Feb 04, 2017
A couple of weeks ago I ran into some of the functional limits of my web search bot, a bot that I wrote for my exocortex which accepts English-like commands ("Send me top 15 hits for HAL 9000 quotes.") and runs web searches in response using the Searx meta-search engine on the back end. This is to say that I gave my bot a broken command ("Send hits for HAL 9000 quotes.") and the parser got into a state where it couldn't cope, threw an exception, and crashed. To be fair, my command parser was very brittle and it was only a matter of time before I did something dumb and wrecked it. At the time I patched it with a bunch of if..then checks for truncated and incorrect commands, but if you look at all of the conditionals and ad-hoc error handling I probably made the situation worse, as well as much more difficult to maintain in the long run. Time for a rewrite.
Back to my long-term memory field. What to do?
I knew from comp.sci classes long ago that compilers use things called parsers and grammars to interpret code so that it can be converted into an executable. I also knew that the parser Infocom used in its interactive fiction was widely considered to be the best anyone had come up with in a long time, and it was efficient enough to run on humble microcomputers like the C-64 and the Apple II. For quite a few years I also ran and hacked on a MOO, which for the purposes of this post you can think of as a massive interactive fiction environment that the players can modify as well as play in; a MOO's command parser does pretty much the same thing as Infocom's IF parser but is responsive to the changes the user's make to their environments. I also recalled something called a parse tree, which I sort-of-kind-of remembered from comp.sci but because I'd never actually done anything with them, I only recalled a low-res sketch. At least I had someplace to start from so I turned my rebooted web search bot loose with a couple of search terms and went through the results after work. I also spent some time chatting with a colleague whose knowledge of the linguistics of programming languages is significantly greater than mine and bouncing some ideas off of him (thanks, TQ!)
But how do I do something with all this random stuff?
Oct 29, 2016
A couple of days ago I got it into my head to upgrade one of my Exocortex servers from Ubuntu Server 14.04 LTS to 16.04 LTS, the latest stable release. While Ubuntu long-term support releases are good for a couple of years (14.04 LTS would be supported until at least 2020) I had some concerns about the packages themselves being too stale to run the later releases of much of my software. To be more specific, I could continue to hope that the Ruby and Python interpreters I have installed could be upgraded as necessary but at some point the core system libraries would be too old and they'd no longer compile. Not good for long-term planning.
First off, whenver you're about to do a major upgrade of anything, read the release notes so you know what you're getting yourself into. You'll also usually find some notes about all the new goodies you'll be able to play with.
In the past I've had nothing but trouble using the documented Ubuntu release upgrade process, so much so that I've had clients sign "I told you so," documents when they pressured me to do so because the procedure could reliably be expected to leave the system completely trashed, and a full rebuild was the only recourse. This time I set up a testbed in Virtualbox which consisted of a fully patched Ubuntu Server 14.04.5 LTS install. I ran through the documented upgrade process, and much to my surprise it went smoothly, leaving me with a functional virtual machine at the end of a 45 minute procedure (most of which was automatic, I only had to answer a few questions along the way). The process consisted of logging in as the root user (sudo -s) and running the updater (do-release-upgrade).
So, if it's so easy, why am I writing a blog post about it? Why worry?
Why worry, indeed. Read on.
Mar 26, 2016
In my last post on the topic of exocortices I discussed the Huginn project, how it works, what the code for the agents actually look like, and some of the stuff I use Huginn's agent networks for for in my everyday life. In short, I call it my exocortex - an extension of the information processing capabilities of my brain running in silico instead of in vivo. Now I'm going to talk about Exocortex Halo, a separate suite of bots which augment Huginn to carry out tasks that Huginn by itself isn't designed to carry out very easily, and thus extend my personal capabilities significantly.
Now, don't get me wrong, Huginn has a fantastic suite of agents built into it already and more are being added every day. However, good design techniques require one to realize when an existing software architecture is suited for some things and not others, and allowances should be made for that. To put it another way, it was highly unlikely that I would be able to shoehorn the additional functionality I wanted into Huginn and have a hope in hell of it working. However, what Huginn has a multitude of are interfaces for getting events into and out of itself, and I could make use of those interfaces for plugging my own bots into it. The Website Agent is ideal for pinging REST API interfaces of my own design; Jabber Agent implements a simple XMPP client which can send events to an address on an XMPP server (assuming that it has its own login credentials); oversimplifying a bit, Webhook Agent basically sets up a custom REST API rail that external software can use to send events into Huginn for processing; Data Output Agent is used for sending events out of Huginn in the form of an RSS feed or a JSON document that can be consumed and parsed by other software.