Ask HN: How to Seriously Start with Machine Learning and AI - <AUTHOR> Hey,
Since years I've been seeing tones of news "how machine learing did smth... " and today that's enough with just reading how other people change the world with AI. I want to join into this area and scientificly understand how it everything works - make my own projects...
-I'm a third-year Computer Science student who just has passed most of the needed courses like obj programming,python course, databases, math statistics, algebra etc... I really enjoy playing with data like projecting databases, programming backed etc...
Everything I know until today - I have learned on my own(swift, python, backend). Mostly by practice and solving problems. Now I really want to start serious journey with Machine Learning and AI.
But by making some small research which made me realised that I don't want just to implement already done frameworks for e.q face recognition (maybe I should?)
I would like to understand the topic really seriously and be able to explore this area... ---but here's a problem because I don't know how to start it. I've got enthusiasm, some ideas for a projects, but still don't know almost anything about how exactly everything works.
When I was starting with programming, I read some books, watched online lecture and bang. I started doing my own projects. How to start in this more scientifically sophisticated area?
Are there any courses, books, online lectures which you can recommend me for a start to understand how it all works? Unfortunately, my university doesn't lead any more interesting courses in this area... People here are just fascinated with it but nothing more complex...
I'm still young so why not to lose time on something that seems to be really fascinating ;) <comment><USER2> I'm probably the worst example of how to get into this field of work, but since I do _actually_ work on developing and applying ML algorithms every day, I think my case might be relevant.

Firstly, my background is not in mathematics or computer science what-so-ever; I'm a classically trained botanist who started came at the issue of programming, computer science, and ML from a perspective of "I've got questions I want to ask and techniques I want to apply that I'm currently under prepared to answer."

Working as a technician for the USDA, I learned programming (R and python) primarily because I needed a better way to deal with large data sets than excel (which prior to 5 years ago was all I used). At some point I put my foot down and decided I would go no further until I learned to manage the data I was collecting programmatically. The data I was collecting were UAV imagery, field and spectral reference data, specifically regarding the distribution of invasive plant species in cropping systems. The central thrust of the project was to automatically detect and delineate weed-species in cropping systems from low altitude UAV collects. This eventually folded into doing a masters degree continuing to develop this project. That folded into additional projects applying ML methods to feature discrimination in a wide range of data types. Currently I work for a geo-spatial company, doing vegetative classification in a wide range of environments with some incredibly interesting data (sometimes).

I think you've got the issue a bit cart-horse backwards. In a sense I see you as having a solution, but no problem to apply it too. The methods are _ALL_ there, and there are plenty of other posts in this thread addressing where to learn the principals of ML. What this doesn't offer you, is a __why __of why you should care about a thing? My recommendation would be to find something of personal interest to you in which ML _may_ play a role.

With out a good reason to apply the techniques that everyone else here is outlining, I think it would be too challenging to keep the level of interest and energy required to realize how to apply these concepts. Watching lectures, reading articles, doing coursework is all very important, but it shouldn't be thought of as a replacement for having personally meaningful work to do. Meaningful work will do more to drive your interests than anything.

<comment><USER3> This reads as an amazing journey. Kudos for your pursuit of a better process.

It seems to me that so many (online) courses jump to applying tf/pytorch to a predefined dataset, whereas most of the work is in preparing the data. I have a personal project I'd like to try out classifying images, and haven't had much luck finding resources on building my own training dataset.

Can you recommend any resources on assembling and collating your own dataset?

<comment><USER2> It should be noted that I deal primarily with geo-spatial image analysis, so there is a not insignificant amount of bias with regards to what data I'm interested in. I like using the USDA NAIP API for imagery, since I can call in imagery using GDAL directly into python or R. I rely heavily on freely available public utility data sets (Parcel level utility data). Beyond that and other than as a starting point, you're training data is always going to be something you've invested in heavily. Good training data is 100% the game. No modeling exercise is going to go well on poor quality training data. Currently as a personal project, I'm trying to develop a platform for developing and training data for geospatial modeling. If you're interested, hit me up on a PM and I can explain it in more detail (after work).

<comment><USER4> PM OTW...

<comment><USER2> Not sure if there are PM's in HN? firstname.lastname@example.com otherwise.

</comment></comment></comment></comment></comment><comment><USER5> IMO, to start with AI, it's best to start as simple as possible so that you understand the algorithms that drive things that are accessible to tweak. If you are a gamer, think about the various AI routines that control pathfinding, resource collection, and strategy for computer players in strategy games. Some of the concepts of BFS and DFS build into Minimax strategies and other types of "simple" approaches to searching decision spaces/states for optimal approaches to problem solving. This gets into the development of heuristics for approximating outcomes. All of this is fantastic baseline for understanding more complex AI and machine learning algorithms. While it's easy to jump into ML or AI at a higher level, without the baseline understanding of the search algorithms and probability landscapes that are underpinnings to advanced work in these fields, you'll probably never feel like you understand what is going on.

You can find some fun tutorials here: [https://www.redblobgames.com](https://www.redblobgames.com)

The CS 188 "Intro to AI" class at Berkeley is excellent: [http://ai.berkeley.edu/home.html](http://ai.berkeley.edu/home.html)

It used to be on edx.org but I think a lawsuit about accessibility required them to remove it? Perhaps you can find it in the edX archives.

Edit: looks like you can find the lecture videos and other resources on the Berkeley site: [http://ai.berkeley.edu/lecture_videos.html](http://ai.berkeley.edu/lecture_videos.html)

<comment><USER6> By far the best response. After this,one could always try the deeplearning.ai or fast.ai courses depending on whether they prefer top-down or bottoms-up approach. Or why not do them together?

</comment></comment><comment><USER7> I don't know much about AI but for ML specifically Elements of Statistical Learning is fantastic. I find its explanations a lot easier to understand than other resources. I recommend you skim through it to get a taste. Additionally if you prefer lectures ETHZ has recordings of their ML class[1].

The best way to learn the details is of course to read the original papers. This is especially true for following along with the latest developments in deep learning.

[1] [https://www.ethz.ch/content/vp/en/lectures/d-infk/2017/autum...](https://www.ethz.ch/content/vp/en/lectures/d-infk/2017/autumn/252-0535-00L.html)

<comment><USER8> I'm working my way through Elements now. Do you by any chance know of any lectures specifically based on it? And solutions to the exercises there? I have found it hard to find (good) solutions.

<comment><USER9> The Machine Learning Course offered by Prof. Ravindran at Indian Institute of Technology (IIT- Madras) uses ESLR. The course is free to enroll and you will have weekly assignments. [https://onlinecourses.nptel.ac.in/noc18_cs26/preview](https://onlinecourses.nptel.ac.in/noc18_cs26/preview)

<comment><USER8> Thanks!!

</comment></comment></comment></comment><comment><USER10> I'm surprised that no one has yet mentioned Andrew Ng's Machine Learning course on Coursera and to go a bit deeper, his deep learning specialization on Coursera as well. Along with the programming assignments it's a solid way to get your feet wet. And definitely second the suggestion of the fast.ai courses as well

<comment><USER11> I took this course. Andrew is a great teacher. However, I wish he worked on his public speaking a bit. He has certain speech patterns, like starting sentences with "it turns out..." and after a while it becomes extremely irritating, at least to me.

Fantastic course though.

<comment><USER11> Lol, apparently we aren't allowed to criticize people HN likes.

</comment></comment></comment><comment><USER12> I'm very adamant that if you really want to be an AI researcher, it starts with the mathematics. Multi-variable calculus, linear algebra,discrete math, probability and statistics are key. The classic books and courses others have suggested are excellent starting points.

I'll also say something here that isn't established, so I may take some heat. I believe there are two main paths in AI that will eventually converge to general AI: symbolic and sub-symbolic reasoning. If you go the path of symbolic reasoning, studying functional programming, theory of computation,type theory, natural language processing/compilers are in your future. If you go the path of sub-symbolic reasoning, you will be closer to optimization methods, neural networks, etc. It really depends on what you want to do. Ex: Computer vision is all about sub-symbolic reasoning, while natural language processing is heavily about symbolic reasoning. Of course advanced applications mix both! If you want to go after general AI, you gotta figure out how to tackle both forms of reasoning.

In the end, if you are serious about being an AI researcher, you will have to be a great computer scientist and mathematician. This is why it seems so difficult to get into. "Do I focus on working on messing with libraries and algorithms or the mathematical theory? And if I do both, how!?"

It's not easy, but just start and keep going. Others have provided great advice. One of my greatest joys in life has been coming to understand the marriage of computer science and mathematics under the banner of AI. It is really something exciting worth living for.

[Never give up!]([https://www.youtube.com/watch?v=KxGRhd_iWuE](https://www.youtube.com/watch?v=KxGRhd_iWuE))

Also, I recently gave a talk on getting into AI and my view on the state of things. It also has a resources section that may be of interest. Slides: [https://docs.google.com/presentation/d/1pDZLkFTFjuZzM8lIKkuC...](https://docs.google.com/presentation/d/1pDZLkFTFjuZzM8lIKkuCqgkFHthCVu6vMgNQge92tSY/edit?usp=sharing)

<comment><USER13> One day 40 years after I graduated I "got" linear algebra.

The eigenvectors of the inverse of the covariance matrix...

Squeeze and twist it into a hypersphere!

Mahalabobis distance is generalized z-score! Oh!

<comment><USER12> That's the ticket. It's all about building that intuition so you can be creative with mathematics. Linear algebra in particular is an area that continues to teach me more over the years.

</comment></comment></comment><comment><USER14> In October, I quit my job to live on savings and work on AI/ML. I am not interested in developing novel AI approaches; my objective is to learn the application of machine learning to solve narrow problems.

In light of that, I believe the technology to have progressed to where one can learn how to use existing libraries to solve specific problems. Lots of businesses have specific problems, and will pay me to solve them.

