Technology

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.

Levelling Up to Claude Code

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So I mentioned at the end of my last post about AI that switching to Claude unlocked a new level of what I was doing with it.

It started small. Someone at work mentioned they’d been using Claude Code to build actual apps, and they’re not a developer either. Just someone with ideas and enough curiosity to see what happens. That stuck with me.

I had a specific problem I wanted to solve. I have some VoIP numbers through my internet provider, actual UK mobile numbers I can receive texts on. Useful for giving out to LinkedIn contacts or anyone I don’t want having my main number. The problem was sending replies meant logging into a clunky website every time. Nobody wants to do that.

So I asked Claude about it. One thing led to another and it said it could help me build a web app for that. When I asked if we could run it in Docker it said of course. I already run Docker on a few systems at home so that made sense. It built me a container, I ran it on my Synology, got it working for one number, cloned it for a second. Then I thought, wait, can we just have one container with a dropdown to switch between numbers? Of course we can. And it did it.

That was the moment I thought, if it can build me an app with this level of input and time, what else can it really do?

So I set up Claude Code properly, gave it access to my Raspberry Pi setup, and had it take the SMS app further. It interrogated my existing config, made some enhancements, deployed everything from the command line. Straightforward, but impressive.

From there I got more ambitious. My blog had been running on YunoHost, which is a self-service VPS platform. Decent enough, but it’s always on an older version of Debian because the open source volunteers take what feels like a long time to update it, and if an app isn’t in their package store you’re out of luck. I’d always wanted to run my own properly configured stack but never wanted to deal with the time to care and feed it.

I had a spare VPS sitting around doing nothing. So I asked Claude, can we design and build my entire website on this thing. WordPress in Docker, proper backups, the works. It said yes.

First I designed the whole thing with Claude, got a proper design document together, then imported that into Claude Code and let it build. About $25 to $40 in API costs later I had a website. And not just a website. Automated daily backups following a daily, weekly, monthly cadence with pruning built in, all replicating to a second location with an immutable copy at the end. Backup infrastructure honestly better than some hosting providers I’ve heard about. I then migrated my site over to it, wiped the old setup, and migrated it back again just to prove I could restore it. All worked.

Then I got bold. I had the spare VPS now freed up, so I used it to build a set of personal tools I’d always wanted but never had the time to set up properly. An open source SSO system with passkey authentication for me and my wife. A Searx search instance sitting behind the SSO. Network monitoring. I didn’t even know I could set up my own SSO until Claude walked me through it. Some API costs and an afternoon later it was running.

Around the same time I set up a proper monitoring stack. My external VPS now watches my home internet connection and my main Raspberry Pi, and sends push notifications to my phone if anything goes down. Not email, because I’m not going to look at an email. An actual push notification. I also have a speed test running every hour on a gigabit ethernet connection straight into the router, and my internet provider, who I’ve never had a bad word to say about, is consistently delivering around 305 megabit on a 350 megabit plan. Seeing that graphed over time is genuinely satisfying.

All of that cost me some API charges, probably a couple of dollars worth, and £4 for a push notification app I now own outright. Not a subscription. Just mine.

Then I built a router.

I’d looked at this before. A router is basically just a computer with extra network cards, and I’d used open source router software in the past. But I’d always bought the manufacturer’s hardware because I didn’t want to deal with building and maintaining my own. This time I asked Claude to help me design and deploy it, and then help me maintain it going forward.

Of course It said yes. It can be a yes man/lady/person a lot.

I researched the hardware with Claude’s help and landed on a Protectli VP2430, a fanless little box with four 2.5 gigabit network ports and an Intel N150 processor. 16 gigs of RAM and a 256 gig SSD. Overkill for a router, which means it’ll last a long time. Then I designed the whole OpnSense configuration with Claude before touching any hardware.

Deployment was more painful than I expected. The biggest issue was needing the new router connected to my laptop while also needing live internet on the same laptop to use Claude to programme it. The address space decisions I’d made complicated things further. I spent six hours one evening and an entire Saturday on it before rolling back. Then I realised what was in the end the hurdle that had stopped me, made one design adjustment, tried again the following week, and had it running in about two hours. It’s been stable since.

I’m not a software developer. But I’ve spent years building and supporting data centres, call centres, and large scale applications, so I understand enough to know when something looks wrong and push back on it. I understand some scripting and basic fundamentals. What I can do is explain what I want clearly, spot when the output doesn’t smell right, and interrogate it until it does. That combination, it turns out, is enough to build some pretty serious stuff.

The things I’ve been able to do aren’t things I couldn’t have done before in theory. But the time to care and feed a self-hosted setup was always more than I was willing to put in. Now I have AI that helps me design it right, build it right, and fix it when something goes wrong. The barrier that used to stop me isn’t really there anymore.

