Wealth Hidden in Infinite Niches

I am glad I am on Twitter. I don’t post relentlessly in this medium. I do keep my presence alive and mostly enjoy the connections. Once in a while a good thing happens. Some one you don’t know connects to you. You don’t know why they did, so you go out to check their profile, a bit of what they post and make a decision to follow.

It happened to me today and I am glad David decided to follow me. That is how I discovered 1000 True Fans and a bunch of very interesting sources. If you are an entrepreneur of any kind, I am sure you would enjoy reading this.

A few snippets:

A creator, such as an artist, musician, photographer, craftsperson, performer, animator, designer, videomaker, or author – in other words, anyone producing works of art – needs to acquire only 1,000 True Fans to make a living.

A True Fan is defined as someone who will purchase anything and everything you produce. They will drive 200 miles to see you sing. They will buy the super deluxe re-issued hi-res box set of your stuff even though they have the low-res version. They have a Google Alert set for your name. They bookmark the eBay page where your out-of-print editions show up. They come to your openings. They have you sign their copies. They buy the t-shirt, and the mug, and the hat. They can’t wait till you issue your next work. They are true fans.

One thousand is a feasible number. You could count to 1,000. If you added one fan a day, it would take only three years. True Fanship is doable. Pleasing a True Fan is pleasurable, and invigorating. It rewards the artist to remain true, to focus on the unique aspects of their work, the qualities that True Fans appreciate.

The key challenge is that you have to maintain direct contact with your 1,000 True Fans. They are giving you their support directly. …You also benefit from the direct feedback and love.

You don’t need a million fans to justify producing something new. A mere one thousand is sufficient.

This formula – one thousand direct True Fans —  is crafted for one person, the solo artist. What happens in a duet, or quartet, or movie crew? Obviously, you’ll need more fans. But the additional fans you’ll need are in direct geometric proportion to the increase of your creative group. In other words, if you increase your group size by 33%, you need add only 33% more fans.

At my company iMorph, we want to serve a small number of customers extremely well. Some of them pay a mere $25 and some of them more than $25,000 every year.  It does not matter. Interacting with customers, the users of your products, listening to their suggestions and watching them use your products in ways you never thought of, is one of the greatest joys. That is why I like the philosophy of 1000 True Fans. A few thousands is a small number, enough to support a  small business and still provide enough time for innovation and improvement and interactions.

Relentless Predator Upon the Obsolete

…a combination of relentless predator upon the obsolete and benevolent solver of the world’s problems. As ways of making money go, that’s pretty good. Startups are often ruthless competitors, but they’re competing in a game won by making what people want.

This is such a cool way to think about startups. I like the image of the relentless predator – some one on the hunt, looking to obsolete wasteful ways of doing things, saving people tons of money and making a few bucks in the process.

So how are startup ideas born?

1. If you are lucky, you will find a list like this to start with. It can fire your imagination and set you thinking to make your own list or flesh out the ideas a bit more.

2. You can watch out for problems and suddenly a better way solve some of them may pop-up in your head.

3. You can watch trends, think a bit ahead and build a few experimental proto-types and see what happens (You may be taking a bit of a risk with this approach and may end up building a solution looking for a problem).

4. Find the gap in an emerging technology space and fill a tiny bit of it with your solution.

5. Leverage a new technology to do something that has not been done before.

6. Pick some great idea that is successful and radically improve the implementation (make it simpler, easier, faster, more scalable).

7. The best, in my opinion, is to scratch your own itch and find something, for which you are the first user and see whether it has one of the above characteristics (an added bonus).

In our own startups , we have tried a few of these approaches. There may be many more. As Paul Graham says:

Consider this list to end with a giant ellipsis.

Dreaming up ideas can become a (nice) habit, so I keep an idealog. Not every idea is a good one or fit for a startup. But ideas trigger ideas and you never know where they may lead.

LinkLog: Some Lively Discussions on Blogs

It is some times nice to start off with a blog post and discover a gold-mine. You keep digging and keep finding more stuff. Here are a few I found this morning:

Explaining Things: Math vs Programming

Who Reads Code Samples – which seems to have started the entire Math vs Programming comment threads

There are some really good arguments here and opinions. I like to think of Math being declarative, abstracted version where code deals with all the nuances and explains things a bit more.

I do like the concept of alternate forms of expression – especially in a programming book. So I will pick up and read this book (learning the Math if needed) just to see whether I can get used to this style.

Here is a nice link on Teaching Mathematics using Programming. If you do not have a formal Math back-ground or studied it a long time ago but forgot most of it (like me), this may help a bit.

we now have new tools: the development of computer programming has provided languages with grammars that are simpler and more tractable than that of conventional mathematical notation. Moreover, the general availability of the computer makes possible convenient and accurate experimentation with mathematical ideas.

LinkLog: The Transient Life of Software

AMK’s Transient Life of Software makes you pause a bit and think about your life – of building software.

I think the days of massive e-mail blasts are drawing to a close; in 10 years we’ll be more concerned with other methods — generating RSS feeds, posting to Twitter or some equivalent site, SMSing to mobile phones, that sort of thing — and at some point we’ll come to a corporate decision to throw away the e-mail code.

