ChatGPT

AI Setup Evolution

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I’ve been writing a lot about AI and tech projects lately, which makes sense since that’s the stuff that ends up on this blog. It isn’t all that’s going on in my life, just to be clear. But my AI setup has quietly evolved in August and September in a way that’s letting me undo a bunch of stuff I only built because of the cloud AI’s limitations, so here we go again.

First, the accounts. I’ve been using Claude a lot more than any other AI system lately, but I’m still mixed between Claude and ChatGPT for coding work. Instead of paying for Claude Max or the equivalent top tier ChatGPT plan, which run about five times the price of the regular subscription, I pay for two mid tier plans instead, the Claude Pro and the ChatGPT Plus. Partly that’s so when I run out of tokens on one, I can switch to the other. But that’s a small reason. The bigger ones are that if one gives me a bad answer, I want to try the other, and sometimes I want a second opinion and will ask both the same question. I also don’t think I’d actually use five times the usage, so paying for the bigger plan on either one would just mean I’m not getting my money’s worth. There are times I max out both in the same stretch, so maybe I am doubling up after all. Who knows.

Interesting side note, I’ve been leaning on Codex more for coding lately, with a bit of chat here and there. I like their interactive audio, though I don’t use it as much as I probably hold onto the idea of using it. I’m building sites and things with Codex now, not because I think it’s better, I started because a friend recommended it after I hit a specific wall with Claude. I still use Claude for coding too, but I keep them separate on purpose. Coding work tends to run a project’s token budget down fast, and I don’t want that eating into the day to day chatting I do in Claude. The coding side is pretty interchangeable between the two since everything gets saved locally to wherever I’m working anyway.

Now, the actual problem I’ve been solving. With ChatGPT, and then Claude once I moved to it, the challenge was always context. Take my car tracking prompts as an example. It’s one of many, holiday planning is another, so is tracking the general inventory of stuff I own, but let’s stick with the car since we’re actually replacing it with another used one right now. I’d feed it a lot of detail about the car, our usage, our spending, all of that. Then every new chat, I wanted it to already know the context, how to respond to me for that narrow kind of conversation. So I had some generic prompts, plus a very specific auto inventory prompt, and I’d load all of that into a fresh chat every time.

Then there’s the data itself. Registration, MOT dates, how much we paid for the car, fuel spend over the last couple of months. All stuff AI is genuinely good at trending and drawing insight from, and useful for research too. But I needed to save it somewhere. My old process was to have the prompt itself describe how to save the data, which in most cases meant getting the AI to structure everything into a JSON file I could download and keep a version of.

The problem is I don’t want to keep an indefinite chat going, especially with Claude, though ChatGPT has some of the same issue. As a chat gets long, the AI starts forgetting the earlier stuff and compacts it, and it even warns you this is happening. I wanted everything solid enough that if I deleted that chat and started fresh, the new one would already have everything the old one had.

So the prompts were only half the system. The other half was the JSON, or a CSV for a pure inventory file, sitting there holding the actual data. I built one of these little systems for the car, one for holiday planning, one for holiday budgeting, a stack of them, each with its own context switch. It worked, but it meant a real proliferation of chats. Start a chat, load the data, double check which version of the file I was working from, and when I was done for the day, have the AI spit out a fresh JSON with a version number in the filename and save it to disk. I ended up with thirty or forty versions of some of these files sitting around. Not done with the topic forever, just done with it for that session, so I’d save and move on, and next time load the latest one and keep going.

That whole dance existed because neither Claude nor ChatGPT could write to a file in its own project folder, only read from one. I could load something into a project, but I couldn’t have it write back out to that same spot. That was the actual limitation driving all of it.

Then, much more recently, within the last month or so, Claude’s Cowork got a lot easier to use and a lot more functional. It’s mid September as I’m writing this, and just yesterday I read that they’d simplified the interactions with it even further, which is great timing for me. With Cowork, I’ve gone back through each of my little systems and rewritten the prompts. Now I can just tell a new chat to look at a directory on my computer, read the markdown file with the instructions, read the JSON, and write straight back to that same JSON. No more versioning gymnastics on my end, because I don’t need to care about it anymore. Time Machine on my Mac is already snapshotting everything hourly and backing it up elsewhere, so the versioning is handled at the file system level, which is where it belonged all along.

