Posts tagged with "Gemini"

Well, what a surprise, nobody could have seen it coming: it does seem to be bait and switch season in LLM/agent land.

As I mentioned earlier today, when I ran up Gemini CLI to have it work on a change to BlogMore in the background, I got a notification that I should be swapping to Antigravity CLI instead. I let Gemini CLI get on with the change anyway, but resolved to install Antigravity CLI and give it a go. While there's still a touch under a month of use of Gemini CLI to go (based on the blog post), it seems sensible to get to know the new tool as soon as possible.

Installing Antigravity was a little bit of a faff. Looking at the documentation, you have to install the main application itself first, authorise with that, and then you can install and use the CLI. Fair enough. Rather than download the DMG from their website, I decided to go with the Homebrew installation (I like to try and keep track of what I have installed and this helps me do that).

So I installed that, ran it up, went through some setup questions, then finally got dropped in something that looked like it wanted to be an IDE of sorts. Nah, I'm fine, I like to work elsewhere. But that was okay given that I just wanted to get to the CLI anyway. Before I did that though, having installed this app, I saw that it was showing a "Restart to update" notification. So I did that, waited a wee while, and then finally was presented with something that looked totally different. Now I had an application that looked almost exactly like the main Gemini website (or the Gemini macOS application).

So that was kind of weird.

Finally I was in a position to install the CLI itself. From what I can see it's not available via Homebrew yet, and the installation instructions are the usual "curl this through bash, trust me bro" affair. Having done that (yes, yes, I know...), I was all set.

Credit where it's due, when I ran it up it just worked. As in: I didn't need to authorise again or anything like that; the fact that I'd set everything up via the main application did seem to have done that job.

After this though, it kind of went a little downhill. The first thing I noticed was the set of models available was rather different from Gemini CLI. I mean, okay, that's fair, I guess you expect things like that to change, but in my inexperienced¹ view of what these agentic tools offer, it looked like all the options were a little more... pricey, perhaps?

Still, I'm sure that sensible defaults are chosen out of the box, so it seemed like a good time to give this new tool a shot. I had a nice little problem for it to work on so that felt like a great test. It's hard to say for sure, but I feel like an issue like that, with the right prompt, would have used up somewhere between 3% and 5% of the daily quota in Gemini CLI, using Auto (Gemini 3). That was the default out of the box and, aside from tapping the models to try and unstick them, I've never really set it to anything else and the results have always been fine. With all this in mind I set Antigravity to work. Given that there didn't seem to be any sort of "Auto" option, I let it go with Gemini 3.5 Flash (High), which is what it was set to out of the box.

Yikes.

As I read that, and as I recall what happened, it took about 25 minutes to get to a reasonable solution to the request, with me pushing back on a couple of wild choices it made about how to change the code around. In doing this it left me with just 20% of the quota free for the next four and a half hours.

Yikes.

This is fine in this particular situation, where I'm conducting a long-term experiment and often letting the tool run at reasonably self-contained problems, in the background, while I get on with other more important things. But if I were to try and use this, as I have Gemini CLI, for an evening of sofa-hacking, refactoring lots of code or adding a handful of new features... that's not going to be sustainable. Any such session is going to grind to a halt pretty quickly. Presumably the intended solution here is that I buy myself lots of "AI credits".

I will experiment more, and intend to try and work out what the point, purpose and impact of each of the models are, as found in Antigravity. Doubtless there's a smarter approach I can take where it'll cost less quota for similar results. What is for sure though is that Antigravity CLI is not a drop-in replacement for Gemini CLI. It seems to be a different way of working, with different models, and different considerations. Also with less openness too.

It's interesting to drop in on the Gemini CLI subreddit, where the members seem to be experiencing what the Copilot folk were a week or so back. People finding they're chewing through their quota in no time, only with the added frustration of having to transition to a whole new application that seems to be lacking some features they're used to.

None of this is shocking to me -- although I'll admit that I thought the Gemini CLI ride might last a wee while longer than it did -- nor, I'd hope, to anyone else, but it continues to be fascinating to watch the squeeze being applied all around this tool space. This is going to be an increasingly worse time for anyone wanting to mess with agents for hobby projects. The idea of a tool that lets you get unambitious projects done for the price of a coffee or two, per month: that was a reasonable prospect. When the real cost turns out to be similar to an actual utility bill for your home... I know some people have expensive hobbies, but this would not seem to be a rewarding one at the sorts of costs we're starting to look at.

Once again, it's going to be interesting to see how engineering departments, and AI-embracing companies as a whole, react, as they become more and more invested in these third-party services, and less able to actually do things themselves, while at the same time the suppliers of those services squeeze them harder to try and make this adventure pay off.

I just sat down at my desk and fired up Gemini CLI to get it to make a change to BlogMore, and I see this:

I've yet to actually look at Antigravity, so I know pretty much nothing about it at this point. After a brief glance at the link that was given it seems like it's a positive change, perhaps. Honestly, I'm not sure. But that's kind of moot, I don't really have a choice. Within a month Gemini CLI is going to stop working anyway.

This is yet another reminder that, while plenty of folk are pushing these tools as the answer to the "problem" of software development, they're not really stable tools, it's not really a stable market, and, to some degree, if you fully rely on these tools, you're constantly at the mercy of the whims of some other company.

I'm glad I have a project where I'm forcing reliance on them as an experiment, so I can see and experience this first-hand, but I'd be very concerned for someone who's fully bought into them.

Perhaps there's a market here for a "Killed by AI" website, much like Killed by Google?

Or, maybe I'm being unfair here; it could be that this is more akin to Google solving the chat problem by constantly moving people from one chat application to another, while also having chat abilities in all sorts of other products...

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.

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.

As I've mentioned a few times recently, I'm using Google's Gemini CLI more at the moment; in part because I have a Gemini Pro account so it makes sense to use it, but also in anticipation of dropping anything to do with Copilot.

While I've had some troubles with it -- as can be seen here, here and here for example -- I'm mostly having an okay time. The code it writes isn't too bad, and while it seems to need a little more direction and overseeing than I've been used to while using Copilot/Claude, it generally seems to arrive at sensible solutions for the problems I'm throwing at it¹.

One difference with working with Copilot CLI that I have noticed, however, is that Gemini doesn't seem to care for cleaning up after itself. When faced with a problem it'll often write a test program or two, perhaps even create a subdirectory to hold some test data, run the tests and be sure about the outcome. This is good to see. It's not unusual for me to do this myself (or at least in the REPL anyway). But it really doesn't seem to care to actually clean up those tests. A handful of times now I've had it leave those files and directories kicking around. I've even said to it "please clean up your test files" and it's gone right ahead and done so, which suggests it "knows" what it did and what it should do.

This also feels like a new source of mess for all the people who commit their executables and the like to their repositories. That should be fun.

The thing I don't know or understand, at least at the moment, is if this is down to the CLI harness itself, or the choice of model, or a combination of both, or something else. I'm curious to know more.

Another evening, and another period of Gemini CLI getting stuck thinking. So this time I thought I'd try something: cancel it while it was thinking and change the model.

I was working on something new for BlogMore and, sure enough, after a wee while, we got stuck in "Thinking..." mode. So I hit Escape and asked to pick a different model. I chose to pick manually, and went with gemini-3.1-pro-preview.

I then literally asked that it carry on where it left off...

...and it did! It worked. No more sitting around thinking for ages.

Watching the quota after doing this, it looks like the model I was using ate quota faster, but that was worth it given I've never come close to hitting full quota with Gemini CLI.

Once the immediate job was done, I went back to auto and it worked for a bit, only to get stuck thinking again. I repeated this process and it did the trick a second time. From now on I'm going to use this approach.

It does, again, highlight how unreliable these tools are, but at least I've found a workaround that works for now.

Having had Copilot crash out the other day, while working on the linter for BlogMore, I decided to lean into Gemini CLI a little more and see how that got on.

When I first tried it out, a week back, I found it worked fairly well but could be rather slow at times. On the whole though, I found it easy enough to work with; the results weren't too bad, even if it could throw out some mildly annoying code at times.

