sacrificing quality.
This post is the exact breakdown of every tool and technology in my current stack, why I chose each one, and which products use what. If you're a solo builder, a product manager who codes, or someone trying to figure out what tools actually matter in 2026, this is for you.
The Development Layer: Claude Code
This is the most important tool in my workflow. Claude Code is an AI agent, rather than an autocomplete tool or a chatbot you paste code into. It lives in your terminal, understands your entire codebase, and can make complex multifile changes with a single prompt.
I use Claude Code for virtually all of my development work now. When I was building MissionOS, I'd describe a feature in natural language ("add a breadcrumb navigation system that supports multitenant org switching") and Claude Code would scaffold the components, update the routing, handle the state management, and wire everything together. It was not perfect every time, but it was close enough that I was editing rather than writing from scratch.
The key to using it well is project memory. I maintain detailed .claude configuration files in each project that describe the architecture, the database schema, the naming conventions, and the patterns I want followed. This context means Claude Code doesn't start from zero every session. It knows that MissionOS uses Supabase Auth with Airtable as the data layer. It knows TAWK uses rumps for the menu bar and PyInstaller for bundling. It writes code that fits the project instead of generic code you have to reshape.
Every product I've built in the last year has been built with Claude Code. It is the foundation of my stack.
The AI/ML Layer: Python
For anything that touches machine learning directly, Python is still the only serious choice. When I built TAWK, the entire audio processing pipeline is Python: OpenAI's Whisper model for speech to text, PyTorch as the runtime, NumPy for audio processing, and rumps for the macOS menu bar interface.
Python's strength extends beyond the ML libraries to the ecosystem around packaging and distribution. PyInstaller lets me bundle the entire Python runtime, the Whisper model, all dependencies, and the application logic into a single .app that runs on any Mac. The bundling process is laborious (you have to manually handle mel filters and tokenizers, and sign every Mach-O binary inside the bundle for Apple's notarization), but it works. TAWK is a signed, notarized, stapled macOS application that users download and run. All Python under the hood.
I also use Python for data processing scripts that power the Support Intelligence dashboard. When you're parsing thousands of support tickets, extracting patterns, and generating insights, Python's data processing libraries are the strongest option I know.
Key Python tools in my stack:
- Whisper: local speech to text model (used in TAWK)
- PyTorch: ML runtime for model inference
- PyInstaller: bundling Python apps into native macOS applications
- rumps: macOS menu bar application framework
- NumPy: audio and data processing
The Web Application Layer: Next.js + TypeScript
For web based products, I use Next.js with TypeScript exclusively. MissionOS is built on Next.js 14 with the App Router. The Support Knowledge Base showcase is Next.js 16. The program management system is React 18 with Vite (an older project), but everything new is Next.js.
Why Next.js over plain React? Three reasons. First, the App Router gives you server components and server actions out of the box, which means less client side JavaScript and faster page loads. Second, the file based routing eliminates an entire category of configuration that wastes time. Third, Vercel's deployment integration is simple: push to main and it deploys, with no CI/CD pipeline to configure.
TypeScript is a requirement for me. When you're working solo and moving fast, types catch the bugs that a code review would normally catch. I've lost count of how many times TypeScript has saved me from shipping broken data transformations or incorrect API responses. The upfront cost of typing everything is repaid many times over in debugging time saved.
MissionOS, for example, pulls data from Airtable's API, transforms it through several layers, and renders it in a real time dashboard. Without TypeScript, the Airtable field mappings alone would be a constant source of runtime errors. With TypeScript, if the data shape changes, the build fails immediately and tells me exactly where to fix it.
The Backend Layer: Supabase
Supabase handles authentication and database needs across multiple products. MissionOS uses Supabase Auth for user management, and the program management system uses it as the primary backend.
What makes Supabase so useful for solo builders is that it removes most of the backend engineering work. Need authentication? It's built in. Need a PostgreSQL database? It's there. Need real time subscriptions? One line of code. Need row level security? SQL policies. Need file storage? Built in.
I don't run servers, manage databases or write authentication logic. Supabase handles all of it, and the free tier is generous enough that you can build and validate a product before spending a dollar.
The Data Layer: Airtable
This is the unconventional choice in my stack, and one of the most useful. Airtable serves as the data source of truth for both MissionOS and the program management system.
For MissionOS, Airtable holds all the OKR data: 44 projects, 200+ tasks, team assignments, pathway structures, and progress tracking. The web application reads from Airtable's API and presents it in a real time dashboard. Why not just use a regular database? Because the people who manage this data (project leads, pathway managers, the operations team) need to edit it directly. Airtable gives them a spreadsheet like interface they already know how to use, while I get a structured API to build applications on top of.
The program management system takes this further. It's backed by an Airtable base with hundreds of records across multiple tables: programs, pathways, schedules, content assets, instructor information. Airtable handles the relational data model, and the application layer handles the user experience.
