This post is mostly data driven (from my own web site's data) to give me a sense of the quality of the books I've read, otherwise individual reviews are all linked in this post or available on my books page. Longest book: Butter - 464 pages Shortest book: The Time Machine - 80 pages Quickest read: 3 days - The Radleys by Matt Haig (336 pages) Longest read/slog: 2 months each Butter by Asako Yuzuki (464 pages) Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI by Karen Hao (496 pages) The Left Hand of Darkness by Ursula K. Le Guin (290 pages) Diversity of authors: Women: 9 Men: 5 Rated books 5 stars Minority Rule: Adventures in the Culture War - 319 pages 4 stars Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI - 496 pages In Bloom - 432 pages Sweetpea - 368 pages The Radleys - 336 pages The Man Who Died Twice - 422 pages The Echo Wife - 240 pages Books by decade 1890 1895: The Time Machine by H.G. Wells 1960 1969: The Left Hand of Darkness by Ursula K. Le Guin 2010 2010: The Radleys by Matt Haig 2015: Sweetpea by C.J. Skuse 2018: In Bloom by C.J. Skuse 2019: Reasons to Be Cheerful by Nina Stibbe 2020 2020: Butter by Asako Yuzuki 2020: The Man Who Died Twice by Richard Osman 2021: The Echo Wife by Sarah Gailey 2022: The Satsuma Complex by Bob Mortimer 2022: Spike Milligan: Man of Letters by Spike Milligan 2023: Making a Killing by Cara Hunter 2024: Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI by Karen Hao 2025: Minority Rule: Adventures in the Culture War by Ash Sarkar

I've been doing these posts ending my years, aiming to publish on the 31st, so I'm pleased that I've managed to get this post out the door. Mostly for my own reading, but perhaps yours too.

Made me hungry and could even smell the foods, but took me a long time to read which made it feel extremely stretched and disconnected. I really don't know why this book took me nearly 60 days to read, but it did which makes the events at the start of the book feel so utterly far away from the end. I'm not sure what drew me to the book but I picked it up with zero expectations. It discusses feminism, the pressure society puts on women to behave and appear certain ways (though this is Japanese culture, I do think a lot of this applies/is experienced by western white women too). There's a strong feature of food and butter in particular (as you'd expect from the title) - and the author (and translator) does an excellent job of writing in a way that left me reading late at night and really wanting to taste the food (and I'm not a foodie). There's also a story of friendship and support through those connections. This part of the book was really lovely to read, but seemed to all be crammed in at the end. The story also includes a very manipulative character, Kajii, and whilst I read I felt sorry (and frustrated) for the protagonist, Rika, that she couldn't see the obvious manipulation (but perhaps that's the point, when we're close up to manipulation, we can't see the wood for the trees). There really is a lot (of meaty subjects) in this book, and perhaps this contributed to my slow reading but it did leave me struggling to carry all the ideas through to the end of the book. I've written about urgency in writing, and that's what helps me turn the page. This book doesn't have that, which isn't bad, but doesn't help me. I suspect I would have enjoyed this more had I been able to digest it a little faster than two months.

I've written about Advent of Code in the past, but that was 5 years ago, so this warrants a new post, and there's an extra opportunity, I think.

