
uv Quietly Won the Python Packaging War — Here's How to Switch in 10 Minutes
pip, venv, pyenv, pipx and poetry just got replaced by one Rust binary. Here's why uv won, and how to switch today without breaking anything.
Working notes from 10+ years of teaching Python, machine learning, and data science. Real code, honest tradeoffs, no AI slop.

pip, venv, pyenv, pipx and poetry just got replaced by one Rust binary. Here's why uv won, and how to switch today without breaking anything.
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Pythonpip, venv, pyenv, pipx and poetry just got replaced by one Rust binary. Here's why uv won, and how to switch today without breaking anything.
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PythonAstral replaced flake8, black, isort and now mypy with Rust tools that are 10–100x faster. Here's the modern lint + format + type-check setup.
LearningYou don't have a knowledge problem — you have a reps problem. Here's the simple ratio that turns passive watching into a portfolio that gets you hired.
Data Science96% of Jupyter notebooks don't reproduce. marimo's reactive, pure-Python notebooks kill hidden state for good — here's how.
A working engineer's honest roadmap for learning machine learning in 2026 — no PhD required, no "master all the math first" gatekeeping. The right order, the myths to ignore, and how to know you're actually progressing.
SQL is the most underrated skill in data science, and you don't need all of it. Here's the honest short list of what matters day-to-day and in interviews, with runnable snippets and a clear marker for where "studied SQL" becomes "uses SQL."
I tutor data science for a living, so I have an obvious bias. Here's the genuinely fair version: when a $30 course beats a tutor, when a tutor beats the course, and the hybrid path that usually wins on value.
Most data science interview prep gets the priorities backwards. Here's what actually gets you hired in 2026 — SQL, clear thinking, and reading messy data — versus the obscure algorithms everyone over-studies and nobody asks about.
SettingWithCopyWarning, KeyError, weird dtypes, exploding merges — almost every pandas beginner hits the same handful of errors. Here's what each one actually means, why it happens, and the real fix (not just a copy-paste that makes the warning go away).
You did everything right — train/test split, cross-validation, the works. Your model gets 94 percent accuracy in dev and 71 percent in production. The bug is almost always the same subtle scaling mistake. Here is how to spot it and the three-line fix.
Free YouTube exists. So does Stack Overflow. So does Claude. Here is the honest case for and against hiring a 1-on-1 tutor in 2026 — written by someone who is one and gets paid to do it. With the four kinds of learner who actually benefit, and the three who do not.
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Every performance tutorial says vectorize everything. Then they show a one-liner that beats a for-loop 100x. Real production code is messier — here is when vectorization actually helps, when it hurts, and the benchmarks to prove both.
Both APIs return the same answer in 90% of cases — so why does anyone teach both? A practical decision guide with benchmarks, three real failure modes, and the rule I use in 1-on-1 sessions.
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GeneralThe complete guide to AI tools every student needs to excel in their studies and future career.
These posts cover the patterns I see most often in 1-on-1 sessions. If you want help applying them to your code, the first session is free — no credit card required.
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