I still keep these as separate projects in Claude, because I want specific context tied to specific folders on my computer. My car information lives under an auto folder inside a broader life inventory folder, my computers have their own folder, my domain names have theirs, each with its own markdown for how I want it handled. In the past I’d have to manually load the data file and the instructions file into Claude every single time I started up. Now the project just has a tiny instruction pointing to a markdown router, a file in the root directory that lists out all the different contexts I’ve got and where to find the detailed files for each one. It knows to go look in those folders, and inside something like auto, it’ll find an instructions markdown, the data file, and whatever else belongs there. I can start one omni chat for anything life inventory related, or a specific car chat, and either way it’s not my job to keep the local files in sync anymore, because that’s just handled.

For a while I could already do a version of this on my own computer in a coding session, but not from the cloud, not from my phone on the couch, or my iPad while we’re figuring out where to go for dinner or nailing down holiday plans. Now with Cowork, I can start a session that runs as long as it needs to. I don’t care if it compacts and forgets things partway through, because all the data that matters is already saved to a file, and it’s got a remote session into my actual computer at home.

In another post I mentioned why I might end up getting a Mac Mini instead of relying on my MacBook Air, and this is exactly why. If I take the laptop with me, none of these sessions work, since there’s no computer at home to remote into. I’m fine with a dedicated home machine for this, because lately the only reason I’ve been taking a laptop anywhere has been to do AI stuff with it in the first place. It’s a bit circular. If I can run everything through a remote Cowork session back to a computer at home, I don’t need to bring a computer anywhere at all.

I’ve been going on holiday without a computer for a while now, aside from that one AI use case, to the point where I’ve packed the laptop a couple of times telling myself I might need it, and then never actually taken it out of the bag. My 11 inch iPad Pro has become my go to screen for everything, and this new Cowork setup makes that even more true than it already was.

So that’s where things have migrated to for now, and we’ll see how it keeps evolving. Ideally I’d love to get to a point where I don’t need a home computer in the loop at all and can just use remote storage instead. But I don’t use a mainstream service like Dropbox, OneDrive, or Google Drive, which is probably where that kind of direct read and write access would actually get supported first. Last I checked, Google at least would let you read from those places but not write back to them. When that changes, it’ll be worth revisiting, but for now I’m sticking with my own storage.

That’s the state of the AI setup as it stands. Check back in a couple of months to see how much of this I’ve already torn apart and rebuilt again.

Teaching Future Scott to Trust the Money Bot

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Simplifying my finances is one of those things that’s been on the list forever and just never got looked at. It sat there, neglected, while everything else got attention instead.

Part of the reason is that I spread certain banking functions across different banks. If you follow personal finance blogs, you already know that’s not the recommended move. I try to follow the You Need A Budget methodology, which is the app I use to manage my budget, and the whole philosophy there is keep things as simple as possible. One account, one clear picture.

But my problem is I don’t want all of my money for the month just sitting in a current account. I don’t want that to be where irregular spending, or spending in general, comes out of. Some banks solve this with what people call pots or envelopes, virtual ways to segregate money inside one account. This bill comes from here, that bill comes from there, savings live somewhere else, all under one umbrella. I’m not going to say which bank I use for this, but not many banks in the US or the UK actually offer it, so if you go looking you’ll probably figure it out. The bank I use also has a developer API, which is both potentially dangerous and potentially powerful.

What I wanted was something that could handle the bills coming out of one pot (mortgage, electric, the recurring stuff), set aside money for the credit card bill so it’s always covered when it lands, round up spending a bit each week and stash the extra somewhere, and keep the actual monthly savings separate from all of that. Then whatever’s left sits in the current account for general spending.

The catch is that thing needs constant care and feeding. Bills are fixed, so you pad them a little and move the money over. Paycheck comes in, and you have to decide how much sits where, because I want money sitting somewhere earning interest until I actually need to spend it. It’s a lot of small administrative decisions that need to happen on a schedule, and none of it is hard, it’s just tedious.

