Carlos Aguni

Highly motivated self-taught IT analyst. Always learning and ready to explore new skills. An eternal apprentice.


June 2021 Interesting Reads

02 Sep 2021 » monthly-awesome
  • https://www.educative.io/courses/competitive-programming-intvw
  • http://allynh.com/blog/adding-a-react-frontend-to-your-flask-project/
  • https://jmmv.dev/2021/04/always-be-quitting.html
  • https://3dlg-hcvc.github.io/plan2scene/
  • [Updated! Aug 14 2020] YouTube recommended encoding settings on ffmpeg (+ libx264)
    • https://gist.github.com/mikoim/27e4e0dc64e384adbcb91ff10a2d3678
  • Install ESXI 6.7 on Dell OptiPlex Desktop
    • https://www.programmersought.com/article/88836730279/
  • Dell EMC imagem personalizada da VMware ESXi a disponibilidade e as instruções de download
    • https://www.dell.com/support/kbdoc/pt-br/000176963/dell-emc-personalizada-image-of-vmware-esxi-availability-and-download-instru%c3%a7%c3%b5es
  • https://rodrigolira.eti.br/isos-esxi-customizadas/
  • pyvmomi collector gettasksbyuser.py
    • https://github.com/jramacha/pyvmomi-community-samples/blob/master/samples/gettasksbyuser.py
  • pyvmomi relocate_events.py
    • https://github.com/vmware/pyvmomi-community-samples/blob/master/samples/relocate_events.py
  • https://awesome.cube.dev/?framework=react
  • Ask HN: Which book or course gave you an unfair advantage?
    • https://news.ycombinator.com/item?id=27636743
      • Fooled By Randomness (NN Taleb): Taleb is a complicated personality, but this book gave me a heuristic for thinking about long-tails and uncertain events that I could never have derived myself from a probability textbook.
      • Designing Data Intensive Applications (M Kleppmann): Provided a first-principles approach for thinking about the design of modern large-scale data infrastructure. It’s not just about assembling different technologies – there are principles behind how data moves and transforms that transcend current technology, and DDIA is an articulation of those principles. After reading this, I began to notice general patterns in data infrastructure, which helped me quickly grasp how new technologies worked. (most are variations on the same principles)
      • Introduction to Statistical Learning (James et al) and Applied Predictive Modeling (Kuhn et al). These two books gave me a grand sweep of predictive modeling methods pre-deep learning, methods which continue to be useful and applicable to a wider variety of problem contexts than AI/Deep Learning. (neural networks aren’t appropriate for huge classes of problems)
      • High Output Management (A Grove): oft-recommended book by former Intel CEO Andy Grove on how middle management in large corporations actually works, from promotions to meetings (as a unit of work). This was my guide to interpreting my experiences when I joined a large corporation and boy was it accurate. It gave me a language and a framework for thinking about what was happening around me. I heard this was 1 of 2 books Tobi Luetke read to understand management when he went from being a technical person to CEO of Shopify. (the other book being Cialdini’s Influence). Hard Things about Hard Things (B Horowitz) is a different take that is also worth a read to understand the hidden–but intentional–managerial design of a modern tech company. These some of the very few books written by practitioners–rather than management gurus–that I’ve found to track pretty closely with my own real life experiences.
      • The Linux Programming Interface
  • https://copdips.com/2018/07/use-pyvmomi-EventHistoryCollector-to-get-all-the-vcenter-events.html
  • https://mgdm.net/weblog/systemd/
  • https://medium.com/python-point/mqtt-and-kafka-8e470eff606b