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Pretty much every project that I actively maintain on my GitHub account has a change log of some description. For a long time now, whenever I add a new entry to the log, I'll include a link to the PR that implements that change. Inevitably, this results in me adding the ChangeLog entry, creating the PR, then doing a follow-up change and commit now that I know the PR number, which allows me to add the link.

So I've created next-gh-pr.el to save me just a little time and let me be just a little more lazy. Inside it I've currently got a next-gh-pr-insert-markdown-link command which, when run, as you might imagine, inserts a link to the next likely PR URL as Markdown.

Working out the next URL is simple enough: get the latest issue and PR number, take whichever is the highest, and add 1. There is the wrinkle that discussions also cause this number to bump, and getting the latest discussion number is a little extra faff that I can't be bothered with right now, but my projects very seldom have discussions taking place anyway.

I've made a minor bump to OldNews, my terminal-based client for TheOldReader. There's no significant change in this release, but it does change the dependency on html-to-markdown.

Since I initially released the application, this library seems to have been through a couple of significant changes, and not every breaking change seems to have resulted in a major version bump. OldNews doesn't pin this dependency to a major version (I try not to, only ever setting lower-bounds for dependencies where possible), so it's fair that a change there can break things. I also think it's fair to hope that minor version changes won't cause trouble.

Recently, I've seen the library update with a minor version change and it's flat-out caused runtime errors, either because the API has changed, or because of an error being thrown by legitimate use of the API.

Most recently, such an error happened, and was fixed by the time I noticed it, but the release that was made never made it up to PyPI. This left OldNews stuck not working. Because of this I had to pin the library to an earlier version.

It's now been updated again and PyPI is correct, so I think it's safe to relax the pin.

Fingers crossed...

Quite a few weeks ago now -- I think it was around the time I started work on blogmore.el and got the new MacBook Air -- I remember sitting in a cafe in Edinburgh and via Mastodon having a conversation with Andy about tweaking better results out of PageSpeed Insights. I seem to remember him correctly observing that one of the big hits on the performance score was the size of images, and also the format, and that some SSG engines would go to the trouble of converting to the likes of WebP and/or generating different sizes that are appropriate to different screens, that sort of thing.

I can't quite remember where we left it, but I think it was considered more work than was worth worrying about, and perhaps swapping all images on our blogs to WebP would solve most of the issues.

For a couple of different reasons, late last week, I decided it was time to play with the problem. For some reason I've been pretty cautious with this PR. I planned it out last Friday night, kicked off work on it on Saturday morning, and have then been tinkering and changing it and testing it and iterating over it all weekend. Something about the nature of the change made me want to go very slowly with this. I think it was an unease about messing with the images that would get served, the nature of the new tags that would get emitted, the fact that there would be even more HTML tinkering going on, the possible complexity of maintaining the cache... lots of things to consider and this is supposed to be a nice, simple, unfussy site generator.

Anyway, I've just released v2.24.0 with this feature added. It's off by default, and is turned on by setting optimise_images to true. Then, when you build your blog, each PNG, JPEG or WebP image will be converted into one or more WebP images stored below static/images/optimised. How many are made for each image will depend on how image_widths is set. The physical size of each image (and how the image looks) can be affected by image_quality.

This does have two very obvious effects:

1. It will result in your generated site being quite a bit bigger, if you have lots of images.
2. It will result in the build time taking much longer.

The first issue is something I can't do anything about; it is what it is. The second issue, however, is something that can be dealt with. Given I've just made a release that speeds up build times, this would be a huge step backwards. So with this in mind, as the optimised images are created, a cache of them is also created in BlogMore's cache directory. This, again, does mean that more space is taken on your local storage to build your site, but it also means that repeated builds will remain fast.

If you run into problems or need space back, don't forget you can easily clear the cache.

So what's the result of all of this? Is it worth the effort? Well, to be sure, before I upgraded the version of BlogMore that I build this site with, I measured its performance.

After upgrading and rebuilding, here is how the same home page measures up.

I was genuinely surprised by the difference. The settings I used were:

optimise_images: true
image_quality: 95

and, of course, almost all the images on this site are now WebP anyway. I think I was expecting it to have a small impact, but even having those WebP images turned into stepped sizes seems to have a very measurable effect.

I'm going to be keeping a close eye on how this works for the next few days. As I say, I've tested this as much as possible and gone over the code as carefully as time has allowed. If this feature does break something I hadn't anticipated I can always just turn it off again anyway. Meanwhile though, the improvement on mobile does seem genuinely worth it.