Yesterday evening though, because of the failure of Copilot, I decided to go just with Gemini and work on the problem of speeding up BlogMore. This worked really well. I found that it followed instructions well¹ when given them, and also did a good job of applying what it was told, constantly, without needing to be told again. I actually found I had a bit of a flow going (in the minimal way that you can get any sort of flow going when you're not hand-coding).

Using it, I tackled all the main bottlenecks in BlogMore and got things working a lot faster (at this point it's generating a site in about 1/4 of the time it used to take). By the time that work was done, I wanted to do some last tidying up.

This was where it suddenly got unreliable. I asked it a simple question, not even tasking it with something to do, and it went into "Thinking..." mode and never came back out of it. I seem to remember I gave it 10 minutes and then cancelled the request.

After that I tried again with a different question, having quit the program and started it again with --resume. This time I asked it a different question and the same thing happened. I hit cancel again and then, a moment later, finally got an answer to the previous question.

From this point onwards I could barely ever get a reply out of it. I even tried quitting and starting up again without --resume, only for the same result.

A quick search turns up reports similar to this issue on Reddit, Google's support forums and on GitHub. It looks like I'm not alone in running into this.

This here is one of the things that concerns me about the idea of ever adopting agents as the primary tool for getting code written: the unreliability of their availability, and so the resulting inconsistency of the output. It feels like any perceived win in terms of getting the code written is going to be lost in the frustration of either waiting and trying again when it just gives up playing along, or in running from one agent to another, hoping you find the one that is capable of working with you at that given moment.

Meanwhile folk talk like it's the solution to the problem of software development. It's especially concerning when those folk are in "engineering leadership" or a position with a similar name. When they talk like this they are either displaying a lack of foresight, or betraying a lack of care for the craft they are supposed to represent (amongst other reasons).

It's very timely that this post from Rob Pike popped up in my feed this morning:

Although trained in physics, I worked in the computing industry with pride and purpose for over 40 years. And now I can do nothing but sit back and watch it destroy itself for no valid reason beyond hubris (if I'm being charitable).

Ineffable sadness watching something I once loved deliberately lose its soul.

Yup.

As mentioned recently, Andy did a nifty bit of testing of BlogMore to measure the performance hit of each of the optional generation features. Performance is something I haven't really spent much time thinking about; I cared more about how the result looked than I did about how quickly a site got generated.

I seem to remember that, early on, I did have a bit of a play with trying to get Copilot to tackle the idea of parallel generation of parts of the site, but I didn't seem to get any sort of win out of it so I closed the PR and gave up on the idea.

Now though, especially having added the elapsed time of each step to the progress report during generation, I am curious about what wins there are. One that came to mind right away, and I have been considering dealing with for a wee while, is the fact that every generation downloads the FontAwesome metadata. Caching that would be a simple win, for example.

But, if I'm going to do this, it makes sense that I stick to the spirit of this project and get one or more agents to tackle the problem. So I asked Gemini CLI to look over the codebase and give me some big easy wins. I made clear I didn't want it to do any work yet, I just wanted it to report back what it could find.

The result made a lot of sense. Here's what it said:

Performance Optimization Recommendations for BlogMore¶

I have conducted a comprehensive review of the blogmore codebase and identified several areas where measurable and significant speedups can be achieved, particularly for larger sites.

1. Backlink Snippet Extraction (Highest Impact)¶

The most significant bottleneck is in src/blogmore/backlinks.py. The build_backlink_map function iterates through every internal link in every post. For each link, it calls _extract_snippet, which re-parses the entire Markdown content of the source post to generate a plain-text snippet.

• The Issue: If a post has 20 internal links, it is parsed 20 times just for backlinks.
• The Win: Modify the logic to insert unique markers for all links in a post and perform a single Markdown-to-plain-text conversion per post.

2. Redundant Markdown Parsing for Metadata¶

The Post object in src/blogmore/parser.py has properties like description and reading_time that are accessed frequently (on the post page, index pages, RSS feeds, search index, etc.).

