"The Art of Insight in Science and Engineering: Mastering Complexity" by Sanjoy Mahajan.

It beautifully treats estimation and problem solving techniques, illustrated by examples from science and engineering. Instead of aiming for a complete, thorough and accurate treatment of problems, its goal is to teach shortcuts to sacrifice some accuracy for much reduced effort. This is a refreshing change to academia where rigor is often pursued at all cost. But in the real world rigor rarely matter, and simplifications are almost always worthwhile, especially initially since we can always refine models if required.

I first read it as an undergraduate and use the estimation and problem solving techniques from it almost daily. Though well hidden, the pdf is available for free on the website of the publisher.

Datadog | Software Engineers | ONSITE (Boston, Denver, Lisbon, Madrid, NYC, Paris, Tel Aviv) and REMOTE | Full-time

Datadog is a monitoring, tracing, logs system, and more, for your infrastructure and services. We build our own tsdb, event store [1], distributed tracing tools, cutting edge visualizations, and more. We love shipping great experiences for customers just like us and are growing fast! We write a lot of Go, Java, Python, Typescript (with React), and a bit of other languages. We run on k8s, and are multi-region and multi-cloud.

We're looking for people who can build systems at scale as we process trillions of events per day. Let us know if that's you!

https://dtdg.co/hnwhoshiring

[1] https://www.datadoghq.com/blog/engineering/introducing-husky

That means Go was easy for those devs to pick up, not that Go is simple.

Rich Hickey gave an all time classic presentation about this seemingly nitpicky but important distinction https://www.youtube.com/watch?v=SxdOUGdseq4

Designing Data-Intensive Applications by Martin Kleppmann https://www.goodreads.com/book/show/23463279 It’s usually number 1 on these lists but definitely deserves it!

"tell me what tools you've made and i'll tell you who you are." Really.

Probably i make one sw tool per month - whatever annoys me enough to go over the lazyness treshold.

Most of my toys (made last 25+ years) stay at https://github.com/svilendobrev/svd_bin

https://github.com/svilendobrev/svd_util (python stuff)

apart of the bigger ones there with own repos

most used stuff seems:

* shell wrappers/configs to make using terminals a more humane+repeatable thing. A system (="language") of plenty of aliases and shortcut keys, for many combinations of x-terms/shells, so regardless of which one it is, it gets same human interface. Remember 4dos? there. F2 is dir/ls..

* similar thing for vim. F2 for save. ctrl-f for find. etc..

* vcs.sh - similar thing for 5-6 revision control systems

...

* sound files searching/cutting/diffing - searching one record within another - think recognizing start and end signals of some radio show, cutting it out from several alternative recording sessions - e.g. digitized analog vs http/rtmp streams; and diff'ing the results ; the timeline can be warped a lot (Wow and flutter, yes, with some charts out)

* hundreds of others.. some of them i dont remember anymore what they were for :/

have fun

Decoder Ring: https://james.darpinian.com/decoder/ to look up error codes. Started with OpenGL, then added Windows error codes, Linux, HTTP, and Vulkan.

I was a bit frustrated always pasting error codes into Google, as it doesn't always come up with the best result. You often have to extract just the code from a larger message, and potentially convert to/from hex or signed/unsigned, e.g. Windows error codes like "-2005270521". My tool handles all that for you. Just paste an error message containing codes in whatever format and it'll find them, and it's incredibly fast.

I also made https://aqi.today during the California wildfires. I was frustrated by other air quality sites that load way too slowly and don't emphasize the one number that matters. Airnow.gov has improved since I made this, but but I still prefer mine for the simplicity, speed, and much better data sourced from Purpleair. Airnow.gov sensors are typically 5+ miles apart, and data is delayed by an hour or more, while air quality can vary on a block-to-block and minute-to-minute basis. Purpleair has far better sensor coverage and data is delayed only 10-20 minutes.

I wrote a tool that reads numbers from stdin and outputs the percentile statistics of those numbers.

https://github.com/zdwolfe/zstat

https://pypi.org/project/zstat-cli/

    $ cat nums.txt
    456
    366
    695
    773
    ...

    $ cat nums.txt | zstat
    p0      =       56
    p50     =       366
    p90     =       773
    p95     =       773
    p99     =       826
    p99.9   =       826
    p100    =       826