saurabhjha
Joined 13 years ago
Karma
146
Submissions
31
Member since
Dec 21
http://saurabhjha.github.io
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Comment · 9 years ago
I thought about this article a lot since it was published. I might be going off the tangent a bit here but it might be relevant here
I do think it is possible to make create a company with properties mentioned in dhh's article that have novel technical challenges. I used to think that only consumer startups can have it because only there you get users and traffic. ITA software and Akamai comes to mind. In fact, scaling a consumer product is fairly common knowledge (every app except Whatsapp and Facebook is sharding databases, adding a cache line to it and going through roughly the same steps).
If you can make a good product for businesses that requires solving a problem only made soluble with recent technology advances (for example, multicores, reduced latency gap of RAM and flash storage), then you never need to worry about monetisations strategies, VCs, exits and other things. Maybe you need but it will be like dessert after dinner rather than salt in Indian food.
Comment · 9 years ago
Not a prep course, but for example Redis has a very good source code overview here https://github.com/antirez/redis#redis-internals
More remarkable is the fact that antirez updated the documentation in response to a post in Reddit. https://www.reddit.com/r/redis/comments/3re0aw/any_pointers_... Thank you antirez! :-)
Comment · 9 years ago
Do you need an understanding of compilers to go through this? What are the prerequisites?
Comment · 9 years ago
Edited original comment. Sorry for the confusion.
Comment · 9 years ago
I have some C programming experience and I wanted to learn C++ so I bought and started reading Stoustrop's book, thinking it's the K&R of C++.
It's the worst programming book I have ever read! It's introduction made me hate C++ and if I didn't had to work with other C++ libraries, I would have avoided C++. The text is full of bloat, for the lack of better word. It's the opposite of crisp writing.
I read some good language books like K&R's C, Joe Armstrong's Erlang book. I loved them and I thought this would be in same league. It's not.
I would like to learn C++ systematically, like not cut-and-paste stuff. "How to think like a computer scientist: C++" seems like a good book. I read the book "How to think like a computer scientist", which is an introduction to programming in Python. I think that would be a good start. Is it a good book for experienced programmers wanting to learn C++?
To people wanting to learn C++, please avoid Stoustrop's book like Plague.
EDIT: I read "The C++ programming language 4th edition" which explains C++11 https://en.wikipedia.org/wiki/The_C%2B%2B_Programming_Langua...
Comment · 9 years ago
I like to print them out. Reading from any type of screen hurts my eyes. Has anyone got experience reading papers in Kindle. How does it feel like?
Comment · 9 years ago
I clicked on this expecting some technical stuff and all I got was this BS social/community whining.
Well, even in the worst case where FreeBSD community is trash, they have one of the best kernel implementations and one of the best open source OS documentation.
Comment · 9 years ago
I read it was something like their hackathons started at 8 PM.
Comment · 9 years ago
I also think there is some corporate backing in C++ compilers. Like a few internal company people are getting paid to fix this kind of stuff.
Regardless, it's great to see C++ compilers incorporating latest standard features. For comparison, check JavaScript standards implementation in browsers (not sure if it's a fair comparison).
Comment · 9 years ago
I remember watching a 90 minute documentary on Chernobyl on television around 2010. It was mentioned that the construction of sarcophagus was 10 year behind schedule.
Glad to see the work done though the world paid a huge, huge price for it.
Comment · 9 years ago
I will name two: Remote and Rework, books by BaseCamp founders
Comment · 10 years ago
I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on.
I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way you can progress beyond lecture 2 (out of 20) without a solid probability background. A solid background in probability/statistics probably means a good first course in Probability or maybe the first five chapters of "Statistical Inference" by Cassias and Berger. Similarly, for SVM, you need a solid background in Linear Algebra and so on. You probably also need a background Linear Optimization. Here are the recommendations by Prof. Michael Jordan https://news.ycombinator.com/item?id=1055389
Not a lot of people want to dive in this much. They have got things to do and who cares about proofs anyway. The thinking goes like "Most of the mathematics is abstracted away by libraries like scikit-learn. Let's get shit done.". Well, I think a lot of competitive advantage of Google/Facebook in ML is because they have staffed their engineering with people who have studied these things for years (by PhD). Compare that to flipkart's recommendations.
However, I don't think this problem is unique to ML/Data Science. It is equally bad in "Distributed systems". Let's use Docker, that's the future!