I just accomplish an online algorithm test for an interview, one of the problems ask me to travel thought a tree structure and calculate how many paths satisfied the condition. It can be easily solved by traditional DFS algorithm. I answered this (pseudocode):

Python makes users easy to operate files by hiding the implementation details for the IO module. This article aim in discourse more details about how it works for people who want to know more about it.

Back to the 1980s, the NLP systems still based on complex sets of hand-written rules. These rules become hard to maintain since it grows exponential depend on the length of sentences. Moreover, new words pop up every day so we also have to maintain the old roles before developing any new one. Most important, we can’t use these rules on other languages since the rules are languages-specify.
With the power of the modern computer, we tried machine learning algorithms like decision trees to replace some of the hand-written rules. As we gain more and more data, we start using statistical models like hidden markov models to predict the probability of the word depends on the input data. Nowadays, with the help from the deep neural network, word embeddings help us to convert a word from higher dimension space to a lower one.

Even though the computer run much faster then we expected nowadays, the speed between the different components are still very large. To solve this problem, we introduce different levels of caches. After access the data from storage, we store the most used (temporal locality) and the nearby data (spatial locality) in the current lever so we can aceesee them directly later. The term storage here is relative. For example, main memory is a storage to CPU cache, and the disk is a storage to main memory. When running a program, it may reach several levels of caches. Let’s look closer to it:

I spent 2 years in a startup as a software engineer, we have 15 engineers and we proud of our products. Like other companies, we desire to hire the best engineers. But hiring is really hard, I remember a popular article in HN, the article quoted from Thomas Ptacek:

Today I’m going to review pull_9302

Today I’m going to review pull_8757, My selection criteria it’s the PR should be

1. Neither too new or too old
2. Passed most of the tests and been labeled awaiting review
3. I can review it in a day.

(Another python contributor Tal Einat gave me so many advises in my yesterday post, Thank you so much, Tal.)

I’m going to review some PR from the Cpython repo this week.