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📅 Today’s Picks

Polars: Lazy CSV Loading with Query Optimization

Problem

Pandas loads entire CSV files into memory immediately, even when you only need filtered or aggregated results.

This eager evaluation wastes memory and processing time on data you’ll never use.

Solution

Polars’ scan_csv() uses lazy evaluation to optimize queries before loading data.

How scan_csv() works:

• Analyzes your entire query before loading any data
• Identifies which columns you actually need
• Applies filters while reading the CSV file
• Loads only the relevant data into memory

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Build Structured AI Agents with LangChain TodoList

Problem

Complex workflows require structured planning. Without it, agents may execute subtasks out of order or miss crucial ones entirely.

Solution

LangChain v1.0 introduces TodoListMiddleware, which gives agents automatic task planning and progress tracking.

Key benefits:

• Decomposes complex requests into sequential steps
• Marks each task as pending, in_progress, or completed
• Ensures agents follow logical execution order

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☕️ Weekly Finds

RAGxplorer [LLM] – Open-source tool to visualize your RAG embeddings and document chunks

nbQA [Python Utils] – Run ruff, isort, pyupgrade, mypy, pylint, flake8, and more on Jupyter Notebooks

prometheus-eval [LLM] – Evaluate your LLM’s response with specialized language models for reproducible assessment

Looking for a specific tool? Explore 70+ Python tools →

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