Tech-savvy? Biz-Savvy?

本文讨论了在当前中国社会环境下,开发者提升自身商务素养的重要性。文章指出,在面对经济压力和社会地位的挑战时,开发者不应仅仅满足于技术专长,还需要具备一定的商业洞察力,以便更好地实现个人职业发展。

An IBM development VP recently answered an interview. One of the interview questions was that what suggestions he'd have for the mass of developers for them to become successful in their careers. The VP mentioned that one important factor was to become biz-savvy.

Well, I believe this holds true for the typical developers who enjoy caffeine and coding through the nights. They do need to look beyond their Dilbert stereotype and become a little bit more biz-savvy if they don't like pointy haired bosses to rule them.

But that also depends on who you are now and who you want to be.

I have personally seen more than a few great developers who shows excellent technical potential, and yet decided to do more business. They just became mundane business people.

This is especially a problem with the highly thriving and restless atmosphere in China. I grew up as a typical technical guy. So I can understand this increasingly heavy pressure from unnamable sources to developers. There are people who are making a lot and lot of money out there, by simply doing "business" stuff. Yet, developers are required to sit in small cubicles staring at computer screens all day while their bosses often don't show enough respect to them.

This is a different story in China than in the US or other western countries. In China, young people have to fight for existence first. Young graduates coming fresh out of school are pressed to find better paid jobs. Their families have spent years of family income for their education. The government doesn't pay for their unemployment. Their parents have retired, with little pension. Housing price has been skyrocketing. They will have to work for almost an entire life to afford a simple apartment. Everything is about money.

Yes, and it seems that the highest paid positions are in doing business. Everyone is talking about successful entrepreneurs. Everyone wants to do management.

I have interviewed dozens of students. And I can feel that for many of them, the sheer passion in technology is the least likely reason that they choose to become a developer. The valid reason is simply that being a developer has a steady pay at the moment. And many fresh employees eagerly seek to become "biz-savvy" even before they become "tech-savvy" enough. Because they believe they see more money in that.

What's the problem? Why are less-than-2-year-experience developers crazy about executive biographies and fancy marketing stories more than ingenious technical stuff? Why do CS majored students long for consulting jobs where software productivity is sarcastically measured by lines of code?

Someone has to fix this problem in China. And I believe it's the employers themselves' responsibility to do so. Treat your developers fair first, before you even dream about your master business plan. Without the tech-savvy folks, nothing will be realized.

源码来自:https://pan.quark.cn/s/a3a3fbe70177 AppBrowser(Application属性查看器,不需要越狱! ! ! ) 不需要越狱,调用私有方法 --- 获取完整的已安装应用列表、打开和删除应用操作、应用运行时相关信息的查看。 支持iOS10.X 注意 目前AppBrowser不支持iOS11应用查看, 由于iOS11目前还处在Beta版, 系统API还没有稳定下来。 等到Private Header更新了iOS11版本,我也会进行更新。 功能 [x] 已安装的应用列表 [x] 应用的详情界面 (打开应用,删除应用,应用的相关信息展示) [x] 应用运行时信息展示(LSApplicationProxy) [ ] 定制喜欢的字段,展示在应用详情界面 介绍 所有已安装应用列表(应用icon+应用名) 为了提供思路,这里只用伪代码,具体的私有代码调用请查看: 获取应用实例: 获取应用名和应用的icon: 应用列表界面展示: 应用列表 应用运行时详情 打开应用: 卸载应用: 获取info.plist文件: 应用运行时详情界面展示: 应用运行时详情 右上角,从左往右第一个按钮用来打开应用;第二个按钮用来卸载这个应用 INFO按钮用来解析并显示出对应的LSApplicationProxy类 树形展示LSApplicationProxy类 通过算法,将LSApplicationProxy类,转换成了字典。 转换规则是:属性名为key,属性值为value,如果value是一个可解析的类(除了NSString,NSNumber...等等)或者是个数组或字典,则继续递归解析。 并且会找到superClass的属性并解析,superClass如...
基于遗传算法辅助异构改进的动态多群粒子群优化算法(GA-HIDMSPSO)的LSTM分类预测研究(Matlab代码实现)内容概要:本文研究了一种基于遗传算法辅助异构改进的动态多群粒子群优化算法(GA-HIDMSPSO),并将其应用于LSTM神经网络的分类预测中,通过Matlab代码实现。该方法结合遗传算法的全局搜索能力与改进的多群粒子群算法的局部优化特性,提升LSTM模型在分类任务中的性能表现,尤其适用于复杂非线性系统的预测问题。文中详细阐述了算法的设计思路、优化机制及在LSTM参数优化中的具体应用,并提供了可复现的Matlab代码,属于SCI级别研究成果的复现与拓展。; 适合人群:具备一定机器学习和优化算法基础,熟悉Matlab编程,从事智能算法、时间序列预测或分类模型研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①提升LSTM在分类任务中的准确性与收敛速度;②研究混合智能优化算法(如GA与PSO结合)在神经网络超参数优化中的应用;③实现高精度分类预测模型,适用于电力系统故障诊断、电池健康状态识别等领域; 阅读建议:建议读者结合Matlab代码逐步调试运行,理解GA-HIDMSPSO算法的实现细节,重点关注种群划分、异构策略设计及与LSTM的集成方式,同时可扩展至其他深度学习模型的参数优化任务中进行对比实验。
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