The last couple of years have been a wild ride. The biggest parts of the conversation around AI for most of that time have been dominated by absurd levels of hype. To go along with the cringe levels of hype, a lot of people have felt the pain of dealing with the results of rushed and forced AI implementation.

As a result the pushback against AI is loud and passionate. A lot of people are pissed, for good reasons.

Because of that it would be understandable for people casually watching from a distance to get the impression that AI is mostly an investor fueled shitshow with very little real value.

The first part of the sentiment is true, it's definitely a shitshow. Big companies are FOMOing hard, everyone is shoehorning AI into everything they can in hopes of capturing some of that hype money. It feels like crypto, or Web 3.0. The result is a mess and we're nowhere near peak mess yet.

Meanwhile in software engineering the conversation is extremely polarized. There is a large, but shrinking, contingent of people who are absolutely sure that AI is something like a scam. It only looks like a valid tool and in reality it creates more problems than it solves. And until recently that was largely true. The reason that contingent is shrinking, though, is that the latest generation of SOTA models are an undeniable step change. Every day countless developers try using AI for something that it's actually good at and they have the, as yet nameless but novel, realization that "holy shit this changes everything". It's just like every other revolutionary tech tool, you have to know how to use it, and when not to use it.

The reason I bring up software engineering is that code is deterministic. You can objectively measure the results. The incredible language fluency of LLMs can't gloss over code issues. It either identified the bug or it didn't. It either wrote a thorough, valid test or it didn't. It's either good code or it isn't. And here's the thing: It is. Not automatically, or in all cases, and definitely not without careful management and scaffolding. But used well it is undeniably a game changing tool.

But it's not just game changing in software. As in software if it's used badly, or for the wrong things, it's more trouble than it's worth. But used well it's remarkable. I'll give you an example:

A friend was recently using AI to help create the necessary documents for a state government certification process for his business. If you've ever worked with government you've already imagined the mountain of forms, policies and other documentation that were required. I got involved because he ran into some issues getting the AI to deliver.

Going through his session the thing that blew my mind was how little prompting it took to get most of the way there. He essentially said "I need help with X application process for X certification" and then he pasted in a block of relevant requirements from the state. The LLM agent then immediately knew what to do, which documents would be required and which regulations were relevant. It then proceeded to run him through a short Q and A to get the necessary specifics for his business and then it just did it. The entire stack of required documentation was done in under an hour versus the days it would have taken him to do it himself. It didn't require detailed instructions or .md files or MCP servers or artifacts, it just did it.

And he's familiar with this process, he has the expertise to look at the resulting documents and say "yeah this is exactly what the state is looking for". It's not surprising that the model had a lot of government documentation in its training data, it shouldn't even really be mind blowing at this point how effective it was, but it blew my mind anyway. Probably because not having to deal with boring, repetitive paperwork is a miraculous thing from my perspective.

This kind of win is now available in a lot of areas of work and business. It's not hype, it's objectively verifiable utility.

This is not to say that it's not still a mess. I could write an overly long essay on the dangers of AI in software, business and to society at large. We thought social media was bad, that the digital revolution happened too fast for society to adapt... AI is a whole new category of problematic. One that's happening far faster than anything else has. There's no precedent.

But my public service message is this: Don't let the passionate hatred of AI give you the wrong idea: There is real value there. I don't mean this is a FOMO way, you don't have to "use AI or get left behind". The truth is that 6 months from now the combination of new generations of models and improved tooling, scaffolding and workflows will likely make the current iteration of AI look quaint by comparison. There's no rush to figure out a technology that's advancing and changing this quickly because most of what you learn right now will be about solving problems that will be solved by default in the near future.

That being said, AI is the biggest technological leap since the beginning of the public, consumer facing, internet. And I was there for that. Like the internet it will prove to be both good and bad, corporate consolidation will make the bad worse. And, like the internet, the people who are saying it's not revolutionary are going to look silly in the context of history.

I say this from the perspective of someone who has spent the past year casually (and in recent months intensively) learning how to use AI in practical ways, with quantifiable results, both in my own projects and to help other people solve problems in various domains. If I were to distill my career into one concept, it would be: solving problems. So I feel like I'm in a position to speak about problem solving technology with expertise. If you have a use for LLM powered AI, you'll be surprised how useful it is.

A simple question. I know LLMs are currently not a replacement for authors. Will that remain true in 5 to 10 years?

EDIT: No. I never expected to earn a living either mostly or exclusively by selling books. There are however many "side gigs" in my country that can greatly benefit from being published by a real company. Ultimately though, I'm not in it primarily for the money. But I wonder what the future holds for fiction as a whole.

I stay current on tech for both personal and professional reasons but I also really hate hype. As a result I've been skeptical of AI claims throughout the historic hype cycle we're currently in. Note that I'm using AI here as shorthand for frontier LLMs.

So I'm sort of a late adopter when it comes to LLMs. At each new generation of models I've spent enough time playing with them to feel like I understand where the technology is and can speak about its viability for different applications. But I haven't really incorporated it into my own work/life in any serious way.

