The show must go on in the AI era. The responsibility - and the opportunity - for experienced software engineers to help grow the next generation is still here. Sharing knowledge and supporting early-career talent is something AI did not and could not change.
A talk on understanding and using functions in Python — what they are, why they matter, and how to start writing your own — at PyLadies Budapest.
Listening to Boris Cherny talk about “coding being largely solved” felt familiar — it describes a shift many of us are already living, even if we don’t always name it.
Anthropic just shipped native git worktree support in Claude Code CLI. Last week I wrote about a hub-and-spoke worktree setup for parallel AI agents --- now it's getting baked right into the tool.
Git worktrees let you run multiple AI agents in parallel — each on its own branch, in its own directory, without stepping on each other. Here's how I use this when I've got a few features cooking at once.
If you've been using Claude Code for a while, you already know **subagents**. They're the workhorses behind the `Task` tool — you send one off to research something, it comes back with results, and you move on. Simple, effective, and cheap on tokens.
Career growth isn't just about moving faster or working harder. At some point, the real risk shifts: climbing efficiently in the wrong direction. As I think more seriously about moving beyond Senior, I've realized that focus — not ambition — is the constraint most of us underestimate.
As 2025 wraps up, I've been thinking about what actually helped me grow this year. Not everything—just the resources that stuck with me on my journey.
Over the last five years, as a Senior Data Engineer on the EM / Staff trajectory, and at times leading teams directly, I’ve learned that one of the most rewarding challenges isn’t just delivering results…
Many people still think that not knowing something is a weakness — that admitting it might make them look less competent. And in tech, this belief is even stronger.
Critical thinking is your most valuable asset in the age of AI-assisted development. Why judgment still matters when building with AI.
Still relying on print statements to debug your Python code? There’s a much better way: clean, structured, multi-destination, non-blocking logging.
A talk on practical Python data structures — tuples, sets, and the collections module — at PyLadies Budapest.