| title | Tipsrundan 51 | |
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| description | Tipsrundan 51 sweeps in with new concepts, great articles and videos! | |
| slug | 51 | |
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👋 Welcome to Tipsrundan! A biweekly newsletter by AFRY IT South with ❤️
Tipsrundan 51 sweeps in with new concepts, great articles and videos!
A new experiment to make Tipsrundan even greater is here. Make sure to join #tipsrundan on Slack!
Psychological Safety is incredibly important as was noted last time in Tipsrundan. Here we go further with a new article from Harvard Business Review which explains why it’s 100% needed to actually succeed with an agile approach.
Many companies try to adopt the Agile Manifesto but fails as they don’t provide the psychological safety. Make sure to read this article to learn five (5) practical ways to increase psychological safety.
Insightful and enjoyable are the two keywords I take from this great presentation by Rich.
Rich makes it not only enjoyable to listen about simplicity but also shares a lot of insights he has made during the years. Rich is without a doubt a great programmer and built Clojure which is known for its syntax and simplicity (as fast as you get past a certain threshold).
Edward Tufte - on of the greatest UX designers of our time has some input on lists, bullet points and much more.
What makes them good? What makes them bad?
Bullet point often summarize things too harshly and loose context that another reader might be missing.
When designing medium to large sized menu navigations on the mobile web the default go-to, for some time now, has been hamburger menus. This isn’t necessarily a bad thing, but there is a simpler alternative for certain use cases.
Before we get into the nitty-gritty details (and a simple demo) of the sausage link concept, let’s take a quick look at the pros and cons of hamburger menus.
Hadoop? Spark? Big Data is expensive with a ton of overhead..
As I was browsing the web and catching up on some sites I visit periodically, I found a cool article from Tom Hayden about using Amazon Elastic Map Reduce (EMR) and mrjob in order to compute some statistics on win/loss ratios for chess games he downloaded from the millionbase archive, and generally have fun with EMR. Since the data volume was only about 1.75GB containing around 2 million chess games, I was skeptical of using Hadoop for the task, but I can understand his goal of learning and having fun with mrjob and EMR. Since the problem is basically just to look at the result lines of each file and aggregate the different results, it seems ideally suited to stream processing with shell commands. I tried this out, and for the same amount of data I was able to use my laptop to get the results in about 12 seconds (processing speed of about 270MB/sec), while the Hadoop processing took about 26 minutes (processing speed of about 1.14MB/sec).
Functional Programming much? Then I’m 100 % recommending trying to find something interesting here!
n.b. there’s a lot of erlang/elixir, but that’s pretty cool 😎
Do you wanna learn more about exactly how GPU, CPU & RAM works together for Deep Learning models (AI)? And internals of the GPU? Then this blog is 110 % made for your enjoyment!
Excerpt:
So, you want to improve the performance of your deep learning model. How might you approach such a task? Often, folk fall back to a grab-bag of tricks that might've worked before or saw on a tweet. "Use in-place operations! Set gradients to None! Install PyTorch 1.10.0 but not 1.10.1!"
Thank you for this time see you in two weeks
- Hampus Londögård @ AFRY IT South