我认为自20世纪80年代以来发明的最好的想法将是我们不知道的。要么是因为它们很小，无处不在，以至于不引人注意，要么是因为它们的受欢迎程度还没有真正起飞。

前者的一个例子是单击并拖动以选择文本的一部分。我相信这是1984年首次出现在麦金塔电脑上。在此之前，您有单独的按钮用于选择选择的开始和结束。相当繁重。

后者的一个例子是(可能是)可视化编程语言。我不是说像hypercard，我是说像Max/MSP, Prograph, Quartz Composer, yahoo pipes等。目前它们确实是小众的，但我认为，除了思想分享之外，没有什么能阻止它们像标准编程语言一样具有表现力和强大的功能。

可视化编程语言有效地加强了引用透明性的函数式编程范式。这对于代码来说是一个非常有用的属性。他们执行这一点的方式也不是人为的——这只是由于他们使用的比喻。

VPL让那些本来不会编程的人也能编程，比如有语言障碍的人，比如阅读困难的人，甚至只是需要简单节省时间的门外汉。专业程序员可能会对此嗤之以鼻，但就我个人而言，我认为如果编程成为一种真正无处不在的技能，就像识字一样，那就太好了。

就目前来看，VPL只是一个小众的兴趣，还没有真正成为主流。

我们应该做些什么不同的事情

all computer science majors should be required to double major- coupling the CS major with one of the humanities. Painting, literature, design, psychology, history, english, whatever. A lot of the problem is that the industry is populated with people that have a really narrow and unimaginative understanding of the world, and therefore can't begin to imagine a computer working any significantly differently than it already does. (if it helps, you can imagine that I'm talking about someone other than you, the person reading this.) Mathematics is great, but in the end it's just a tool for achieving. we need experts who understand the nature of creativity, who also understand technology.

But even if we have them, there needs to be an environment where there's a possibility that doing something new would be worth the risk. It's 100 times more likely that anything truly new gets rejected out of hand, rather viciously. (the newton is an example of this). so we need a much higher tolerance for failure. We should not be afraid to try an idea which has failed in the past. We should not fully reject our own failures- and we should learn to recognize when we have failed. We should not see failure as a bad thing, and so we shouldn't lie to ourselves or to others about it. We should just get used to it, because it is just about the only constant in this ever changing industry. Post mortems are useful in this regard.

One of the more interesting things, about smalltalk, I think, was not the language itself, but the process that was used to arrive at the design of smalltalk. The iterative design process, going through many many revisions- But also very carefully and critically identifying the flaws of the existing system, and finding solutions in the next one. The more perspectives, and the broader the perspectives we have on the situation, the better we can judge where the mistakes and problems are. So don't just study computer science. Study as many other academic subjects as you can get yourself to be interested in.

电可擦可编程存储器，概括为非易失性读/写存储器，目前最著名和最普遍的是Flash。 http://en.wikipedia.org/wiki/EEPROM列出了这个发明于1984年。

通过赋予存储介质与处理单元相同的物理特性、功率要求、大小和稳定性，我们消除了在设计处理器位置时的限制因素。这扩大了我们如何以及在何处为如此多的智能设备(以及以前根本不被认为是智能的东西)赋予“智能”的可能性，以至于我们仍然被这股浪潮所吸引。Mp3播放器只是其中的一小部分。

互联网本身早于1980年，但由蒂姆·伯纳斯-李提出并实现的万维网(“通过简单机制分布式超文本”)始于1989/90年。

虽然超文本的概念以前就存在(Nelson的Xanadu曾尝试实现分布式方案)，但WWW是实现分布式超文本系统的一种新方法。Berners-Lee以一种强大且易于实现的方式将简单的客户机-服务器协议、标记语言和寻址方案结合在一起。

我认为大多数创新都是以一种原创的方式重新组合现有的作品。万维网的每一部分以前都以某种形式存在过，但两者的结合只有在事后才变得明显。

我确信你现在正在使用它。

我相信单元测试、TDD和持续集成是1980年之后的重大发明。

Eclipse内存分析器:

使用Lengauer-Tarjan支配树算法进行内存使用分析。

我没有资格在一般意义上回答这个问题，但仅限于计算机编程?并不多。

为什么?我思考这个问题已经有一段时间了，我认为我们缺少两样东西:历史感和客观评价我们所创造的一切的方法。并非所有情况都是这样，但大体上是这样。

For history, I think it's just something not emphasized enough in popular writing or computer science programs. Take language features, for example. A canonical source might be HOPL, but it's definitely not common knowledge among programmers to be able to mark the point in time or in which language a feature like GC or closures first appeared. And of course after that there's knowledge of progression over time: how has OOP changed since Simula? Compare and contrast our sense of history with that of other fields like maybe political science or philosophy.

至于判断，这确实是我们寻求成功的客观衡量标准的失败。给定foobar，它以什么可衡量的方式改进了编程行为中的某些方面，其中foobar是任何设计模式，敏捷方法，TDD等等。我们有没有试过测量这个?我们到底想测量什么?正确性，程序员的生产力，代码的易读性等等?如何?软件工程确实应该着手解决这些问题，但我还没有看到。