Invent the products that replace your best-sellers

I’m thinking of this in terms of Glass replacing Android handsets. Apple doesn’t wait for a competitor to offer something better than their products — they control the killing-off of their best-sellers with each next-generation release. While some companies delay innovation in attempt to extract every ounce of profit (and increasing margins) out of their products, Apple doesn’t leave their timelines, forecasting, and supply-chain in the hands on it’s competitors.

Don’t compete head-on (Android v. iOS)

Android is making great progress, but it’s an head-on fight; and advancements are always compared to the iPhone. Sure, there’s money to be made with Android handsets — but it’s a “me too” game.

Change the conversation (iPad vs. everything else)

Tablet computing is hot right now; but so far, trying to capture a piece of the iPad market (AKA the Tablet market), is a losing proposition. Apple owns this segment. Instead of playing catch-up, change the conversion. Invent a new platform, and own it’s market segment instead.

Of course, as nice as all of this sounds, daringfireball has the right interpretation: This vaporware R&D fluff looks more like Microsoft or Nokia than Apple. Apple wouldn’t say “we’re exploring ideas for a future product that will change the world.” Apple would just do it.

def cache_method_results(fn):
    def _view(self, *args, **kwargs):
        var_name = '_{n}'.format(n=fn.__name__)

        if var_name in self.__dict__:  # Return the copy we have
            return self.__dict__[var_name]

        else:  # Run the function and save its result
            self.__dict__[var_name] = fn(self, *args, **kwargs)
            return self.__dict__[var_name]

    return _view

You might use it like this:

class Foo(object):
    @cache_method_results
    def some_expensive_operation(self):
        ...calculate something big and unchanging...
        return results


f = Foo()
print(f.some_expensive_operation())  # This first call will run the calculation
...
print(f.some_expensive_operation())  # but this one will used the cached result instead

It’s not rocket science, but these little tricks add to the fun of using Python.

The book covers a number of topics that come up frequently in data mining: reworking algorithms into a map-reduce paradigm, finding similar items, mining streams of data, finding frequent items, clustering, and recommending items. Unlike many texts on the subject, you won’t find source-code in this book; but rather, extensive explanations of multiple techniques and algorithms to address each topic. This lends itself to a better understanding of the theory, so that you understand the trade-offs you might be making when implementing your own systems.

There are easier texts to get through, but if you’re getting started with recommendation or data-mining systems, and haven’t read this book, I’d encourage you to do so.