Most data platforms don’t break overnight; they grow into complexity, query by query. Over time, business logic spreads across SQL scripts, dashboards, and scheduled jobs until the system becomes a “SQL jungle.” This article explores how that happens and how to bring structure back.
Pydantic's Rust core enables high-throughput validation, but only when used intentionally. This article examines four common gotchas and explains how aligning model design with the validation engine improves clarity, scalability, and performance.
If adding a feature feels like open-heart surgery on your codebase, the problem isn’t bugs, it’s structure. This article shows how better architecture reduces risk, speeds up change, and keeps teams moving.
PostgreSQL is fast. Whether your Python code can or should keep up depends on context. This article compares and benchmarks various insert strategies, focusing not on micro-benchmarks but on trade-offs between safety, abstraction, and throughput and choosing the right tool for the job.
MCP is a key enabler into turning your LLM into an agent by providing it with tools to retrieve real-time information or perform actions. In this deep dive we cover how MCP works, when to use it, and what to watch out for.
Take anything from loggers to data to functions straight from your request in your endpoints
Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can autonomously search through files that the user explicitly allows it to access. Full control, 100% private, all the benefits of LLM without the privacy leaks, token costs or external dependencies.
Atlas and other AI browsers are a hot mess when it comes to security, privacy and censorship. In this post we'll explore how hackers can access your private data and how privacy is dead when using Atlas.
Supercharge Your FastAPI with Middleware: Practical Use Cases and Examples
Automatic Python dependency injection to make your code more testable, decoupled, uncomplicated and readable.
How to log breadcrumbs using Python's built-in logging package
Easy, user-friendly caching that tailors to all your needs