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News & links about Python programming.
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How we optimized Dash's relevance judge with DSPy

Dropbox used DSPy to turn prompt engineering for our relevance judge into a measurable, automated optimization loop, improving task performance, cost, and how reliably it works in production.

https://dropbox.tech/machine-learning/optimizing-dropbox-dash-relevance-judge-with-dspy
Reinventing Python's AsyncIO

The post explores a redesign of Python’s async runtime, arguing that the current async/await and event-loop model adds unnecessary complexity, and proposing a simpler runtime where concurrency is handled automatically without explicit async syntax.The author experiments with a new runtime approach that can run async workloads 2–3.5× faster than traditional asyncio, suggesting Python’s co...

https://blog.baro.dev/p/reinventing-pythons-asyncio
The Hidden Mechanism Behind Clean Python APIs (Descriptor Deep Dive)

Descriptors define how Python resolves attribute access, explaining why values sometimes come from the instance, class, or elsewhere in non-obvious ways. Understanding descriptor rules enables cleaner, more reusable designs by giving you precise control over attribute behavior.

https://www.youtube.com/watch?v=7SUzTOkUVLY
Pydantic AI - Intro to Agentic AI with Pydantic AI framework

We'll look at using Pydantic AI to build agent-based workflows, starting with simple fundamentals, and building up to more complex examples that use vector databases, RAG, multi-agent workflows and more.

https://www.youtube.com/playlist?list=PL-2EBeDYMIbSWGoDzOFm33_5W_ShO-VIi
Fixed Python autocomplete

The post suggests that heavy LSP and static analysis approaches are unnecessary for many common autocomplete scenarios. It shows a lightweight, pattern-based approach can deliver faster, more responsive suggestions without full semantic analysis.

https://matan-h.com/better-python-autocomplete
How Clean Code Turns Into Overengineering

This video is about how code that looks clean can still hide a bad design, and why overusing tiny abstractions can make a program harder to understand and change. It refactors a Python reporting example by simplifying the structure, making the pipeline explicit, and focusing on cohesion over smallness.

https://www.youtube.com/watch?v=U4sPMwAiXco