You posted the link to the README, which is different from the link to the repo itself. Since these two aren't really duplicates, I don't think it's a fault of HN's algorithm.
Since the root page of a repo renders the README, the two URLs are not very different. A sophisticated duplication detection algorithm could recognize that.
The algorithm would have to examine the HTML returned by the URLs to determine they are duplicate pages. There is no way to determine they are duplicate pages by just analyzing the URLs. It is not impossible to have two different posts to HN that are legitimately different content that have the same domain and even partially matching paths. In order to work in a dependable way, the algorithm would have to examine the HTML returned by the URLs to determine they are duplicate pages. In this case, that wouldn't even work because they aren't actually duplicate pages. In order to mark these two URLs as duplicate I think you might need to use some machine learning and then it would only yield some level of confidence that these were indeed duplicate pages as a result.
Or they could just special-case GitHub repos and their readme files. But what is duplicate detection if not machine learning? One can't be sure the same URL is the same document if submitted at different times.
I'm not sure how it works. Sometimes, you'll go to post something, and it will turn that into an upvote on the already-existing story, and other times it will post a dupe.
That's more lectures than a student could see. Now we need a full concept chart dependency tree and an adaptive content suggestion system to guide students through this sea of lectures.
I'd like to have a system where you take tests to rate your understanding level and decide where to start.
It depends a lot on what you want to specialize in. There are a few entry level classes that are good at preparing you for one of many paths you can take. For the very basics, I've heard good things about harvard's cs50. For an intro to systems, I'm currently going through CMU's 15-213 which I think should be a class in all CS departments.
Sometimes I find it easier to find good conference talks than to wade through a lot of these classes, but some of them overlap with what I took in school.
One of my biggest complaints about a lot of the stuff found in YouTube is the lack of preparation.
Those without teaching experience think it is enough to fire-up the screen recorder and go for it. Sure, that can work, but it often results in a bad or mediocre product.
The most egregious of issues being when they destroy your concentration because they are making mistakes live as you follow along. "No! Wait! That was a period. No a slash. No, I forgot to define the class. Oh, yeah, the import was wrong. Wait, a database table is missing." ... and more.
Some could argue that this can be interesting because you see errors. Point taken. This is the wrong way to teach about errors. It destroys the student's train of thought and detracts from learning.
The right way to teach about errors it to explicitly teach them. In other words, you do it correctly and then say "let's see what happens if we delete this column in the database". You can then expose the issues and how to deal with them. To unexpectedly run into issues during a tutorial due to lack of preparation only confuses students. Imagine a Calculus professor fumbling as he/she explains course material, it would be maddening.
If you want to teach, please take the time to prepare the material and the script. Have at least a second monitor on your machine so you can view the script as you record your lesson on the main monitor. Never take students down a path you have to reverse from, it can be very confusing.
EDIT: By "script" I don't mean "code" but rather what you are going to say and do and when. Yes, you need to have your code visible on a separate monitor as well just to make sure you don't make mistakes.
I highly recommend MIT's 6.006 for a good intro to data structures and algorithms. My college's course wasn't great, but watching the MIT lectures and working through CLRS helped me gain a really solid intuitive understanding of complexity and approaches to different classes of problems. At the very least it helped a lot with interviews!
I made an Elasticsearch querying tool that lets you construct multiple dependent queries.
If I hadn't taken an Algorithms class, I wouldn't have thought to use a directed acyclic graph, and to topologically sort the different queries so that they are resolved in order.
That was actually the first time I'd used something from a theory class on the job! It was pretty exciting, though probably mundane for most engineers :).
That's actually a great question and I don't think I can really answer it yet since I've only done internships and I'm set to start my first full-time job in January. I find some satisfaction in trying to optimize the small problems I've faced so far but it's mostly been bug fixes and glue code. I like the answer Feuilles_Mortes gave, hopefully I'll find some useful scenarios in the future.
Excellent resource. I'm almost at the end of Andrew Ng's ML course on Coursera and was looking to learn more about convex optimization. I will definitely try out some of the links there (Machine Learning > misc machine learning topics)
Thanks for posting this. As a bootcamp grad, I just bought a Intro to CS textbook to start learning the basics, but I can't wait to look at some of these too
This is cool. I think "visual learners" or whatever it's called are a bit on the fringe in CS but we do exist. When starting something new, I learn much more easily by video or discussion with people. As I become more comfortable with a subject, technical documentation becomes progressively more useful.
I'm the same way, I find a good series of lectures to be the best way for me to wrap my head around something. I think it's a combination of "visual" and "global" learning styles. A global learning style means you work your way from the big picture down to the details as opposed to "sequential" style where you are better off diving into a detailed problem and building your knowledge outward from there [0].
Can I ask you how much difficult or advanced find you those lectures ?
are those graduate level ?
upper ungraduated/first year graduate ?
Or material is only bachelor level ?
I'd say it's bachelor level. It definitely requires some prior knowledge of digital logic and general computer systems concepts. Here's a link to the course page where you can find the prerequisites and a syllabus and some other info:
