Potential impacts of Large Language Models on Engineering Management – this post is an essential starter for a discussion, and I can think of other impacts. For example – how interviewing \ assessing skills of new team members affected by LLMs? What skills should be evaluated those days (focusing on engineering positions)?
One general caveat for using LLMs is completely trusting them without any doubts. This is crucial for a performance review. Compared to code, if the code does not work, it is easy to trace and fix. If the performance review needs to be corrected, it might be hard to pinpoint what and where it got wrong, and the person getting it might need more confidence to say something.
https://www.engstuff.dev/p/potential-impacts-of-large-language
FastAPI best practices – one of the most reasoned and detailed guides I read. Also, the issues serve as comments to this guide and are worth reading. Ideally, I would like to take most of the ideas and turn them into a cookie-cutter project that is easy to create.
https://github.com/zhanymkanov/fastapi-best-practices
How Product Strategy Fails in the Real World — What to Avoid When Building Highly-Technical Products – I saw all in action and hope to do better in the future.
1 dataset 100 visualizations – I imagine this project as an assignment in a data visualization/data journalism course. Yes, there are many ways to display data. Are they all good? Do they convey the desired message?
There is a risk in being too creative, and there is some visualization there I cannot imagine using for anything reasonable.
https://100.datavizproject.com/
Automating Python code quality – one additional advantage of using tools like Black, isort, etc., is that it reduces the cognitive load when doing a code review. The code reviewer should no longer check for style issues and can focus on deeper issues.
https://blog.fidelramos.net/software/python-code-quality
Bonus – more extensive pre-commit template –
https://github.com/br3ndonland/template-python/blob/main/.pre-commit-config.yaml
Tom Ron 