That changed recently when I decided to lean all the way in to agent assisted coding for a project after getting some impressive boilerplate out of one of the leading models (I don't remember which one). That AI can do a competent job on basic coding tasks like writing boilerplate code is nothing new, and that wasn't the part that impressed me. What impressed me was the process, especially the degree to which it modified its behavior in practical ways based on feedback. In previous tests it was a lot harder to get the model to go against patterns that featured heavily in the training data, and then get it to stay true to the new patterns for the rest of the session. That's not true anymore.

Long story short, add me to the long list of people whose minds have been blown by coding agents. You can find plenty of articles and posts about what that process looks like so I won't rehash all the details. I'll only say that the comparisons to having your own dedicated junior or intern who is at once highly educated and dumb are apt. Maybe an even better comparison would be to having a team of tireless, emotionless, junior developers willing to respond to your requests at warp speed 24/7 for the price of 1/100th of one developer. You need the team comparison to capture the speed.

You've probably read, or experienced, that AI is good at basic tasks, boilerplate, writing tests, finding bugs and so on. And that it gets progressively worse as things get more complicated and the LoCs start to stack up. That's all true but one part that has changed, in more recent models, is the definition of "basic".

The bit that's difficult to articulate, and I think leads to the "having a nearly free assistant" comparisons, is what it feels like to have AI as a coding companion. I'm not going to try to capture it here, I'll just say it's remarkable.

The usual caveats apply, if you rely on agents to do extensive coding, or handle complex problems, you'll end up regretting it unless you go over every line with a magnifying glass. They will cheerfully introduce subtle bugs that are hard to catch and harder to fix when you finally do stumble across them. And that's assuming they can do the thing you're asking then to do at all. Beyond the basics they still abjectly fail a lot of the time. They'll write humorously bad code, they'll break unrelated code for no apparent reason, they'll freak out and get stuck in loops (that one suprised me in 2025). We're still a long way from agents that can actually write software on their own, despite the hype.

But wow, it's liberating to have an assistant that can do 100's of basic tasks you'd rather not be distracted by, answer questions accurately and knowledgeably, scan and report clearly about code, find bugs you might have missed and otherwise soften the edges of countless engineering pain points. And brainstorming! A pseudo-intelligent partner with an incomprehensibly wide knowledge base and unparalled pattern matching abilities is guaranteed to surface things you wouldn't have considered.

AI coding agents are no joke.

I still agree with the perspectives of many skeptics. Execs and middle managers are still out of their minds when they convince themselves that they can fire 90% of their teams and just have a few seniors do all the work with AI. I will read gleefully about the failures of that strategy over the coming months and years. The failure of their short sightedness and the cost to their organizations won't make up for the human cost of their decisions, but at least there will be consequences.

When it comes to AI in general I have all the mixed feelings. As an artist, I feel the weight of what AI is doing, and will do, to creative work. As a human I'm concerned about AI becoming another tool to funnel ever more wealth to the top. I'm concerned about it ruining the livelihoods of huge swaths of people living in places where there aren't systems that can handle the load of taking care of them. Or aren't even really designed to try. There are a lot of legitimate dystopian outcomes to be worried about.

Despite all that, actually using the technology is pretty exciting, which is the ultimate point of this post: What's your experience? Are you using agents for coding in practical ways? What works and what doesn't? What's your setup? What does it feel like? What do you love/hate about it?

I was trying to find out why Lidarr wasn't matching my copy of The Cure's Greatest Hits. Found out I've got some bootleg Russian release that's catalogued on discogs (I eventually found the musicbrainz release and updated my profile to include bootlegs). So I search "Lidarr use specific discogs release" and the duck duck go search assist spat out some text about Lidarr not using discogs and cited this Tildes post.

It's curious because that post is 3yrs old and doesn't talk about discogs integration in Lidarr, just one mention of discogs in the post and some folks talking about Lidarr in the comments (It did cite a relevant GitHub issue about it though). The AI response mentioned that some users track new releases with Lidarr and downloads disabled, while covered in the post, it seems fairly tangential to my query.

I'm curious why it decided to check or cite a tildes post. No tildes posts came up in the first couple pages of search results. I use tildes from the same location, though on my phone where this query was on my desktop, and have done a couple DDG queries using "site:tildes.net" on my phone.

Has anyone else seen a search assist cite an unexpected site? Not unexpected as in irrelevant, that's all too common, but small and specific sources.

I've been trying to get a more rounded understanding of the impacts that "AI" has had since ChatGPT went viral back in 2022.

I've found it easy to gather a list of negative impacts, but have struggled to point to many positives.

I was curious if there were folks who have used any of these AI tools, and would willing to share any positive impacts those tools have had in their lives. I'm particularly interested in the text, audio, image, and video generation tools that have appeared since ChatGPT went viral, but please share anything else that you think fits.

so Google's AI Studio used to have an option to finetune gemini flash for free by simply uploading a csv file. but it seems they have removed that option, so I'm looking for something similar. I know models can be finetuned on colab but the problem with that is it's way too complicated for me, I want something simpler. I think I know enough python to be able to prepare a dataset so that shouldn't be a problem.

My understanding of how large language models work is rooted in my knowledge of statistics. However a significant number of people have never been to college and statistics is a required course only for some degree programs.

How should chatgpt etc be explained to the public at large to avoid the worst problems that are emerging from widespread use?