Context: back in March 2025 I decided to put aside my scepticism and try AI driven development for the day. I appreciate that in 8 months, the AI landscape, particularly around agentic software dev has moved along, and perhaps this should have been posted originally back in March. All the same, maybe this is useful to some degree, if only to capture what it was like in time. Whilst I sit squarely in my AI-sceptic seat, I was recently prompted to try a different tact by two post I read a few weeks back. The first was Bruce's colonoscopy post, yes, that one. It was in fact that he was using a local LLM to create a generative image to commemorate his visit. I'd been using chatgpt (for code and electronics, basically a NLP version of a search engine), and hadn't considered that perhaps I could host myself to take some responsibility for one of the two problematic aspects of LLMs today (first being power consumption, second being the global theft). The second post was Simon Willison's post on how he created a mini tool (the post is much broader than that, a useful read). It wasn't the post so much but the tool he was using: Claude Code. So far with chatgpt I'd copy back and forth errors and tweaks when it was helping me. Although I also have copilot enabled in VS Code it really holds down to autocomplete at a pretty junior level. I'll often have typescript errors (for work) that copilot claims it can help fix, only resulting in even more typescript errors - so as a rule, I tend to avoid generating code fixes with copilot. But what Simon showed with a shared transcript between himself and Claude Code was the software making the changes and offering diffs. So that was what prompted a mini journey, and here's how it went. Offline LLMs Previously I had installed command line tools and even Ollama on my Mac without having the faintest idea how to use them effectively - so they sat idle and unused. I'm not sure how, but I came across Msty, a tool that purported to make using local LLMs very easy. For a change, that seems to be true. Since I still had Ollama running (though I should have probably ejected it), Msty quickly linked up to this and discovered (though I'd forgotten) that the DeepSeek R1 model was loaded already. I figured since I was primarily using LLMs for software development, it made sense to find a model that suited those requirements. Not quite that simple, or certainly not if you (ie. me) doesn't have the LLM lingo and terminology down. I did some searching online, found a few articles, which themselves read as if they had been generated by AI, offering feature reviews of LLM models, but I couldn't find anything that said "model X is excellent for Y software development". Additionally, I found some models on huggingface.io, and figured that 7B was going to be easier/more viable to run than a 70B (big numbers need big machines? I was guessing…). However putting the name of the model into Myst yielded a lot of results and I couldn't really tell you what the difference was. More importantly, I couldn't quite be sure of the provenance (and whether that even matters…I'd assume it did). I ended up installing the following models: llama 3.2 (2gb) deepseek coder v2 (which blew up) codellama (3.8gb) codellama 13b WizardCoder (I think the 3gb one?) (it just didn't reply on the query) Wavecoder-ultra-6.7b (literally never worked for me) I tried a number of technical problems without much success and did capture the output for a number of these models to try to solve a Home Assistant automation process (which you can see here). It's entirely likely there's better models out there for what I wanted to do. I did know I didn't need a model to know everything, so some focus (and thus: reduced size) would make sense. However, not one of these gave useful replies to my questions. My favourite (fail) was giving code-llama a question that read: this query gets all the lessons matching these lesson slugs (I gave it the actual SQL query), how do I get a list of all the slugs that didn't match?. The LLM happily reported that I was trying to learn Spanish: The provided code contains a list of sentences that are written in Spanish, and the goal is to translate them into English. However, there are some errors in the provided code that need to be fixed before it can function properly. Here are some issues with the current code: Missing curly braces around the translate function definition: The translate function should be defined as a separate named function expression, like this: { translate } = require("google-translate");. This will ensure that the function is properly closed and can be used in other parts of the code. For some reason it was using the slugs in the query as the most important part and completely ignoring the technical query. For the most part, the responses I got were fairly hand wavy, text heavy (which I didn't want since I was asking about code), and in most cases irrelevant to my task. I think em0ry42 on BlueSky sums up what I was seeing: Smaller self hosted models will always under perform the larger ones. I think your experience and those of the other commenters are consistent with the current reality of these technologies. They can't do what people are promising. Yet. I'm sure there are people who can tune the hell out of their setup, but sadly, running any decent LLM locally as a useful code assistant, is just not here for the rest of us. So I parked that for a while, and turned my attention to Claude Code. Coding without touching code I've no clue how new Claude Code was at the time, though I've gathered it's