The lesson: not every data store needs to be a traditional database. If your data has nontechnical stakeholders who need direct access, Airtable works well as a backend.
The Design Layer: Tailwind CSS
Every interface I build uses Tailwind CSS. This website, MissionOS, the Support Knowledge Base showcase, and the program management system all use Tailwind.
For a solo builder, Tailwind's value is time. I don't write CSS files, name classes, or context switch between HTML structure and CSS styling. Every design decision happens inline, right next to the markup it affects. This sounds minor, but across thousands of components, it saves days of development time.
Combined with Claude Code, Tailwind becomes even more useful. I can describe a UI ("a card component with a subtle border, 16px padding, rounded corners, and a hover effect that lifts it slightly") and Claude Code generates the exact Tailwind classes. The design vocabulary is standardized and predictable, which makes AI assisted development significantly more reliable.
The Deployment Layer: Vercel
All web applications deploy to Vercel. MissionOS, the Support Knowledge Base showcase, and this blog all run on Vercel's infrastructure.
The deployment flow is: push code to GitHub, Vercel automatically builds and deploys. Every pull request gets a preview URL. Every merge to main goes to production. There's no Docker, no Kubernetes, no server configuration. It does for deployment what Supabase does for backends: it removes an entire category of work.
For TAWK, deployment is different because it's a native macOS application. That goes through GitHub Releases, with the signed and notarized .dmg uploaded directly. The website at gettawk.com handles distribution and Stripe handles payments.
How the Stack Fits Together
Here's a quick mapping of each product to its stack:
- TAWK: Python, Whisper, PyTorch, PyInstaller, rumps. Desktop native, offline first, no web stack.
- MissionOS: Next.js 14, TypeScript, Supabase Auth, Airtable data, Tailwind CSS, Vercel. Full web application with real time sync.
- Program Manager: React 18, Vite, Supabase, Airtable, Auth0, Tailwind CSS. Internal tool with SSO.
- Support Intelligence: Next.js, TypeScript, Tailwind CSS, Vercel. Data visualization and pattern analysis.
- TwoSpreads: early stage, built on the same Next.js + Supabase + Tailwind foundation.
The pattern is clear: Next.js + TypeScript + Supabase + Airtable + Tailwind + Vercel for web products. Python + specialized ML libraries for native AI products. Claude Code for building everything.
Why This Stack Works for Solo Builders
The common thread across every tool in this stack is that it removes a category of work. Supabase removes backend engineering, Vercel removes DevOps, Tailwind removes CSS management, and Airtable removes the need to build admin interfaces. Claude Code narrows the gap between knowing what to build and actually building it.
When you combine these, what's left is the actual product work: deciding what to build, designing how it works, and making sure it solves a real problem. That's the work I want to spend my time on. Everything else is overhead.
I'm not saying this is the right stack for everyone. A team of 50 engineers building a high scale consumer product would make different choices. But for the growing number of people who are building real, production AI products as solo operators or small teams, this stack has held up in production. These are working products rather than demos. TAWK has paying customers. MissionOS manages real OKRs for real teams. The program manager orchestrates 100+ live education programs.
The tools are now good enough that one person can build what used to require a team, with the right stack. This is the one I use.
Frequently Asked Questions
What tech stack is best for AI products in 2026?
The best stack depends on what you're building, but a proven combination for 2026 is: Claude Code for AI assisted development, Python for ML/AI workloads (Whisper, PyTorch), Next.js with TypeScript for web applications, Supabase for backend and authentication, Airtable for flexible data management, Vercel for deployment, and Tailwind CSS for rapid UI development. This stack optimizes for shipping speed while maintaining production quality.
Is Python or JavaScript better for AI development?
You don't have to choose; use both. Python is stronger for AI/ML workloads, model inference, data processing, and desktop applications that bundle ML models. JavaScript (specifically TypeScript with Next.js) is better for web applications, real time dashboards, and user facing interfaces. The most productive AI builders use Python for the AI layer and TypeScript for the application layer, choosing the right language for each job.
What deployment tools do AI builders use?
For web based AI products, Vercel is the leading deployment platform due to its seamless Next.js integration, automatic previews, and edge functions. For desktop AI applications, PyInstaller handles bundling Python apps with ML models, though code signing and notarization require additional tooling. Supabase provides managed backend infrastructure. GitHub handles version control and release distribution. The key is choosing tools that minimize DevOps overhead so you can focus on building the product.
Can one person build production AI products?
Yes. With AI assisted coding tools like Claude Code, a single builder can ship and maintain production AI products. The key is choosing a stack that minimizes boilerplate and infrastructure management: managed services like Supabase and Vercel, frameworks like Next.js, and AI coding assistants that handle implementation details. Products like TAWK, MissionOS, and Support Intelligence were all built and shipped as solo operations alongside a full time executive role.