The Fast.AI course (part 1 v2) is the best way to get started, IMO. The fastai library wraps up a lot of boilerplate, and gives you a simple recipe w/ conceptual understandings to achieve state of the art results (top 20% on just about any kaggle competition) in just a couple months of intensive study.

<comment><USER15> I've been doing Coursera and got done with Udacity but both are just too philosophical. (I'll probably cruse the deep learning class too.)

Does FAST.AI get into the grit of actually doing or is it more philosophical?

<comment><USER16> Fast.AI is 100% focused on getting into DOING stuff. Its guiding philosophy is that teaching the math first instead of teaching you how to actually do some machine learning is backwards. The lectures start with a dozen lines of code that learns how to discriminate between cats and dogs and then follows up on that by slowly removing layers of abstraction. It's very cool.

That said, the lectures are pretty rough around the edges compared to something like a coursera class. And his goal is to get you to play around with stuff on your own using his stuff as a jumping off point. It works well for some people but not as much for others.

</comment></comment></comment><comment><USER17> Pattern Recognition and Machine Learning by Christopher Bishop.

Book: [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%...](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)

Notes: Its very very math heavy but if you really want to grasp the concepts and the idea around each topic, this is one of the way to go.

Online Lectures: [https://www.youtube.com/watch?v=mbyG85GZ0PI&list=PLD63A284B7...](https://www.youtube.com/watch?v=mbyG85GZ0PI&list=PLD63A284B7615313A)

I like how he explains stuff and adds some context behind the math of each topic.

Alternative: [https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)

A pretty good youtube channel that follows up on modern machine learning and he has all of his video tutorial demos on GitHub.

Hope this helps fellow Machine Learner :)

<comment><USER18> Bishop's book is horrible. I've got a PhD in machine learning and yet it makes me feel stupid.

I'd go with David Barber's book every time.

</comment></comment><comment><USER19> I started with Andrew Ng's ML courses, which take a bottom-up approach beginning with the math. After finishing the deeplearning.ai track, I started rounding out my skillset with some data science and R programming classes, so I could be more comfortable working with unfamiliar data.

The fast.ai courses are a more top-down approach to ML, and there are plenty of good reasons for taking this approach. You'll start getting practice with libraries like Tensorflow right away. However, if you have a fairly strong math background and linear algebra doesn't give you nightmares, I highly recommend the Andrew Ng courses. A deep (ha!) understanding what's going on "under the hood" in ML will help your debugging, inform your strategy, and make your code better in the long run.

</comment><comment><USER20> I wouldn't dive in to a full machine learning course as the first step.

1\. Watch short tutorials about TensorFlow on Youtube

2\. Look for lectures about specific topics that sound interesting.

3\. Read NIPS papers that sound interesting

4\. Check out the Deep Learning textbook, but maybe don't read the whole thing ( [http://www.deeplearningbook.org/](http://www.deeplearningbook.org/) )

5\. By this point you should have a very rough idea about the current and past states of machine learning. You didn't have to spend any money or put in any exhaustive mental effort. If it still sounds interesting and you are motivated you can try a full online course or buy some paid books.

</comment><comment><USER21> These were to 2 books I had when I took AI classes in college(~1998) pre-deep learning, CNN etc..

[http://aima.cs.berkeley.edu](http://aima.cs.berkeley.edu)

[https://www.allbookstores.com/book/compare/9780201533774](https://www.allbookstores.com/book/compare/9780201533774)

might be good for some foundational stuff. I felt they were pretty readable.

I remember having to do backprop in excel as one assignment.

</comment><comment><USER22> Apparently Kaggle just opened a training/education branch.

[https://www.kaggle.com/learn/overview](https://www.kaggle.com/learn/overview)

EDIT: I also second the fast.ai suggestion(s) as well

</comment><comment><USER23> This video got me started, it runs through building a simple neural net in C++ from scratch: [https://vimeo.com/19569529](https://vimeo.com/19569529)

After that I extended it to solve the hand written digits problem. Tweaking it to get past about 80% accuracy taught me a lot of intuition about how the learning rate/other parameters interact.

After that going through Andrew Ng's machine learning course will fill in some of the underlying math.

</comment><comment><USER24> This doesn't help answer your question (I don't think) by my observation...

I have the same question and I got my degree 12 years ago.

I remember my AI course in college consisted of implementing a neural net in Lisp and memorizing a ton of dense text.

It is really easy to take a Tensor flow model someone already wrote and tweak the parameters to make it work for your use case. I think that code reuse and open source is the largest advancement in AI in the last 10 years.

So a lot of companies can use AI in their products without even really knowing how it work.

Now once you start training your own models the hardest part to me is the vocabulary. So many tutorials and classes say "use this algorithm" or "take this code and modify it"... I want to know why I chose that algorithm, what were the other choices, and how do I write that code if I don't have an instructor to do the boilerplate for me. It is very frustrating.

Hopefully some of the answers here will answer some of those questions.

As another note, a lot of the advanced AI uses Calculus. algebra is not enough. I don't know what college you went to or what accreditation it has but by third year you should at least have taken Calculus II... maybe even Differential Equations. If you haven't, don't worry, 99% of programming jobs won't use them. But parts of AI will.

</comment><comment><USER25> I'd say to "seriously" start anything you can take a first step by building something, whatever it is. Even if you just follow a tutorial it gets you wet enough to keep going I think.

</comment><comment><USER26> I would highly recommend starting with course.fast.ai. Really helped me.

<comment><USER27> +1. I started with a bunch of theory courses before jumping in to the fasi.ai course but I wish it had been available at the time for me to start with. The theory is a lot easier to internalize when you're working on real networks instead of toys.

</comment></comment><comment><USER28> I would recommend working on as many Kaggle competitions as you can. Try your best to understand manipulating data quickly and efficiently (use Python). Using the correct algorithm is typically trivial and a solved problem for most typical business issues (classification of text, imagery or audio data). At first you'll have to look at other people's code and copy/paste/edit/learn/iterate. Also, don't underestimate the complexity of creating good training, validation and potentially test sets.

95% of your time will be spent massaging data. People say it so often, it may sound ridiculous, but I promise you, it's not. This skill is also readily transferrable to other domains.

I would start with classifying text using sklearn and Facebook's Fasttext.

Then try the dogs/cats image classification challenge and get familiar with Keras and its utilities. I created a few hours of content around recognizing Bill Gates or Jeff Bezos, then trying to recognize 2 types of dog breeds. I outline the challenges of creating "good" training and validation sets.

[https://www.youtube.com/watch?v=O3hffX- jC98&list=PLImyDqSBQb...](https://www.youtube.com/watch?v=O3hffX- jC98&list=PLImyDqSBQbdl_dd5GTZjiUzPZ2-63h10j)

For every hour you spend looking at some equations, you missed an hour expanding your skill-set to manipulate and get data into a format which can be fed into well understood and maintained algorithms. Once you feel like you get produce results, go back and think about the underlying mechanics. My 2 cents from experience.

</comment><comment><USER29> "Be fearful when others are greedy, and greedy when others are fearful" \- Warren Buffett

I don't want to try to dissuade you in particular, but I think more young people should apply this principle to the question of what field to enter. I've seen dozens of "How do I get into AI/ML?" posts in the last couple of years.

<comment><USER30> If that were true young people shouldn't get into tech at all let alone AI since it's overhyped at the moment.

</comment></comment><comment><USER30> Think you should pick a project that seems reasonable and then start working on it, learning what you need to along the way.

</comment><comment><USER31> 1\. Get yourself grounded in theory

Learning from Data by Yaser Abu-Mostafa, and its companion book. Very theoretically heavy but worth the trouble (this is Caltech course)

Do all assignments in the course with Python/Numpy/Scikit-learn

2\. Choose your niche

You need to NARROW down your interest. You can start broad just to know enough basics, but gradually pinpoint to making/hacking stuff that is most fun for you. Test waters on computer vision, speech, NLP, and games/robotics (reinforcement learning), or other less popular fields.

E.g. Start with computer vision -> Basic convnet image classification -> Encoder-Decoder architectures -> End-to-end ConvNet monocular RGB to depth image generator

Read papers that have published code on github, this let you reproduce and understand how things work, so that after awhile you can hack your own models and stuff

</comment><comment><USER32> With the shortest introduction, created by myself ;) [http://lausbert.com/2018/01/14/the-shortest-introduction- to-...](http://lausbert.com/2018/01/14/the-shortest-introduction-to-machine- learning/)

</comment><comment><USER33> For now, I'd recommend you start the two courses on: [http://fast.ai/](http://fast.ai/)

They will guide you through machine learning and deep learning basics without being overwhelming with math. Good luck!

</comment><comment><USER34> > I'm a third-year Computer Science student

Make sure you take, as soon as possible, all the AI & ML courses your department offers. If prerequisites are holding you back, try to negotiate or audit. (Some of my key insights date back to graduate-level AI classes they humored me to sit in on when I was a freshman.)

MOOCs are great for people who don't have that option, but you have the opportunity to ask questions, get course credit for your work, bolster your GPA, show it on your transcript, network with classmates, etc.

</comment><comment><USER35> I'm starting the fast.ai courses as a recent stat grad looking to expand my knowledge. Heard many good testimonials. Besides that, I think Andrew Ng's Coursera offerings - intro to ML and the newer NN specialization - are great first steps. Personally I have a hard time learning from videos, so I refer to my copy of Pattern Recognition and Machine Learning by Bishop.

