Don't lecture me about performance when computers today are on the order of 1000 times faster than when I learned programming, and when even non-JIT scripting languages are 100-200 times slower than native code, we still have an order of magnitude speedup over native apps of the 80s. IMHO all that matters today is developer time, which is being squandered by languages like Swift and Rust that encourage a trees over forest view of productivity. My favorite non-mainstream languages are HyperTalk (AppleScript, ActionScript) and MATLAB (Octave) which provide tremendous leverage in few lines of code. Or better yet, code-less functional environments like Excel or FileMaker where you only drop down to a macro language when necessary. Or even better that than, declarative environments like the web that were built with relational data models and markup before AJAX opened the floodgates to callback hell and all the workarounds since (why the web lost WYSIWYG editing is a profound tragedy to me).
This turned into a rant but I simply don’t think code is supposed to be the way it is today. We’ve made a mistake. Yes, it has advanced computer science remarkably. But the opportunity cost of that is that we spend our days writing boilerplate and dealing with nondeterministic distributed computing issues informally rather than working at an appropriate level of abstraction.
I believe there are certain personalities drawn to software development, who are very detail-oriented and love technical challenges, to the detriment of the end result - forest vs trees. Just look at people who want to make games and end up writing a game engine, vs people who jump into Flash and make do with the tools available.
Personally, I would much prefer to just feed in a specification, or set of constraints, to a program, and have it spit out a working program. Program synthesis seems like an interesting area of research, but fairly basic at the moment. I imagine any useful synthesis will be domain-specific - generating a JSON web service/API will be quite different to writing a music visualizer.
Then in current practice, it's often back to square one, but it shouldn't be. We have opininated frameworks like Rails and Django that save a lot of time, but only cover the featureset of a CRUD app. There is a lack of opinionated frameworks that work at a higher level, but still below that of drag-and-drop templated website generators.
I am very intrigued by Lia [0] as an example for how languages can be embedded inside of one another for useful effects. See Will Crichton's great blog post for more [1].
However, I am wondering why Python and PHP were chosen. I can't think of a compelling use for having both languages simultaneously. They are both dynamic scripting languages with similar design objectives.
[0] https://github.com/willcrichton/lia [1] http://notes.willcrichton.net/the-coming-age-of-the-polyglot...
> Depending on how one chooses to classify programming languages, Python and PHP can appear similar—most obviously, both are dynamically typed. From our perspective, however, there are a number of tricky differences: PHP has multiple global namespaces which can span multiple files, whereas Python uses one global namespace per file (semantic friction); most of PHP’s core data-structures are immutable, whereas many of Python’s are mutable (semantic and performance friction); and PHP’s sole collection data type is a mapping, whereas Python separates the notion of mappings from that of sequences (semantic and performance friction). As this may suggest, this combination of languages presents a number of design and implementation challenges which have no obvious precedent. We show that PyHyp’s solutions to these challenges allow interesting case studies to be implemented.
- R has lazy evaluation semantics (with caveats), and Python is eagerly evaluated. The ggplot library in Python recently posted to HN sheds some light on these issues.
- R has like 2 or 3 class systems; Python has a C++-like class system (without static typing, but gaining it in Python 3)
- They differ in semantics with respect to closures (Python 3 changed things a bit)
- R's built-in types are all vectorized, but that might be a good thing, so you can use Python semantics for scalars and R semantics for vectors/data frames/matrices, etc ?
- Python has decorators, generators, coroutines, etc.
I tend to write my Python and R in a pretty small common subset, but yeah they are in fact quite different.