Frequently in programming, we can make a tradeoff: use less (or more) CPU in exchange for using more (or less) memory. Lookup tables are a great example: use a big pile of memory to turn complicated calculations into O(1) operations.

So, for example, implementing itoa, the C library function for turning an integer into a character array (aka, a string), you could maybe make it more efficient using a lookup table.

Alexandar sends us some C# date handling code. The best thing one can say is that they didn't reinvent any wheels, but that might be worse, because they used the existing wheels to drive right off a cliff.

try
{
    var date = DateTime.ParseExact(member.PubDate.ToString(), "M/d/yyyy h:mm:ss tt", null); 
    objCustomResult.PublishedDate = date;
}
catch (Exception datEx)
{
}

"Don't use exception handling for normal flow control," is generally good advice. But Andy's lead had a PhD in computer science, and with that kind of education, wasn't about to let good advice or best practices tell them what to do. That's why, when they needed to validate inputs, they wrote code C# like this:

    public static bool IsDecimal(string theValue)
    {
        try
        {
            Convert.ToDouble(theValue);
            return true;
        }
        catch
        {
            return false;
        }
    } 

Early in my career, I had the misfortune of doing a lot of Crystal Reports work. Crystal Reports is another one of those tools that lets non-developer, non-database savvy folks craft reports. Which, like so often happens, means that the users dig themselves incredible holes and need professional help to get back out, because at the end of the day, when the root problem is actually complicated, all the helpful GUI tools in the world can't solve it for you.

Michael was in a similar position as I was, but for Michael, there was a five alarm fire. It was the end of the month, and a bunch of monthly sales reports needed to be calculated. One of the big things management expected to see was a year-over-year delta on sales, and they got real cranky if the line didn't go up. If they couldn't even see the line, they went into a full on panic and assumed the sales team was floundering and the company was on the verge of collapse.

Loading times for web pages is one of the key metrics we like to tune. Users will put up with a lot if they feel like they application is responsive. So when Caivs was handed 20MB of PHP and told, "one of the key pages takes like 30-45 seconds to load. Figure out why," it was at least a clear goal.

Combing through that gigantic pile of code to try and understand what was happening was an uphill battle. Eventually, Caivs just decided to check the traffic logs while running the application. That highlighted a huge spike in traffic every time the page loaded, and that helped Caivs narrow down exactly where the problem was.

Alice picked up a ticket about a broken date calculation in a React application, and dropped into the code to take a look. There, she found this:

export function calcYears(date) {
  return date && Math.floor((new Date() - new Date(date).getTime()) / 3.15576e10)
}