If you want a linear algebra text, I enjoy Strang's Introduction to Linear Algebra

</comment><comment><USER21> some free courses:

[https://www.class-central.com/subject/ai](https://www.class- central.com/subject/ai)

Some guys self made Master's program: [https://hackernoon.com/my-self-created- ai-masters-degree-ddc...](https://hackernoon.com/my-self-created-ai-masters- degree-ddc7aae92d0e)

</comment><comment><USER36> This is kind of a masters degree course i created for myself to get knowledge of Machine Learning from bottoms up

First, you need a strong mathematical base. Otherwise, you can copy paste an algorithm or use an API but you will not get any idea of what is happening inside Following concepts are very essential

1) Linear Algebra (MIT [https://ocw.mit.edu/courses/mathematics/18-06-linear- algebra...](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra- spring-2010/) ) 2) Probability (Harvard [https://www.youtube.com/watch?v=KbB0FjPg0mw](https://www.youtube.com/watch?v=KbB0FjPg0mw) )

Get some basic grasp of machine learning. Get a good intuition of basic concepts

1) Andrew Ng coursera course ([https://www.coursera.org/learn/machine- learning](https://www.coursera.org/learn/machine-learning))

2) Tom Mitchell book ([https://www.amazon.com/Machine-Learning-Tom-M- Mitchell/dp/00...](https://www.amazon.com/Machine-Learning-Tom-M- Mitchell/dp/0070428077))

Both the above course and book are super easy to follow. You will get a good idea of basic concepts but they lack in depth. Now you should move to more intense books and courses

You can get more in-depth knowledge of Machine learning from following sources

1)Nando machine learning course ( [https://www.youtube.com/watch?v=w2OtwL5T1ow](https://www.youtube.com/watch?v=w2OtwL5T1ow))

2)Bishops book ([https://www.amazon.in/Pattern-Recognition-Learning- Informati...](https://www.amazon.in/Pattern-Recognition-Learning-Information- Statistics/dp/0387310738))

Especially Bishops book is really deep and covers almost all basic concepts.

Now for recent advances in Deep learning. I will suggest two brilliant courses from Stanford

1) Vision ( [https://www.youtube.com/watch?v=NfnWJUyUJYU](https://www.youtube.com/watch?v=NfnWJUyUJYU) )

2) NLP ( [https://www.youtube.com/watch?v=OQQ- W_63UgQ](https://www.youtube.com/watch?v=OQQ-W_63UgQ))

The Vision course by Karparthy can be a very good introduction to Deep learning. Also, the mother book for deep learning ( [http://www.deeplearningbook.org/](http://www.deeplearningbook.org/) )is good

<comment><USER37> hey neel8986, I know linear algebra is very important for large scale calculations. But how much calculus and statistics do you need for ML? Also, if you can touch what applications of calculus and statistics are used in ML that would be awesome :]. THANKS!

<comment><USER36> Regarding calculus, I think basic multivariable calculus can be enough for starting. If you need a refresher you can look for ([https://ocw.mit.edu/courses/mathematics/18-02sc- multivariabl...](https://ocw.mit.edu/courses/mathematics/18-02sc- multivariable-calculus-fall-2010/index.htm))

Also the basic idea of chain rule is important for deep learning.

Regarding statistics, I already mentioned the probability course which describes most of the important statistics concept you need. Also, some idea of Hypothesis testing can be helpful

<comment><USER37> right on thanks neel8986.

</comment></comment></comment></comment><comment><USER38> datasciencemasters.org was reccumended by someone working inside the Tensorflow team at Google. It is a sophisticated collection of courses and books (Stanford curriculum...etc).

You would find that as a superset of what you need, you can just scroll till you find what you need to learn. (They are kinda sorted from basics first)

</comment><comment><USER39> Make sure you load up on mathematics, in both pure and applied. Try to find work where you'll either be immersed in statistical models or have the freedom to experiment with them.

Capital markets such as equities trading will probably be the best place to look for work experience.

</comment><comment><USER40> > I want to join into this area and scientificly understand how it everything > works

That's kind of funny, given that one of ML's biggest criticisms is that not even the field's foremost experts truly understand how it works.

</comment><comment><USER41> For deep learning: Surprised no one's mentioned Nando de Freitas's YouTube lectures, great series! Hugo Larochelle's YouTube course is great as well!

</comment><comment> gm-conspiracy Do you recommend using rented GPU instances, or building a some kind of Nvidia rig?

<comment><USER42> I'd personally say it depends on your budget, what you want to do, and the amount of data you plan to process. It may also depend on how comfortable you are setting up your own rig, if you go that route.

For instance, last year I completed Udacity's "Self-Driving Car Engineer Nanodegree" course. Large parts of the course needed us to train neural nets (tensorflow and similar) on data we either generated or downloaded. We were provided the option to use AWS instances for training (free credits), but I opted to use my local box.

It took me about a day or so of playing around before I finally got everything working properly (CUDA, etc) with my NVidia 750ti GPU. This is a very low-end GPU, but it honestly performed quite well for the course. It could only handle a limited amount of data, and sometimes training cycles took a while for turnaround (depending on the task), but it ran the resulting models quite easily (while still handling the 3D rendering tasks of the vehicle simulator).

For learning purposes, it will all depend - if you already have a machine with a decent GPU, CPU and RAM (say something equivalent to the 750 or better, 4 cores or more, and say 8 gig of RAM), it might make sense to try to do things locally - if you think your skills are up to the configuration challenges (I got my system working, but I ended up breaking Ubuntu's update system, because I'm on 14.04 LTS, and I had to hand-install many things to fix dependencies and such for Python, Tensorflow, C/C++, etc - in order to complete the course).

However, if you are planning on processing a huge amount of data for a large model, but aren't planning on doing this constantly - then an AWS instance might be a better option, as a custom rig for this kind of thing I'd imagine would be a bear to spec out and configure - not to mention cost.

I'm certainly not an expert on all of this, though...ymmv.

</comment></comment><comment><USER43> Start with python. Then learn scikit-learn

</comment><comment><USER44> Fast.ai is the only thing I recommend

</comment><comment><USER45> Just recently I have written a "Machine Learning for Web Developers in JavaScript" blog post [0]. If you or someone else is a web developer, it might be interesting. It outlines my approach of learning it and gives a couple of great resources for JavaScript enthusiasts. Otherwise, I will just post a couple of the materials I used for myself to learn about ML below.

\- [0] [https://www.robinwieruch.de/machine-learning-javascript- web-...](https://www.robinwieruch.de/machine-learning-javascript-web- developers/)

Podcast:

\- [http://ocdevel.com/podcasts/machine- learning](http://ocdevel.com/podcasts/machine-learning)

Courses:

\- [https://www.coursera.org/learn/machine- learning](https://www.coursera.org/learn/machine-learning)

\- [https://eu.udacity.com/course/machine-learning-engineer- nano...](https://eu.udacity.com/course/machine-learning-engineer-nanodegree-- nd009)

\- [https://www.coursera.org/specializations/deep- learning](https://www.coursera.org/specializations/deep-learning)

\- [http://course.fast.ai/](http://course.fast.ai/)

Books:

\- [https://www.amazon.com/gp/product/B014X01SS0/](https://www.amazon.com/gp/product/B014X01SS0/)

\- [http://www.deeplearningbook.org/](http://www.deeplearningbook.org/)

\- [http://neuralnetworksanddeeplearning.com/](http://neuralnetworksanddeeplearning.com/)

\- [https://www.safaribooksonline.com/library/view/deep- learning...](https://www.safaribooksonline.com/library/view/deep- learning/9781491924570/)

Math:

\- [http://www.fast.ai/2017/07/17/num-lin- alg/](http://www.fast.ai/2017/07/17/num-lin-alg/)

\- [https://www.khanacademy.org/math/linear- algebra](https://www.khanacademy.org/math/linear-algebra)

\- [https://www.khanacademy.org/math/statistics- probability](https://www.khanacademy.org/math/statistics-probability)

\- [https://www.khanacademy.org/math/calculus- home](https://www.khanacademy.org/math/calculus-home)

JavaScript ML:

\- [https://bri.im/](https://bri.im/)

\- [https://github.com/javascript-machine- learning](https://github.com/javascript-machine-learning)</comment>


Where does a tree get its mass? - <AUTHOR> http://thelastpsychiatrist.com/2008/11/where_does_a_tree_get_its_mass.html <comment><USER2> The article started by pointing out a logical incongruency that most people have. I know better than to think the tree is made of the ground, however I know many people are not.

However he's talking mass and reneges on his statement that trees are made from the air by stating that many trees are over 50% water. Well IMO when talking about mass the majority-share counts as the subjects mass origins.

The plant breaks down CO2 into C + O2 via photosynthesis. The C makes up the plant. This is the simplified version he starts with, which was an ample metaphor. However then he states that the tree can be predominantly water. Meaning the plant is made up of say 60% water and 40% carbon.

However the plant is made up of predominantly of water trapped within cellulose membranes. If we talk dry weight, we're still talking 6 carbon atoms (72), 10 hydrogen atoms (10) and 5 oxygen atoms (80). This means roughly 66% of the trees mass comes directly from water from the ground sequestered in its cellulose.

This means roughly only 20% of the tree actually comes directly from carbon sequestered from the air.

So really, where does the plant get its mass? The ground.

If you're asking where the plant gets its dry weight, still the ground by ~6%.

By tackling a logical fallacy with scientific thinking he opened the window to be completely corrected by it himself. Trees grow because they take the carbon out of the air. However you talk about mass and we're talking about molecular weights.

Now if he'd have been as smart of Feynman he would have dismissed the water quite logically again. The rain falls from the sky. Save for a few very large trees that actually reach into the water table, trees well inland rely solely on the rain for their supply of water. That means the 50% of its mass that is water still technically comes from the air from moisture being condensed in cold air and forming water droplets.

<comment><USER3> >The rain falls from the sky. Save for a few very large trees that actually reach into the water table, trees well inland rely solely on the rain for their supply of water.

But they draw up that rain from the local soil using transpiration as I was explaining to my 4yo yesterday, I'm pretty sure he'll have forgotten it by now, I digress.

What I'm not sure about is rainwater capture - do shrubs capture (internally) water higher up the plant (eg in at limb connections) or are leaves simply directing the water to fall close to the "trunk" to maximise root take-up.

It's a good catch though as I'm sure I'd fall to the leap from knowing that "trees grow by taking in CO2 and that is where they get their mass of carbon from" to the false assumption that "tree mass comes primarily from the air" without really thinking about it.

