CTO Insights - Issue #126
Software Engineering and Technical Leadership
Welcome to CTO Insights #126!
The newsletter is based on top Software Engineering and Technical Leadership content from the past week, it is free and made for your weekly reading.
This week’s favorite - Three-Bucket Framework for Engineering Metrics.
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Netflix has its own machine-learning platform - Metaflow. With it, Netflix successfully optimizes video encoding, predicts user preferences, and improves the streaming experience.
Any machine learning enthusiast can probably imagine the amount of insightful data there is in this article. So, enjoy reading through all 2000+ words inside.
When organizations become too complex, it significantly hurts scalability. It also negatively affects decision-making, innovation, and so on.
The first thing you’re going to read inside this one is how this complexity can arise. Where it comes from.
Then, a few very effective strategies to save the ship before it’s too late.
Out of so many engineering metrics, which ones should you stick to? This can be a tough dilemma.
Hopefully, by learning more about them, you’ll understand exactly which metrics fit your case best. This guide can help! It introduces a promising framework that should make your troubles go away.
As an organization, you want what’s best for everyone that’s a part of it. You also want a positive and supportive environment for teams.
Here’s how team health checks can contribute greatly to this. You can ensure that they result in creating a culture of psychological safety and promoting open communication and collaboration.
Inside this post: a few very convincing reasons why focusing on achievement is much better than being busy all day with countless activities.
If you’re a product manager, you’ll find plenty of insight here on how to shift your focus to achievement. There are different methods and ways to try.
If you’re wondering whether to pursue a role in product engineering, or you just want to know what it’s like, you might want to read what a very satisfied product engineer has to say about this role.
Trunk-Based Development, or TBD, allows you to integrate frequent code changes into a shared codebase or "trunk”.
The main benefit of this is to significantly reduce merge conflicts. Developers can merge their changes into the trunk any time during the day, maintaining a more stable codebase.
Find out how to properly implement this approach in this guide.
In this post, Lyft offers a detailed explanation of its machine learning tool used to create embeddings of entities within the Lyft platform, such as users, drivers, and rides.
It’s interesting to read about the deep learning techniques they use to create high-quality embeddings that can capture complex relationships between different entities.
Overall, it’s a great article showing the capabilities of machine learning within the transportation industry.
Sometimes, if the market changes or if the customer wants something else, as a developer you will have to experience changing requirements. It’s not a pretty sight, but it happens.
So, how can you deal with this more easily and effectively? You’ll find several strategies to do just that in this guide. One of them includes involving the customer in the development process, but you can take your pick yourself.
The right metrics and data can be your best friends as a manager. They allow you to make correct and effective decisions objectively (the good way) instead of subjectively (the not-so-good way).
But, which metrics should you use? How to avoid the negative consequences that may come with using metrics to make decisions? And, what are some of the amazing benefits that come when doing this properly?
Find out all of that, inside.
Thanks for making it this far! 🙏
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