501–510 of 620 posts
I also invested time in learning Symfony and React for a personal web project I've been developing. For the same reason, after working with Docker for multiple years, I finally started to use it with an orchestrator and I chose Docker Swarm for that.
* Finally understood the concept of lifetime in Rust.
* Learned Go programming language. I loved it, but wished it had generics (waiting for Go 2).
* Golang - I built a simple key-value store based on Raft consensus protocol.
* Golang - I also built a distributed hash table with Kademlia.
* Did some lock-free programming in Golang to implement Lamport timestamps.
* Learned Docker and Kubernetes. I also dived into concepts that are behind Docker like namespaces, cgroups and securing them.
* Learned to use Terraform and AWS.
* Learned some Elixir - haven't done anything serious yet.
* Learned Ruby, Python and some shell scripting - I still look up stackoverflow a lot here. I use them to automate some boring stuffs during development. (like I needed to setup cluster configuration every time I try running the Raft based KV store).
* At work, I learned the Spring framework and a few details about its internals. I got a few questions like - is type erasure a lie? it is just that getting generic types is not so straightforward!
* Played with MongoDB and Node.js - implemented a simple expense tracker backend.
After all of this, I decided I'm a bit overstretched and decided to stick with building cloud/distributed systems mostly with Golang and learn Docker, K8S.
I've also dabbled in saw sharpening for woodworking. I got started with disposable Japanese saws that can't be sharpened but want to move to Western saws which need sharpening. I was successful with rip saws but all my attempts with cross cut saws ended in failure. I'm hoping to find some time to try again, filing back the teeth and filing them again.
Learned modeling for Machine Learning. Took Andrew Ng's ML course then Coursera's Deep Learning specialization. I already had a data science background, doing NLP data collection, processing, and building annotation tools (for text, images, audio, and video) at my day job (Ruby on Rails mostly). I did have to do a lot of brushing up on long-neglected math skills (Khan Academy is amazing!) and continue to do so. Also…
I wasn't aware that Ruby was used in data science. As someone looking to learn Ruby for machine Learning, are there any good books or MOOCs?
Real World Embedded Systems. Built a dishwasher that cleans and sets your dinner table. C++, Arduino, Electrical Engineering, and worked with a mechanical engineer on the design execution. Here is the story, skip to the bottom if you want to see the youtube video- Started this year working on a finance App, this one needed to be perfect. React Native, Laravel PHP mysql. The app is almost finished, and is worthy of a…
I implore you to start a company if you're at all interested in working on this further. Seriously, please consider productizing it. I would pay thousands of dollars for a polished version of this in my home, especially if it matched the style and decor of my dining room furniture.
No offense intended for this project personally, this is really cool. But what was that joke about how since most founders are young single males, most tech companies make products nobody really needs that are just designed to replace your Mom?
Julia :) For scientific programming, it's a lot more fun than Python. And so far the community if friendly and responsive.
Then the package authors, who often do research in the relevant fields, will start long discussions about the application context and the best approach to take (beyond Julia implementation).
That's both fun and a wonderful opportunity to learn, especially across narrow academic fields.