Thanks for your analysis.

</comment></comment><comment><USER4> Feynman knows:

<http://www.youtube.com/watch?v=N1pIYI5JQLE>

<comment><USER5> Can't upvote you enough for this. By the way, Feynman also knows the difference "between knowing the name of something and knowing something": <http://www.youtube.com/watch?v=05WS0WN7zMQ>

<comment><USER6> This. He really articulates clearly the difference between two ways of thinking. Thinking by really understanding then translating the thoughts into words and thinking by manipulating words.

</comment></comment></comment><comment><USER7> This reminds me of a snippet from Buckminster Fuller's "Critical Path". He's talking about specialists and generalists and how we rarely see the big picture of what's important.

"Nobody is born a specialist. Every child is born with comprehensive interests, asking the most comprehensively logical and relevant questions. Pointing to the logs burning in the fireplace, one child asked me, "What is fire?" I answered, "Fire is the Sun unwinding from the tree's log. The Earth revolves and the trees revolve as the radiation from the Sun's flame reaches the revolving planet Earth. By photosynthesis the green buds and leaves of the tree convert that Sun radiation into hydrocarbon molecules, which form into the bio-cells of the green, outer, cambium layer of the tree."

[http://www.maebrussell.com/Critical%20Path/Critical%20Path%2...](http://www.maebrussell.com/Critical%20Path/Critical%20Path%20excerpts%201.html)

</comment><comment><USER8> The concept of uncertainty is hard to grasp. In my high school (1979) we were required to use slide rules for the first year of science class, despite the availability of inexpensive calculators. The reason was to impress upon us the importance of significant figures, and also to develop our intuition for orders of magnitude. Unless your slide rule is really expensive, you just can't get more than 3 significant figures from it, which is a good match to the accuracy of just about every measurement in high school. It also tells you the figures without the magnitude--you have to determine in your head whether you just calculated microns or kilometers.

</comment><comment><USER9> An interesting thing here is that this article assumes that the way science is taught is wrong. I do not intend to make any conclusions about this, but I just graduated from high school and I just realized _why_ people don't learn science as opposed to a bunch of meaningless phrases.

Although a lot of it is a broken system, but the truth is that they aren't expected to. What is expected of them is to get good grades, and since that is possible through rote almost all of my peers take that route. If you ask them in a moment of honesty they say; "why should I do it? I'll just get the grades get into a good college a good job... Why should I waste my time learning this?".

I used to gripe about the system whenever I heard that reply and why they just didn't get it, but now I realize that they are simply reflecting society in general. Most parents quite frankly don't care that they _know_ science or anything as long as they get good grades. Most teachers hardly give a damn about teaching and they aren't even remotely interested in science. Most employers upon employment cannot check and do not look at how much the individual understands anything. They just ask for a degree from a reputable institute.

I admit that it is harder to change the mindset of a population, but this is something that has to be done. Changing the school system and bringing out new books will have limiting impact unless the foundations of this problem (societies attitude toward learning) aren't addressed.

</comment><comment><USER10> An interesting topic, but I couldn't get more than a few paragraphs in. It's really poorly written.

EDIT "couldn't" (I suppose if I'm going to gripe about grammar, I should at least proof read...)

<comment><USER11> I stopped reading at "Whatever you just said, it's wrong" as it was evident from there that it was the usual HN hyperbole article. Not to say HN is dominated by such articles, but their number has been steadily growing and one is already too much.

</comment></comment><comment><USER12> The analogy at the end (section IV) made the whole article easier to "get".

      ...an answer cannot be more precise than its data.  Or its question.

<comment><USER13> Except the example he gave was also wrong. 3 + 4.2 is 7.2, using exact arithmetic, which should be the default assumption.

If you're measuring values under uncertainty, then his explanation makes some sense. But that's a property of measurement and uncertainty, not of numbers or arithmetic.

<comment><USER14> Default assumption for whom?

For a scientist, or someone working with real world data, this should never be the default assumption.

Besides, in the paragraph with the arithmetic he explicitly talks about significant figures. In context, the answer is unambiguously 7 .

<comment><USER15> It's 7.2. Without context (and he gives none for the expression) everyone (including me as a scientist) must assume abstract numbers. What's more, we know he's not using measurements because there are no units...

</comment></comment></comment></comment><comment><USER16> The example is not all that potent. before the link even loaded, the following flashes through my head:

'well of course carbon from the air. ... oh, and water. They have a lot of water.'

He tries to use something pretty clear cut, like mass and trees (mass is mass, and trees are pretty typically also trees) as an analogue to something inherently vague- psychiatry in this case. Whether someone is bipolar is way more up to interpretation than whether something that weights 100kg has a mass of 100kg or not.

</comment><comment><USER17> Three vaguely related observations:

1\. When we lose weight due to eating right and/or exercising (excluding water weight), it's because the air that we breathe out is slightly heavier than the air that we breathe in.

2\. When we're burning wood in a fireplace, the heat it gives off is essentially the sunlight that the tree has collected over its lifetime -- i.e. the heat difference between full sun and the shade of the tree.

3\. A solar cell producing peak power is slightly colder than one that is either open- or short-circuited.

<comment> a-priori Re #1: You're ignoring metabolic byproducts that are excreted in urine or feces or sweat. Are there any byproducts that are released in the breath?

<comment><USER17> I should have qualified that by saying it's true to roughly the same degree as the claim that a tree's mass comes from the air. I.e. if you wave your hands and ignore a lot of potentially-important second-order effects, it more or less holds water :).

</comment></comment></comment><comment><USER18> Excellent. After the first line I deliberately closed my eyes and tried to re- reason it (and got it right, thankfully). It was quite refreshing to have a blog post make you actually stop and _really_ think for a second.

The point was good as well. ;)

<comment><USER19> It's the whole blog, not just the post :) Really worth bookmarking.

<comment><USER18> Damn straight. I just finished reading one of his latest entries; acidic, critical and brilliant IMO.

[http://thelastpsychiatrist.com/2010/06/are_certain_behaviors...](http://thelastpsychiatrist.com/2010/06/are_certain_behaviors --_and_jo.html)

EDIT: money quote: _The science error of our generation is this: If A is strongly associated with B, and B is strongly associated with C, then A is strongly associated to C._

</comment></comment></comment><comment><USER20> _stupid mode_

Opens up more questions than it answers. It seems when trees die, they become earth or even oil. How come they don't completely dissolve into air again? And if they don't completely dissolve into air, how come all the CO2 is not bound in the earth yet (trees pluck it out of the air, then die and bind it to the ground)? There would have to be bacteria or something that converts soil into air (CO2), but then, why don't they dissolve everything?

<comment><USER21> Trees do dissolve. Forest fires are the most elegant way -- the reaction liberates much of the carbon and all of the oxygen into gasses including CO2, and the cycle is complete.

Bacteria that break down the rotting remains of a dead tree also consume the energy stored in the cellulose and release gasses like carbon dioxide and methane. At some point the remains don't contain bonds with easily freed energy and you have regular old dirt. If they had a way to break down the dirt further they would, but the cost is too high.

</comment></comment><comment><USER22> Did anyone else think this was going to be about the Higgs boson?

<comment><USER23> Higgs boson is also wrong. The biggest part of the mass is created by the strong interaction between the quarks in the nucleon.

</comment></comment><comment><USER24> I agree with the ultimate point in this article, but the way he got there seemed to be rather jumpy. I also had a hard time following his train of thought. I will say that the way the article ended was excellent.

</comment><comment><USER25> This forget's about all the H as a tree get's most of it's hydrogen from water.

EX:"Glucose" (C6H12O6) is from 6 * H20 + 6 * C02 => C6H12O6 + 6 * O2.

So a reasonable ansewer would be mostly air and water with some minirals from the soil.

<comment><USER18> Uh, the second "section" on the page basically says about the same thing :)

<comment><USER25> _Some trees are more than 50% water by mass_

This is true, but missing my point. A lot of a trees weight is water but the H in it's DNA is _from_ watter but not still water.

<comment><USER16> Yes, and the question was where does the tree get it's mass from.

I suppose you could look at it in the twisted sense of 'what in the tree contributes so much to it's mass', but that's a rather tortured use of English.

<comment><USER25> If the meant "50% of the mass of some trees is from water" that then he should have said so. _Some trees are more than 50% water by mass_ is a true statement, but it means something else.

The author saying something related to the truth, but I think he has a poor understanding of what is going on. Reading that I felt like a teacher who wants to give some created for a student using the correct words while realizing they don't quite get it.

As to precision it's more accurate to say 3 +/- 0.5 times 101 +/- 0.5 = 256.25 to 355.25 or 305.75 +/- 49.5 than to just say 300. For an extreme example (1 +/- .1) ^ 1,000 can be any ware from ~2.47e41 to ~1.748e-46.

PS: "Dead stars" is a reasonable if incomplete answer to the same question, but context matters so it's not acceptable on a botany or chemistry exam. In the same way that Air + Soil + Water would be missing the point on an astronomy exam.

<comment><USER18> > As to precision it's more accurate to say 3 +/- 0.5 times 101 +/- 0.5 = > 256.25 to 355.25 or 305.75 +/- 49.5 than to just say 300.

Ah, problem is you've changed the question now. Which is a reasonable thing to do when face with 3 + 101.5 = ?, but not the point the author was making (or, actually, it is the exact point the author is making) :)

<comment><USER25> Ehh, I was trying to show why "significant digests" are a poor approximation of accuracy.

His explanation in terms of addition is incomplete. _Since "3" only has one significant digit (3.03.0 is a more precise number with 2 significant digits) the answer itself can have no more than one significant figure._ 3 +/- .5 + 101.5 +/- .05 = 104.5 +/- 0.55 which has 2 significant digits. Now in his example 3 + 4.7 the three does limit the question to 1 significant digit, but your example has more than that even though your also adding 3 to a number (104.5 +/- 0.55)

PS: This is all assuming the accuracy based on the number of digits given you could easily have 3 +/- .0001 and 101.5 +/- 70.