fairly new. It's a solid product from my experience (where I even managed to lose track as to which company owns which weirdly named AI thingy). Setup and interface is entirely on the command line, so already we're speaking my language. I'd seen demos of developers who've been able to join up their entire codebase to the LLM but each time I'd dabbling, I would quickly get lost and give up. Claude Code does exactly this without the walls I'd experienced in the past. I am however, acutely wary that Claude is running on a remote machine, and likely to be chonking through so much power that we're just throwing away water to keep machines from burning up. Let's stick a pin in that (and gosh, I loath myself already for that). The very first problem I wanted it to solve was where I was trying to download 1,000s of videos and they all needed to be added to one massive tarball (context: this is for work, to allow users to bulk download our assets). I'd hit a problem where the tar process kept throwing an unhelpful exception the evening before and no amount of documentation on the library I was using helped me. Overnight I had a suspicion as to the cause and it gave me an idea to try - but I thought I'd let Claude try first, see what it does. Without any specific direction (ie. my idea for the fix), and only the name of the file and the function the problem happened, Claude Code suggested the same solution I had in my head. The UI then offered a syntax highlighted diff of the change it wanted to commit to disk. I was able to review it (very much how I'd approach a code review) and all I then needed to do was hit enter to accept. I tested the code in a separate terminal and indeed the change worked. Given this positive start, I then spent most of the working time split between the Claude Code UI and in the terminal to run the main program (which was sequencing a very large dataset). The code changes for the most part were always good and code that I accepted. The experience was…weird. I'd heard of LLMs being referred to as junior developers but when I was going back and forth between chatgpt and vscode (again, for me, copilot never really came in useful), because of the amount of interaction that was required from me, it felt even less than working with a junior. But this was a much closer experience. I'd describe the change and logic, sometimes pointing to filenames that would offer useful context, and Claude would spend some time (and money) thinking, then it would ask me to check a diff. Weirdly I spent more time sitting and staring out the window waiting for code to come back than I did looking at code. It was a weirdly hands off experience. I can't tell where I sit on that. The main criticism that I had is that, because we use specific rules for typescript (no any and types are defined, which I think seems okay), Claude wouldn't really follow those strict rules, so I needed to go in at the end to clean that part up. The secondary criticism is more a matter of taste. The code (and logging) was verbose to my taste. Additionally, being outside the code for the majority of the work period felt really strange. Sort of like a self-driving car took me the majority of my journey, deciding itself the navigation, for me only to be needed for the final arrival through some tight country lanes. Or something! A cost Since I was freestyling my way on Claude Code, I did manage to rattle through $5 of credit. I did think this was (somehow) linked to my Google business account, but I'm now suspecting it was free credit to introduce me to their API. After running through this credit now twice (I switched to my personal account for a second run) I've discovered there's tools to help manage that sprawling cost (such as /compact and /clear to reduce how much context the LLM is fed before giving me a result). I'd like to play with this more to get an idea of how much I'm really prepared to pay. Also after writing (most of) this post, I came across an interesting project that takes the Claude UI and lets you connect up your own backend. I've not tried it yet, but I'd be interested to see if I can connect to a local LLM and try out results (though going by the current experience, it's going to have a hard time competing). Since it was conversational... I decided to hack together a simple keyboard keycap (I had spare) with an ESP32 board to emulate a keyboard. Then this would send a (fairly) unique keycode that then launched a python command which started a whisper based script that let me talk, then pasted the text into whatever was focused. This meant I had: press button, say the thing, press the button, wait for it to be done. It wasn't great because it was a little clunky, but it definitely felt futuristic! How I felt afterwards (I'm now writing this 8 months late, but I remember how it felt on the day). Even though I was surprised at the progress of the work, both for how terrible the local code solving was and how impressed I was with Claude Code - it did leave me with a feeling of disconnect. There's certainly the issue with the maintainability of pure vibe-coded software, but this was something more. There's a creative input that I put into my coding process. A sense of purpose and achievement in solving some complicated problem, or writing a line of code that I'm particularly pleased with. There wasn't really of that feeling of connection with the output. Having written this retrospectively I know that my perspective has changed somewhat, but I do remember have this weird dissonance between the outcome and the experience of getting there.

