5 interesting things (09/02/2024)

Closing the women’s health gap: A $1 trillion opportunity to improve lives and economies – a McKinsey report that highlights the gender health gap and points to the opportunity – potential for a $1 trillion economic gain with additional societal impact. One interesting point is that there are gaps and flaws throughout the value chain – drug effectiveness, therapy access, research functions, etc. This hints that there are many opportunities out there that can make a significant impact.

https://www.mckinsey.com/mhi/our-insights/closing-the-womens-health-gap-a-1-trillion-dollar-opportunity-to-improve-lives-and-economies

Slashing Data Transfer Costs in AWS by 99% – one of the costs developers often forget or dismiss when considering architecture is the cost of data transfer. The solution described in this post is elegant and demonstrates the effect of deep knowledge and understanding of the domain. Simple to trivial architectural decisions can cost so much.

https://www.bitsand.cloud/posts/slashing-data-transfer-costs

3 questions that will make you a phenomenal rubber duck – I previously mentioned that debugging skills are essential, and it is important to iterate and refine them. I especially liked the 3rd question – “If your hypothesis were wrong, how could we disprove it?” as it forces one to think the other way around and see a slightly bigger picture.

https://blog.danslimmon.com/2024/01/18/3-questions-that-will-make-you-a-phenomenal-rubber-duck

Product Managing to Prevent Burnout – burnout is more common than we think and can have many causes. Moreover, different people would react differently to different cultures and would burn out or not burn out accordingly. The most important takeaway is that managing and controlling burnout is a team sport; it is not only the concern of the direct manager, but product managers can also participate in this effort. (I strongly recommend the honeycomb blog)

https://www.honeycomb.io/blog/product-managing-prevent-burnout

The “errors” that mean you’re doing it right – I was able to identify or witness almost all the errors mentioned in the post. I also think some of those errors, such as Letting someone go soon after hiring, Pivoting a strategy just after creating it, etc, could be attributed to the sunk cost fallacy. And if we want to make the opening sentence more extreme – “If you don’t make mistakes, you’re not working”.

https://longform.asmartbear.com/good-problems-to-have

5 interesting things (06/07/2023)

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.

https://review.firstround.com/how-product-strategy-fails-in-the-real-world-what-to-avoid-when-building-highly-technical-products

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