Leading Without Deep Technical Knowledge

In my previous jobs, when I’ve been promoted to a leadership role it has been as a result of being the most experienced member on the team. Having a deep knowledge of the business, the code base and the technologies we’re using meant I was already an authority on most topics of the team needed to discuss, and could weigh in on a discussion with a well formed and considered option.

When I changed companies at the end of last year I came to Ocado Technology as a team lead for an existing team, using a technology stack I wasn’t familiar with. In fact Ocado are a Java based company, which I had never used before, so not only was I not familiar with the frameworks and libraries used, but I wasn’t even familiar with language the code was written it either!

Leading in a situation like this required a complete change in how I approached problems. When a stakeholder or the product owner approached me with a challenge rather than immediately being able to respond with a rough solution, and vague estimate or a timeline I need to defer to my team, and let them propose a solution, estimate it, and then I could fit it into our schedule. I might challenge them on some points, but it was their plan. I quickly needed to learn who knew the most about which systems, so I could get the right people involved in discussions early.

Accessing FitBit Intraday Data

For Christmas my wife and I brought each other a new FitBit One device (Amazon affiliate link included). These are small fitness tracking devices that monitor the number of steps you take, how high you climb and how well you sleep. They’re great for providing motivation to walk that extra bit further, or to take the stairs rather than the lift.

I’ve only had the device for less than a week, but already I’m feeling the benefit of the gamification on FitBit.com. As well as monitoring your fitness it also provides you with goals, achievements and competitions against your friends. The big advantage of the FitBit One over the previous models is that it syncs to recent iPhones, iPads, as well as some Android phones. This means that your computer doesn’t need to be on, and often it will sync without you having to do anything. In the worst case you just have to open the FitBit app to update your stats on the website. Battery life seems good, at about a week.

The FitBit apps sync your data directly to FitBit.com, which is great for seeing your progress quickly. They also provide an API for developers to provide interesting ways to process the data captured by the FitBit device. One glaring omission from the API is any way to get access to the minute by minute data. For a fee of $50 per year you can become a Premium member which allows you do to a CSV export of the raw data. Holding the data, collected by a user hostage is deeply suspect and FitBit should be ashamed of themselves for making this a paid for feature. I have no problem with the rest of the features in the Premium subscription being paid for, but your own raw data should be freely available.

Losing Games

I’m not a quick game player. I don’t rush out a buy the latest games and complete them on the same weekend. Currently I’m most of the way through both Alan Wake and L.A. Noire.

Alan Wake is a survival horror game where you’re fighting off hordes of people possessed by darkness. L.A. Noire is a detective story that has you solving crimes in 1940s Los Angeles. Both feature an over the shoulder third person camera, and both have excellent graphics. They also both have a film like quality to the story. In Alan Wake the action is divided up in six tv style “episodes”, with a title sequence between each one. It also has a number of cut scenes and narration by the title character sprinkled throughout the game which help to drive the story forward.

Scalable Collaborative Filtering With MongoDB

Many websites have some form of recommendation system. While it’s simple to create a recommendation system for small amounts of data, how do you create a system that scales to huge amounts of data?

How to actually calculate the similarity of two items is a complicated topic with many possible solutions. Which one if appropriate depends on your particularly application. If you want to find out more I suggest reading the excellent Programming Collective Intelligence (Amazon affiliate link) by Toby Segaran.

We’ll take the simplest method for calculating similarity and just calculate the percentage of users who have visited both pages compared to the total number who have visited either. If we have Page 1 that was visited by user A, B and C and Page 2 that was visited by A, C and D then the A and C visited both, but A, B, C and D visited either one so the similarity is 50%.

Steve Jobs and the Lean Startup

On my 25 minute train journey to work each morning I like to pass the time by reading. The two most recent books I’ve read are The Lean Startup: How Constant Innovation Creates Radically Successful Businesses by Eric Ries and Steve Jobs by Walter Isaacson (both links contain an affiliate id). Although one is a biography and the other is a book on project management they actually cover similar ground, and both are books that people working in technology should read.

Walter Isaacson’s book has been extensively reviewed and dissected so I’m not going to go into detail on it. The book is roughly divided into two halves. The first section is on the founding of Apple, Pixar and NeXT. This section serves an inspirational guide to setting up your own company. The joy of building a great product and defying the odds against a company succeeding comes across very strongly. The later section following Job’s return to Apple is a much more about the nuts and bolts of running a huge corporation. While it’s an interesting guide to how Apple got to where it is today, it lacks the excitement of the earlier chapters.