codex-lb
Load balancer for ChatGPT accounts. Pool multiple accounts, track usage, manage API keys, view everything in a dashboard.
https://github.com/Soju06/codex-lb
Load balancer for ChatGPT accounts. Pool multiple accounts, track usage, manage API keys, view everything in a dashboard.
https://github.com/Soju06/codex-lb
GitHub
GitHub - Soju06/codex-lb: Codex/ChatGPT multiple account load balancer & proxy with usage tracking, dashboard, and OpenCode-compatible…
Codex/ChatGPT multiple account load balancer & proxy with usage tracking, dashboard, and OpenCode-compatible endpoints - Soju06/codex-lb
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
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
YouTube
Stop Checking for None Everywhere
🧱 Build software that lasts. Join the Software Design Mastery waiting list → https://arjan.codes/mastery.
'None' seems harmless at first. But once it starts spreading through your codebase, every function needs more checks, more validation, and more defensive…
'None' seems harmless at first. But once it starts spreading through your codebase, every function needs more checks, more validation, and more defensive…
💩1
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/
https://www.reddit.com/r/Python/comments/1uyervv/20_itertools_functions_sorted_by_how_often_youd/
Reddit
From the Python community on Reddit
Explore this post and more from the Python community
💩1
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/
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/
adamj.eu
Django: introducing django-orjson - Adam Johnson
Just as cars painted red are known to be faster, libraries implemented in Rust are also known to be faster. Today’s example is orjson, a Rusty replacement for Python’s built-in json module, boasting 10x faster serialization and 2x faster deserialization.
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
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
Medium
How Airflow is using AI to make data engineering more resilient, not more complex
Your pipeline failed at 2am. What if it could fix itself?
huggingface / speech-to-speech
Build local voice agents with open-source models
https://github.com/huggingface/speech-to-speech
Build local voice agents with open-source models
https://github.com/huggingface/speech-to-speech
GitHub
GitHub - huggingface/speech-to-speech: Build local voice agents with open-source models
Build local voice agents with open-source models. Contribute to huggingface/speech-to-speech development by creating an account on GitHub.
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/
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/
Bernát Gábor — Python packaging, tox, virtualenv & open source
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.