manca

Awesome! What's the plan for Cadence? Do you plan to maintain both projects moving forward or you'll start shifting community to Temporal and deprecate Cadence?

I ask because internally we just deployed our first Cadence cluster a few weeks ago -- it'd be good to know what to expect and what the founders of the project suggest to do now that Temporal is officially released to production?

I am very excited to see a company built around Cadence! I am just trying to understand what would be the main differentiator between Cadence and Temporal, except providing a SAAS solution?

Do you plan to maintain both projects, or plan to deprecate Cadence in favor of Temporal?

I had 7 meetings in total today, starting at 8am. Almost the entire work day (8 hours) spent in meetings. I am principal engineer and usually have say in many technical discussions and decisions.

But on a light day I have around 2-3 meetings a day. One of them usually being an interview with the candidate (either phone or onsite).

I agree with all you said. However, 99% of major tech companies employ exactly the same process of hiring software engineers. I've been on hundreds of interview loops and unfortunately just one bad interview in a loop of 5-6 could make the candidate look like a "bad" fit and thus receive no offer. That particular candidate might be in fact great fit, but the way he was tested could not reveal that.

I don't know the solution to this problem. Eliminating tests whatsoever and just talking with the candidate about his experience and probing his knowledge on different topics is not efficient either. There are a lot of talking heads around who when given a simple task fail miserably.

We as a tech community need to come up with better ways to assess other people's competency while also making sure those people fit within our company's culture, work efficiently with others and after all create value. This is a hard problem. So, we try to simplify the problem by imposing the "proven" way of finding such people -- give them arbitrary tests and hope they pass them.

This is exactly what I said in my retweet to PG's essay. Having hackable bad tests in the very tech industry proves the point that artificial tests are still the way of thinking for many and something we collectively need to unlearn. The question is how.

Wow!

I use Google's, but don't follow it to the word. I use their linter with couple of my own rules related to C++11 headers (chrono, etc), lines width, whitespaces, etc.

But in general, I try to follow good practices from it like: namespace formatting, as well as anonymous/unnamed namespaces, names and order of includes, #define guard (although I prefer #pragma once), use of smart pointers (fixed owners for dynamically allocated objects), explicit constructors, etc.

Really nice list of algorithms here...

Math is definitely foundational for Computer Science and for that reason one of the most interesting algorithms I've personally encountered is the one for computing Principal Component Analysis (PCA)[1] using Singular Value Decomposition (SVD)[2].

It's just amazing how you can easily represent N dimensional objects in 2D. It's used a lot in Machine Learning. One of the interesting applications of the algorithm is Face recognition (eigenfaces method).

[1] https://medium.com/100-days-of-algorithms/day-92-pca-bdb6684...

[2] https://web.stanford.edu/class/cme335/lecture6.pdf

Algorithms I and II by Tim Roughgarden on Coursera -- amazing teaching style with a lot of depth and math background.

ML by Andrew Ng is also another great example of a MOOC that could be executed very well.

From non CS related courses I really liked Intro To Finance by Gautam Kaul, also on Coursera.

As others mentioned, Effective Modern C++ by Scott Meyers is a solid modern C++ book with nice tips and tricks. Other than that, check out the official C++ Guidelines by Bjarne and co. http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines

Good luck!

I believe the guy just talked with one of the non-tech recruiters from the agencies, not the actual Google employed recruiter.

There are bunch of those non competent recruiters playing smart asses...

Definitely one of my favorite talks of all time.

Thanks for reminding me to rewatch this again...

Almost 50% of all L1 petitions get RFE (Request for evidence). It's becoming harder to use this visa to bring people to the US these days, too. It's especially difficult for big outsourcing companies (proving and supplying enough evidence for the "specialized knowledge" (L1B) category is really non trivial for the companies and often not clearly understood by the USCIS officers, hence the RFE).

However, USCIS has recently released a new L1B adjudications policy for public feedback (http://www.uscis.gov/news/uscis-posts-updated-l-1b-adjudicat...) where they clearly define the "specialized knowledge" category. Maybe this will help companies to properly understand what's needed to demonstrate the specialized knowledge in the future, but I really doubt it.

I was going through this painful process two years in a row. Last year there were around 170k petitions filled. A year before that, around 125k.

We did not win the lottery both times, and opted for L1B instead this year. It is available only to the companies who have international offices in order to bring people from abroad to the US. Still waiting on this...

I'll probably write a blog post about the whole experience, once everything is over. Stay tuned.

P.S One thing is certain - US immigration desperately needs fix.

Great idea guys. I hope you get a chance to host some brilliant people from all over the world.

What's been the rate of visitors/job offers since you began hosting?

I think learning Python is a great decision if you're new to programming. It'll expose you to fundamental programming concepts like: statements, loops, conditions, functions and even go deeper into object oriented concepts like abstraction, encapsulation and inheritance.

I always recommend people who are new in programming to start with Python because it's really flexible, has rich set of libraries and it's easy to get started.

It's good to hear that a lot of people are experimenting with functional languages like Haskell and Erlang, but I think we should step back a little and take a look at C++ and its development over the years.

As you probably know C++11 is already around and majority of compilers already support it. What most people don't know is that C++14 is ready to kick some ass too! It introduces for the first time in C++'s history functions as first class objects and allows you to pass them around as function parameters! I think this is really exciting and I also encourage as many people to take a look at this new and very promising developments of C++ language.

On the other hand, Apple's Swift looks promising too - functional elements, type inference, speed, good compiler and so on. Not to mention it's being used with tons of great frameworks Apple provides for iOS and OS X development. It sure doesn't hurt to take a look into it too.

All in all, it really is an exciting time for us developers. There are really tons of cool tools and languages that allow us to get things done at the speed never imagined before.

Now, we should also look back at the fundamentals and ask ourselves could we design, not only program? That's not the skill you can pickup in couple of days reading bunch of tutorials and forums about your next cool language. I think, learning how to think about particular problem and how to approach it is more important than barely know how to code the first solution that comes to your mind in the new ultra fancy language. Think about it...

Yeah, being first on Hackers News is not good for your box :)