Programmer’s dilemma

本文探讨了资深程序员在长期服务于稳定技术栈的大公司后,可能会遭遇的职业发展瓶颈——即所谓的“专家陷阱”。作者通过面试经历揭示了许多资深程序员在面对基本技术问题时表现出的能力缺失,并分析了造成这一现象的原因。此外,还提出了个人与团队层面的解决方案。

Recently I interviewed tens of candidates for a kernel programmer’s position. These candidates are from big, good companies, which are famous for chips or embedded OS/systems. Many of them claimed they have at least 10 years on-job experience on kernel. Their resumes look fairly shiny — all kinds of related projects, buzz words and awards…

But most of them cannot answer a really basic question: When we call the standard malloc function, what happens in kernel?

Don’t be astonished. When I ask one of the candidate to write a simple LRU cache framework based on glib hash functions, he firstly claimed he had never used glib — that’s what I expected — I showed the glib hash api page and explained the APIs to him in detail, then after almost an hour he wrote only a few lines of messy code.

I don’t know if the situation is similar in other countries, but in China, or more specifically, in Beijing, this is reality. “Senior” programmers who worked for big, famous foreign companies for years cannot justify themselves in simple, fundamental problems.


Why did this happen?

The more I think about it, the more I believe it is caused not only by themselves but also by the companies they worked for. These companies usually provide stable stack of code, which has no significant changes for years. The technologies around the code wraps up people’s skills, so that they just need to follow the existing path, rather than to be creative. If you happened to work for such kind of code for a long period and did not reach to the outer world a lot, one day you will find yourself to be in a pathetic position — they called you “EXPERT” inside the team or company, yet you cannot find an equally good job in the market unfortunately.

This is so called “Expert Trap”. From day to day, we programmers dreamed of being an expert inside the team/company; however, when that day really comes we trapped ourselves. The more we dig into existing code, the deeper we trapped into it. We gradually lose our ability to write complete projects from scratch, because the existing code is so stable (so big/so profitable). What’s the worse, if our major work is just to maintain the existing code with little feature development, after a while, no matter how much code we’ve read and studies, we will find we cannot write code — even if the problem is as simple as a graduate school assignment. This is the programmer’s dilemma: we make our living by coding, but the big companies who fed us tend to destroy our ability to make a living.


How to get away from this dilemma?

For personal —

First of all, Do your own personal projects. You need to “sharpen your saw” continuously. If the job itself cannot help you do so, pick up the problems you want to concur and conquer it in your personal time. By doing so, most likely you will learn new things. If you publish your personal projects, say in github, you may get chances to know people who may pull you away from your existing position.

Do not stay in a same team for more than two years. Force yourself to move around, even if in the same organization, same company, you will face new challenges and new technologies. Try to do job interviews every 18 months. You don’t need to change your job, but you can see what does the market require and how you fit into it.

For team/company —

Give pressures and challenges to the employees. Rotate the jobs, let the “experts” have chance to broaden their skills. Start new projects, feed the warriors with battles.

Hold hackathon periodically. This will help to build a culture that embrace innovation and creation. People will be motivated by their peers — “gee, that bustard can write such a beautiful framework for 24 hours, I gotta work hard”.

根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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