Over the weekend I read a comment, I think it was on Hacker News, where someone said they were having fun building things using AI. This was in response to someone saying that using AI took the fun out of programming. In their reply, the person qualified their answer with something along the lines of "the highs are higher and the lows are lower".

I think I agree.

My first ever exposure to any sort of computer was a Sinclair ZX80 that my maths teacher brought into school. After a class he plugged it in and let me and a friend take a look. To this day I still remember looking in the manual, looking at the tutorial, and at some point typing...

When I hit NEW LINE and a 2 appeared on the screen I was thrilled, I was hooked. I'd typed something that appeared on a TV screen and then I did something that made the answer appear on the TV screen. This felt like magic.

I've been hooked on writing code ever since.

In that time the highs have been high, and the lows have been low, but I think it's fair to say that I've been doing this for long enough (it's now 45 or 46 years since I typed that first instruction) that things have settled down. I still get a thrill when writing code, and I still get fed up with it from time to time, but the distance between the two isn't what it once was.

Which brings me back to the comment I read: I think I can safely say that, while properly experimenting with agents, while building BlogMore to test this approach out, I have been through a period of higher highs and lower lows when it comes to how I feel about the code and the project itself. When I kicked off development it was genuinely thrilling to have gone from an empty repository to a comprehensively-working initial version in just a matter of hours. Likewise it was thrilling to have gone from nothing to rebuilding this blog with the tool in just a few days. It would be a lie to suggest that it wasn't fun and exciting to see the result.

But, as I wrote back then, I was also very mindful of how empty the process felt at times, how I missed the whole "flow state" connection to building out the application. There have also been many moments along the way, which I've documented at times on this blog, where I've felt the project was getting stuck down a dead-end with respect to how the code was going.

And then there's all the times Copilot and/or Gemini CLI just plain stopped getting stuff done.

Given this -- given the highs especially -- I can see why some people get totally hooked, go all in, get consumed by the illusion of how powerful these tools are. I can see why they'd buy into and embrace the mindset that trots out the AI-equivalent of the crypto-hype "stay poor" retort to those who display any level of scepticism.

The first company I worked for full-time had two offices. One in the south of England, another in the north. Despite being a northern lad, I'd somehow found myself working in the southern office. While the company used a few languages, there was a split between the two offices, mostly driven by the fact that the northern office was more minicomputer-based (lots of DEC stuff as I recall), whereas at our office it was more PC-based (we were an Apricot dealership, amongst other things). At our office, the predominant language was Clipper (later to be called CA-Clipper).

At one point, at the other office, they hired someone to start doing Clipper coding up there too, and he was handed his first project, to add a new report to an existing system. After around three weeks, he just didn't turn up for work one day, called in to say he'd quit (or so I was told). Meanwhile, the work he had done didn't seem to be working. If someone took the newly-compiled system and ran the new report, nothing happened.

When the code was looked at, it became clear why. The new module had one line of code. Well, not one line of code exactly: it had a one-line comment.

* This is too hard. I can't do this.

That was it. He'd spent those weeks appearing to work on the requirement, but never produced a single line of actual code.

I felt really bad for the guy. He'd somehow managed to make it through the interview, somehow managed to convince others, and himself, that he was capable of working with Clipper and writing code (probably made easier by the fact that the office in question wasn't a "Clipper shop"). But when it came to actually getting on with a job, he'd been unable to get it done (and, apparently, had felt unable to ask anyone around him for help, which probably says a lot about that office and the industry at the time¹).

I bring this up because I was reminded of this story when I was tinkering with Gemini last night. While working on the optimised images PR, towards the end of the session, I asked it to make a particular change. It then started "thinking", and after a couple of minutes appeared to get to work on the problem. It kept printing, scrubbing out and printing again, lines of text of what it was apparently doing. This went on for something like five minutes. Eventually it announced that the work had been done, explained what it had changed, and how it had implemented the requirement.

I flipped to another terminal to test out the work and... no changes. Zero changes. Nothing to diff, nothing to commit.

I flipped back to the CLI app, mentioned that nothing had changed, and it then very quickly made some edits; nothing spectacular, a 14-line diff affecting five lines (to start with).

This is the first time I've seen this, and I guess yet another thing I need to keep an eye out for. Of course I would notice if I asked for some work to be done and it wasn't done (I did), but it feels like another method via which this "productivity tool" can make you less productive.

If you give me the under-qualified, solution-paralysed, entry-level developer who doesn't know how to proceed, I can help them. Their current inability to actually bash on the keyboard and make code appear isn't the problem here. Giving them a tool that will busy-work for five minutes and produce nothing isn't going to help them, neither will things improve if they're given a tool that does emit all the code. Removing the human element is going to remove safety, growth and also domain knowledge. I feel it's going to rot software engineering departments from within, if handled badly.

