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