Below are the five most recent posts in my weblog. You can also see a chronological list of all posts, dating back to 1999.

I do a lot of my work in a virtual machine running Red Hat Enterprise Linux. In order to distinguish the terminal windows that are running shells within the VM from the ones running on my laptop's natively, I configure the shell prompt to look different.

For my other systems, I use my punctual prompt system, but for this VM I thought it would be more fun to have something a bit more on-brand.

A Red Hat prompt

Here's a bashrc snippet to achieve this:

hat="🎩"
export PS1="\[\e[31m\]$hat\[\e[0m\]"

I ended up switching to zsh for my shell in this VM because of a bash bug exposed by trying this out. You shouldn't hit it for the snippet above, but I originally specified the red colour using 256 color escape codes, and this caused width-error bugs when long lines wrapped. zsh doesn't have the same bug, but it can be avoided in bash anyway by using the much older 8 colour escape codes, as above.

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Russ Allbery of the Debian project writes reviews of books he has read on his blog. It was through Russ's review that I learned of "Deep Work" by Cal Newport, and duly requested it from my local library.

I've a long-held skepticism of self-help books, but several aspects of this one strike the right notes for me. The author is a Computer Scientist, so there's a sense of kinship there, but the writing also follows the standard academic patterns of citing sources and a certain rigour to the new ideas that are presented. Despite this, there are a few sections of the book which I felt lacked much supporting evidence, or where some obvious questions of the relevant concept were not being asked. One of the case studies in the book is of a part-time PhD student with a full-time job and a young child, which I can relate to. The author obviously follows his own advice: he runs a productivity blog at calnewport.com and has no other social media presences. One of the key productivity tips he espouses in the book (and elsewhere) is simply "quit social media".

Through Newport's blog I learned that the title of his next book is Digital Minimalism. This intrigued me, because since I started thinking about minimalism myself, I've wondered about the difference of approach needed between minimalism in the "real world" and the digital domains. It turns out the topic of Newport's next book is about something different: from what I can tell, focussing on controlling how one spends one's time online for maximum productivity.

That's an interesting topic which I have more to write about at some point. However, my line of thought for the title "digital minimalism" spawned from reading Marie Kondo, Fumio Sakai and others. Many of the tips they offer to their readers revolve around moving meaning away from physical clutter and into the digital domain: scan your important papers, photograph your keepsakes, and throw away the physical copies. It struck me that whilst this was useful advice for addressing the immediate problem of clutter in the physical world, it exacerbates the problem of digital clutter, especially if we don't have good systems for effectively managing digital archives. Broadly speaking, I don't think we do: at least, not ones that are readily accessible to the majority of people. I have a hunch that most have no form of data backup in place at all, switch between digital hosting services on a relatively ad-hoc manner (flickr, snapchat, instagram…) and treat losing data (such as when an old laptop breaks, or a tablet or phone is stolen) as a fact of life, rather than something that could be avoided if our tools (or habits, or both) were better.

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I'm in a perpetual state of downsizing and ridding my life (and my family's life) of things we don't need: sometimes old computers. My main (nearly my sole) machine is my work-provided Thinkpad T470s: a fantastic laptop that works so well I haven't had anything to write about it. However, I decided that it was worth keeping just one spare, for emergencies or other odd situations. I have two candidate machines in my possession.

In the blue corner

left: X61S; right: R600

left: X61S; right: R600

Toshiba Portégé R600. I've actually owned this now for 7 years, buying it originally to replace my beloved x40 which I loaned to my partner. At the time my main work machine was still a desktop. I received a new work laptop soon after buying this so it ended up gathering dust in a cupboard.

It's an extremely light laptop, even by today's standards. It compares favourably with the Apple Macbook Air 11" in that respect. A comfortable keyboard, but no trackpoint and a bog-standard trackpad. 1280x800 16:9 display, albeit TN panel technology with very limited viewing angles. Analog VGA video out on the laptop, but digital DVI-D out is possible via a separate dock, which was cheap and easy to acquire and very stowable. An integrated optical media drive which could be useful. Max 3G RAM (1G soldered, 2G in DIMM slot).

The CPU is apparently a generation newer but lower voltage and thus slower than its rival, which is…

In the red corner

x61s

x61s

Thinkpad X61s. The proportions match the Thinkpad X40, so it has a high nostalgia factor. Great keyboard, I love trackpoints, robust build. It has the edge on CPU over the Toshiba. A theoretical maximum of 8G (2x4) RAM, but practically nearer 4G (2x2), as the 4G sticks are too expensive. This is probably the "heart" choice.

The main drawback of the X61s is the display options: a 1024x768 TN panel, and no digital video out: VGA only on the laptop, and VGA only on the optional dock. It's possible to retro-fit a better panel, but it's not easy and the parts are now very hard to find. It's also a surprisingly heavy machine: heavier than I remember the X40 being, but it's been long enough ago that my expectations have changed.