<comment><USER18> _PS: This is all assuming the accuracy based on the number of digits given you could easily have 3 +/- .0001 and 101.5 +/- 70._

That is the whole point :)

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER26> I had this same though recently after thinking about how much shrub/tree/grass growth I have pruned out of my yard over the last 4 years ;-). Keeping a compost pile of all trimmings keeps this in perspective. Conversely, the amount of mass left after you burn wood is quite minimal. There was a nice scene about that topic in the move "Smoke". Screenplay dialog here: [http://www.script-o-rama.com/movie_scripts/s/smoke-script- tr...](http://www.script-o-rama.com/movie_scripts/s/smoke-script-transcript- paul-auster.html)

</comment><comment><USER15> As far as I know, most of the CO2 in plants goes to respiration for energy production. Not saying it doesn't go to mass, as well, but it's the energy side of things that people learn in elementary school.

We're not stupid, and this man needs to think about his accusations before he blogs about them...

<comment><USER27> Are you aware that cellulose is just interlinked glucose molecules? A tree is basically a giant hunk of hydrated sugar byproducts with some minor mineral and other components.

<comment><USER15> Yes, very. I could tell you all about the glycosidic linkages.

Some observations: Polysaccharides are not sugars. Mono- and di-saccharides made of carbohydrates are. Glucose has other uses than becoming cellulose or starch or glycogen. The life cycle of a plant is not linear or simplistic. CO2 is not only used as a substrate in anabolism.

</comment></comment></comment><comment><USER6> I asked this question to my brother who is in high school and he got it right without me steering: CO2, water and a little bit from the ground. Education works :)

</comment><comment><USER28> Adam: Who invented the cotton gin?

Bob: That's easy, Eli Whitney

Adam: What's a cotton gin?

Bob: Shrug

</comment><comment><USER29> I got the water part without even thinking, and photosynthesis danced in my head as an answer, but carbon didn't even cross my mind.

</comment><comment><USER30> And here in the animal kingdom, when you exercise and lose thirty pounds, much of that weight is breathed out.

</comment><comment><USER31> reminds me of Minds of Our Own <http://www.learner.org/resources/series26.html>

</comment><comment><USER32> As the author of the article, I thought I'd add my observation about the comments here.

The point of my post was twofold.

First, an observation about what we think we know. When you say, "tiananmen square massacre" to people, most have a basic understanding of what happened, EXCEPT that the single image they have associated with it is the one of the student in front of the tank. That incident happened, of course, but it was the image selected to "teach" you about that incident, and that's what stuck. 1) This allows you to think you know something (one image), thought you don't know really anything (what was the point of the whole thing?) 2) you know only what someone else decided to tell you. If they had chosen to show you a pic of a student dropping a hand grenade in a tank, well, there you go.

Second, the most pointed criticisms on this board come from scientists (e.g. electromagnetic). This is because you're not relying on words, you're actually trying to understand the process. Some will do it better than others, fine; but my point was that most people are not taught that that process is important, they are taught only to "know" things. In college I knew a guy who could identify a lot (30?) dinosaurs. By name. Is he an expert? Everyone thought he was.

Point: when I asked him what the modern descendant of the triceratops was, he said it was a rhino. And no one disagreed with him, even after I pointed out that triceratops laid eggs and rhinos don't. He didn't know that, but even once he knew it it did not change his thinking; he was taught to think in terms of names and appearances, and he applied himself accordingly. (I write that story up, in case anyone cares.)

That he thought he was an expert is fine; that he didn't know dinos laid eggs is also fine, but it is of importance to understand the divergence between egg layers and mammals. That's where most people fail, and that's entirely the fault of the way we learn: the news.

It's not school-- we barely remember much. But watch a segment on the news, and you think you're informed, and then you extrapolate from that "information" entirely on the force of prejudices you've acquired from watching other news...

Who thinks China is mean to students? Ask around. Then ask them why they think that. I hope it obvious that had the desire of the news media been to tell you that the Chines govt. was nice to them...

I'm probably preaching to the choir here, but let me spin it around. While I'm guessing most of you are technically oriented, how many would bristle if, after a catastrophe, the government refused to send in crisis counselors and mental health support people to the site? It's not been shown to be effective, and could be counterproductive. But your intuition might suggest to you that it should be done, and so the govt. is left defending itself. Or perhaps you have a strong opinion on what Paulson should have done with AIG. Or whatever-- there is some field out of your expertise where you abandon your logical and scientific mind, in favor of prejudice and presumption in the form of empty words. And you don't know it.

The criticism that I am a poor writer is a common one. I know, I know.

I hope I made my point here (and perhaps enticed some readers?) without offending anyone, which is something I try very hard not to do.

Cheers.</comment>


Judge Rules That Airbnb Is Illegal in New York - <AUTHOR> http://gizmodo.com/judge-rules-that-airbnb-is-illegal-in-new-york-509021604 <comment><USER2> <https://news.ycombinator.com/item?id=5742925></comment>


Ask HN: Is it illegal to display false metrics on your SaaS site? - <AUTHOR>

I'm referring to metrics like "number of users", "number of X processed by our users", etc.
I've seen some VERY newly launched SaaS sites that claim to be "used by thousands of customers" which I find hard to believe. Of course they could have had a lengthy private beta where they got those signups but I'm still curious.
Is it illegal? Does it depend on the country where their company is incorporated? Does it constitute misleading advertising? <comment><USER2> I suppose it is possible to fake metrics without lying...you just have to be very misleading. The "number of users" metric, for example, is not clearly defined. Does it mean total number of warm bodies who signed up? That's hard to fake. Is it the number of rows in your user table? That's easy. If people can register multiple accounts, then 1000 people could have 10,000 accounts and claim 10,000 unique "users".

It is also easy to get users by having a free tier. You could have 100,000 free users and 10 paid, and claim to have "100,000 customers". Or if your service is targeted at businesses, who then resell to their customers (an MLS service for example), then you might claim that your service is "used by 100,000 customers" even though you only sold to 100 businesses.

You also have to read the wording carefully. There is a dentist near my house that advertises "We specialize in Zoom teeth whitening, as featured on Extreme Makover". You might assume the dentist was featured on the TV show, when in fact it was only the Zoom product/technique that was featured.

</comment><comment><USER3> Lying is, at least where I live, not illegal.

<comment><USER2> I live in the US, and there are many cases where lying is illegal, including perjury, libel, slander, fraud, filing a false police report, failing to disclose certain known facts during a sale, misrepresenting earnings of a corporation, and so on.

<comment><USER3> A fair point, and also true where I live. Let me modify my original statement: lying, at least where I live, is in most cases not illegal.</comment></comment></comment>


Official World Cup API - <AUTHOR> http://live.mobileapp.fifa.com/api/wc/matches <comment><USER2> (duplicating my comment from the other API thread)

T's and C's on FIFA's site are pretty clear:

      (i)            FIFA Content

      5.1          All FIFA Content, including FIFA feeds, is owned by, or
         licensed to, FIFA. FIFA Content is provided to You "AS IS" and may
         not be used, reproduced, distributed, transmitted, broadcast,
         displayed, sold, licensed or otherwise exploited for any other
         purposes than their access and usage on the FIFA Digital Platforms.
         For that sole and exclusive purpose, FIFA grants to You a limited,
         revocable, non-exclusive license to access and use the FIFA Digital
         Platforms privately for non-commercial purposes, in accordance with
         these Terms.

"FIFA Digital Platforms" is defined as the website and their App.

So... doesn't really sound like an "official API" in the sense of being something you could use to create your own world-cup data-based apps or web sites...

<comment><USER3> You can quickly display the data using the online JSON editor: [http://cdn.rawgit.com/jdorn/json- editor/master/demo.html](http://cdn.rawgit.com/jdorn/json- editor/master/demo.html)

This is "small data", you can always say that you watched all the matches and collected the information yourself ;-)

And, depending where you live this license can/cannot be legal.

</comment></comment><comment><USER4> Shocked to see the data type used in column names (I guess these aren't necessarily direct representations of the columns however) in 2014.

Edit: Or is this more common than I thought?

<comment><USER5> In my experience, these days far less people use Hungarian or Leszynski (more commonly applied to db column names, though the form seen here isn't following Leszynski) naming conventions (or variants thereof) than don't.

However, they are still common enough that I'm not too surprised when I see them, particularly in in-house code as opposed to open source software. It tends to be the sort of thing encoded in the 'process' of a company at some point that is difficult to extricate out later on due to legacy code, overall friction to change, etc.

</comment></comment><comment><USER6> You can also have the blog feed of each event of the matche: [http://live.mobileapp.fifa.com/api/wc/match/300186492/en/blo...](http://live.mobileapp.fifa.com/api/wc/match/300186492/en/blog)

</comment><comment><AUTHOR> With the number of the match, then you can get detailed information [http://live.mobileapp.fifa.com/api/wc/match/300186492/en](http://live.mobileapp.fifa.com/api/wc/match/300186492/en)

<comment><USER7> Yet more data unceremoniously dumped from a database and munged into JSON.

I like the player "PosX" and "PosY" decimal values, presumably used to position the player on a team formation graphic.

