I think this is a strong point.  If rails is yesterday&#x27;s technology, then what is today&#x27;s?<p>To be clear, by no means am I trying to claim that Rails is the clear winner over other technology choices, or that a good answer to this question needs to be one single, unambiguous answer.  I&#x27;ve learned a lot about other possibilities.  Node of course, but it&#x27;s still relatively new, and you need to prefer (or at least be ok with) javascript instead of ruby.  Pheonix and Elixir sound promising.  My own personal prediction (probably better to call it a suspicion, since I&#x27;m wary of predictions) is that an &quot;isometric&quot; approach like Volt will become more common (the approach, not necessarily that specific implementation, though it is of course a possibility as well).<p>But for now?  Some will do this, but I wouldn&#x27;t use Volt, though that has more to do with the requirements of my job than anything wrong with Volt (there really is no need for me to go so bleeding edge in what I do).  I personally am hesitant to invest too much in Javascript, even on the client side, since there is still a great deal of churn.  Again, this may simply not be a possibility for everyone, it depends on the kind of app you&#x27;re writing.<p>So it&#x27;s not so much that I don&#x27;t see good, perhaps preferable alternatives to Rails.  I just don&#x27;t see a sufficiently unambiguous case that Rails is out of date to be calling it yesterday&#x27;s technology - yet.<p>And lastly, as I mentioned in he other thread about this, when Rails isn&#x27;t working for me, either because of code clarity or performance, I tend to go to older, more established technologies, not newer ones.  For very elaborate sql, I use… sql, not contortions in ActiveRecord that I think are much harder to read and understand than a sql statement.  I also often go to raw sql for performance reasons (mass inserts, queries that result in very large result sets that need to be optimized for speed).  Unix, networking, specialized languages and environments for scientific computing, machine learning, stats, or other specialized tasks.  That tends to be where I go.