So naturally, when you’re a tech person with an API, and a private server sitting at home that you’ve locked down tight, my answer was to build something.

I got access to the developer API and dictated all of it into my notes app first, walking through every use case. Then I fed that into Codex, since that’s what I’m building with these days, and told it what I wanted. It came back and said, yes, absolutely doable, confirming the one thing I already knew I’d need. Everything had to live in one bank, using pots instead of separate current accounts, because the API can only do things within that bank itself, not across open banking connections. That part wasn’t news, it’s why I’d spent the past month or so slowly relocating everything into one place beforehand, betting the API work would do what I thought it could. I was playing the long game on it.

I know at least one or two friends who’ve played with this same API before and had good things to say about it. Someone else at work has built something much bigger with it too, which I’ll get into another time once I’ve actually built my own version of that.

What I ended up with is a website with a UI that tracks the different use cases and, more importantly, the logic rules, because there have to be very specific rules in place so it never moves money it doesn’t have or shouldn’t touch. I built it with a few maturity levels. At the bottom, it’s purely informative, just telling me what’s going on and what it suggests. Above that, semi-manual, where it recommends a move and I go execute the transfer myself. That’s roughly where I am right now, moving from just getting notified to getting a recommendation I still have to act on.

After that, the plan is a warning stage, a day or two heads up before it makes a move on its own, and eventually full automation, where it just tells me after the fact that it moved money, on top of whatever notification I’d get from the bank itself anyway.

The whole point is that if I’m not around to do it, my wife doesn’t have to think about it, and it saves me from manually shuffling money around every week or month. The only thing I’ll still need to do is reconcile it in You Need A Budget, since it’ll pull the transactions in through open banking like anything else. Because the moves are predictable, I’ll know what I’m looking at when they show up.

The logic itself is actually pretty simple. It’s the fact that it touches money that makes me cautious, which is why there are four or five approval levels instead of just flipping it on. The plan is to ratchet it up gradually over a few months, and if it’s consistently accurate after five or six months, I’ll trust it with full automation.

That’s where the AI part gets more interesting, past the point of just building the thing. Once it’s built, I don’t really need AI for it anymore beyond patching bugs or tweaking the UI. The real question becomes whether what I built for manual human intervention is actually the smart way to do it. Is the amount of headroom I gave the bills pot right? Are the intervals right? What actually makes sense here? I’ve asked it to help me look back at a couple months of data eventually and suggest improvements, which feels like the more useful use of AI in this than the building itself.

There’s a part of my brain that’s irrationally nervous about handing this over, even though it can’t make trades and can’t send money anywhere outside my own accounts, and I’ll see everything it does. It’s managed risk, I’d say. But if it works the way I think it will, it saves real time every month, so it’s worth the caution.

Next up, I want to see if there’s a US equivalent I could set up something similar with. My early research says no, but we’ll see.

Censoring My Own News, And Couldn’t Be Happier

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Following on from my post about doom scrolling through the news and feeling the way I do right after eating that second or third piece of cake M told me I shouldn’t have, I finally focused on trying to actually fix my news problem instead of just avoiding it. I asked ChatGPT the other day whether it could build me a rule structure for news that would, in essence, censor things I read. Things I don’t want, things I do want. Turns out it could.

I consume most of my news through RSS feeds in an app called Reeder. My first attempt was simple. I exported my OPML file, the list of everything I subscribe to, handed it to ChatGPT, told it exactly what I wanted and didn’t want to see, and let it build the logic. I ran it once or twice. It took ten to fifteen minutes to process each time, was very compute intensive, and just wasn’t doing what I wanted.

So I had a different thought. I already had this RSS feed file. Could I get AI to build me an actual tool to do the heavy lifting, with the AI only handling a smaller slice of the logic on top? I’ve had good luck with that pattern before, where the AI builds a local tool for the grunt work and only steps in for the parts that actually need it. Turns out yes, it could, and of course once I proposed it the AI agreed it was a much better approach than my first idea. Which, seriously, if it was that much better, why didn’t it just suggest that in the first place and save me the time? That’s a separate emotional issue I need to unpack in its own therapy session about AI use.