I've been wanting to write and share my experience of this year's event but a number of things have slowed me down - not least of all that it was Julie's birthday the following Tuesday (the first year her birthday was entirely swallowed by the event). So now as I sit writing this a full eight days later - sat on the side of the swimming pool as many kids, including my own, do their swimming lessons - I'm trying to collect my thoughts on the day.

As I think about how command line AI tools can be used, I know it's already a bit of the wild west not disimilar to the way we install npm modules. I've seen a bit of writing about how these tools should be run in a controlled sandbox (because they can mess with the surroudning environment), and this is a nice digestable post with working examplse of Dockerfiles. Source: thingsithinkithink.blog

A simple but effective fix to working with web components and VS Code. I wanted to get syntax highlighting and prettier support (to auto fix indenting, quotes, etc) in my component's templates. The extremely quick read is, add /* HTML */ to the front of the template. Case sensitive and space sensitive (though hopefully one day it won't be so strict). Now highlighting and prettier (with save and fix) works. Note that you need the es6-string-html VS Code extension for this to highlight correct (something I had forgotten I had installed).

Version control for your data: Automerge is a local-first sync engine for multiplayer apps that works offline, prevents conflicts, and runs fast. Interesting project, possibly for mini personal projects that want to share some data (possibly without a database? unsure). But also in some kind of collaborative code editor, ala codecasting from JS Bin old days. Beautifully designed landing page though. Source: automerge.org

Besides the name, the entire core foundation has changed. This question came up recently by someone who had either attended back in the early days or that knew of our event, but they (understandably) saw us as it was in 2009. I think the vibes you came away with in the 2009 and 2010 events would be recognised, but content and the core messaging has changed quite significantly.

I accidentally upgraded to Tahoe (I didn't know it existed and thought I was moving to Sequoia and the UI design is all over the place, and it's constantly reminding me how bad it is. This excellent article takes what was an attention to detail that we took for granted (because tech is supposed to "just work"), and calls out just a handful of the failings that Apple's OS now ships with (including on iOS, that I thankfully don't have to suffer). My fear, based on experience with bad Apple UI - (like the notifications that couldn't be quickly dismissed forcing us to click EXACTLY in the right place, and with the "suitable" amount of delay) - is that it simply won't be fixed or even improved. /via venerable Bruce Lawson Source: blog.johnozbay.com

I'd recommend anyone who wants to understand the emergence of AI and OpenAI in particular to read this book. I do struggle reading non-fiction, and this book was no different. It took me nearly 2 months to finish reading (in my evening snippets) - but it was definitely worthwhile. It was this 90 minute interview by Novara Media with Karen Hao that made me immediately purchase the book. I don't think I can fully review this book and do it justice, but I can share what I learnt from the book where my original assumptions were wrong about AI and AI companies. The book is focused primarily around OpenAI and Sam Altman, and in particular how his ousting in November 2023 came to be, through hundreds of interviews and documents, and paints a very insightful picture of the messiah complex (though not in her words) that Altman has. Here's a short summary of things I didn't know but learnt through reading this: Altman and friends started conversations (and business, albeit initially as a non-profit, though that didn't last) in 2015 because they were going to build AGI and that it was inevitable. The company was formed with scientific researchers originally, again as an open company with the intent of sharing, though spoiler alert: this all changed I had always assumed it was cowboy bro developers working on the code, but it was, originally, academic based engineering AI safety was constantly there and initially significant - but as we all know now, eventually lost a battle to make an impact, left "hobbled" and thrown rather to the wayside. The training data was, after GPT-2, ingested wholesale and attempt to clean/sanitise would happen on the results coming from prompts - i.e. the inputs were not cleaned, which means applying dizzying array of filters to catch on the output and edge cases. Common Crawl was introduced at GPT-3 - which is also where the input filtering stopped happening AI, or Western AI companies including OpenAI (but also Google and Microsoft) put their data centres in the Global South, additionally sourcing their data annotators from the poorest countries allowing them to pay (via third parties) literal pennies per hour for the work (which could also come with terrible mental health side effects as the worker would read and view the generative content that AI could come up with based on the unfiltered dark corners of the web) Sam Altman lies. Little lies, but from a great deal of documentation, a lot and often to tell people what they want to hear whilst (we guess?!) having some ulterior motive The path that OpenAI decided to take to head towards what they believe will be AGI, effectively requires unlimited compute power, when in reality, there are lots of different applications of AI that don't need that level of power, Stable Diffusion being one such example trained using 256 GPUs (still not a desktop computer, but not hundreds of thousand GPUs either) OpenAI's approach, to close off it's scientific findings, close it's source and refusing to share methods means that there's no way to verify any of their progress, but more importantly is stripping the academic scientific community of it's researchers (as someone who has visited CERN on two occassions, seeing science being shared is incredible and incredible for society) My only complaint about the book (and it's likely to be my own fault) is I had trouble with the jumping backwards and forwards in time - I'd often be unsure where we were in the timeline. If you work in tech, I'd absolutely recommend this book. If it's not possible, then definitely the interview I linked above.

