Yeah. I get that many HN comments are just complaints (heck mine was too and just as negative and shaming). But how bad of a day must you be having to try to shame someone about how they choose to write up an experience they thought was neat. Whatever, free speech and all that. Hope OC's day gets better.
It doesn’t read like shaming to me. It’s, in the grand scheme of HN comments, definitely on the more constructive side of the criticism. Maybe it could have been reworded, but I think the author of the post could very easily find it actionable in the future. I too had to stop reading the article at that point, so I think if the author wants more people to read, my advice for them is to just write like themselves. We’ve entered the start of a new Instagram filter age where many people feel they need to have LLMs reword their writing presumably for the same reasons as the original filter age. I share OC’s sentiment of pushing against the recent trend of implicitly shaming people for their individualistic writing styles.
Only because so many of the articles posted on HN now are AI-written, and badly, too. A lot of tech people are so impressed with LLMs’ capabilities in code that they fail to recognize how bad they are at writing enjoyable prose. And it feels like a chore to write out a whole blog post by hand when the machine could do it for you! But the result we get is so, so much worse and more annoying.
I dislike AI prose too, the cadence of it really rubs me the wrong way, but, that said we've had a lot of great, informative articles lately, written with AI help, where you just have to grit your teeth and get through them to get the underlying knowledge.
I don't think that commenting on every article is going to make the posters suddenly decide to go back and rewrite it by hand. Some of them probably don't even speak English natively. The comments are getting more tiresome than the AI prose at this point.
Hopefully in a year or so the LLM output won't be so janky and obvious, so this might just be a phase everyone has to pull through.
> If you care about how your content moves through the world now, including through AI systems, you have to care about caching. Not as a performance optimisation for human browsers, but as infrastructure for machine readership.
That doesn’t answer the question at all, and I wonder if it’s actually true? A cache is not magic; it is, itself, just a static file server in the end. If I self host a static page website on an nginx box, do I actually need cache to serve today’s crawlers?
The screenshot in the image says 3k req/day. That’s 2 requests per minute (amortized). At that rate, you can serve it with cgi and Perl.
Cache is only relevant if you have a lot of traffic AND dynamic pages, or if you care about latency (which is only relevant for humans).
I think about what I do in these verbose situations; I learn to ignore most of the output and only take forward the important piece. That may be a success message or error. I've removed most of the output from my context window / memory.
I see some good research being done on how to allow LLMs to manage their own context. Most importantly, to remove things from their context but still allow subsequent search/retrieval.
Feedback loops are important to agents. In the article, the agent runs this build command and notices an error. With that feedback loop, it can iterate a solution without requiring human intervention. But the fact that the build command pollutes the context in this case is a double-edge sword.
If you really need that, the easy solution here is to get a list of errors using an LSP (or any other way of getting a list of errors, even grep "Error:"), and only giving that list of errors to the LLM if the build fails. Otherwise just tell the LLM "build succeeded".
That's an extremely simple solution. I don't see the point in this LLM=true bullshit.
> The noticeable spike [~20 percentage points] in May in the figure above [tool invocations] was largely attributable to one sizable account whose activity briefly lifted overall volumes.
The fact that one account can have such a noticeable effect on token usage is kind of insane. And also raises the question of how much token usage is coming from just one or five or ten sizeable accounts.
It is quite interesting to ponder these usage statistics, isn't it?
According to their charts they're at a throughput of something like 7T tok/week total now. At 1$/Mtok, that's 7M$ per week. Less than half a billion per year. How much is that compared to the total inference market? And yet again, their throughput went like 20x in one year, who knows what's to come...
Yes, but that token growth chart looks linear to me. There's the usual summer slump and then growth catches up once the autumn begins, but if you plot a line from the winter growth period at the start of 2025 you end up roughly in the right place except for an unusual spike in the most recent month (maybe another big user).
I'd have liked to see a chart of all tokens broken down by category rather than just percentages, but what this data seems to be saying is that growth isn't exponential, and is being dominated by growth in programming. A lot of the spending in AI is being driven by the assumption that it'll be used for everything everywhere. Perhaps it's just OpenRouter's user base, but if this data is representative then it implies AI adoption isn't growing all that fast outside of the tech industry (especially as "science" is nearly all AI related discussion).
This feels intuitively likely. I haven't seen many obvious signs of AI adoption around me once I leave the office. Microsoft has been struggling to sell its Copilot offerings to ordinary MS Office users, who apparently aren't that keen. The big wins are going to be existing apps and data pipelines calling out to AI, and it'll just take time to figure out what those use cases are and integrate them. Integrating even present-day AI into the long tail of non-tech industries is probably going to take decades.
Also odd: no category for students cheating on homework? I notice that "editing services" is a big chunk of the "academia" category. Probably most of that traffic goes direct to chatgpt.com and bypasses OpenRouter entirely.
Good points here, particularly the ends not justifying the means.
I'm curious for more thoughts on "will drive more and more people out of jobs”. Isn't this the same for most advances in technology (e.g., steam engine, computers s, automated toll plazas, etc.). In some ways, it's motivation for making progress; you get rid of mundane jobs. The dream is that you free those people to do something more meaningful, but I'm not going to be that blindly optimistic :) still, I feel like "it's going to take jobs" is the weakest of arguments here.
It happened before, and it was an issue back then as well.
Mundane job may be mundane (though note that it is sometimes subjective), but it earns someone bread and butter and it is always economic stress when the job is gone and many people have to retrain.
If we were to believe those of us who paint this technology as mind-bogglingly world-changing, that someone is now nearly everyone and unlike the previous time there is no list of jobs you could choose from (that would last longer than the time it takes to train).
If we were not to believe the hype, still: when those jobs got automated back then, people moved to jobs that are liable to be obsolete this time, except there is also just more people overall, so even purely in terms of numbers this seems to be a bigger event.
Okay, maybe that's beer and closer to Waltham =P
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