PythonHub

In-House LLM Serving at Netflix Netflix built an in-house LLM serving platform using vLLM and NVIDIA Triton, integrating self-hosted models into its existing production infrastructure through unified gRPC and OpenAI-compatible APIs. The article details production lessons around model packaging, version compatibility, zero-downtime deployments, observability, and scaling constrained decoding by moving bottlenecks from s... https://netflixtechblog.com/in-house-llm-serving-at-netflix-a5a8e799ea2c

WrenAI Open-source GenBI: generative BI for AI agents. https://github.com/Canner/WrenAI

Lucen a Python compiler that parallelizes for-loops via comment pragmas https://github.com/fcmv/lucen

Python Hub Weekly Digest for 2026-07-26 https://pythonhub.dev/digest/2026-07-26/

Python 3.15’s Ultra-Low Overhead Interpreter Profiling Mode The post explains how CPython 3.15’s JIT uses a low-overhead interpreter profiling mode based on swapping dispatch tables instead of running a separate profiling interpreter or adding branches to the normal interpreter. It shows how this “dual dispatch” design lets Python record execution traces for JIT compilation with much lower overhead, while raising the tradeoff between performance ... https://fidget-spinner.github.io/posts/ultra-fast-tracing.html

yifanfeng97 / Hyper-Extract Hypergraph is more powerful. Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command. https://github.com/yifanfeng97/Hyper-Extract

Ossie Apache Ossie is a vendor-neutral standard for sharing semantic metadata across analytics, AI, and BI tools. Its goal is to define metrics and business meaning once, then reuse them everywhere. https://github.com/apache/ossie

Ban commits/transactions using AST analysis and linters The post explains why database commits and transactions should be owned only by the DB access layer, because hidden commits and leaking DB models can silently break atomicity or lose data. It shows how to enforce those boundaries with AST tests, flake8 rules, and a narrow LLM review step to catch DB models being returned where domain models should be used. https://www.droppedasbaby.com/posts/db-commits/

tempolocus Tempolocus is a time-series activity patterns and approximate location inference https://github.com/ail-project/tempolocus

ProtoLink Build autonomous Python agents with native Agent-to-Agent (A2A) communication. https://github.com/nMaroulis/protolink

What Every Python Developer Should Know About the CPython ABI https://labs.quansight.org/blog/python-abi-abi3t

Fstache Fast dependency-free, typed Mustache renderer for Python 3.12+ https://github.com/servletcloud/fstache

Mebus / cupp Common User Passwords Profiler (CUPP) https://github.com/Mebus/cupp

Building a fast HTML toolkit in C for Python How turbohtml builds a fast HTML toolkit in C for Python: SWAR, SIMD, zero-copy, interned atoms, IDNA, LTO/PGO, and honest benchmarking, 3-22x faster. https://bernat.tech/posts/blazing-fast-html-parser/

huggingface / speech-to-speech Build local voice agents with open-source models https://github.com/huggingface/speech-to-speech

How Airflow is using AI to make data engineering more resilient, not more complex The post explains how Airflow is adding AI-assisted reliability features that detect schema drift, resume long-running jobs from saved state, and classify failures using team runbooks. It shows AI being used inside data infrastructure rather than as an app layer, making pipelines more self-healing without forcing data engineers to rebuild their workflows around agents. https://blog.dataengineerthings.org/how-airflow-is-using-ai-to-make-data-engineering-more-resilient-not-more-complex-36ff44fd8df7

Django: introducing django-orjson The post introduces django-orjson, a new package that gives Django and Django REST Framework drop-in replacements powered by the faster Rust-based orjson library. It also points to a Django proposal for pluggable JSON backends, which could make faster JSON serialization and deserialization easier to adopt across Django itself. https://adamj.eu/tech/2026/07/15/introducing-django-orjson/

20 itertools functions, sorted by how often you'd actually reach for them (not the docs order) https://www.reddit.com/r/Python/comments/1uyervv/20_itertools_functions_sorted_by_how_often_youd/

GeoSQL Turn Claude/Codex into geospatial analytics agent. https://github.com/dekart-xyz/geosql

Stop Checking for None Everywhere The video explains why overusing None makes code harder to reason about by forcing checks, validation, and defensive handling throughout the system. It shows practical alternatives like better defaults, fail-fast validation, explicit state modeling, and the Null Object pattern to keep uncertainty at the edges and simplify core logic. https://www.youtube.com/watch?v=h8ZwhU3PpVw