Startups – The Earliest Phase Is The Most Productive

From Jessica Livingston. Founders at Work: Stories of Startups’ Early Days

Apparently sprinters reach their highest speed right out of the blocks, and spend the rest of the race slowing down. The winners slow down the least. It’s that way with most startups too. The earliest phase is usually the most productive. That’s when they have the really big ideas. Imagine what Apple was like when 100% of its employees were either Steve jobs or Steve Wozniak.

The striking thing about this phase is that it’s completely different from most people’s idea of what business is like. If you looked in people’s heads (or stock photo collections) for images representing “business,” you’d get images of people dressed up in suits, groups sitting around conference tables looking serious, Powerpoint presentations, people producing thick reports for one another to read. Early stage startups are the exact opposite of this. And yet they’re probably the most productive part of the whole economy. Why the disconnect? I think there’s a general principle at work here: the less energy people expend on performance, the more they expend on appearances to compensate. More often than not the energy they expend on seeming impressive makes their actual performance worse.

It is one of those books that you can keep reading and re-reading.

On Respecting the Intrinsic Limitations of the Human Mind

 On respecting the intrinsic limitations of the human mind and approaching the (programming) task as Very Humble Programmers – From The Humble Programmer

… the amount of intellectual effort needed to design a program depends on the program length. It has been suggested that there is some kind of law of nature telling us that the amount of intellectual effort needed grows with the square of program length. But, thank goodness, no one has been able to prove this law. And this is because it need not be true. We all know that the only mental tool by means of which a very finite piece of reasoning can cover a myriad cases is called “abstraction”; as a result the effective exploitation of his powers of abstraction must be regarded as one of the most vital activities of a competent programmer. In this connection it might be worth-while to point out that the purpose of abstracting is not to be vague, but to create a new semantic level in which one can be absolutely precise.

the tools we are trying to use and the language or notation we are using to express or record our thoughts, are the major factors determining what we can think or express at all! The analysis of the influence that programming languages have on the thinking habits of its users, and the recognition that, by now, brainpower is by far our scarcest resource, they together give us a new collection of yardsticks for comparing the relative merits of various programming languages.

Programming will remain very difficult, because once we have freed ourselves from the circumstantial cumbersomeness, we will find ourselves free to tackle the problems that are now well beyond our programming capacity.

Hierarchical systems seem to have the property that something considered as an undivided entity on one level, is considered as a composite object on the next lower level of greater detail; as a result the natural grain of space or time that is applicable at each level decreases by an order of magnitude when we shift our attention from one level to the next lower one. We understand walls in terms of bricks, bricks in terms of crystals, crystals in terms of molecules etc. As a result the number of levels that can be distinguished meaningfully in a hierarchical system is kind of proportional to the logarithm of the ratio between the largest and the smallest grain, and therefore, unless this ratio is very large, we cannot expect many levels.

I do not know of any other technology covering a ratio of 1010 or more: the computer, by virtue of its fantastic speed, seems to be the first to provide us with an environment where highly hierarchical artefacts are both possible and necessary. This challenge, viz. the confrontation with the programming task, is so unique that this novel experience can teach us a lot about ourselves. It should deepen our understanding of the processes of design and creation, it should give us better control over the task of organizing our thoughts.

Once in a while you come across an essay that is timeless. A lot has changed in the world of software development, since this talk was delivered (in 1972). By a funny coincidence, my programming career started in 1972 and I was blissfully ignorant of the challenges Dijkstra was talking about. It has been both an exhilarating and humbling experience to be a developer for a while.

A Few TED Talks on Education

For the past few days I have been watching a few  TED talks on Education.

I want to share a  couple of my favorites.

Shimon Schocken and Noam Nisan developed a curriculum for their students to build a computer, piece by piece. When they put the course online — giving away the tools, simulators, chip specifications and other building blocks — they were surprised that thousands jumped at the opportunity to learn, working independently as well as organizing their own classes in the first Massive Open Online Course (MOOC). A call to forget about grades and tap into the self-motivation to learn.

Daphne Koller is enticing top universities to put their most intriguing courses online for free — not just as a service, but as a way to research how people learn. With Coursera (cofounded by Andrew Ng), each keystroke, quiz, peer-to-peer discussion and self-graded assignment builds an unprecedented pool of data on how knowledge is processed.

With Coursera, Daphne Koller and co-founder Andrew Ng are bringing courses from top colleges online, free, for anyone who wants to take them

Some observations:
  • I like the approach taken by the self organizing computer course going from fundamental principles (NAND gates) to building a computer, writing an OS, a compiler and a game. It may be worth starting a community just to do that for interested students and enthusiasts.
  • The Coursera talk was fascinating. MOOCs are a popular but also a controversial topic. Daphne, in her talk mentions some of their learning from teaching students online. It was cool to see that there were using machine learning to spot some trends and how they started personalizing certain aspects of the course based on their analysis.
  • I think online learning and learning communities can help existing educational institutions. They do not replace teachers or class room learning, but complement them.
  • Anything that sparks interest or curiosity, help students follow some specific path (even if it is not part of the curriculum) of their own interest will be great tools to improve learning experience.
  • Learning by doing is probably one of the better methods of learning but the existing labs do not seem to fulfill that need.
  • Finally, teachers need help. We need to help teachers use more interesting tools to make learning engaging.
I think some of the autonomous colleges take some of these ideas and adopt them for their own needs or offer them as optional courses to interested students.

Recommended Reading – July 8, 2013

This is a list of some of my tweets and some context so that you can decide which ones are worth reading.

Notes by Tim Berners-Lee: These statements of architectural principle explain the thinking behind the specifications. http://bit.ly/12mkLUp 

I have been reading some of these notes and found them pretty inspiring. You can get a sense of how web evolved, the kind of thinking that goes behind some of the standard efforts at W3C. If you want to track a list of W3C standards and drafts, you may want to take a look at this list.

Writing as a Thinking Tool http://wp.me/pe0H-1k2

There are times when a tweet is not enough. I feel that I should take a fragment from an article and make it a LinkLog (a style of blog post which is basically a link to a recommended post with a teaser). Sometimes, you like the title of a post/article. You read it and you immediately like the author’s style and content. That is what happened in this case. It was an email alert from LinkedIn that got me following Ben Casnocha. 

Mastering a skill involves hundreds of stages of incremental improvement over a very long period of time. From How to Draw an Owl.

This is a really short but very compelling article on what it takes to master a skill. The lessons from this apply to developing products or an innovation too.

If You Aren’t Taking Notes, You Aren’t Learning

A very different perspective on note taking. I take a lot of notes so when I saw this post, I was really interested. I highly recommend both reading and practicing. A bonus link includes some cool note keeping techniques from Tim Ferriss’s extreme “take notes like an alpha geek

Good Read: Good Life?

This article made my day. Maria Popova uncovers gems for your reading pleasure in her aptly named Brain Pickings.

There is no shortage of good days. It is good lives that are hard to come by. A life of good days lived in the senses is not enough. The life of sensation is the life of greed; it requires more and more. The life of the spirit requires less and less; time is ample and its passage sweet. Who would call a day spent reading a good day? But a life spent reading — that is a good life. A day that closely resembles every other day of the past ten or twenty years does not suggest itself as a good one.