fohara

Fixed. Thanks again for the report and sorry for the hassle!

Ouch, thanks for the heads up! Will have a fix up shortly.

Thanks! It generates mutant by parsing the source code (via tree-sitter) and then using a collection of operators to make changes such as invert booleans, flip comparison operators, etc.

It is built with certain testing libraries in mind, especially for defaults, but it is also extensible such that you should be able to get it working with your custom testing library. The `flawd init` command should provide some meaningful context, and I would be happy to lend a hand with config if you're up for sharing some more context. Feel free to contact me through the site or via email (in profile)

The associated paper references Judea Pearl's theories on causality, but curiously doesn't mention the DoWhy implementation [0], which seems to have some recognition in the causal inference space.

[0] https://github.com/py-why/dowhy

Apparently some of the software in Nikola vehicles was written in Crystal [1]. [1] https://manas.tech/blog/2020/02/11/nikola-motor-company/

Did it compile to wasm and run at the edge at scale?

The reactive data store bits seem very similar to baobab.js [0]. Baobab's api seems a bit more terse though, which I prefer.

[0] https://github.com/Yomguithereal/baobab

I'm surprised Kivy [0] doesn't get more attention for desktop app development. Apps are developed in Python, it's cross-platform and MIT licensed. Running the hello world example uses just ~2.5% cpu and ~57mb memory on a 2015 mbp.

[0] https://github.com/kivy/kivy

This seems very similar to GitXiv: http://www.gitxiv.com . Maybe there is an opportunity to join forces.

Does Substrate VM have cross-platform support for major operating systems (i.e. for Linux, Windows, MacOS)? If so, can a binary compiled on MacOS run on Windows, and vice versa?

Seems similar to OpenWhisk: https://github.com/apache/incubator-openwhisk . The built-in metrics reporting of OpenFaas looks useful though.

Nice. Maybe it's finally time to upgrade to the latest Firefox for testing. We had been pegged at an older version because we ran into some issues with the new (at the time) MarionetteDriver[1], but I imagine the migration might be smoother now.

[1] https://developer.mozilla.org/en-US/docs/Mozilla/QA/Marionet...

I'm wondering how this compares to Xtext[1], which is another framework for developing domain specific languages. Xtext feels more approachable to me, but maybe that's because there appears to be a lot more documentation and tooling.

For example, the docs on Xtext's grammar language[2] seem very intuitive to me, even though I'm not experienced in compilers or language design. I don't have quite the same intuition when looking over the AnyDSL docs[3]. Maybe the Xtext docs are just more goal-oriented, i.e. "Five simple steps to your first language".

[1] https://eclipse.org/Xtext/index.html [2] https://eclipse.org/Xtext/documentation/301_grammarlanguage.... [3] https://github.com/AnyDSL/anydsl/wiki/Tutorial

"This code prints 'Hello, world!'

  밤밣따빠밣밟따뿌
  빠맣파빨받밤뚜뭏
  돋밬탕빠맣붏두붇
  볻뫃박발뚷투뭏붖
  뫃도뫃희멓뭏뭏붘
  뫃봌토범더벌뿌뚜
  뽑뽀멓멓더벓뻐뚠
  뽀덩벐멓뻐덕더벅

"

I wonder how this compares to BurlapCraft[1], which is a mod of Minecraft integrated with BURLAP- a reinforcement learning and planning library.

[1] http://h2r.cs.brown.edu/announcing-burlapcraft/

I think this is solid advice. I continue learning by picking problems/inconveniences I encounter while working during the day, and then doing whatever research is required to make those things less inconvenient. In the process of researching the problem space, I'm often introduced to new programming concepts and tools that I would have otherwise never known about. Overall, I find it easier to learn new things if I have a real problem I'm trying to solve.

Automated layout analysis is pretty tricky. The data reported from the DOM is not always reliable, so it's helpful to couple it with computer vision data. I spoke about an integrated approach at Selenium Conf 2013: http://www.youtube.com/watch?v=DF0QUD0kuiQ

I'm wondering if there were any specific reasons as to why Weka was used instead of Mahout for the machine learning chapter, as both are in Java.