Apparently mobile devices only need one size of image each, rather than havingn various pixel density buckets. For some reason, the Android image is twice the size of the iOS image:

"c_IosImage": "[http://live.cdn.mobileapp.fifa.com/awards/ios/manofthematch....](http://live.cdn.mobileapp.fifa.com/awards/ios/manofthematch.png"), "c_AndroidImage": "[http://live.cdn.mobileapp.fifa.com/awards/android/manofthema...](http://live.cdn.mobileapp.fifa.com/awards/android/manofthematch.png")</comment></comment>


Inside Palantir's War with the U.S. Army - <AUTHOR> https://www.bloomberg.com/news/articles/2016-10-28/inside-palantir-s-war-with-the-u-s-army <comment><USER2> Being only a few miles from Palantir's headquarters, I can confirm that no war is going on. JK. People here are talking about the issues all the time, and I'm curious to see what happens.</comment>


Climate talks break up with no agreement on carbon trading - <AUTHOR> https://www.ft.com/content/33f5d282-1f3f-11ea-92da-f0c92e957a96 <comment><USER2> Many years ago, I actively engaged the political discussion surrounding carbon markets, as I thought I might be able to help. I've been directly involved in similar types of international negotiations, albeit at a smaller scale and unrelated to carbon markets. Talking to people involved in the carbon market negotiation made it abundantly clear why there will never be a useful outcome.

Most of the governments involved in negotiating a common international agreements on carbon markets unofficially represent for-profit interests seeking advantage in the structure of the carbon market. There is an enormous amount of profit that can be realized here, and the good faith actors are in the minority. This is quite normal even in small international agreements but it becomes exponentially harder to arrive at an agreement that is minimally corrupted with respect to its nominal purpose as the profit incentive to corrupt the agreement increases. The last time I looked in on the current state of the carbon market negotiations, it had been corrupted beyond redemption. I have no faith that something useful will result.

This is why international agreements are unlikely to have a material impact on climate change. International agreements tend to only be effective when there is minimal profit or advantage to be gained by corrupting the purpose of the agreement. Climate change activities simply involve too much money for an international agreement to produce a credible outcome. It is the wrong tool for the job.

<comment><USER3> Do you think carbon taxes can work without international agreement? If so what approach gives you hope?

<comment><USER4> Carbon taxes are easy. You tax anyone who pulls it out of the ground or imports it. With international agreements you might drop the import tax and let to source country tax the extraction. The producers will just pass the added cost on to the users. Even this simple solution will be shot down by politics.

<comment><USER5> It's a fat regressive tax at a time when the lower working class is already rebelling. Invest more in technology and instead increase subsidies and build transmission lines

<comment><USER6> You can make it non-regressive by paying a dividend that is funded by the carbon tax revenues. This is also a possible mechanism for making it revenue- neutral, which could be politically advantageous.

I believe the common name for this approach is "fee and dividend". I'm surprised I don't see it discussed very much. I would think that it should be palatable across the political spectrum.

</comment></comment></comment></comment></comment><comment><USER7> Carbon trading is a scheme for bankers to profit from climate change. The solution is simple —carbon tax. Additionally it is ideal for the carbon tax to be revenue neutral.

<comment><USER8> Carbon trading means no additional tax burden is placed on the economy, the biggest emitters simply pay to those that go green.

By limiting the number of certificates, you can steer exactly how much CO2 will be emitted in any given year.

And the cost of CO2 means that procedures that reduce CO2 emissions become financially viable, starting with the most effective ones.

Assuming you can track CO2 emissions correctly, it's a genius instrument.

<comment><USER9> A carbon tax would have similar incentives. Green producers and products get taxed less than brown producers. It also doesn't matter if the product comes from Timbuktu or Savannah, it just matters how green or not green it is.

Carbon trading would result in billionaires and large funds finding a new way to make more money at the expense of everyone else and solve little.

</comment></comment></comment><comment><USER10> This is not very surprising, is it? How many more data points do we need to conclude that COPs never achieve anything? I think it's time to acknowledge that our "leaders" have failed us.

<comment><AUTHOR> This is the thesis of Naomi Klein books. Current political system and pseudo- market solutions are incapable of solving the climate problem. And so far, unfortunately it is proven right.

I do agree in principle (with some reservations perhaps).

[] [https://www.goodreads.com/book/show/21913812-this-changes- ev...](https://www.goodreads.com/book/show/21913812-this-changes-everything)

<comment><USER11> Except that if we instituted a simple carbon auction it's provably effective (not just theoretical, it very effective for NOx and SOx). I suppose that's not a pseudo market solution, that's a real market solution, unlike all of the trading proposals being put forth at the moment.

<comment><USER12> An auction is a tax in disguise, the only difference is that if the government doesn't get the quantity exactly right it will either make the whole thing toothless or destroy its own economy.

About NOx and SOx, those are local pollutants. Local problems are much easier to fix.

<comment><USER11> NOx and SOx are not local pollutants. NOx/SOx emissions in one part of the world cause acid rain in other parts.

</comment></comment></comment></comment></comment><comment><USER13> There won't be a carbon tax in Germany. People are allergic to anything that has tax in the name. Instead Angela Merkel has chosen to strengthen the CO2 certificate trade in Germany which is known to be ineffective by design [0]. So if international carbon trading doesn't work either then that means there is no effective policy that will allow us to reach our CO2 goals.

The only hope that we have left is that speculative investors drive up the price. The more middlemen the better. If everyone bought 100€ worth of certificates every year and only sold when the price goes up it and keep them forever if the price goes down then this would at least simulate the effects of a CO2 tax because the effects would ripple through the entire economy without requiring action from politicians.

Paying directly for your own carbon sequestration doesn't work because it only has local effects, doesn't encourage prevention of emissions and it is effectively a letter of indulgence ("I'm free of my sin").

[0] many sectors are exempt, a base amount of certificates is handed out for free, the price is always low because there are too many CO2 certificates

</comment><comment><USER14> Given that a lot of the representatives flew to the climate talks, and did not reach any sort of climate deal, the COP25 resulted in a lot of CO2 production for no benefit.

<comment><USER15> The "...but they fly" comment comes up a lot.

Flying results in 2% of global CO2 emissions a year. That's hundreds of thousands of flights a day over an entire year.

Coal powered electricity results in an incredible amount of green house emissions, and it's just plain bad for the environment. How about we stop complaining about flying and stop using coal? Please, note I didn't say fossil fuels, I said coal:

[https://h4labs.org/ive-got-another-stupid-idea-to-deal- with-...](https://h4labs.org/ive-got-another-stupid-idea-to-deal-with-climate- change/)

We might actually buy ourselves some real time on climate change.

<comment><USER16> "We" could stop using coal tomorrow and it wouldn't make the slightest difference. China's out-of-control coal mine fires alone amount to more CO2 emissions than most countries and that is still a tiny fraction of their total.

Flying is relevant because nothing undermines the message more than "you must curtail your lifestyle but I won't mine". Exactly how many climate summits have been held over videoconferencing? My employer has massively cut back on flying to meetings, we have a big screen in every meeting room instead, it works pretty well. Meanwhile politicians and celebrities refuse to cut back on private jets. There's no truer form of climate denial than that!

<comment><USER17> China's CO2 production per capita is half of that of US

[https://www.google.com/publicdata/explore?ds=d5bncppjof8f9_&...](https://www.google.com/publicdata/explore?ds=d5bncppjof8f9_&met_y=en_atm_co2e_pc&idim=country:CHN:RUS:IRN&hl=en&dl=en#!ctype=l&strail=false&bcs=d&nselm=h&met_y=en_atm_co2e_pc&scale_y=lin&ind_y=false&rdim=region&idim=country:CHN:RUS:IRN:POL:USA&ifdim=region&hl=en_US&dl=en&ind=false)

<comment><USER16> _China 's CO2 production per capita is half of that of US_

... and their total emission is already double that of the US. Are you saying China take no action until they achieve parity per capita? I can think of no other reason you would mention "per capita".

<comment><USER18> This has been explained to you before: [https://news.ycombinator.com/item?id=21596722](https://news.ycombinator.com/item?id=21596722)

<comment><USER16> Nice condescension, but the "per capita" argument is still complete nonsense. When China achieves parity "per capita" with the US - which it is on track to do within the next decade - but with 4x the population, will you finally understand?

<comment><USER18> Unless you can make the case that the average person in the US is entitled to put more CO2 into the globally shared atmosphere than the average person in China, per capita is the only way to fairly access whether a country is contributing more or less than its fair share.

Yes, it would be bad if Chinese per capita use rose to match US per capita use, because US per capita use is already way over what is sustainable globally.

<comment><USER16> The environment doesn't care what anyone is "entitled" to and the West knows this and is a case study about how hard it is to reduce. I find it bizarre that anyone suggests it's sensible for China to get there and only then start to reduce, because they are "entitled to".

Of course, the West is already on that downward trend and China is soaring upwards with no signs of even plateauing. Within 10 years China will be technologically independent of the West and the West will have even less influence over its environmental policy than we do now.

<comment><USER18> > I find it bizarre that anyone suggests it's sensible for China to get there > and only then start to reduce, because they are "entitled to"

No one has suggested that. You, however, have suggested that there is no point in the US lowering its emissions because China emits more total than does the US.

The atmosphere does not care about arbitrary political boundaries. There's a total amount of greenhouse gases we can put in it consistent with keeping global warming under any given target. A ton of CO2 from a person in China is the same as a ton of CO2 from a person in the US as far as the atmosphere is concerned.

So if a random person in China being responsible for putting about 8 tons in per year is unacceptable, then by what logic is a random person in the US putting in over 16 tons a year fine? By every physical measure, that US person is doing at least twice the harm to the atmosphere.

The US needs to be the leader on this, not making excuses not to reduce its own emissions.

<comment><USER19> Which is exactly why there is no difference between sixteen tons from eight people and sixteen tons from sixteen people. The emissions per capita alone is totally meaningless.

The reason that per country emissions are important is that countries are convenient political and demographic units.

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER20> By the way, IMF says that globally, fossil fuel subsidies represent $4.7 trillion / year...