It suggested FreshRSS, an open source RSS reader I’d actually used before, back on an earlier version of my VPS. I’d stopped using it at the time because Reeder already syncs everything to my devices through iCloud, so self hosting the same feeds felt redundant. But I’m generally in favor of hosting my own stuff when I can, so we brought FreshRSS back with all my existing feeds, added another container alongside it to do the preprocessing and staging, and then a third layer that’s the actual AI step, pulling the processed data and turning it into a summary rather than a raw doom scrolling feed.

The first couple of summaries it gave me were pretty thin, partly because my source list was thin. For months I’d been whittling down my RSS feeds because Claude or ChatGPT kept telling me two sources were basically duplicating each other, so I’d drop one. Now that AI is doing the curation instead, it’s going the other direction. It’s telling me to add more sources so it can build a fuller, more complete picture on the topics I actually care about.

One example. I’m an American living in the UK, and I also work in financial services, so I want to track the FX rate between the US and UK. Instead of just pulling from general news sites, it started going straight to the Bank of England and the US Fed for specific announcements. Something I would never bother doing manually, but a computer doesn’t mind, and then it digests it down for me.

It’s been running for about a week now, and it’s genuinely doing what I asked. I find it pretty funny that if a news provider censored my news like this without asking, I’d be outraged. But since I’m the one asking for it, I couldn’t be happier. There are hints here and there of touchy topics, since some hinting is unavoidable given what I’m tracking, but nothing that pulls me in to go dig further. Which, again, is exactly the point.

I’ve since added a bunch of topics I’d forgotten about in the first round, and it’s giving me good paragraph length blurbs with source links if I actually want to go read more. It’s a real step change from doom scrolling and bookmarking articles for later that I never get back to. I’m not trying to be comprehensive here. I want a picture of what’s going on, fifteen to thirty minutes of reading, and then I stop and I’m not depressed about the state of the world. I know I’m building myself a bit of a bubble, but I’m trying to stay reasonably well rounded by excluding specific things rather than whole topics.

One thing it set up that I haven’t tested yet is a weekly digest of blogs and general interest reading, more of a how-to and hobby mix. Sort of a Sunday paper edition, which I thought was a nice touch, even though I’m not sure I actually want that on a Sunday. So that’ll need some tweaking. For now the plan is it generates once a week but I can also just run it on demand. Still early beta stage since I don’t have a few weeks of usage on it yet.

Behind the scenes, this is all running on a public VPS I keep separate from the one hosting my blog, set up specifically for stuff like this. It’s pretty locked down, but the AI agents can hit the API to pull the processed data out of the app when needed. Next up is probably tuning the sources further, and I’d like to get it to a point where it publishes the results to a private webpage or file I can load in a browser and use Safari’s read aloud option on, instead of me manually asking Siri to read sections. That’s not solved yet.

The other thing I didn’t fully appreciate until I started this project is how much of a time sink visiting news sites had become, the same way social media is. It truly is an addictive habit you don’t realize you have until you’re trying to stop it. I catch myself now with a free second reaching to check the BBC or CNN, and stopping myself, and I hadn’t really noticed how often I was doing that until I started noticing myself not doing it. My to do list has gotten better as a result, since I’m spending that time on things I’ve actually said I want to do instead. More on that another time.

Collecting AI Models Like It’s a Hobby

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It’s almost like I collect AI models and providers. Each one has its own use case, and I probably could consolidate. But in some situations it’s not even worth the effort to do it.

As of writing this in early May 2026, and I mention the date because I usually have a backlog of a couple of months before things go live, my main tool is Claude. Well worth the money and then some.

But in addition to that I’m paying for Venice.AI as my privacy AI. Very cheap per year, limited use case, but for what it does it’s worth every penny.