Transience doesn’t mean writing software isn’t a worthwhile activity; it helps work get done today, hopefully simplifies the lives of the users, and can provide an enjoyable occupation. T

Software, especially good software has an amazingly long life compared to the time spent building it. In most cases it seems to last for ever. “For ever” is very relative, though.

Understanding Expertise

Have you watched an expert at work? You can sense  a level of comfort and fluency. I often watch expert programmers code, debug, fix problems in software.  I get the same sense watching painting shows on TV. A stroke here, a stroke there and suddenly you have a beautiful creation taking shape, right in front of you.

I always wondered what makes experts experts. I thought it was intelligence or just a lot of experience. It seems to be a lot more than that. I never dug deeper into the subject and suddenly I stumbled into this, while exploring a completely unrelated subject.

People who have developed expertise in particular areas are, by definition, able to think effectively about problems in those areas. Understanding expertise is important because it provides insights into the nature of thinking and problem solving. Research shows that it is not simply general abilities, such as memory or intelligence, nor the use of general strategies that differentiate experts from novices. Instead, experts have acquired extensive knowledge that affects what they notice and how they organize, represent, and interpret information in their environment. This, in turn, affects their abilities to remember, reason, and solve problems.

… the study of expertise shows what the results of successful learning look like…

We consider several key principles of experts’ knowledge and their potential implications for learning and instruction:

    1. Experts notice features and meaningful patterns of information that are not noticed by novices.
    2. Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of their subject matter.

    3. Experts’ knowledge cannot be reduced to sets of isolated facts or propositions but, instead, reflects contexts of applicability: that is, the knowledge is “conditionalized” on a set of circumstances.

    4. Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort.

    5. Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others.

    6. Experts have varying levels of flexibility in their approach to new situations.

Now we know. Or at least, have some theories to explore. The fragments above, were taken from the second chapter – How Experts Differ from Novices of a really fascinating book – How People Learn.

Good Reads: An Element of Beauty in Learning

There is an element of beauty versus duty in learning most things. When the task is all duty, you may do it, but you may never like it. Indeed, you may come to hate it and stop altogether when the external forces that keep you on task (your teammates, your sense of belonging) disappear. When you enjoy the beauty of what you are doing, everything else changes.

From Eugene’s Math and Computing as an Art.

I think I finally found my morning reading. I stumbled upon this blog following threads of a controversy about a new CS curriculum and ending up in A Small Curricular Tempest. I spent a few hours, reading many of his posts, before I realized that I spent a few hours. This is what used to happen when I was a teenager and in early twenties. Endless hours of reading, engrossed and not even noticing the passage of time.

Thanks Eugene for making my day a bit better and giving me lots of stuff to read.

Wikis and Knowlets – A Concept Web for Knowledge Representation

Stumbled upon this site today. Seems to be taking the wiki based collaboration one level higher.

Wikiprofessional’s Concept Web Initiative is a global collaboration to innovate how knowledge is represented and expanded on the Internet.

Knowlets

The Knowlet summarizes the relations between the concepts and presents the strength of the relationship based on a value derived from three main factors: factual (F) statements found in scientific databases, the co-occurrence (C) of two concepts in a text, and a predictive (P) parameter based on the conceptual overlap of the two concepts.

An extension to MediaWiki

The Wiki is an extension of the MediaWiki software that enables Wiki editing capability in a relational database structure.

Just the beginning

The first incarnation of the wiki is for Life Sciences called WikiProteins.

While the Life Sciences are the starting point for the Concept Web, the Wikiprofessional collaboration intends to expand the knowledge representation and expansion dynamics of the Concept Web systematically in disciplines and languages throughout the world.

This will be an interesting development to watch. With an impressive list of Collaboration and Research Partners, WikiProfessional seems to be off to a good start.

Many questions remain.

  • How will this be different from DbPedia and other similar efforts?
  • What is the connection to the Semantic Web?
  • Is the code an open source like MediaWiki (WikiProfessional is based on MediaWiki)
  • Is Knowlets the right meme for knowledge representation? In all fields of interest?

LinkLog: Programmer Competency Matrix

On a winter day in Boston, I sat through a two hour lecture on B-Trees. There was snow outside and we all sat spell bound as Greg Basset, our instructor taught us how Digital’s RMS-11K (the record management system) worked. The concept of incremental loading, fill factors, splitting data and index buckets and compression of duplicate keys, is still fresh in my memory. I gave that talk several times later in my life when I was teaching the subject. In each repetition, I learned a little bit more, answering questions. A few years later, I had the chance to use many of the ideas when we built C-Trieve and Objectrieve, two record management systems on top of which we built an SQL relational database. That was more than a couple of decades ago.

Looking at the Programmers Competency Matrix brought back lots of those memories. I think I am going to make a custom version of this matrix, make a poster and put it up on the wall of my class. A lot of these topics are not needed for the application programming today. But for a few of those intensely curious students, this is a kind of road map.

Meta:

Reddit is a great source of interesting posts, especially for programmers. I should thank the kind soul who posted this link.