Niels Leenheer goes through a detailed setup on how to take a single HDMI to projection into a mini event setup with holding screens. The technical detail is superb too, specifically considering power draw you would need but also, importantly, the length of cables and types to handle data rates. Definitely one for the back pocket read when wanting to upgrade the meetup game to something that has a lot more polish. Source: nielsleenheer.com

Very cool CSS gradient tooling by Adam Argyle.It's so pretty all I want to do is play with UI forgetting that it'll actually give me CSS.I also like that the URL is sharable and savable (perhaps through a bookmark or in a markdown doc).His latest changes include:- better import design- more resilient parser (understands more syntax)- supports multi-layered gradient imports Source: gradient.style

Very recently I was forced to sign up to Meta due to a product purchase (don't at-me!) and I had forgotten what it was like to be part of the algorithms. Our entire family browse the internet (the web and internet) from behind a DNS proxy that blocks a lot of social media including Facebook/Meta/Insta/whatever it's actually called. Unblocking these different services just to get a physical product to work (naively I thought it worked mostly offline) felt like I was exposing myself to a pretty gross network. Then had to make an account for my child, whom if there were over the age of 10 and under the age of 13, could be managed under my account. Once they turned 13 (in the eyes of Meta) they were completely unrestricted (which I've got A LOT to say about, but I digress). This combined with having already having to lift the protection I had in place was too much. I quickly deleted both accounts and parked the problem for the day. After some reflection, I decided that I would create a single account, that we'd all share that way at least I could monitor all activity and the data Meta collected would be (hopefully) extremely mixed due to different people using the account. Which reminded me of a story I heard about years ago where villagers in some areas of Africa (and likely other places in the Global South) were sharing a single device to visit Facebook and the like, which made it nigh impossible for algorithms to profile the user and made targetting and ads effectively useless. Why don't we do this now? Why don't we share accounts to large social media sites to pollute the signal we're putting out? I understand if you want to create an account so you can build your brand or post what you're up to, this doesn't really work. But if you're visiting these sites to keep in touch with your communities and post short comments or questions - then it seems to me that this is a viable solution. If I were to propose implementing this, it would be that each shared-user would have 10-20 real people using the account, I can see how it might be chaotic with more people - plus there's an aspect of trust required in your group. If someone decides to change the password you're all of a sudden blown up. Funnily enough, I've written about this same thought some 6 years ago, prompted by this fun pollution tool. What do you think? What downsides am I missing here?

I'd been waiting for the grief to find me. I wasn't actively looking for it, I know which memories to poke to feel real pain, but I wanted to create space for it to find me, and throughout the month of August, this year, it couldn't find me. Until today. 30th August. This day is the knife edge. The day, 15 years ago, that Tia still had a heartbeat, still kicked, was on her way. Julie was in (long) labour. On this same day, at some point, her heart gave out, she died before she could take her first breath and the midwives had to tell us that they couldn't find that heartbeat any more. Julie was in labour, so Tia was coming. Except that her delivery at 3am on 31st August would be the other side of our lives. The side we live on today: our derailed and rebuilt lives that exist in now.

No quite the same murderfest from Sweetpea 1, but still enjoyably angry at the world. I think I expected the same kind of murder spree from the first book, and it's not that at all (although Rhiannon, the protagonist, desperately wants that). It's actually written on the tin, In Bloom is Rhiannon's experience of being pregnant - which, for a change, rolls up all the shitty experiences into one. I enjoyed, and laughed at, the cutting turn of phrases - the kind of brutal honesty that you might reserve for the closest of friends knowing that it's for the "inside voice". I can see myself reading the next instalment in the book (though perhaps not so soon) and I'm not entirely sure where the character can go given the way the book ends, so that's definitely intriguing. But just for the the record (my record!), Rhiannon is a terrible person!

It's not an intuitive process and requires a few speciality commands to work, so it made sense that I write up the process so I can duck myself later on.

Honestly not sure what I thought of the book… I'm unsure how I got this on my to-read list. I think I was looking for funny books from the last few years. I didn't laugh once reading this book though. It was a sweet story, set in the 80s, but I'm not sure where the arc was. The story is told from the perspective of Lizzie, an 18 year old young woman who moves out of home to work as an assistant to a dentist. The dentist himself is fairly sexist and xenophobic. The mother (of the protagonist) is … hard to describe. In short, she's a pain in Lizzie's arse - though she definitely loves her. I think there's some part of flying the nest and finding your own feet, but Lizzie somehow ends up in a very similar place by the end of the book. There's times I was reading and thinking surely she's going to realise that she herself is actually a good dentist and will take that path, but nope, she ends up as a clothes store assistant, so … I don't know. It was cute to have 80s nostalgia peppered throughout the book, the highlight being the salad spinner (which my own mother still owns and I definitely remember spinning it for fun). I guess I was left with the sense of why did I read what I read?

