Researching UK train tickets with an AI agent

In my last post, I briefly mentioned spending part of a Sunday getting Bob, my dot, to research train tickets and collect the results in Google Sheets. There was quite a bit hidden in that sentence, time to unpack it a little, and think about how much better train ticket purchases could be if you actually want to optimize for something specific.

I had two trips to work out, some flexibility over when to travel, and an interest in reasonably priced First Class tickets (which I often see pop up, but no idea when to really expect them). I do these trips multiple times a month often on regular class tickets, and have been for the past few years, and often end up with all sorts of ticket purchases, advanced ones on the cheap, off peak returns, and many purchased last minute. I wanted to see the options without running slightly different versions of the same search myself, then trying to remember which one had produced the useful fare, over multiple days, multiple possible destination stations etc.

I’m expecting after writing this, someone might come and tell me about the perfect UK train ticket purchase and comparison website, but to my knowledge it doesn’t yet exist?

I ended up with hundreds of recorded comparisons of individual ticket prices, and four preferred direct trains costing on average £42.90 each (all for first class tickets), totaling £171 (Or £157 with split tickets). The off peak return price for these journeys would be £102.80, so only around £70 (or £50) more for 4 first class trains, which hopefully will mean that I actually get a seat on all of these trains, vs my last trip on Friday where I didn’t even get on the train as it was full…

The loose brief

The starting point was fairly ordinary. Two trips, approximate dates, one adult and no Railcard. There were three destination stations that would work for me, so fixing the search to one station would have thrown away useful options before we had even started.

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I’m here to ramble (about AI ‘n stuff): a look at the last few months

The last post about AI that I wrote was about GitHub Copilot moving to AI credits, rather than their prior model of something akin to per prompt billing. That was in May, and to be honest, through June, July and August, not much seemed to change for me in the landscape for my productive working hours. (Various things changed and happened in my periphery that I might be able to explore in my personal time).

However September and October really saw another wave of changes that actually lead to impact during my working hours. From the new wave of GPT 6 6.1 models, to the OpenAI dots, and our increasing internal usage of Notion AI for our new internal knowledge base and task tracking, I’m seeing myself rambling on to various LLMs day in day out, and leaving more and more for them to do in the background, vs following along closely with for all of the steps (being an avid GitHub Copilot user before this).

The tides have turned, and I primarily use Codex these days as part of VsCode. With Copilot, the only model that I seem to be able to gel with well and make productive for my usage right now is GPT 5.6 Terra, auto always selects something that seems to take too much time, or perform poorly, meanwhile Codex seems to generally just get shit done, without needing so much human interaction. I imagine I’ll continue using GitHub Copilot for more hand holdy type tasks, and also for “code review” and some basic coding tasks within GitHub Copilot Cloud.

Where the “power” is really coming from in my working day now is having an AI accessible and curatable knowledge base and task tracking system, IDE integration, and consistent chat and context experiences across devices. Along with my first dot, called Bob…

Bob (the Dot)

An OpenAI dot is an always-on, autonomous AI agent inside ChatGPT powered by GPT-6 Astra. I have no doubt that some folks home hosted LLM setups come close to this sort of thing (only with arguably less effective and likely slower models), however for a working setup having this as an off the shelf product is mighty powerful.

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