Then there’s Lumo from Proton. I’m about one week into an experiment of not using it to see if I actually miss it. The honest answer is probably not, because I’m now running local models on my new MacBook Air with 32 gigs of RAM, which I got specifically to be able to do this. I’ve been running Mistral 3 3B locally and early results suggest it might actually be more private and more capable than Lumo for what I need it for. So that £120 a year might be on its way out.

I also have Perplexity Pro, which I don’t actually pay for directly. It comes bundled with another subscription I have. It’s slightly better than the others for web search type queries so I use it for that. Would I pay for it separately? Probably not. But I have it so I use it.

And then there’s ChatGPT. I touched on this in the last post but I’m currently trialling whether running Claude Code and ChatGPT’s Codex side by side is actually cheaper than paying for the next Claude tier up. The overages on Claude Code have been real, and the maths might just work out. Still figuring that one out.

So yes, I have more models and services than I probably need. Lumo is the obvious cut. Beyond that I’m genuinely not sure. The ecosystem as a whole is delivering value right now, even if it’s a bit sprawling.

What I do know is that two years in, I’m using these tools in ways I couldn’t have imagined when I first paid for Copilot on a 30 day trial just to see what would happen. And the people I talk to who haven’t really dug in yet, I get it, it can feel overwhelming. But the gap between what you can do with these tools and what most people are doing with them is pretty wide right now. And that gap is only going to get wider.

Switching to Claude

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I’ve been on this AI journey for a while now, and for most of it ChatGPT was my main tool. But in February 2026 that changed.

Someone whose opinion I trust recommended I give Claude a try after voicing my frustrations with ChatGPT. When I actually sat down to evaluate it I did something that in hindsight was pretty funny. I asked Claude directly why I should use Claude over ChatGPT.

It told me not to.

Based on what I told Claude I wanted, it said I’d probably get better results from ChatGPT. So naturally I didn’t trust that answer and kept pushing. As I interrogated it further it started explaining that it was slower and more thoughtful, and from its previous read of what I was looking for, it figured I wanted fast straight answers. And that’s when something clicked for me.

One of my biggest frustrations with ChatGPT was exactly that. I’d ask it something and it would just fire back an answer. Fast, confident, and often not what I actually asked for. Like if I asked for specific instructions on how to do something on an Apple product, it would give me generic steps that didn’t even exist in the actual interface. I’d have to stop it and say don’t give me fluff, give me the actual thing. Then it would have to go look it up and either admit it didn’t know or finally give me something useful.

What Claude was describing as a weakness, slower and more considered, was exactly what I wanted. So I signed up and started using both in parallel.

Early on I was genuinely impressed with what was coming out of Claude. I was using Sonnet, the middle tier model, and the difference in output quality was noticeable pretty quickly. The concern then was whether I’d end up paying for two services. I was already paying for Venice and Lumo on the privacy side and the last thing I wanted was more sprawl.

But it became clear fairly fast that Claude was where I wanted to be. Which meant I had to migrate everything I’d built in ChatGPT over the previous six months or so. Custom GPTs, saved prompts, all of it. I had to extract everything, make sure I had backups, build a little text based database of all my prompts, and systematically move it all across.

I got it done within a month and managed to avoid paying for both services at the same time for more than one month. Then I downgraded ChatGPT to the free tier and haven’t looked back.

From a reliability standpoint Claude is better. Not perfect, and everything I said in the last post about not trusting it still applies. But it’s a meaningful improvement. And honestly, switching to it unlocked a whole new level of what I started doing with AI. Which is what the next post is about.

What I Actually Started Using AI For

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I’ve been writing about my AI journey for a few posts now, and I’ve talked a lot about which tools I use and how much I trust them. But I haven’t really gotten into what I’m actually doing with them day to day. That’s what this one is about.

Trip planning was one of the first things that clicked. When you’re searching for flights and hotels, every website limits what details you can give it. With AI I could be hyper specific. If I’m flying with just the kids it’s this configuration. If my wife is with us it’s that one. These are the types of hotels we like, these are the amenities we need. It could actually hold all of that and work with it.

It didn’t book anything, but it helped me build out exactly what we were looking for and where to go look for it.