I've been immersing myself in the AI news for the last few months, trying to get understanding of the landscape, what and why there's excitement, whether the ethics (both from a copyright and climate change perspective) are being considered, and what's the impact of my own usage. In particular lately I've been looking at the idea of vibe coding. I've been playing with Claude Code which, as an experienced developer, is hard not to like (at time of writing!). The short version of what I want to say is: vibe coding seems to live very squarely in the land of prototypes and toys. Promoting software that's been built entirely using this method would be akin to sending a hacked weekend prototype to production and expecting it to be stable.

Over the years we, as a family, have been moving our purchases away from Amazon, except in one single place: Kindle ebooks. For me it's that I'm incapable of reading physical books (but my kindle unlocked my reading), and with a Kindle, I was limited as to where I buy my books. When I read that it was relatively easy to jailbreak all the Kindle models I used this as my opportunity to move to buying epub books and hopefully more of that money goes to the authors (in an ideal world…). Here's how it's going so far. In short: not quite as well as I'd like.

Funny, unwavering and dark. Rhiannon, Sweetpea, is according to BuzzFeed, a serial killer - and probably a psychopath. For the first half of the book I just read it as she's a bit crazy and has decided to act on her impulses. Except as I carried on reading, I realised that what she was doing was listing the things that piss her off all the time and just landed at the inevitable conclusion that the only way to get rid of these things is to get rid of these things. Not unlike a lot of us except we don't go all the the way to fully extinguishing someone. There were some really funny turns of phrases throughout the book and I just wondered how I could possibly end but suspected it would end the way it did which isn't a bad thing. I definitely plan to read a few more of these books in a series. Graphic, explicit, raw, quite angry (and annoyed), funny. Good stuff!

An easy read, good for the holiday. I chomped this down in a matter of days (though it helped being by the pool to spend more time reading). I've read a number of Matt Haig's books and find I prefer his older material over the newer books, and this book fitted nicely into that category. Without examining the story too carefully (because I think the balance of humour and darkness, in retrospect, is a little off balance and the wheels sort of come off!) - it was a fun read. Definitely reading the book in 2025 rather than the years which the Twilight stories were at their height probably makes the story a little more refreshing for me (it's about a British vampire family - not a spoiler, you'll get that from the blurb). The characters were fairly simple to follow and switch between on each (nicely short) chapter. I didn't really feel much sympathy for the parent characters (I'm not sure if I was supposed to, but like I said, it doesn't want too close a look), but there's some fun twists on the vampire genre (or perhaps I'm not that familiar with the genre!). Some fun gory bits, a decent mini twist here and there, very easy to read - a good choice for the holiday read.

A book of letters…kinda made me want to write letters… I picked this up based on "funny books" though I'm really not sure how I landed on this book. I know, sort of, Spike Milligan's work, but only from TV. I didn't know he was an author, particularly from funny war stories so I missing considerable context. Arguably I'm not sure why I read this book. The back cover quotes appeared almost immediately at the start of the book and it was pretty clear that this read like a bottom-of-the-barrel book, just printing every letter that could be found. There was the odd occasion that the correspondence was funny, but unfortunately not laugh-out-loud-funny, just a bit of a snicker. In the last part of the book (part 4: words of support), the letters are about mental health (dating back to the late-60s), which was interesting to read as it feels like in the real world we've only started to talk openly about mental health in the last decade or so. Milligan would write letters offering support to others, and would often talk about how the NHS support was missing the love and understanding and rather delivered pills (which he says helps, but only as a band-aid). It was these last group of letters, mostly about sending out love and positivity to the world that brought this up from 2 stars to 3. It definitely made me want to actually write words down to people rather than the usual email, and it was nice to see some silly humour throughout his decades.

300 pages but wow it felt 4 times longer... I found the writings quite difficult to consume. A lot, if not all the names were sounds but without any familiar rhythm, and Le Guin was describing, with I assume great accuracy a world with locations, cities, regions and mountains - but I was struggling to map it all. Then the story was thread through this detail, which left me struggling to read and struggling to enjoy the book. It's only when I hit around 60% and the two characters embark on a journey across the ice did I finally manage to engage properly. I really did feel the characters relationship change through their journey and felt their struggle, which I think is what turned this book, for me, from 2 star to 3. There's a scene towards the end when the protagonist revisits the King of {whatever the name of the city was}, and the protagonist reflects on how the time has passed since his first visit much earlier in the book. I could empathise! It felt like a lifetime! Probably a great book. Maybe passed me by though.