A paraphrasing of Gerald Jay Sussman's explanation for MIT's switch from Scheme to Python in its undergraduate computer science program, focusing on the changing nature of programming and the need to adapt to modern systems and libraries.
A list of Python libraries that helped the author improve their automation scripts and turn duct-tape code into something trustworthy.
* **pathlib:** Simplifies file path manipulation, making it cross-platform compatible and more readable.
* **tenacity:** Provides a decorator for automatically retrying failed operations (like API calls) with configurable settings.
* **rich:** Enhances logging with features like progress bars, colored output, and detailed tracebacks for better observability.
* **schedule:** A more readable alternative to cron for scheduling tasks in Python.
* **pydantic:** Enforces data validation, ensuring inputs conform to expected types and structures.
* **python-dotenv:** Manages environment variables, keeping sensitive information (like API keys) separate from code.
* **loguru:** A streamlined logging library that requires minimal configuration.
* **watchdog:** Monitors filesystem changes and triggers actions based on those changes (event-driven automation).
* **typer:** Creates command-line interfaces (CLIs) for Python scripts, making them more user-friendly as tools.
* **pathlib:** Simplifies file path manipulation, making it cross-platform compatible and more readable.
* **tenacity:** Provides a decorator for automatically retrying failed operations (like API calls) with configurable settings.
* **rich:** Enhances logging with features like progress bars, colored output, and detailed tracebacks for better observability.
* **schedule:** A more readable alternative to cron for scheduling tasks in Python.
* **pydantic:** Enforces data validation, ensuring inputs conform to expected types and structures.
* **python-dotenv:** Manages environment variables, keeping sensitive information (like API keys) separate from code.
* **loguru:** A streamlined logging library that requires minimal configuration.
* **watchdog:** Monitors filesystem changes and triggers actions based on those changes (event-driven automation).
* **typer:** Creates command-line interfaces (CLIs) for Python scripts, making them more user-friendly as tools.
A curated list of awesome tools and libraries for large language models.
Christensen believes the reason for these mistakes occuring so often is ease; we know how to use shared libraries and we too often optimize for the short-term since it feels more productive. He notes though that delaying the cost of decoupling is very high and that we already know the solution, it takes a bit of extra thought and effort to put the tools in place from the beginning but when they are in place they are straightforward to use. He therefore recommends looking beyond the short-term ease, avoiding binary coupling and instead leveraging contracts and protocols to get the benefits of the microservices architecture.
During the Q&A after his talk he emphasized that he thinks it’s OK to use even a large framework as long as it’s used inside a self-contained service, but not if the architecture of the whole system adopts this large framework because that leads to long-term coupling.
During the Q&A after his talk he emphasized that he thinks it’s OK to use even a large framework as long as it’s used inside a self-contained service, but not if the architecture of the whole system adopts this large framework because that leads to long-term coupling.