So now I’m looking at what’s next. I have mockups for a couple of actual apps I want to build. Things I want for myself that don’t exist quite the way I want them. A colleague at work just builds whatever he thinks of now. I’m getting there.

The biggest current headache is cost. Claude Code API charges add up fast when you’re doing serious work, and last month I had enough overages that I’m now trialling whether running both Claude Code and ChatGPT’s Codex together is actually cheaper than paying for the next Claude tier up. Early signs are interesting.

And separately, with my new MacBook Air running 32 gigs of RAM, I’m finally in a position to run proper local models. I’ve started downloading and testing, and the early results suggest I might be able to replace some of what I’m using Lumo for with a local model that’s actually more private and possibly better. That’s an ongoing experiment.

It’s a lot. But honestly, talking to people now, whether friends, colleagues, or people in the industry, so many are either just scratching the surface or not looking at it at all. I was over a year late getting serious about this. I don’t think I’m late anymore.

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.

Use AI Like It’s Lying To You, Because It Is

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I’ve touched on this in passing across a few posts now, but it deserves its own space. Because as useful as AI has been for me, it is not sunshine and rainbows.

AI is a great tool. I genuinely believe that. But I also think the future gets pretty dystopian if people don’t use these tools with their eyes open. And right now, a lot of people aren’t.

One thing I read recently that stuck with me: it can do super advanced calculations but it can’t tell time right. That sounds like a joke but it isn’t. Some of the things I’ve asked it, it will be absolutely insistent it’s correct. You interrogate it because something smells off, and eventually it folds. Oh yeah, you’re right, I was wrong. I’ve had situations where I knew it was wrong, kept pushing, and it took a surprisingly long time before it admitted it.

So what I tell people, my kids, colleagues at work, is this. Use it. But do not trust it. Assume it’s going to lie to you. If you go in with that mindset and you scrutinise the output, it can be really good. But you have to be able to scrutinise it. That’s the part people skip.

That’s also the part that makes it genuinely dangerous in the wrong hands. I can ask it something about cybersecurity and I’ll know pretty quickly if the answer looks right or completely off. But I can’t ask it to do my taxes. I don’t know tax law. So if it tells me I can do something, I have no idea if it’s true. That’s a problem. And it’s why you see things like lawyers submitting court filings with citations that don’t exist because a judge caught them using AI and not checking the output. People just throw stuff in and take whatever comes out.

I ran into this myself about a year or so ago when I was doing some budget planning. Nothing super sensitive, just the savings pots I set aside for predictable expenses throughout the year so I’m not hit with an inconsistent spend later. I do it all in a spreadsheet and it gets involved. I figured let me see if AI can handle it.

It did it, and then it didn’t. The numbers were inconsistent. Flat out wrong in places. I tried for a few months and I just could not rely on it. So I stopped and went back to my spreadsheet.

More recently I tried again with a different model, and I’ll get into that in the next post. But it’s actually working now. Two months in and the output is consistently accurate. I’ve also gotten smarter about how I prompt it, asking it to show its work and export the data in a way I can verify. So it’s a combination of the models improving and me getting better at using them.

But the overall lesson hasn’t changed. The reading of the tea leaves is the hard part. Sometimes the output is exactly what you wanted and better than you could have done yourself. Sometimes it’s close but slightly off in a way that’s easy to miss. And sometimes it’s just wrong and completely confident about it.

The tool is getting better. That’s real. But so is the risk of people treating it like it’s infallible. It isn’t. Not even close.

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.

The Case for a Private AI

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So when I started paying for ChatGPT, I’d hesitate before putting anything into it. I had to make conscious decisions about what I was okay sharing and what I wasn’t. In some cases it was easy. I don’t care about this, so fine. In others it was something I did care about, but the convenience won out and I’d bend my own rules a bit.

Come May or June 2024, I read about Venice.AI. It was intriguing because I wanted a private AI, and what these guys had built was designed from the ground up around privacy. Nothing stored, no logs kept. Yes, there’s still that moment in time where they’re processing your data, but they’re keeping nothing after that. Their entire business model is built on trust.

Are they 100% trustworthy? No. The only way to truly guarantee that is to run your own model. But they were offering something real, so I was intrigued.

The reason I hadn’t gone the local model route already was my hardware. I had an M3 MacBook Air with 16 gigs of RAM. I could download LM Studio and run stuff, but it was slow and clunky. Just not the experience I was looking for. I looked into cloud-hosted GPU options too, the kind of thing a friend had mentioned, but it was a lot of configuration and effort I just didn’t want to deal with. Funny enough, nowadays I could probably have Claude Code help me set that up in an afternoon. But I’m getting ahead of myself.

