💥 The Engineering Digest #7 is here💥
👉👉👉 Building an intelligent experimentation platform at Uber.
👉👉👉 Take OpenTracing for a HotROD ride. OpenTracing is a new, open standard for instrumenting applications and OSS packages for distributed tracing and monitoring developed in Uber.
👉👉👉 Detecting abuse at scale: locality sensitive hashing. With five million plus Uber trips taken daily worldwide, it is important for Uber engineers to ensure that data is accurate.
👉👉👉 We analyzed thousands of technical interviews on everything from language to code style. Here’s what we found.
👉👉👉 Beginner’s Guide to…
💥 The Engineering Digest #6 is here💥
Did I run out of space cats? Absolutely not! So, after a season break the digest is back at its normal cadence 😃
👉👉👉 Extreme Event Forecasting at Uber with Recurrent Neural Networks. At Uber, event forecasting enables us to future-proof our services based on anticipated user demand. The goal is to accurately predict where, when, and how many ride requests Uber will receive at any given time.
👉👉👉 Network protocols for anyone who knows a programming language. The network stack does several seemingly-impossible things. …
💥 The Engineering Digest #5 is here💥
This digest isn’t going away, it just took two extra days to collect some awesome stuff to read 😃
👉👉👉 Kappa Architecture. Kappa Architecture is a software architecture pattern. Rather than using a relational DB like SQL or a key-value store like Cassandra, the canonical data store in a Kappa Architecture system is an append-only immutable log
👉👉👉 A huge collection of static analyses tools for pretty much every production programming language. Some of this stuff we are also using at Uber.
👉👉👉 Storage engine design…
💥 The Engineering Digest #4 is here💥
This week’s digest is short, but very informative :)
👉👉👉 HOODIE: UBER Engineering’s Incremental Processing Framework on Haddop
👉👉👉 Five Things I’ve Learned As A New Manager At Google. One Googler explains how she’s learned not to (just) be a “crap umbrella” for her team members.
👉👉👉 Meet The Man Who Makes Facebook’s Machines Think. Nearly 3,000 miles away from Facebook’s Menlo Park headquarters, in an old, beige office building in downtown Manhattan, a group of company employees is working on projects that seem better suited…
💥 The Engineering Digest #3 is here💥 — Don’t forget to subscribe in Telegram (https://t.me/engdigest) and now on Medium (https://medium.com/@yasik)
New fact about Jeff Dean — Jeff Dean once shifted a bit so hard it ended up on another computer.
👉👉👉 How To Spot Symptoms Of Employee Burnout On Your Engineering Team. A new survey shows that stress-induced burnout is going to be a big problem for managers in 2017.
👉👉👉 Despite its title, a great view on productivity, focus and achieving great results by working smart.
“Figures as different as Charles Dickens, Henri Poincaré, and Ingmar Bergman, working…
💥 Tech digest #2 is here💥 — And now in Telegram as well https://t.me/engdigest
The hero of this digest is Jeff Dean (https://research.google.com/pubs/jeff.html). Jeff Dean is Chuck Norris of computer science. Fact about Jeff Dean: Jeff Dean puts his pants on one leg at a time, but if he had more than two legs, you would see that his approach is actually O(log n). Check out other facts about Jeff, they are hilarious :)
👉👉👉 Q+A With Jeff Dean: The Brain Behind Google’s Artificial Intelligence
👉👉👉 How Google is remaking itself as a “Machine Learning First” company and the…
I’m starting my new weekly digest that will be primarily focused on 👉 engineers and tech savvy entrepreneurs 👈.
A lot of folks are reaching out to me lately asking about what should they read or study to become better engineers (Type 1 https://teachyourselfcs.com), how to prepare for the interviews in Silicon Valley companies, where to find their next ideas or what challenges to take on next in their career.
I read A LOT and filter out a lot of not worthy readings (it takes a lot of mental energy btw…). So, consider me your personal online bullshit filter :)
Meet Keras. Keras is a high-level neural networks library, written in Python.
Since the rise of neural networks in production environments–those with practical real life applications–the field has been primarily a privilege for scientists specializing in machine learning and complex math theories. I like to compare the state of AI by drawing a line back to 50’s, when the same statement applied to early days of computer science. Earlier, to write a program, a scientist had to implement a decent amount of math and then to translate it into machine codes, which ran on a particular hardware.
It was true…