🐍 Python in Practice — Tips, Tools & Techniques for Everyday Development

Over the years, Python has become my go-to for quick automation, AI experiments, and building production-ready microservices. Here’s what I’ve learned along the way.

🛠 My Python Use Cases

• CLI tools for data migration
• AI/ML prototypes with OpenAI, LangChain, and HuggingFace
• Web APIs with FastAPI and Flask
• Data transformation scripts for ETL pipelines

📌 Tips That Made a Difference

1. Type Hints Are Worth It — They make your code self-documenting and improve IDE assistance.
2. Know Your Standard Library — collections.Counter, itertools.product, and functools.lru_cache are lifesavers.
3. Environment Management — Use pyenv + pipenv or poetry to avoid dependency hell.
4. Profile Before Optimizing — cProfile and timeit reveal the real bottlenecks.

📚 Lessons Learned

• Python isn’t slow if you use the right data structures and algorithms.
• Readability > clever one-liners.
• Test early — pytest’s fixtures make integration tests painless.

💡 Next Steps

I’ll be sharing deep dives on:

• Python for AI pipeline automation
• Building production-ready FastAPI services
• Lesser-known standard library gems