So when Venice came out with a pro plan at $49 a year as their introductory offer, which has since tripled, though as of my last renewal I was still grandfathered in, I figured for that price it was worth trying. It’s definitely more rudimentary than ChatGPT, but the privacy confidence is real. I’m still careful about what I put in it, but I’m more willing to share certain things there than I am with the public models.

They’ve since launched different models with different privacy levels, which is worth knowing. Some are fully private, some are anonymised but not fully private. You have to pay attention to which is which.

Fast forward to summer 2025. Proton, who I’ve been writing about for over ten years now, and at this point calling them just my email provider doesn’t really cover it, they do storage, VPN, and a bunch of other stuff, launched Lumo, their own privacy-focused LLM. I gave it a try.

The free version was pretty limited, so I added their paid tier for a few months while waiting for my main Proton plan to renew in December. The jury’s still a bit out on it. It did some things okay. From a pure trust perspective I probably trust it more than Venice just because I’ve been a paying Proton customer for a decade. But the way Venice has architected things, it’s actually more private. Lumo is more convenient though, and private enough for most of what I need.

One of the trade-offs with Venice’s full privacy mode is that nothing persists. No data moves between devices or browsers when you log in. Lumo does sync, but you’re trusting that it’s still zero-knowledge on their end.

I’ve actually been using Lumo recently for some things that are genuinely private, things I wouldn’t put near a public model. My logic is simple. I’ve been paying Proton for years to store sensitive documents privately. So why not use that same platform’s LLM to process those same documents? I’m not going to get into specifics here for obvious reasons, but it’s been useful.

The broader point is that I don’t always trust the public models, and honestly you shouldn’t either. But over time I’ve become more relaxed about certain things. It’s a constant cost-benefit calculation. The privacy models are getting better, and even the public ones will sometimes tell you that for certain tasks you don’t need high-level reasoning anyway, so a privacy model is probably fine.

The hard part now is knowing which model to reach for. Which is a whole other post.

As with the first post in this series I used AI to generate my banner image. I am not saying it’s good. I am just saying what I did.

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.

The Great Hotel TV Failure

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hotel tv media centre

This is an older story from April 2024. We were visiting M’s parents and staying at an Aloft Marriott. Pretty good hotel, actually. Kids didn’t like the breakfast, I did. There was a pool. Close to the in-laws. Great value overall.

There were a couple days where M had the car and it was just me and the girls at the hotel. We decided we’d watch a movie, which kicked off the classic “how do we get the thing on my device onto the TV” problem.

I’ve debated traveling with an HDMI cable. It works. It’s just annoying to bring, and I never feel like packing it. So when I noticed the TV actually had an AirPlay option in the menu, the tech part of my brain started geeking out. This felt like one of those rare moments where hotel tech was finally catching up with reality.

At least in theory.

In practice, it refused to work. At all. I tried everything. And as someone who works in tech, there’s always that moment of “I cannot be defeated by a hotel TV,” but eventually you either give up or swallow your pride. I called their support guy. He came upstairs. He didn’t even know the feature existed until I showed him the menu for it. Still no luck. Completely dead.

So now it’s me, two kids, and no movie.

My workaround? I ended up ordering a Chromecast through DoorDash. Someone literally drove to a store, picked it up, and brought it to the hotel. Kind of wild. I don’t even know if that’s a thing in the UK. If it is, I’ve never tried it.

The Chromecast itself was tiny and cheap and came with its own little HDMI tail. Plugged it in, powered it up, connected it to my phone, and that was that. The hardest part was waiting for the movie to download on the hotel Wi-Fi. After that, everything just worked.

We watched Ghostbusters: Afterlife. The kids loved it so much we ended up seeing the sequel in the theater later.

The bigger point here is that the fancy casting feature Marriott advertised was a complete fail. Maybe they’ve fixed it since. I hope so because letting people cast from their own devices is safer and easier for everyone. I’m definitely not signing into a hotel TV with my Netflix credentials. No thanks.

I still have that little Chromecast. I don’t use many Google devices anymore, but this one is so small and so useful that I keep it around. Do I actually remember to travel with it? No. Should I? Probably. But whatever.

Downsizing the Pi Network

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I keep a blog idea board in my Trello app so I can track all the things I want to write about, because I’ll come up with an idea for a post and then promptly forget it. Of course, sometimes I also add it to the board and still forget about it for years. By the time I rediscover it, the topic isn’t relevant anymore, or I’ve already written something similar. But I digress.

One of those old notes was about the state of my Pi network. I wrote it down a year ago, maybe updated it earlier this year, and now I’ve finally made enough changes that it’s worth revisiting.

I’ve owned pretty much every generation of Raspberry Pi. Actually, I think I’ve owned all the main variants from each generation, not the tiny ones or the 400/500 models that are built into keyboards, but all the standard boards. I bought the original Raspberry Pi 1, set it up, played with it, and then never really deployed it for anything meaningful. By the time I wanted to, the Pi 2 had already come out, so I bought one of those. At some point I got rid of the Pi 1, maybe gave it away, but I’ve kept just about every other one since.

