thebeardedone

It's been a while but I would benchmark how ClamAV fares. At $(day job) we implemented scanning for file uploads and after our security team tested they noticed that it detected things fairly poorly (60% was on the high side) for very well known malicious content iirc.

We ultimately scrapped it, if anyone else has any better experience I'd love to hear it.

I have been victim of 3 evos myself, i didnt get very far into the configuration process (db, transmission, etc) before them more or less being rendered useless. The ssd evos are great but the sd cards are simply horrible, tragically I saw them locally bundled with pi starter kits. I ended up buying a sandisk which has not had a single hiccup even after a little bit of abuse.

I recently had a similar experience; our product at work is a monolith not in the greatest shape as it has technical debt which we inherited and our product is usually used condescendingly when talking to other teams working on different products. To our surprise when we started testing it with cloud deployments, it was really lightweight compared to just one of the 25 java micro-services from the other teams.

Their "microservices" suffered from the same JVM overhead and to remedy this they are joining their functionalities together (initially they had 30-40).

Sorry for the mix up, I was replying to rb808's post using the HN app on my tablet, no idea what happened :(.

Regarding HPC, from what I remember they usually have old kernels which are running (2.6) for compatibility reasons where Docker usually is not supported (unless it is backported like in RHEL).

It really depends on what you are trying to do with it, for example we have around 15 different integration test configurations that run every night for which VMs may better suited as we want to test installation and deployments automatically for 3 distributions (6 different versions ex. Ubuntu 14.04, ubuntu 16.04, centos 6, centos 7 etc..) and the last 2 windows server. The good thing is that they reproduce the customer environment.

But they have large down sides as well which slow us down. They are a pain to maintain as they are somewhat undocumented (you make a poc for 1 and management always wants more without improvments), a lot of edge cases cause issues which are tough to reproduce (locally sometimes impossible and waste a lot of time) and it takes them a while to start, run, etc.

This is not too tragic for nightly tests as we get the results in the morning but for tests which are started every hour, you do not want to wait that long to verify your changes work/didnt break anything. You can do these in stages, where you create different images based on the result of a previous job (run basic tests that cover base functionality that should always work, then run more in depth tests, then run performance tests at the end to ensure no significant degradation was introduced, etc..) and send out notifications asap in case of failure. The Dockerfile is essentially the documentation as you can see what is installed/configured. You can run everything locally just as it would in a k8 env. which for some reason every one always struggles with.

I am sure there are also edge cases with Docker that are a pain as well but the other selling points show it may be the right direction. You just havevto find use cases and evaluate them.

Thank you for the reply but sadly there is another issue.

Similarly to you, I was educated in North America where ironically integrals were not part of the curriculum, but when I arrived in Europe I had to take 3 entrance exams: English (the exam is very awkward for the native speakers, you get 2 questions: what is your name and why are you hear after they notice that you do not have an accent), math (their curriculum with the missing content I did not have) and German.

As I understand, if a local does not have a matura in the required fields, they have to take similar examinations which should prepare them.

I think you had a very nice professor though.

Amusingly I had a similar experience while holding a tutorial for math 2 at my university. After an hour a student asked me what the integral symbol is, as if she has never seen it before. Now I do not find this very tragic as I would gladly recap anything and working throughout my studies I know one might not be able to get through all of the content, but good luck studying when you are missing the basics to even understand the problem at hand.

Woops read the wrong parent comment. Ignore me :).

Care to elaborate? I havent done any frontend stuff in a few months but it appears to be the only solution there. Even when using typescript, you still want and need to know js.

Regarding Java, i think its a good starting point if you want to hack your first web project together. You sort of see how everything works together. Before that I always recommend looking at C just to know what you are missing out on. If there is some calculation happening you can always call some C/C++ code to see if you get some speed ups by optimizing for the cache (just an interesting experiment if youve never done it before.

I did not know that there is a clause in the license agreement about not being allowed to disclose benchmarks of databases.

Recently I came into discussion regarding why someone chose to use Oracle instead of Postgres; the argument that someone brought up was that they did not know how Postgres scaled. After pointing out that the data that will be stored will be most likely be a few hundred GB (in an exaggerated worst case scenario) and that Postgres is said to handle 100's of TB of data, they capitulated and said that customers trust Oracle (even though they never see or touch the database).

Personally I would be very interested in seeing comparisons between PG, Oracle and MSSQL as well for different data-sets/use-case scenarios. This would really help as a reference in the future when someone else is making critical decisions which might not appear to make sense.

EDIT: This sounds like a very shady clause; anyone with law insights know whether this would be enforceable in the EU?

Really cool and interesting visualization.

Since this is most likely a common chart for people looking at climate change and it may be explained by the parents feedback, I was wondering if anyone knew what shades are held in reference to? Are they being compared to the average temperature of the last century, average compared to 1849, or whether its sort of a running diff to the previous year?