The winner

Surprising myself perhaps more than anyone else, I've ended up opting for the Toshiba. The weight was the clincher. The CPU performance difference was too close to matter, and 3G RAM is sufficient for my spare laptop needs. Once I'd installed a spare SSD as the main storage device, day-to-day performance is very good. The resolution difference didn't turn out to be that important: it's still low enough that side-by-side text editor and browser feels crowded, so I end up using the same window management techniques as I would on the X61s.

What do I use it for? I've taken it on a couple of trips or holidays which I wouldn't want to risk my work machine for. I wrote nearly all of liquorice on it in downtime on a holiday to Turkey whilst my daughter was having her afternoon nap. I'm touching up this blog post on it now!

I suppose I should think about passing on the X61s to something/someone else.

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I started using the Ruby programming in around 2003 or 2004, but stopped at some point later, perhaps around 2008. At the time I was frustrated with the approach the Ruby community took for managing packages of Ruby software: Ruby Gems. They interact really badly with distribution packaging and made the jobs of organisations like Debian more difficult. This was around the time that Ruby on Rails was making a big splash for web application development (I think version 2.0 had just come out). I did fork out for the predominant Ruby on Rails book to try it out. Unfortunately the software was evolving so quickly that the very first examples in the book no longer worked with the latest versions of Rails. I wasn't doing a lot of web development that at the time anyway, so I put the book, Rails and Ruby itself on the shelf and moved on to looking at the Python programming language instead.

Since then I've written lots of Python, both professionally and personally. Whenever it looked like a job was best solved with scripting, I'd pick up Python. I hadn't stopped to reflect on the experience much at all, beyond being glad I wasn't writing Perl any more (the first language I had any real traction with, 20 years ago).

I'm still writing Python on most work days, and there are bits of it that I do really like, but there are also aspects I really don't. Some of the stuff I work on needs to work in both Python 2 and 3, and that can be painful. The whole 2-versus-3 situation is awkward: I'd much rather just focus on 3, but Python 3 didn't ship in (at least) RHEL 7, although it looks like it will in 8.

Recently I dusted off some 12-year old Ruby code and had a pleasant experience interacting with Ruby again. It made me wonder, had I perhaps backed the wrong horse? In some respects, clearly not: being proficient with Python was immediately helpful when I started my current job (and may have had a hand in getting me hired). But in other respects, I wonder how much time I've wasted wrestling with e.g. Python's verbose, rigid regular expression library when Ruby has nice language-native regular expression operators (taken straight from Perl), or the really awkward support for Unicode in Python 2 (this reminds me of Perl for all the wrong reasons)

Next time I have a computing problem to solve where it looks like a script is the right approach, I'm going to give Ruby another try. Assuming I don't go for Haskell instead, of course. Or, perhaps I should try something completely different? One piece of advice that resonated with me from the excellent book The Pragmatic Programmer was "Learn a new (programming) language every year". It was only recently that I reflected that I haven't learned a completely new language for a very long time. I tried Go in 2013 but my attempt petered out. Should I pick that back up? It has a lot of traction in the stuff I do in my day job (Kubernetes, Docker, Openshift, etc.). "Rust" looks interesting, but a bit impenetrable at first glance. Idris? Lua? Something else?

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I'm long overdue writing about what I'm doing for my PhD, so here goes. To stop this getting too long I haven't defined a lot of concepts so it might not make sense to folks without a Computer Science background. I'm happy to answer any questions in the comments.

I'm investigating whether there are advantages to building a distributed stream processing system using pure functional programming, specifically, whether the reasoning abilites one has about purely functional systems allow us to build efficient stream processing systems.

We have a proof-of-concept of a stream processing system built using Haskell called STRIoT (Stream Processing for IoT). Via STRIoT, a user can define a graph of stream processing operations from a set of 8 purely functional operators. The chosen operators have well-understood semantics, so we can apply strong reasoning to the user-defined stream graph. STRIoT supports partitioning a stream graph into separate sub-graphs which are distributed to separate nodes, interconnected via the Internet. The examples provided with STRIoT use Docker and Docker Compose for the distribution.

The area I am currently focussing on is whether and how STRIoT could rewrite the stream processing graph, preserving it's functional behaviour, but improving its performance against one or more non-functional requirements: for example making it perform faster, or take up less memory, or a more complex requirement such as maximising battery life for a battery-operated component, or something similar.

Pure FP gives us the ability to safely rewrite chunks of programs by applying equational reasoning. For example, we can always replace the left-hand side of this equation by the right-hand side, which is functionally equivalent, but more efficient in both time and space terms:

map f . map g = map (f . g)

However, we need to reason about potentially conflicting requirements. We might sometimes increase network latency or overall processing time in order to reduce the power usage of nodes, such as smart watches or battery-operated sensors deployed in difficult-to-reach locations. This has implications on the design of the Optimizer, which I am exploring.

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