The backbone of my original home network was built on Raspberry Pi 2s. I had five of them running my early private cloud backup network. Over time, I upgraded them with new cases, official Wi-Fi adapters, and less reliance on Ethernet. Then the Pi 3s came out, and I added a few of those for compute jobs. Then came the Pi 4s, and I gradually shifted everything over again.

Eventually, I stopped using the 2s and most of the 3s, and my little network of Pis became mostly 4s. I think I had around four or five of them running various workloads. When the Pi 5s came out, I didn’t jump immediately, but I have about four of them now.

Funny thing is, I’m using less compute now than ever. The main purpose of the Pi cluster used to be my Docker setup, which ran parts of my media center, a Minecraft server, and Homebridge for connecting Ring cameras to Apple HomeKit. Most of that has since moved or shut down.

I replaced the Ring cameras, so no need for Homebridge. Plex moved to my Synology DiskStation because the transcoding works better there. The Docker stack was easy enough to migrate, so that freed up another Pi.

These days, I’m really only using:

One Resilio Sync node for backups One BorgBackup setup for immutable backups One Pi as a Tailscale exit node And a Raspberry Pi 4 running Pi-hole for DHCP and DNS in the house

Everything else runs on Pi 5s, though even that’s more power than I need. When I built out the 5s earlier this year, I decided to stop using SD cards and external drives. I got cases with SSD add-ons, most with 256 GB drives, and one with a 2 TB SSD for backups. It’s a neat all-in-one box setup.

The Pi 4, especially the 8 GB version, is still a perfectly good piece of kit, but I just don’t have enough for it to do. So I’ve started selling them off on eBay. I’ve already sold a couple of the 4s and gotten a surprisingly good return for hardware that’s several years old. I’ve sold all of my 3s and am now selling the 2s. Apparently, people still buy them for nostalgia or small projects.

A few of my old ones are in official cases, and two are even in LEGO cases with camera kits. They look great, but I’m trying to pare everything down so I’m left with only the Pi 5s, and maybe I’ll move the Pi-hole over to a 5 while I’m at it.

Maintaining the hardware takes a decent amount of time and effort, and with what I’m doing now, renting a virtual private server is just simpler. My web hosting, where this blog lives, runs on a VPS with 2.5 GB of RAM, a single virtual core, and about 40 GB of storage, all for around $23 a year. It’s based in Dublin and does the job beautifully.

Building a Pi 5 setup can easily cost around four times that, even if it gives me more power than I’ll ever use. I’m not planning to get rid of the 5s I already have since they’re great machines, but I’ve reduced what I actually run on them. Justifying keeping all the older models sitting around isn’t really there anymore. For most of what I run these days, a VPS or my upgraded DiskStation handles it fine.

So yes, I’m officially downgrading, selling off hardware, consolidating services, and simplifying. I still love tinkering, but the Pi 5s are plenty. The rest? Off to eBay.

Please Take My Money (Sort Of): Zizzi Edition

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Here’s another entry in my ongoing series about payment apps and POS systems, otherwise known as “please, for the love of all things, take my money.”

This time, it’s about Zizzi.

We like Zizzi. Pretty good for a chain, consistent, close to home. The kids enjoy it, and I’m a sucker for a solid lasagna, so it works.

A while back, they introduced the option to order right from your table. You scan, browse the menu, and place your order online. In theory, convenient. In reality, not so much.

The first time we tried it, the whole thing collapsed like a bad soufflé. I think we spent 15 minutes trying to order before admitting defeat. Then the waiter came over, who, to their credit, also had trouble with the system. When the staff can’t make the app work, that’s not a user problem. That’s a “this is broken” problem. Logging in, confirming the order, something always failed. After twenty minutes of tech support cosplay, we gave up and just ordered the old-fashioned way.

Since then, I’ve avoided the “order at the table” gimmick. My appetite doesn’t need a debugging session before pasta.

That said, Zizzi redeems itself with their payment setup. That’s where they actually get things right. You scan the QR code, pay with Apple Pay (no logins, no fuss), and it just works. Recently we had one of the kids’ birthday dinners there, large table, chaos, cake, the usual. The service was great, and paying through the app was quick and clean.

There was one hiccup: the app didn’t let us add a tip. And we really wanted to, because the staff had gone above and beyond with the birthday stuff. We ended up having to flag someone down, who couldn’t add it either. Apparently, the built-in service charge meant we were done. Nice in theory, but awkward when you actually want to leave extra.

Still, credit where it’s due. Ordering? Fail. Paying? Solid.

Zizzi gets a mixed review from me, half frustration, half appreciation. The tech that takes my money works great. The tech that takes my order? Not so much.