On the Edge, by Brian Bagnell, tells the history of Commodore, from their entry into and development of the personal computer industry, to their massive collapse just 15 years later.

Told mostly through new interviews with company engineer, this is a fascinating story of the good and bad management (mostly bad), internal politics, technology design, and short-sighted business practises that built Commodore up into a massive industry force, and yet led to its ultimate demise.

The book also describes how Commodore engineers created the new computers. Commodore was in the unusual position of owning a chip manufacturing company: “vertical integration” is how then CEO Jack Tramiel put it. This allowed the engineers to rapidly prototype new chip and board designs, which meant their product cycles were extremely low: entirely new systems were being created in 3 or 4 months before being demoed at CES conferences. Unfortunately, this rapid development would bake in bad architectural decisions, which following product teams would struggle with.

In some ways this is a very inspirational story. The development teams at Commodore were tiny: five or six core engineers would work on a system, and create entirely new chips, boards, enclosures, and peripherals — like the famous SID sound chip of the Commodore 64. And they would do it in extremely short time frames.

These small teams created the PET, the VIC-20, the Commodore 64, and the Amiga.

The book makes extensive use of interviews with Commodore employees; many chapters are almost entirely quotes. This, and the relatively light touch by the author, makes for an engaging and fast read. But it’s not great literature by any means.

The author does a good job of showing the differing accounts and memories of his subjects, and it is very interesting to see how differently people remember the same series of events.

The book makes a point of showing that Commodore, and not Apple, should be considered the creator of the personal computer. The case presented is good, but comes across somewhat too fervently, and makes the author seem a bit nutty at times.

This is a great read, and one that made me nostalgic for my old C-128D. Recommended.

Programming Collective Intelligence is a book about applying data mining techniques to analyse collections of data. There is submerged information in Ebay prices, in Facebook profile networks, in collections of movie reviews, in news sites, in the stockmarket; this book by Toby Segaran shows ways to extract, visualise, understand, and predict that information.

Each chapter explains and explores a different data mining algorithm, and builds up a working example in Python, while presenting different methods and parameters of the implementation. I hadn’t really worked with Python before, but found the code easy to follow, and picked up some interesting Python idioms that I haven’t seen in other languages before. Chapters end with a set of exercises to follow that build your understanding.

As you follow the examples you build up a reasonably generic code base that allows you to swap in and out different implementations, and reuse previous code to add to new applications.

The examples use live examples from the web: sites like Ebay, Facebook, and Yahoo Finance, and this makes the book more interesting and the results more visceral than some other books on the subject which use more contrived or obscure examples. Even though there is a strong web (or web 2.0) focus on the examples, the methods and the understanding is useful for a whole range of applications.

Some of the topics covered:

• Bayesian classifiers to detect spam, or to file news articles into site sections
• Hierarchical and k-means clustering to discover groups of similar items in massive sets
• Euclidiean distance, Pearson Correlation Coefficient, Tanimoto Coefficient: ways to measure the distance (or difference) between items
• Neural networks to predict user behaviour and improve search result ordering
• Optimisation methods like hill climbing, simulated annealing, and genetic algorithms
• Non-negative matrix factorization
• Support vector machines and kernel methods to go where linear regression can’t

I found it exciting to read — it’s one of those books that give you a whole bunch of new ideas for things to build as you read it. The presentation is very good: no background is assumed, and it doesn’t talk down to those more experienced.

Recommended.