[https://www.imf.org/en/Publications/WP/Issues/2019/05/02/Glo...](https://www.imf.org/en/Publications/WP/Issues/2019/05/02/Global- Fossil-Fuel-Subsidies-Remain-Large-An-Update-Based-on-Country-Level- Estimates-46509)

</comment><comment><USER21> high profile failures like this are exactly why i'm taking things into my own hands and starting a company dedicated to making the voluntary purchase of carbon credits/offsets accessible to the public and to small/mid-sized companies.

we can't wait for the political leadership to get their game together any longer, and we can't wait for the part of the population that doesn't belive there is a crisis to come around either.

we need to start financing the prevention or removal of carbon from the atmosphere today, using the voluntary carbon markets that we have. there aren't another five or ten years of indecision that we can afford to waste. and we don't have any better system for emissions reductions, yet.

those of us who understand the gravity of our present climate situation have the burden of carrying everyone else on our backs to the extent that we are able. addressing our planet's health is not something that will be fair in terms of the responsibilities that we need to take. an idealistic few must come forth and create the means for our mutual salvation, and they can expect only to thanklessly toil in the short-term.

it's a bitter pill to swallow, but we need to accept it and start moving forward rather than getting mired in spite towards those who won't help, or fatalism that we can't change our situation. taking action will be our protection against depression, and chipping away at our planet's climate crisis will inspire others to join our efforts.

send me an email (it's in my profile) if you want to help/join me. i need anyone and everyone who wants to contribute.

but be warned: if you want to join my effort, at present i can promise you nothing but my appreciation and the assurance that you're trying to be one of the good guys.

<comment><USER22> Carbon offsets are a scam, they do not prevent CO2 to be ___added_ __to the current levels we have now.

It's just for capitalists to believe they can keep flying by planting trees.

<comment><USER13> You can buy EU CO2 certificates and resell them for a profit which makes emitting CO2 uneconomical because of all of the middlemen who are taking their cut. If you never sell them below the purchase price they won't result in emissions. It's a win win situation.

</comment></comment></comment><comment><USER23> [http://archive.is/qnLyL](http://archive.is/qnLyL)

</comment><comment><USER24> There are hundreds of news articles about the COP 25 and somehow we chose the _Financial Times_ as the article we should comment on. That says a lot about our bewilderment (cf. their self-serving focus on carbon _trading_ ).

<comment><USER25> What does it say to you?

What it says to me: I know that climate change is an extremely serious problem we need to solve and I believe economics is the right place to look for the tools we'll need to incentive people to do better.

<comment><USER26> Economics has already given us the solution long ago. Climate is an externality. You solve problems with externalities by pricing them into goods and services. That was clear for climate change back in the 90's. Everything since then has not been in the realm of economics but of politics. If the climate change problem will be solved, the solution will have to be political.

</comment></comment></comment><comment><USER27> Corporate and consumerism waste needs to be addressed first.

-Leasing new cars every 3 years -New iPhone every 2-4 years -New clothes every season -Corporate flights vs teleconferencing -Amazon should be there own investigation

And so on.

<comment><USER28> Wouldn't bundling the costs of the negative externalities associated with the production of the new cars, iPhones, and clothes as a tax levied against the manufacturers "address" the "consumerism waste"? Manufacturers are very good at passing costs on to consumers, and innovating to drive costs down in the face of competition. If your goal really is to reduce environmental impact (and not just being "anti-consumerist") then it would seem like using the market to do that would make more sense vs. trying to change the habits of mass numbers of individuals.

</comment></comment><comment><USER24> Who think we need carbon __trading __to fight climate change, except traders, their friends (e.g Financial Times) and their clients?

</comment><comment><USER29> Anyone have a non paywall source? Might be more of a conversation if we had something to read.

<comment><USER30> Fair point.

For the record, though, if you ever _do_ decide to pay for news, FT is one of a few that are actually worth the money.

<comment><USER31> It's actually part of the HN submission guidelines that any paywalled article has to have a known workaround:

[https://news.ycombinator.com/newsfaq.html](https://news.ycombinator.com/newsfaq.html)

I flagged this submission for that reason.

</comment></comment></comment><comment><USER32> This doesn't surprise me one bit.

Government is 100% of the wrong place to solve this. And that's because they can't no matter what they do it's impossible for them to do that.

This can only be solved in the research field with better energy sources.

And it can be solved in the finance field with cheaper energy sources.

Government regulation cannot solve this.

<comment><USER13> >And it can be solved in the finance field with cheaper energy sources.

In Germany the market prices for electricity go down but at the same time the residential rates go up massively. There are two reasons for that. Firstly coal plants can't throttle down but PV and wind farms get paid anyway through a surcharge. Secondly large industrial consumers are exempt from the surcharge. This means residential users are paying the electric bills of aluminum smelters, etc. In other words. The cheaper the electricity in Germany the higher the final price. Isn't that absurd?

The surcharge wouldn't be needed if there was a working CO2 tax or carbon trade system because all coal plants would be shut down and replaced by load following natural gas plants which only run when truly necessary.

</comment></comment><comment><USER33> Greed will kill us all.

</comment><comment><USER34> The only way this is getting sorted is if a benevolent AI takes the world by force. And that's not going to happen on this timeline. So we are pretty much assured to be descending into resource wars, mass migration and civil unrest. Trump, Boris and Brexit are the first signs of the developed world pulling up the drawbridges. It's kind of like an emotional response within the brain. We feel trouble significantly before we can reason about it. The emotional response is the fast, unthinking systems of the mind which respond to danger. People on the ground are feeling the danger even though they can't quite put their finger on the reason. And so we get polarisation and isolationist policies.

</comment><comment><USER35> The cost of removing carbon from the atmosphere far outweighs the cost of not putting it there in the first place. We need bold leadership.

</comment><comment><USER36> From personal perspective - and I would definitely be classified here as a carbon tax obstructionist - thanks to the likes of Al Gore and all the tax happy socialist liberals - go after the visible pollutants. Diesel trucks and coal burning for example. When the politicians of my state (Oregon) start to restrict, tax and outlaw things like natural gas I fight them on everything they do whether it's right or not because I now believe them to be liars snd idiots. Lets plant trees and harvest them for building. I like those ideas. Tax carbon? Promote something do-able to the common sense public at large or fail. This post is not meant to incite any discord but rather just give an honest viewpoint.

<comment><USER13> The obsolete technologies are artificially kept alive through direct and indirect government subsidies. The only thing that stops us from reducing carbon emissions is that we don't choose to reduce carbon emissions.

Electric cars are too expensive? Just build more of them and the price will go down. Battery storage is too expensive? Just build more and the price will go down. Renewables are too expensive? Just build more and the price will go down.

It's a matter of choosing the new technology and right now nobody is choosing. Lots of people are proud about polluting the environment and I don't think I can change their mind, the government can't. We all know what this means: nothing can.

</comment></comment><comment><USER37> Carbon is not a synonym for carbon dioxide. [https://www.cfact.org/2019/04/25/carbon-is-not-a-synonym- for...](https://www.cfact.org/2019/04/25/carbon-is-not-a-synonym-for-carbon- dioxide/)

<comment><USER38> In common parlance it absolutely is.

</comment></comment><comment><USER39> Brings to mind an absurd idea of software development companies gathering to discuss _the international exchange of stack pointers_.

</comment><comment><USER40> Without the US, China and India, these programs are all pointless.

The US, China, and India are not willing to commit.

So the rest if us might as well go home. I know its encouraging to pretend things are working. But they're not. The time for non binding commitments ended in the 90s. Now is (and has long been) the time for cold hard facts.

<comment><USER41> Steep tariffs are needed on goods from countries that won't play ball.

<comment><USER40> If it were the US OR China I might agree. But I don't think we can isolate both at the same time. Maybe with trump sabre rattling, the Chinese will get on board. Who knows

</comment></comment></comment><comment><USER42> Carbon trading is when a polluting economy/state throws its weight around to keep polluting. It's that simple and that wrong.

There should be no such "credits", just emissions reduction plans - and the states which should be required to reduce the most and the fastest are those which pollute the most per capita, and the most period.

Of course, this won't happen because the largest polluters are also the most politically powerful, so they rig things in their favor.

Let's name the names: China, US, India, Russia, Japan, Germany, Iran, South Korea, Canada, Brazil. [https://www.countryaah.com/top-10-countries-that- contribute-...](https://www.countryaah.com/top-10-countries-that-contribute- most-to-global-warming/)

What should be negotiable IMHO is:

* How to account for the unfairness in the fact that some countries got to pollute freely for a couple of centuries while others didn't get this "free pass" and are still behind in grid development. * The question of whether absolute pollution should count more than pollution per capita or the other way around. * The extent and the form of aid by richer/more industrially-developed economies should be committed to help with technology to poorer economies which already pollute a lot and will have difficulties adjusting independently.</comment>


Why .NET? - <AUTHOR> http://engineering.gopangea.com/2015/12/10/why-dot-net.html <comment><USER2> I've never been on a Go project so I can't speak to that but I do find myself moving between Java/C# clients every 12 to 18 months or so. Prior to the lambdas support in Java C# was a clear winner in the language category for me. Sure, there's some minor things that can be frustrating in the Java world (ahem generics) but for the most part I find myself at home after a week. Now, the Java platform is an entirely different matter and that's something MS has been gunning to improve recently. I always thought that Java being on Linux gave the platform some real strength because of being able to take advantage of so much server side stuff that's typically only available on Linux. That seems to be about to change and I'm excited.

</comment><comment><USER3> I am looking forward to playing with .Net again. I used C# and .Net for my personal projects for years, but left it for Linux and Python a few years ago. As Microsoft has made it more open, .Net becomes an option again. Windows 10 even seems OK.

I would add one additional negative against Java though: Java is bureaucracy incarnated as code. The degree of over-engineering present in almost everything I've ever seen written in Java entirely turns me off as a developer.

</comment><comment><USER4> While not very deep, this is a good general outline of why .NET is still great. I think most startups and SV companies get caught in their echo chamber and use whatever is shiny/fancy without realizing that .NET powers many of the businesses that you interact with daily. Practically every company in the Fortune 500 uses it and if you're looking for one tech stack that can do everything, this is it.

