In Seven Tips for Performant Async Python I focused on plain asyncio. That’s the right place to start, because LangGraph doesn’t replace Python’s event loop or make blocking code magically concurrent. If an async LangGraph node calls a blocking library, the graph still waits. If …

In an earlier post we generated embeddings and searched them by computing cosine similarity across a small in-memory list. That approach is perfect for learning and prototyping, but it doesn’t scale. When your corpus grows to hundreds of thousands or millions of documents, iterating over every …

Adding async and await to a Python function feels like an easy win. You’ve heard it makes code faster, so you sprinkle the keywords in, run your program, and it seems to work. But async Python has a habit of looking correct while quietly running no better or even worse than the synchronous …

The earlier posts in this series built self-contained graphs: one graph, one task, one run. But real workflows often span multiple stages, where each stage produces output that the next stage needs. The pipeline pattern I describe here isn’t an official LangGraph pattern — it’s an …

Real-world agents rarely do one thing at a time. They fetch data from multiple sources, run independent checks in parallel, and combine the results before moving on. LangGraph supports this natively with concurrent nodes, but there is a subtle catch when those nodes all write to the same piece of …

LLMs are impressive, but they are limited to the knowledge baked in at training time and can’t take actions in the world on their own. Tools are what change that. By giving an LLM access to tools, you turn it from a system that is frozen in time into an agent that can look up live data, run …

Environment variables are the default way many Python apps handle configuration. They are simple, portable, and work in every deployment environment. But as soon as you add more than a handful of settings, raw env vars become fragile and hard to reason about.

Pydantic used to solve this with …

Consistency and quality are the foundations of maintainable Python code. As projects grow and teams expand, maintaining these standards becomes increasingly challenging without the right tools. Ruff is an extremely fast linter and code formatter written in Rust that helps teams improve their code …

The next rule to implement for the Sudoku solver is Nishio. This will make all outstanding puzzles solvable because it effectively brute forces the removal of an invalid candidate until other rules can take over.

If you like this post and you’d like to know more about how to plan and write …

Embeddings let software work with meaning rather than just matching words, so you can search by intent, find related content, and give LLMs the right context to answer questions. In this post we will start from first principles, show what these vectors actually are, explain their useful properties, …

Large language models are incredibly versatile, but when your code depends on predictable data structures, free‑form text can be a headache. The same information can be expressed in countless ways, making downstream processing error-prone. Structured output bridges this gap: by defining a schema, …

In software engineering, it’s typically very good to have a retrospective after a sprint of work. The goal isn’t just to celebrate successes or catalogue frustrations, but to step back and deliberately reflect: what worked well, what didn’t, and what lessons are worth carrying forward? Advent …