Watching people talk about agents as if they're the solution, and that writing code is now a solved problem, really troubles me. I won't question the idea that it can be a very useful tool -- goodness knows I've found it useful recently -- but I do question the common assertion that it finally is a silver bullet. I find this to be lazy, dangerous and harmful thinking.

Well, this week didn't quite go according to plan, so I didn't get to test out the Steam Controller straight after it arrived. This afternoon, after messing about with code and agents for a few hours, I decided it was high time I stopped doing coding things (and to some extent day-job learning things) and instead did gaming things.

I dragged the Steam Deck and its dock into the living room, hooked everything up, and had a few hours of running and shooting and stuff. Before I could get started I had to plug the "puck" into the Deck with the supplied cable, and let it update. I then had to unplug the puck and plug in the controller to do the same. Finally I had to plug the puck back in (I hadn't quite realised, but it's the wireless connector for the controller) and I was good to go.

I messed around with a few things, including Brotato, DooM II, Cyberpunk 2077 and a lot of Suit For Hire. All felt good. All worked well. For some reason, this more than ever, makes me look forward to the Steam Machine.

I have a funny feeling I'll be joining the reservation queue when I get the chance.

As for the controller itself: it's a controller. It feels good in the hand. It feels comfortable. All the buttons feel in the right place. As someone who's used a Deck for a while it feels like having all the benefits of the Deck without having to hold the Deck itself. If you are already partly in the Valve hardware ecosystem it makes perfect sense as a thing to own.

Yes, I did try dropping it (carefully). No, I didn't manage to make it scream.

Following on from this morning's initial experiment, I think I'm settling on a winner. Rather than be annoying and have you scroll to the bottom to find out: it's Gemini CLI. Here's how I found the process played out, and why I'm settling for one over the other.

Gemini CLI¶

Initially this was an absolute mess. After letting it initially work on the problem, the resulting code didn't even really run. The first go, and the three follow-up prompt/result cycles that followed, all resulted in code that had runtime errors. I'm pretty sure it didn't even bother to try and do any adequate testing. This is odd given I've generally seen it do an okay job when it comes to writing and running tests.

Once I had the code in a stable state, with all type checking, linting and testing passing, it still didn't work. No matter how I tried to use the new facility it just didn't make a difference. No images were optimised. In the end I dived into the code, with the help of its attempt at debugging (it added print calls to try and get to the bottom of things -- how very human!), diagnosed what I thought was the issue (it was looking in the wrong location for the files to optimise), told it my hypothesis and let it check if I was right. It concluded I was and fixed the problem.

Since then I've had a working implementation of the initial plan.

Once that was in place it's been a pretty smooth journey. I've asked it questions about the implementation, had my concerns set to rest, had some concerns addressed and fixed, improved some things here and there, added new features, etc.

All of this has left me with 18% of my daily quota used up. While I think this is the highest I've ever got while using Gemini CLI, it still feels like I got a lot of things done for not a lot of quota use.

GitHub Copilot¶

Initially I thought this had managed to one-shot the problem. Once it had finished its initial work the code ran without incident and produced all the optimised files. Or so I thought. Doing a little more testing, though, it became clear it was only optimising a subset of the images and it didn't seem to be producing the actual HTML to use the images.

On top of this it didn't even follow the full plan that was laid out in the issue it was assigned. For example: once I'd got it doing the main part of the work, it became apparent that it had pretty much ignored the whole idea of using a cache to speed this process up. I had to remind it to do this.

At one point I switched from the in-PR web interaction with Copilot, and used the local CLI instead. When I ran that up it warned me that I was already 50% of the way through some sort of rate limit and this wouldn't reset for another 3 hours. I think I was about 40 minutes into letting it try and do the work at this point.

After a bit more testing and follow-up prompts, I got to a point where I had something that looked like it was working; albeit in a slightly different way from how Gemini CLI did it (the Copilot approach was writing the optimised images out to the extras directory, mixing them in with my own images; Gemini opted for having a separate directory for optimised images within the static hierarchy).

At this point I will admit to not having carefully reviewed the code of either agent; that's a job still to do. But while Gemini got off to a very rocky start, with a bit of guidance it seemed to arrive at an implementation I'm happy with, and one that seems to be working as intended. While it didn't anticipate all the edge cases, when I asked about them it easily found and implemented solutions for them. Moreover, the fact that I could do all of this and confidently know the "cost" made a huge difference. Copilot seems to generally approach this like a quota or rate limit should be a lovely surprise that will destroy your flow; Gemini has it there and in front of you, all the time.

