CTO Insights - Issue #128
Software Engineering and Technical Leadership
Welcome to CTO Insights #128!
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 - The Best Managers Don’t Fix, They Coach.
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A Journey Through Slack's Notification Backend Systems
Slack achieved 100% sampling of notification traces, simplified trace instrumentation, and improved customer experience.
The approach they took involved tracing the flow of notifications across its systems.
To prevent spoiling all the fun, the most I will say here is that they standardized the data format and semantics of events, created a notification spec, and used SlackTrace to model notification flows as separate traces for better trace instrumentation.
How eBay Modernized the Most Important Page on the Platform
eBay’s “View Item” page is undoubtedly one of the most important pages on the platform - it receives over 250 million views per day. No significant changes have been made to that page in the last 10 years.
Okay, so imagine how exciting revamping this page could be. You’ll find all of that in this story.
The Best Managers Don’t Fix, They Coach
If you’re a manager and you love to fix everyone’s problems, this post will show you plenty of reasons why you shouldn’t be doing that.
Instead, coaching those who need their problems to be fixed has a much better outcome, for everyone!
In addition to 4 great coaching tools for managers, the post also explains when not to use coaching. It’s a pretty insightful article.
How to Plan as an Engineering Executive
Planning is an infinite game, and there’s always a bit of mess at every stage. The key thing is to continue improving bit by bit!
This is a fairly comprehensive article highlighting what it’s like to plan a great roadmap.
It discusses annual budgeting, maintaining Engineering’s target functional allocation, and more critical aspects of the job.
The Journey to Becoming a Product-Led Company
There’s a lot to go over in your journey to becoming a product-led company. There’s also a lot that can go wrong.
This article highlights the most important things to consider when going from a service-led to a product-led business model.
If that’s something that you need to learn more about, you have an entire case study here that will prepare you for some very important scenarios.
Can You Survive 90 Days as an Engineering Manager?
Find out what it’s like to transition from an Individual Contributor to a full-time Engineering Manager at a software engineering company.
You will have to quickly adjust to an entirely new domain to you, deal with pretty high expectations, and manage administrative responsibilities such as sick leaves and vacation requests.
So, can you do it? There are a few tips inside that may give you all the help you need.
How ChatGPT Works: The Model Behind The Bot
We’re learning more and more about ChatGPT each and every day. By now, millions of people are using the tool each month.
But, do we fully understand how it works yet? 🤔
There’s still much to learn about it, and this guide will be a big help in that quest. So, if you want to dive into the technical side of ChatGPT, here’s a post you’ll love.
Using Metrics Layer to Standardize and Scale Experimentation at DoorDash
If you’re interested in learning about the importance of metrics to improve experimentation, product development, and overall business performance, you might want to check out DoorDash’s approach.
Viral Spam Content Detection at LinkedIn
Viral spam content is about to start disappearing on LinkedIn.
In fact, LinkedIn explains in this post the approach they are taking to reduce such content, which involves launching multiple AI models.
If all works out, we should start seeing less spam content that doesn’t even belong on LinkedIn in the first place.
Big Savings On Big Data
Find out what contributes the most to the cost savings and increased productivity in Lyft's ML platform. 🔍
In the article, Lyft explains how the company saves a lot of money and time on big data and ML workloads. I’m sure all of us wouldn’t mind having some of those benefits.
So, there are enough charts inside to explain the whole thing and potentially let you enjoy some of the benefits yourself.
Thanks for making it this far! 🙏
I hope you enjoyed this week’s issue. Use the thumbs up/down buttons to rate and don’t forget to share the newsletter with fellows.