tl;dr in tech - Issue #84

Recognition And Rewards At Work#

By: Lara Hogan

tl;dr: Lara Hogan emphasizes that “What we recognize is what we reward.” She provides an exercise to help us better understand how we recognize and reward our teams, highlighting the importance of reinforcing positive behaviors in the workplace. A great read for software engineers looking to improve their team dynamics.

Inside Uber’s Move To The Cloud: Part 1#

By: Gergely Orosz

tl;dr: Gergely Orosz provides an in-depth look at Uber’s history of data centers, challenges of operating them, and incentives for moving to the cloud. He also offers a primer on data centers, regions, and availability zones. A must-read for software engineers interested in cloud computing and data center management.

How Discord Stores Trillions Of Messages#

By: Bo Ingram

tl;dr: Bo Ingram discusses Discord’s struggles with their Cassandra cluster and their migration to ScyllaDB, a Cassandra-compatible database written in C++. A fascinating read for software engineers working with databases and seeking insights into real-world scalability problems.

Project Management For Software Engineers#

By: Kevin Sookocheff

tl;dr: Kevin Sookocheff breaks down the five phases of managing an engineering project according to the Project Management Institute. This informative article covers initiation, planning, execution, monitoring and control, and closure. Essential reading for software engineers in management roles or looking to improve their project management skills.

Hired’s 2023 State Of Software Engineers#

By: Hired

tl;dr: Hired’s 2023 report reveals trends such as engineer optimism, the impact of layoffs on junior and non-traditional engineers, the high demand for remote engineering talent, and shifts in the most in-demand coding skills and software engineering roles. A valuable resource for software engineers to stay updated on industry trends.

Scaling Media Machine Learning At Netflix#

By: Netflix Tech Blog

tl;dr: Netflix shares their approach to building ML infrastructure to reduce time from ideation to productization, including accessing and processing media data, training large-scale models efficiently, productizing models in a self-serve fashion, and storing and serving model outputs for consumption. A captivating read for software engineers working with machine learning and media processing.

Some Possible Reasons For 8-Bit Byte#

By: Julia Evans

tl;dr: Julia Evans explores the question of why the x86 architecture uses 8-bit bytes and evaluates various possible reasons, including historical accidents, optimal sizing, or a mix of both. A thought-provoking read for software engineers curious about the origins of computing architecture.