evandev

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> Today, I use the SteelSeries Sensei models which kind of are modern versions of this mouse: https://steelseries.com/gaming-mice/sensei-raw [0]

0: https://news.ycombinator.com/item?id=19851273

The bolt gun is not to stun them, but an ethical method of killing an animal.

> A captive bolt stunning gun kills the animal and reduces it instantly unconscious without causing pain.

https://www.grandin.com/humane/cap.bolt.tips.html

A lot of the structure is wood.

They are talking about changing the company's name behind Wikipedia to have the same name as the product.

There are still many that have heating oil. I live in NH and have heating oil. I also had heating oil while living in central PA.

To be fair, there are problems with the mirror test.[0] Not being able to replicate, reading into animal's behaviors that isn't there.

[0]: https://www.quantamagazine.org/a-self-aware-fish-raises-doub...

The issue with getting a rabies shot if you come into contact with them is that in the USA, it can cost $3,000 easily ($1634 to $8415)[0]. Which when you assume most people live in a household, can be quite expensive. And if you look at the exposure to bats vs. human infected rate of rabies, it is so exceedingly rare.[1]

[0]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5525325/ [1]: https://academic.oup.com/cid/article/48/11/1493/347031

USA is one of those locations, but bats have approximately 0.5% infected rate estimated. 6% measured, but when you take into account that most of the tested bats were sickly, injured, easily captured, etc. Overall, it is very hard to measure, so it is pretty safe to assume around 0.5%[0]

[0]: https://batworld.org/rabies-info/

The P in HIPAA doesn't stand for Privacy.

They said "adding property owners may need to run their water for a few minutes to clear their service lines." I would think that their hot water tank got some of the pink water into it and unlike the cold line the pink will slowly dissipate.

Even better than that in my opinion is the snowpeak collapsible pour over [0]. It's metal, sturdy and packable. I bring it in my carry on on all trips I go on plus backpacking. Pair with a hand grinder and you're set.

0: https://snowpeak.com/collections/gifts-under-100-dollars/pro...

It has its own problems: https://jhalderm.com/pub/papers/ivoting-ccs14.pdf

Along with contacting Senators, there is also a Whitehouse petition.[0]

0: https://petitions.whitehouse.gov/petition/do-not-allow-myron...

My thoughts (a little of a Rant) on R as the Lead Engineer at a data science focused company is that R is a great statistical language, but a poor programming language. I use the term programming language as a language which is very versatile for a variety of needs (web app, commandline app) such as python, ruby, etc. R has the capabilities to use as a programming language like a climbing rope can be used as a belt. It can, but shouldn't because of some points I have below.

It is great for exploratory analysis, as it is forgiving and easy to use in the console for testing things; but once it needs to be put into practice, it has issues. For a non-programmer, grasping R isn't too hard thanks to some great developers in the community.

There is a lot of good in the R community, but people are focused on making it isn't. Just look at deploying R into production, that can be a nightmare. I've spent days looking over code to figure out where an error in production lies. One of the errors was a package of a package which was updated for the first time in years. That package depended on another package which my package called another function that called the first one; basically it was a mess of dependencies. And there are some misconceptions, while doing the engineering work in R and learning I learned not to use for loops. Then one day I timed it and the for loop was 10x+ faster than any apply/plyr function including using a gpu.

The things that separate a programming language from a statistical language are a programming language have more than one of these:

* Good dependency management

* Easy deployment into production environment

* A clear way to setup environment (e.g. naming, folder conventions)

* Ability to do most of the things you want with the base packages

* Good documentation about the above.

Basically, I believe a good data scientist is someone who can use R (or something else) to explore data and then create the algorithm in a compiled language to be put in production. And for someone who just needs to create analysis for research or a paper, R is the perfect use case. R is an excellent language for its use cases, just don't think about using it for general programming. It has caused a lot of extra dev hours working on issues with it.

Little plug, we wrote a piece on hiring data scientists.[0]

[0]: https://gastrograph.com/blogs/gastronexus/interviewing-data-...

Awesome! I really like how it is "git push to deploy". This is pretty perfect for a couple ideas are company is planning.

We're based near a large University and hire Masters/PhD students about to graduate first as a part time/intern then full-time. It has worked out pretty well for us.

That would be great. It's surprising to me that every docker startups is focused on running large clusters, instead of a few focused on running very few images on small embedded devices. I could see a really nice continuous integration pipeline for the devices and it would speed up deployment for new users wanting to explore IoT.

I work for a startup[0] that does quality control through machine learning for the beverage industry. We're focused most on beer, followed by coffee and spirits.

[0]: https://gastrograph.com

I disagree with R being more useless without r studio. I'm not a fan of R overall, but I run everything in tmux+vim and R is the same way. I prefer it to Rstudio. It's popular, because it makes a few choices which are different many programming languages to be geared towards writing scripts for statistics. (e.g. index 0, assignments)

The site isn't https. http://mcafee16.com/

I'm running arch on my x230. Make sure you don't get a x230 with Realtek. If you do, quickly replace it with a Intel card[1].

[1] http://www.amazon.com/gp/product/B008V7AAJU/ref=oh_aui_detai...

I have a X230 that had one of those Realtek cards. It would drop a lot and be rather funky since I run linux 99% of the time. Then it finally died in February so I spent $13[1] for a replacement non-realtek. It has worked absolutely great, even with a minimalist Arch setup.

[1] http://www.amazon.com/gp/product/B008V7AAJU/ref=oh_aui_detai...

Actually, the startup[0] I work for has found a way to quantify subjective flavor space. We use data science, machine learning, and advanced algorithms to analyze human sensory. We're focused on the craft beverage market as of right now, mainly in quality control and flavor profiling.

[0] https://www.gastrograph.com

I really enjoyed this!

I'm really interested in knowing more about the tech stack you are using. I work for Analytical Flavor Systems[0], where we do heavy data science in R and then display everything in R Shiny. Currently I'm looking at different methods of moving off the R Shiny platform as our application has gotten really complex.

The main problem is we are constantly running our algorithms when new data comes in per producer and we have to display it.

[0] https://www.gastrograph.com

The biggest problem I have overall is pushing new code. When you push new code to git, do you then stop a container and restart it to get a new container working? (Assuming you do something like git clone in the Dockerfile)

Covering a beer up with a lot of hops and malts isn't so much to hide the off-flavors. Rather, some producers like to weave together complex malts and hops into a balanced product.

And the off-flavors aren't a by-product of the size of the producer. I work in the industry doing quality control at the same company[1] as another commenter, and we see inconsistencies with producers the size of a local brewpub to large producers that sell to 7+ states.

Using technology can help reduce inconsistencies, but the end goal it to never let the consumer taste a bad batch. It is cheaper to dump a bad batch than to ship it, as this may result in loss of customers.

Bigger producers know this and will more readily dump a bad batch than a smaller producer.

Lastly, I have seen a producer that ships to 3 states have a manual brewing system, while a small neighborhood brewpub that just started distributing uses a fully automated system.

[1] www.gastrograph.com