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A few months ago I was asked to take charge of onboarding a new developer in my company.
I made a list of topics to be included in the training program, set them in a spreadsheet, gathered all the guides and knowledge-shares I worked on for the team & company since I started working in this position, and created an A-Z training plan for my new mentee.
Now that I can reflect on the process, and say it was quite smooth, I want to share with you some tips I find to be effective for onboarding.

1. Today’s Documentation Is Tomorrow’s Onboarding Material — Record Training Sessions & Document Low Hanging Fruits

A successful onboarding starts…

This is an appendix article, written following the article Log Your Flow.
The purpose of this article is to summarize and exemplify the different log levels that we use in our code.

The levels explained below are ordered by severity level - ascending order.
When we read logs, we can expose the desired severity level. Exposing a specific severity level - exposes to us, the readers, the higher levels as well.
So if we choose “info” level, for example, warning & error & critical will be visible to us as well. …

Have you ever had a bug in prod, and you opened your logging system only to figure out that the logs are not indicative enough to easily trace what happened and why?
Well, I can promise you it never happened to me ;-)

But if it had, I’d be sure to learn the lesson and make my logs much more informative and indicative for the future.

Today I will share with you the conclusions I have gathered regarding how to make our logs as useful as possible for future debugging. And may your features never have bugs in prod!

Use Indicative Entity IDs


In the previous article, I introduced FuzzyWuzzy library which calculates a 0–100 matching score for a pair of strings. The different FuzzyWuzzy functions enable us to choose the one that would most accurately fit our needs.

However, conducting a successful project is much more than just calculating scores. …

About a year ago, I saw a colleague of mine working on a large data set, aiming to measure the similarity level between each pair of strings. My colleague started to develop a method that calculated the “distance” between the strings, with a set of rules he came up with.

Since I was familiar with the FuzzyWuzzy python library, I knew there was a faster and more efficient way to determine if a pair of strings was similar or different. …

Naomi Kriger

I’m a software developer with previous experience in risk & data analysis, working in a FinTech company.

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