Then it evolved. I used to use Trello boards to track trips day by day, reservations, what we were doing when. Now I do all of that inside the AI itself. It exports as a YAML file I can save and reload if I need to start a fresh chat. Since privacy matters to me I’ll delete a chat when I’m done with it, but the file means I don’t lose anything. I have a general preferences file I keep updating, and then a separate file for each trip. It works really well.

I did something similar for days out with the kids. I took a week off last August and it was just me and the girls. I built out a history file of things we’d done and liked, threw in our preferences, and used it to plan the week. On Tuesday we have this, I need to figure out Wednesday, here’s a restaurant nearby that fits. I’d already been doing some of that in a neglected Trello board, but this way it was all queryable and easy to update.

Date nights with my wife got the same treatment. I’d go through our preferences, confirm them with her, and then present her with suggestions. I’ll be honest, I wasn’t exactly hiding where the ideas came from. More like, hey, I told the magic box what we like and this is what it came up with. Sometimes it was completely off. Sometimes it was genuinely spot on.

More recently it’s been helping with meal planning for the kids. Mine are picky eaters, so it was a lot of back and forth on what might actually work. It helped me land on a couple of new meals and then built a schedule to track them. Basic stuff on the surface, but genuinely useful in practice.

One that I found unexpectedly cool was using it to wrangle my Trello data. I have boards for things to watch and books to read. When I tried to pull that data into an AI the file was massive. So I had the AI help me write some scripts to strip out everything I didn’t need, took a two meg file down to about 50K, and then I could actually work with it.

Which brings me to audiobook recommendations. I was a little cautious about feeding it my reading history since it builds up a fairly personal picture of you. But I write about what I read anyway, so it felt like a reasonable trade. I’d give it what I read, when I read it, what I thought of it, and ask for recommendations.

Hit or miss, honestly. Some suggestions were weird and when I pushed back on them it would just fold immediately. That should always give you pause. But when it got it right it was actually pretty useful, and interrogating the reasoning often got me somewhere interesting even when the initial answer was off.

Which is a good lead in to something I want to dig into a bit more. It does some genuinely impressive things, but it’s far from perfect, and that part matters too.

From Sceptic to Subscriber: Beginning of My AI Story

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I can’t believe I’m only now really starting to talk about some AI stuff, and ChatGPT launched in November 2022.

Looking back, I really didn’t do very much with it for over a year. The first six months was kind of like, okay, that’s cool, fine. I did a lot of reading about it separately, but I really didn’t do a heck of a lot until February 2024.

So over a year later, things were mature enough that I decided to take the plunge and try one of the paid services. Through the summer of 2023 I was definitely doing things here and there, but I was sceptical on what it could do. I was sceptical on its privacy. Well, I’m still sceptical on its privacy. But I didn’t pay for anything, and I was what you’d consider a light, casual user.

February 2024, I upgraded to Copilot. I also upgraded the family to the Microsoft 365 family plan at the same time, which you kind of need for Copilot Pro, or don’t, I forget. But there was a reason I did both at the same time. I treated it like a trial. Paid for it, but gave myself 30 days to see if I’d actually use it.

And I liked it. But the main reason I’d gone with Copilot was for the Microsoft Office integrations. That’s what sold me on it for personal use. In practice though, they just didn’t meet expectations at the time. And once I started talking to friends about it, the logic became pretty clear. Copilot is powered by ChatGPT anyway, and ChatGPT at the time had more plugins and a lot more flexibility. So why was I paying for the middleman?

I only used Copilot for about a month before switching to ChatGPT in March 2024.

I started using that on and off. In the beginning I’m not sure I really got my money’s worth, but it was worthwhile to have something and actually use it. I was able to use it for things like tutoring the kids — there’s literally a way to set it up so it won’t just give them the answer, it walks them through the problem. Stuff like that. A whole bunch of different use cases.

But what became apparent straight away was that there were things I was very hesitant to do with it, because it was, and still is, unclear what they actually do with your data.

For context I ran this story through an AI image generator to get a banner for this entry and after 3 tries it came up with what I used.