Month: July 2023

5 interesting things (27/07/2023)

Designing Age-Inclusive Products: Guidelines And Best Practices – I have a 91-year-old grandmother who, in the last 10 years, cannot book a doctor’s appointment herself as she does not use a smartphone and cannot follow voice navigation. Even without a personal perspective, I am very interested in accessibility, and I try to pay attention to inclusivity and accessibility topics wherever relevant. However, I always wonder if those are general best practices or are limited to specific cohorts. Specifically, in this case, younger people usually have more technology literacy than older people and therefore can achieve their goals with less optimized flows and UI.

https://www.smashingmagazine.com/2023/07/designing-age-inclusive-products-guidelines-best-practices/

On Becoming VP of Engineering – A two-part blog post series by Emily Nakashima, Honeycomb’s first VP of Engineering. The first part focuses on her path – coming originally from design, frontend, and product engineering and becoming VP of Engineering that also manages the backend and infrastructure. 

The second part talks about the day-to-day work and the shift in focus when moving from a director position to a VP position. I strongly agree with her saying, “Alignment is your most important deliverable,” and also think it is one of the hardest things to achieve.

https://www.honeycomb.io/blog/becoming-vp-of-engineering-pt1

https://www.honeycomb.io/blog/becoming-vp-of-engineering-pt2

Project Management for Software Engineers – “This article is a collection of techniques I’ve learned for managing projects over time, that attempts to combine agile best practices with project management best practices.”. While a degree in computer science teaches lots of algorithms, software development, and so on, it does not teach project management and time management. Those skills are usually not required in junior positions but can help you have a more significant impact. Having said that, one should find the exact practices that fit him or her and that can evolve over time.

https://sookocheff.com/post/engineering-management/project-management-for-software-engineers/

Designing Pythonic library APIs – A while ago (2 years+-), I looked for a post/tutorial / etc. regarding designing SDK best practices and could not find something I was happy with. I like the examples (both good and bad examples) in this post. If you are in a hurry, all the take aways are summarized in the end (but sometimes hard to understand without context).

https://benhoyt.com/writings/python-api-design/

Fern – “Fern is an open source toolkit for designing, building, and consuming REST APIs. With Fern, you can generate client libraries, API documentation, and boilerplate for your backend server.”. I haven’t tried it myself yet, but if it works, it seems like cookie-cutter on steroids. In the era of LLMs, the next step is to generate all of those from free text.

https://github.com/fern-api/fern

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

Did you Miss me? PyCon IL 2023

Today I talked about working with missing data at PyCon IL. We started with a bit of theory about mechanisms of missing data –

  • MCAR – The fact that the data are missing is independent of the observed and unobserved data.
  • MAR – The fact that the data are missing is systematically related to the observed but not the unobserved data.
  • MNAR – The fact that the data are missing is systematically related to the unobserved data.

And deep-dived into an almost real-world example that utilizes the Python ecosystem – pandas, scikit-learn, and missingno.

My slides are available here and my code is here.

3 related posts I wrote about working with missing data in Python –