Predicting the Future Everyone claims they can’t predict the future. But mathematically, we can do better than guessing. The real problem isn’t the lack of predictive tools, it’s that most people never learned to use them. Let’s say we’re trying to predict if it’ll rain tomorrow. Last year, during this same month it rained 5 out of 30 days. A simple way to calculate the probability is by dividing the rainy days by the non-rain days: ...
Scheduling is an interesting optimization problem that we encounter every day. You schedule things like brushing your teeth, driving to work, and eating a meal, whether consciously or subconsciously. I’m sure most people (me included) don’t care to fully optimize their personal schedule in favor of adding a buffer between tasks. Someone who is trying to optimize their schedule is in for a monumental task. This type of scheduling problem is similar to an NP-hard problem in computer science, sometimes called the “Job-Shop Scheduling” problem. NP-hard problems are problems in which new inputs increase complexity combinatorially. In other words, adding a new task to your schedule would greatly increase the time required to fully optimize it. And that is the key piece of information here: fully optimize. What benefit do you actually get from having the most streamlined schedule if you have to schedule 2 hours a day optimizing your schedule tomorrow? ...
Caching involves storing frequently accessed data in a temporary storage location to improve performance and reduce the need to access slower storage systems. Deciding when to evict items from the cache when it reaches capacity is a key challenge. If important items are removed, performance can degrade. Some common cache eviction strategies are: Least Recently Used (LRU): Removes items accessed the longest time ago. Least Frequently Used (LFU): Removes items that have been accessed the least number of times. First in First Out (FIFO): Removes the items added the longest time ago. As a real-world example, a library “caches” books at the front that are frequently requested, so that patrons can access them quickly without having to wait for the book to be retrieved from the shelves. ...
Note: The following post is not financial advice. It is for informational purposes based on independent research. Money is easy to spend and difficult to acquire. After learning more about personal finances though, I found it easier to get ahead of money problems, and start planning instead of reacting. And I mean that in an empowering way: that anyone can pursue financial freedom. Here’s a overview of a few basic concepts and some specific investment options related to personal finances. I’ll try to cite another source for some of the topics, in case you want to look more into it. But if you only look into one other source, here’s my personal favorite book on personal finance and a quote from it: ...
The third chapter of Algorithms to Live By is about sorting. Some examples of sorting you might encounter: Ordering books on a shelf (if you still have physical ones) Participating in a tournament orders individuals/teams by skill (with varying degrees of reliability) Sorting food in your fridge so you know where to look for things With more digitization these days, computers will handle most of the mundane sorting like ordering documents, but it’s interesting to realize how we naturally follow some of these algorithms. Let’s take an example of ordering your todo list by priority: ...