SAT & CSP are criminally under utilized in CS classes, because profs have no clue about them.

That's why in so many industries they prefer to hire engineers and OR grads and teach them python, than hire SWE and teach them modeling

   coins = [100,50,25,10,5,1]
   change = 1234;
   result = [0,0,0,0,0,0];
   for(i=0:i<coins.length;i++){
     while(change>coins[i]){
       result[i]++;
       change-=coins[i];
     }
   }
   //[12,0,1,1,4]

Coudnt help myself sorry

  function coin_change(change) {
    const coins = [25, 10, 5, 1]
    for (const coin of coins) {
      const n = change / coin | 0
      change -= n * coin
      console.log(coin, n)
    }
  }

  coin_change(25+10+5+1)

As an interviewer, I gave one pretty simple task (people solved it in as little as 8 minutes), wasn't using any real CS, even though I'm good at it.

The reason was that aboint 70% of candidates couldn't write a simple loop -- to filter those out. The actual solution didn't matter much, I gave a binary decision. The actual conversation matters more.

Interview:

> We can solve this with a constraint solver

Ok, using your favorite constraint solver, please write a solution for this.

> [half an hour later]

Ok, now how would you solve it if there was more than 100 data points? E.g. 10^12?

MiniZinc is a really great modeling language for constraint programming. Back in August I gave a talk at NordConstNet25 on how we used it to build a product configurator in what's (probably) the worlds largest MiniZinc model

https://pierre-flener.github.io/research/NordConsNet/NordCon...

My Leetcode ability is unpredictable. I either ace the test or I can't finish it in time. The only way to make outcomes predictable is practice, but I have too much agency and not enough time for that.

Leetcode requires a very different set of skills from software engineering. Software engineering isn't so much about solving puzzles as it is about making good decisions. It's about knowing what's important and knowing where the boundaries are. It's about anticipating problems in their broadest form; creating just the right amount of flexibility and allowing the solution to solidify as your understanding of the problem deepens.

I avoided all this just by becoming a contractor, i ship solution, no me tests me for leetcode ability

Whoever agrees to do LC problems during interview has zero dignity.

An interesting meta problem is to determine antagonistic set of denominations, like the [10,9,1] example given in the post, to maximize the number of coins selected by the gradient method.

Been working on a calendar scheduling app that uses a constraint solver to auto schedule events based on scheduling constraints (time of day preferences and requirements, recurrence rules), and track goal progress (are you slipping on your desired progress velocity? Get a notification). It’s also a meal planner: from a corpus of thousands of good, healthy recipes, schedule a meal plan that reuses ingredients nearing expiration, models your pantry, estimates grocery prices, meets your nutritional goals. Constraint solvers are black magic.