We're a small startup/biz in the adtech space and we went with .NET and it's allowed us to be more agile and build better faster and better technology than all of our competitors. The real test isn't blog posts or endless dev debates but actual productive output and when looking at that, .NET is one of the most successful options available.

</comment><comment><USER5> Were doing a coupling to a soap backend. It has been 7 years since I last did .NET and now I've decided to never go back.

<comment><USER6> .NET WCF to another WCF integration is fine, but if you try and hook a .NET SOAP webservice to one written in Java, and you'll have pulled all of your hair out after two weeks. The reverse is just the same. They all have slightly different implementations of the specifications.

Make that one week if you have to start dealing with the monster that is called WS-* a.k.a. WS-WTF.

I wrote an article on this many moons ago. It is still one of my most frequently read posts: [https://benpowell.org/supporting-the-ws-i-basic- profile-pass...](https://benpowell.org/supporting-the-ws-i-basic-profile- password-digest-in-a-wcf-client-proxy/)

It just goes to show how many SOAP integrations are still going on in the "enterprise".

</comment></comment><comment><USER7> If you are building for client side Windows machine there is no better solution. Its just that simple.

Visual Studio is the best IDE available, bar none. Eclipse has everything you'd need but takes a long time to configure. Visual Studio "just works".

I'm not a Microsoft troll, I've just been around long enough, seen the different koolaid commercials and tasted a few.

</comment><comment><USER8> Most of these requirements seem like they were picked after the fact based on .Net's features. Things like a "rich ecosystem" are very subjective and easily rule out several valid options as part of this justification.

What happened to ever just saying "we want to roll with Microsoft's solutions so we did" ?

<comment><USER9> As the last paragraph states, we saw the writing on the wall with the open sourcing of ASP.NET MVC which is why we were comfortable with sticking with .NET.

We actually _don 't_ "want to roll with Microsoft's solutions so we did" which is why we use MySQL not MS SQL, AWS not Azure, git not TFS, RabbitMQ not MSMQ.

</comment></comment><comment><USER10> What ORM are you using?

I heard that EF 7 which is compatible with ASP NET 5 doesn't support MYSQL database.

Also the disk space when asp net 5 app is published is realy high.

Imagine you have 20 application, it with ~70Mb each one on disk you'll end up with +1GB of packages that you depend on.

I think something like Go, scala or Node JS is better.

<comment><USER4> EF6 is still the recommended ORM and amazing at what it does. EF7 is still missing major features like Lazy-Loading so while you can use it in production, the team has repeatedly said that it's not recommended or necessary unless you have a specific reason to use it. Summer 2016 is when EF7 is supposed to be at full release.

EntityFramework can also support any underlying database and EF6 has providers for sql server, mysql, postgre, oracle, sqlite and many others. EF7 will even support nosql options like MongoDB.

What exactly is the issue with disk space for a published running app? The GAC (global assembly cache) in Windows was originally designed just for this: to share references and avoid having multiple copies of the same binaries. All this led to was a complicated situation with maintenance and reference nightmares. Disk space is exceedingly cheap and a strange to worry about when picking a tech stack.

<comment><USER4> You can read more about EF7 RC1 here: [http://blogs.msdn.com/b/dotnet/archive/2015/11/18/entity- fra...](http://blogs.msdn.com/b/dotnet/archive/2015/11/18/entity- framework-7-rc1-available.aspx?WT.mc_id=DX_MVP4025064)

They currently have working providers for SQL server, SQLite, SQL Server Compact, and PostgreSQL

<comment><USER10> Great ! looking forward to see the changes in RTM version.

When I was talking about disk space I was referring to linux operating system where there is no GAC.

<comment><USER4> Well there's no other way around it - you can either publish all the dependencies with each project or share them in a central place.

Central places don't work very well and again, file space is very cheap. Most binaries compress down very small so there really shouldn't be a disk space issue. And you can always remove dependencies if necessary.

It's just not something that has ever been a problem in over a decade of building and publishing .NET apps.

<comment><USER10> yeah, you're probably right !

</comment></comment></comment></comment></comment></comment><comment><USER11> The brightest part of Java is actually Scala.

Don't forget about F# for .NET too. Type Providers are a killer feature.

</comment><comment><USER12> The article says:

>download a free and open source version of Visual Studio on your Mac

But the link for that takes you to Visual Studio Code. I thought that was just a text editor. That's not the full blown Visual Studio.

It's not possible to run full blown Visual Studio on Macs yet without a VM or Bootcamp right?

<comment><USER4> You're right - VS Code is a open-source text editor with Intellisense and refactoring and plugins and about 80% of the functionality found in Visual Studio. It works well and does almost everything you need if you're working with ASP.NET 5 / .NET Core stuff - which is the only thing you can deploy cross-platform right now anyway.

Visual Studio the full edition will likely never be running on Mac or Linux, but it doesn't need to.

<comment><AUTHOR> > ASP.NET 5 / .NET Core stuff - which is the only thing you can deploy cross- > platform right now anyway.

I understand what you're saying, but it's wrong. You can deploy earlier .NET apps to Linux and OSX via Mono, and get 95% of what you'll get from .NET on Windows. Alas, at a bit slower performance, but it works. Many of the most popular Linux desktop apps are actually written in C#/.Net and run on Mono with GTK#.

</comment></comment></comment><comment><USER13> It's hard to take anyone who's building new .Net applications on C# very seriously. F# is about 1,000 times better--as evidenced by the fact that the C# team themselves are stealing features from F# left and right.</comment>


VentureRate - Share and rate startup ideas - <AUTHOR> http://www.venturerate.com/

<comment><USER2> awesome, cool stuff!</comment>


Ask HN: Rss vs. Direct Hit - <AUTHOR>

I have been using RSS and twitter for a while to keep up to date and informed. Since RSS became popular we've seen points about rss dying, twitter replacing rss, etc.
I'm wondering, with the recent "UI/UX explosion" in web and mobile, does this provide more of a reason for people to hit  websites directly and read them for favorite blogs/news sites? What do you think?
Would you rather hit a page directly if the blog has great content and an enjoyable UI or would you still prefer reading it through Google Reader? <comment><USER2> I have all the blogs I read in my RSS reader.

However, most of these blogs deal with source code and thus far I haven't seen any RSS reader which does syntax highlighting and proper formatting on source snippets.

Therefore I usually read most of the interesting posts on the site itself. But I never visit a blog directly, always through the reader.

</comment><comment><USER3> The issue is if you want to read 50-60 news sources you have to aggregate them using a reader. I use google reader which works fine and sort of works on my android phone. Sometimes I click through but it depends more on the article. Twitter doesnt come close to replace RSS at the moment.

<comment><AUTHOR> perhaps a good differentiator would be a blog/site with content and/or ui design that people enjoy so much they visit it directly? meaning it breaks through the rss feed, and they goto it directly. thoughts?</comment></comment>


Angular 3 is hot on the heels of Angular 2 - <AUTHOR> http://www.infoworld.com/article/3139891/javascript/angular-3-is-hot-on-the-heels-of-angular-2.html <comment><USER2> > when features are to be deprecated, the Angular team will let developers > know ahead of time and keep supporting that feature for two major release > cycles

> Wormald promised a new major version twice a year from now on.

So... Support for deprecated features for one year? Hahahahaha. <sniff> Whooo, feels good to laugh.

</comment><comment><USER3> [http://dayssincelastjavascriptframework.com/](http://dayssincelastjavascriptframework.com/)

</comment><comment><USER4> Keeping up with the treadmill is just getting exhausting. I'm too young to be getting this cynical. I'm happy that most of my codebase is built on battle- tested, well-understood, libraries that are 3-5 years old... Happiness really is a boring stack.

</comment><comment><USER5> Why would Google want that? Why would developers want that? I hate when features are deprecated and what I hate more than that is meaningless versioning. If you're going to constantly change something, keep it in beta channel until you decide. I can see adding a few features every so often but a major version twice a year?

</comment><comment><USER6> Oh good, I was just thinking we needed more churn in javascript frameworks.

</comment><comment><USER7> lol, I thought they had already included every programming concept into ng2, what's left for 3? ;)

<comment><USER8> As a Java guy (who is very into Java 8 and very not into old-school software patterns), I was very amused when I learned that Angular has factories.

This just reinforces my view that JavaScript is going through the same phase Java frameworks did a while ago -- lots of people trying to figure out which kinds of frameworks and patterns to use, until one reaches the AbstractFactoryFactory and the whole ecosystem becomes a punchline.

<comment><USER9> One of the Redux utility functions I reuse across projects [1] would live in an ActionDispatcherFactoryFactory class in Java, but it can just be an 8 line function in JavaScript.

[1] [https://gist.github.com/insin/526b7b006039c16f8b2987a8037217...](https://gist.github.com/insin/526b7b006039c16f8b2987a80372170c)

<comment><USER8> That could be done in a Java 8 stream in a similar number of lines.

</comment></comment></comment></comment><comment><USER10> Someone asked me why I thought using Angular was a bad idea for a long term view.

All I had to say was "ask the knockoutjs guys how they're finding new recruits on the market".

<comment><USER11> You'll have a much easier time finding a decent JavaScript dev who can quickly get up to speed with Knockout (or Vue/Mithril/etc)... compared to finding someone with " _3 years experience in Mega Framework X_ ", where _X_ is only a couple of months old.

Now, if you might have an argument if you mean recruiting in terms of, " _What sexy thing will help me attract rock star 10X JavaScript devs, who haven 't used X professionally yet but are dying to do so?_". However, the problem with that argument is that people who come to you for the latest trendy edge will leave if you don't chase the next trend every six months. In fact, even today they're already more interested in the React shop next door.

Frankly, if you're willing to be a bit less sexy with your front-end tech stack, you can hire more stable and affordable developers and probably be better off altogether.

</comment></comment><comment><USER12> Disregard

<comment><USER13> [https://github.com/angular/angular/milestones](https://github.com/angular/angular/milestones)

<comment><USER12> d'oh, must've missed that, thanks</comment></comment></comment>