As for the general idea that I'm working on: I think I'm going to implement it. Weirdly I'm slightly nervous about building the blog such that it won't be using the images I created, but I also recognise that that's a little irrational. Meanwhile I'm very curious about the impact this might have on the PageSpeed measurement of the blog. While it's far from horrific, image size optimisation and size declaration seem to be fairly high on the things that are impacting the performance score (currently sat at 89 for the front page of the blog, as I type this).

The other thing that gives me pause for thought about merging this in, and then subsequently using it, is that I've just finished migrating all images to webp, and so saving a lot of space in the built version of the blog. Generating all the responsive sizes of the images eats that up again. With this feature off, the built version of the blog stands at about 84MB; with it on, this rises to 133MB. That extra 49MB more than eats up the 24MB saving I made earlier.

On the other hand: storage is a thing for GitHub to worry about, what I'm worrying about here, and aiming to improve, is the reader's experience.

I'm going to sit on this for a short while and play around with it, at least until I get impatient and say "what the hell" and run with it.

Given I'm almost certainly going to drop GitHub Copilot starting next month, I'm using Gemini CLI more and more for BlogMore. Yesterday evening, I used it to plan out an idea for a change to the application. Now that I've migrated all images to WebP, I thought it might be interesting to look at the idea of having a responsive approach to images. This is something I don't know a whole lot about (never having needed to bother with it before), but it also happens that I need to read up on this anyway for something related to the day job; given this, it felt like a good time to experiment.

Together with Gemini CLI a plan was created.

This morning, over second coffee, I've kicked off the job of implementing it and, honestly, Gemini CLI is really struggling. It "implemented" the change pretty quickly, within minutes, but it just plain didn't work. Since then I've had it iterate over the issue four times and now it's struggling to make it work at all. It's still beavering away on this as I type, and consuming daily quota at a fair rate too.

So, while I still have GitHub Copilot, this feels like a good point to play them off against each other at least one more time. Having saved the plan Gemini wrote last night as an issue, I've assigned it to Copilot (using Claude Sonnet 4.6). As I type this, I have Gemini racing to get this working in a terminal window behind Emacs, meanwhile there's Claude doing its thing in GitHub's cloud.

It'll be interesting to see if Copilot manages to one-shot this, for sure Gemini is far off a one-shot implementation.

The job is finally done. After considering moving all the images in the blog over to WebP, and then finally getting the migration under way, I'm all done.

As I mentioned before: I've done this by hand, one post at a time, also adding missing covers as I go. The process went faster than I anticipated and I found that adding linting support to BlogMore really helped with this process. Each time I made a batch of changes I could run the linter to make sure I'd not broken any image links.

As for the result: I've brought the total size of images on the blog down from around 56MB to about 32MB, give or take (keep in mind the latter figure also includes all the WebP images I've added while blogging since I started this process). While I don't really have to worry so much about the storage costs of these images (I'm using GitHub Pages after all), overall, over time, there should be savings in the time it takes for readers to load any given page.

It's now well over a year since I released Braindrop and it's in constant use by me. I continue to find raindrop.io a really useful resource, and more often than not manage, edit, tag, and review what I save there with Braindrop, including which become public, and which don't.

I've made a few small changes to the application in the past year and a bit, but not much. It's been stable and useful. But on the back of a recent change I made to OldNews, I felt I needed to make the same change here.

So with the release of v1.1.0 I've added three new commands to the application:

• JumpToNavigation - Jump to the navigation panel; bound to 1 by default
• JumpToRaindrops - Jump to the main raindrops list panel; bound to 2 by default
• JumpToDetails - Jump to the details panel for the selected raindrop, if the panel is visible; bound to 3 by default

Now it's just a little easier and quicker to get around the UI.

If raindrop.io is your thing, and you want to work with your saved bookmarks in the terminal: Braindrop is licensed GPL-3.0 and available via GitHub and also via PyPI. It can also be installed using uv:

uv tool install braindrop

If you don't have uv installed you can use uvx.sh to perform the installation. For GNU/Linux or macOS or similar:

curl -LsSf uvx.sh/braindrop/install.sh | sh

or on Windows:

powershell -ExecutionPolicy ByPass -c "irm https://uvx.sh/braindrop/install.ps1 | iex"

If uv isn't your thing then it can also be installed with pipx:

pipx install braindrop

Once installed, run the braindrop command.