#python

Python's Ellipsis literal (...) is a real object (type(...) is <class 'ellipsis'>), not just a placeholder. Beyond type stubs and numpy slicing, NVIDIA's NOOA framework repurposes it as a runtime dispatch marker: methods with ... bodies are completed by an LLM at runtime, while methods with real bodies stay deterministic Python. The boundary between "code I control" and "code the model controls" becomes greppable: grep -n '\.\.\.' agent.py. Clever reuse of existing syntax that linters and type checkers already accept.

django-crawl by Adam Johnson: BFS-crawls your Django site using the test client, no real HTTP overhead. Origin story: diffing CSP headers during a Django 6.0 migration accidentally caught 7 non-CSP bugs in a 100%-coverage project. HTML parsing via Rust (html5ever), follows links/sitemaps/feeds. Works as a management command or a TestCase smoke test that raises ExceptionGroup on errors. discuss

Python 3.15 datetime docs now officially recommend import datetime as dt in a Tip block, to disambiguate the module from the datetime.datetime class. The tip cites Adam Johnson's 2019 post — 6 years from community convention to CPython docs. Fixes both traps: from datetime import datetime shadows the module, and import datetime reads ambiguously as datetime.datetime.now(). via

Django Modern REST seems to be an emerging REST API library with focus on performance, scalability and pluggable serializers. Supporting msgpack, CBV and less of sync_to_async. Might evaluate it against the my current preferred django-ninja. gemini

zoocache is a sematic dependency based cache manager, that support in-memory, LMDB or redis backends. Integration with Django looks interesting.

The free-threaded version of python can be added to uv and github actions with t suffix. e.g. 3.14t and 3.13t source

how uv got so fast. UV performance is because of design decisions while rust contributes to micro optimizations.

"LangGraph is an orchestration framework for building stateful multi-agent applications using LLMs. It provides low-level primitives such as nodes and edges, along with built-in features that give developers granular control over agent workflows, memory management and state persistence. This means developers can start with a simple pre-built graph and scale to complex, evolving agent architectures. With support for streaming, advanced context management and resilience patterns like model fallbacks and tool error handling, LangGraph enables you to build robust, production-grade agentic applications. Its graph-based approach ensures predictable, customizable workflows and simplifies debugging and scaling."