Your approach matters more than the technical details

DynamoDB is an eventually consistent database. There's a lot of fear around that. In this post, we'll discuss how to think about eventual consistency in DynamoDB as well as strategies to work around it.

There are 48 YouTube videos in my Linear Algebra for Machine Learning course, each of which is detailed in this blog post.

In 12 months, @aarondfrancis changed his life by bypassing fear and embracing risk. Now, he’s working his dream job @tuple. Get his full story on The ReadME Project:

Interesting facts from the [software] testing world sorted by date - GitHub - kiwitcms/history-of-testing: Interesting facts from the [software] testing world sorted by date

Supporting a programming language at Meta is a very careful and deliberate decision. We’re sharing our internal programming language guidance that helps our engineers and developers choose the best…

FauxPilot - an open-source alternative to GitHub Copilot server - fauxpilot/fauxpilot

Slack=Chat groups + 65 tool integration (SMS, Google Drives, Twitter, Trello, Asana, Github, Dropbox, Salesforce, Zendesk, etc)+ File…

This post reviews the key takeaways from the DynamoDB Paper, an excellent 2022 paper that details learnings from running and improving DynamoDB.

Note, that this collection of links and topics does not cover the ML/DL part of the exam, in order to prepare for that I’d recommend reading through the book “Geron Aurelien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” and doing some…

A deep technical dive into all things Redis. Covering various Redis topologies, data persistence and process forking.

How to make container images better? What does a better container image really mean? Image size vs the number of vulnerabilites. Pros and cons of using Alpine as a base image. Alpine and distroless alternatives.

A free online textbook — introduction to the theory of distributed algorithms

Learn how to design systems at scale and prepare for system design interviews - karanpratapsingh/system-design

Contribute to google/silifuzz development by creating an account on GitHub.

The code from the Machine Learning Bookcamp book. Contribute to alexeygrigorev/mlbookcamp-code development by creating an account on GitHub.

25 questions I've been asked in previous interviews

Approaches for shipping code to production reliably, every time.

The job of a query optimizer is to reduce the cost of queries without changing what they do. Optimizers cleverly manipulate the underlying data pipelines of a query to eliminate work, pare down expensive operations, and optimally re-arrange tasks.

A tour of recent work to re-engineer Git’s garbage collection process to scale to our largest and most active repositories.