• The Issue: Post.description calls extract_first_paragraph, and Post.reading_time calls calculate_reading_time. Both of these functions perform a full Markdown conversion every time they are called because they are not cached.
• The Win: Use functools.cached_property on these fields. Additionally, these should be calculated by extracting text from the html_content that has already been generated during the initial parse, rather than re-parsing the raw Markdown.

3. Sequential Processing¶

The generator currently processes everything sequentially in a single thread.

• The Issue: For a site with hundreds or thousands of posts, the initial Markdown parsing and the subsequent Jinja2 rendering are strictly serial, leaving multi-core CPUs mostly idle.
• The Win: Use multiprocessing (e.g., ProcessPoolExecutor) to parallelize the parsing of Markdown files and the rendering of HTML pages. Since these tasks are CPU-bound and independent, this would scale almost linearly with the number of CPU cores.

4. Asset Management Inefficiencies¶

The AssetManager in src/blogmore/generator/assets.py performs some operations that could be optimized:

• FontAwesome Metadata: It downloads icon metadata from GitHub on every build to optimize CSS. This can be cached locally to avoid network latency.
• Icon Generation: It regenerates favicons and Apple touch icons on every build. This should be skipped if the source icon hasn't changed.

5. Minor Algorithmic Improvements¶

• Navigation Lookups: In PageGenerator.generate_post_page, the code uses all_posts.index(post) to find the current post's position for prev/next links. This is an O(N) operation called N times, leading to O(N^2) behavior. Pre-calculating a mapping would make this O(1).
• Markdown Instance Reuse: Several utility functions (markdown_to_plain_text, extract_first_paragraph) create a fresh markdown.Markdown instance on every call. Reusing a single instance (and calling .reset()) or caching the instance creation would reduce overhead.

Summary¶

The "Big Wins" are definitely optimizing backlink snippet extraction and implementing parallel parsing/rendering. Together, these would likely reduce generation time for large sites by 60-80% on modern hardware.

At first glance they all seem pretty sensible, and I like that it seems to have found some really obvious big improvements, and also the little wins that might have a cumulative effect. I strongly suspect the parallel generation suggestion is again going to be a non-win (and so for now I'm going to pass on it), but most of the other suggestions seem doable and worth trying out.

I think what I will do, rather than work through them all in one PR, is tackle one at a time. So, over the next few days, expect to see some PRs turn up and get merged (hopefully), as I try and tease some speed wins out of the code.

At the moment I'm working on a linting command for BlogMore. Having given up on Copilot/Claude for this, I've been having quite a bit of success with Gemini CLI. But while doing this, I've noticed some odd things with it. It does have this habit of cargo-culting some changes, or just rewriting code that doesn't need it.

For example, the tests for the new linting tool: it keeps adding import pytest near the top of the test file despite the fact that pytest doesn't get used anywhere in the code. Every time, I'll remove it, every time it adds more tests, it'll add it back.

Another thing I've noticed is it seems to be obsessed with adding indentation to empty lines. So, if you've got a line of code indented 8 spaces, then an empty line, then another line of code indented 8 spaces, it'll add 8 spaces on that empty line. That sort of thing annoys the hell out of me¹.

But the worst thing I just ran into was this. It had written this bit of code:

def lint_site(site_config: SiteConfig) -> int:
    """Convenience function to run the linter.

    Args:
        site_config: The site configuration.

    Returns:
        0 if no errors, 1 if errors were found.
    """
    linter = Linter(site_config)
    return linter.lint()

On the surface this seems fine: a function that hides just a little bit of detail while providing a simple function interface to a feature. But that use of a variable to essentially "discard" it the next line... nah. I dislike that sort of thing. The code can be just a little more elegant. So seeing this I edited it to be (removing the docstring for the purposes of this post):

def lint_site(site_config: SiteConfig) -> int:
    return Linter(site_config).lint()

Nice and tidy.

I then had Gemini work on something else in the linting code. What did I see towards the end of the diff? This!

Sneaky little shit!

Now, sure, the idea is you review all changes before you run with them, but knowing that it's likely that any given change might rewrite parts of the code that aren't related to the problem at hand adds a lot more overhead, and I wonder how often people using these tools even bother.