Don’t Use Boolean Flags in Python, Use Policies Instead
The Policy Pattern replaces large conditional blocks by breaking rules into small, composable components that can be combined into flexible pipelines. This approach makes code easier to extend, test, and manage, especially when dealing with feature flags and configuration changes.
https://www.youtube.com/watch?v=wYeDGkdMi3g
The Policy Pattern replaces large conditional blocks by breaking rules into small, composable components that can be combined into flexible pipelines. This approach makes code easier to extend, test, and manage, especially when dealing with feature flags and configuration changes.
https://www.youtube.com/watch?v=wYeDGkdMi3g
YouTube
Don’t Use Boolean Flags in Python, Use Policies Instead
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In this video, I show how to replace growing conditional logic with the Policy Pattern. Instead of one big function, you split rules into small, composable…
In this video, I show how to replace growing conditional logic with the Policy Pattern. Instead of one big function, you split rules into small, composable…
google-deepmind / gemma
Gemma open-weight LLM library, from Google DeepMind
https://github.com/google-deepmind/gemma
Gemma open-weight LLM library, from Google DeepMind
https://github.com/google-deepmind/gemma
GitHub
GitHub - google-deepmind/gemma: Gemma open-weight LLM library, from Google DeepMind
Gemma open-weight LLM library, from Google DeepMind - google-deepmind/gemma
What's new in pip 26.1
pip 26.1 adds support for dependency cooldowns, experimental support for reading/installing from standard lockfiles (pylock.toml), fixes several long-standing limitations of the 2020 resolver, and drops support for Python 3.9.
https://ichard26.github.io/blog/2026/04/whats-new-in-pip-26.1/
pip 26.1 adds support for dependency cooldowns, experimental support for reading/installing from standard lockfiles (pylock.toml), fixes several long-standing limitations of the 2020 resolver, and drops support for Python 3.9.
https://ichard26.github.io/blog/2026/04/whats-new-in-pip-26.1/
Richard Si
What's new in pip 26.1 - lockfiles and dependency cooldowns!
pip 26.1 adds support for dependency cooldowns, experimental support for reading/installing from standard lockfiles (pylock.toml), fixes several long-standing limitations of the 2020 resolver, and drops support for Python 3.9.
ml-intern
An open-source ML engineer that reads papers, trains models, and ships ML models.
https://github.com/huggingface/ml-intern
An open-source ML engineer that reads papers, trains models, and ships ML models.
https://github.com/huggingface/ml-intern
GitHub
GitHub - huggingface/ml-intern: 🤗 ml-intern: an open-source ML engineer that reads papers, trains models, and ships ML models
🤗 ml-intern: an open-source ML engineer that reads papers, trains models, and ships ML models - huggingface/ml-intern
honker
SQLite extension + bindings for Postgres NOTIFY/LISTEN semantics with durable queues, streams, pub/sub, and scheduler
https://github.com/russellromney/honker
SQLite extension + bindings for Postgres NOTIFY/LISTEN semantics with durable queues, streams, pub/sub, and scheduler
https://github.com/russellromney/honker
GitHub
GitHub - russellromney/honker: SQLite extension + bindings for Postgres NOTIFY/LISTEN semantics with durable queues, streams, pub/sub…
SQLite extension + bindings for Postgres NOTIFY/LISTEN semantics with durable queues, streams, pub/sub, and scheduler - russellromney/honker
Single file Python CLIs when do you split, when do you keep it monolithic?
https://www.reddit.com/r/Python/comments/1t0crw2/single_file_python_clis_when_do_you_split_when_do/
https://www.reddit.com/r/Python/comments/1t0crw2/single_file_python_clis_when_do_you_split_when_do/
Reddit
From the Python community on Reddit
Explore this post and more from the Python community
Easily Stream LLM Responses with Django-Bolt and PydanticAI
A guide showing how easy it is to start using django-bolt and PydanticAI agents together.
https://www.caktusgroup.com/blog/2026/04/27/django-bolt-easy-pydanticai-streaming/
A guide showing how easy it is to start using django-bolt and PydanticAI agents together.
https://www.caktusgroup.com/blog/2026/04/27/django-bolt-easy-pydanticai-streaming/
Caktusgroup
Easily Stream LLM Responses with Django-Bolt and PydanticAI | Caktus Group
A guide showing how easy it is to start using django-bolt and PydanticAI agents together.
Choosing a Python Logging Library in 2026
Compare Pythons standard logging module structlog and Loguru with real benchmarks OpenTelemetry integration paths and frameworkspecific guidance for Django FastAPI and Flask.
https://www.dash0.com/guides/python-logging-libraries
Compare Pythons standard logging module structlog and Loguru with real benchmarks OpenTelemetry integration paths and frameworkspecific guidance for Django FastAPI and Flask.
https://www.dash0.com/guides/python-logging-libraries
Dash0
Choosing a Python Logging Library in 2026 · Dash0
Compare Pythons standard logging module structlog and Loguru with real benchmarks OpenTelemetry integration paths and frameworkspecific guidance for Django FastAPI and Flask
❤1
Databases Were Not Designed For This
The post defines defensive databases as systems designed to protect data from buggy, noisy, or autonomous applications through safeguards such as idempotency, auditability, soft deletes, controlled writes, and strict permissions. As AI agents and distributed services generate more unpredictable traffic, data stores must actively preserve integrity rather than assuming every client behave...
https://arpitbhayani.me/blogs/defensive-databases
The post defines defensive databases as systems designed to protect data from buggy, noisy, or autonomous applications through safeguards such as idempotency, auditability, soft deletes, controlled writes, and strict permissions. As AI agents and distributed services generate more unpredictable traffic, data stores must actively preserve integrity rather than assuming every client behave...
https://arpitbhayani.me/blogs/defensive-databases
Arpit Bhayani
Databases Were Not Designed For This
There is an implicit contract at the foundation of every database architecture decision you have ever made. You probably never wrote it down. Nobody does. It just… existed.