The AI Tools I Use Every Day in 2026

My Daily Driver Tools

People often ask what tools I use for AI development. Rather than giving a theoretical answer, here is what I actually opened and used in the last week. Every tool on this list is something I depend on in my daily work, not something I tried once and bookmarked.

AI Models and APIs

Claude (Anthropic)

Claude is my primary model for complex reasoning, document analysis, and code generation. I use the API through the Anthropic Python SDK for production systems and Claude Code for development work. The combination of strong reasoning and reliable structured output makes it my default choice for tasks where accuracy matters most.

GPT-4o (OpenAI)

I use GPT-4o as a secondary model, particularly for dual-model scoring systems and for tasks where I want a different perspective from Claude. The function calling capabilities are also excellent for structured data extraction.

Gemini (Google)

Gemini 2.0 Flash is my go-to for high-volume, lower-complexity tasks where speed and cost matter more than peak reasoning quality. Metadata generation, content classification, and quick summarization tasks all run through Gemini.

Development Tools

VS Code with Claude Code

My primary editor is VS Code, but increasingly I am working through Claude Code for AI-assisted development. The ability to have Claude understand my entire codebase and make changes across multiple files has dramatically accelerated my development speed.

Python and FastAPI

Python is my primary language for all AI work. FastAPI is my framework of choice for building AI-powered APIs. The combination of type hints, automatic documentation, and async support makes it ideal for LLM-backed services.

Pydantic

I use Pydantic extensively for data validation throughout my pipelines. Every input and output in my AI systems has a Pydantic model that validates the data structure and types. This catches errors early and makes debugging much easier.

Infrastructure and Data

Supabase

Supabase is my database of choice for AI applications. PostgreSQL under the hood gives me reliability and pgvector for embeddings. The auth, storage, and real-time features mean I rarely need additional backend services. Row-level security keeps data safe without application-level authorization code.

VPS with PM2 and Nginx

All my production services run on a VPS managed with PM2 for process management and Nginx as a reverse proxy. This setup handles nine concurrent AI projects with excellent reliability and predictable costs.

Git

Every project uses Git for version control. I also use Git-backed rollback systems for autonomous AI processes, which gives me a safety net when AI systems make changes that need to be reverted.

Monitoring and Communication

Telegram Bots

All my pipeline monitoring flows through Telegram. I get real-time alerts for errors, daily digest reports, and can even trigger certain actions by sending commands to my bots.

Supabase Dashboard

For data monitoring and ad-hoc queries, the Supabase dashboard is invaluable. I can quickly check pipeline results, run SQL queries against production data, and monitor usage patterns.

Content and Media

MJML

For email templates, MJML is essential. It compiles to responsive HTML that works across all email clients. I use it for both marketing emails and system notification templates.

Google Cloud Text-to-Speech

For AI video production pipelines, Google Cloud TTS provides the voiceover audio. The quality has improved dramatically and the cost is very reasonable for my volume of usage.

Playwright

When I need to collect data from the web, Playwright handles the browser automation. It is more reliable than traditional scraping tools, especially for JavaScript-heavy sites.

What I Stopped Using

Just as interesting as what I use is what I dropped:

• LangChain: Too much abstraction for my needs. I prefer direct API calls with lightweight wrappers
• Docker: For my scale, PM2 provides everything I need without the overhead
• Pinecone: Switched to pgvector in Supabase. One less external service to manage
• Notion for project management: Moved everything to plain text files and Git

The best tool stack is the one with the fewest tools that still gets the job done. Every additional service is another point of failure and another bill to track.

The Philosophy Behind My Choices

You will notice a bias toward simplicity and consolidation in my stack. I prefer fewer, more capable tools over a sprawling collection of specialized services. Supabase replaces what would otherwise be three or four separate services. PM2 replaces more complex orchestration tools. This approach reduces cognitive overhead, simplifies debugging, and keeps costs predictable.

Your ideal stack will look different from mine, but the principle of choosing tools that reduce complexity rather than add it applies universally.