alt Hacker NewsPerhaps the real takeaway is that there really is only one product, two if you count image generation.
Perhaps the only reason Cursor is so good is because editing code is so similar to the basic function of an LLM without anything wrapped around it.
Like, someone prove me wrong by linking 3 transformative AI products that:
1. Have nothing to do with "chatting" to a thin wrapper (couldn't just be done inside a plain LLM with a couple of file uploads added for additional context)
2. Don't involve traditional ML that has existed for years and isn't part of the LLM "revolution."
3. Has nothing to do with writing code
For example, I recently used an AI chatbot that was supposed to help me troubleshoot a consumer IoT device. It basically regurgitated steps from the manual and started running around in circles because my issue was simply not covered by documentation. I then had to tell it to send me to a human. The human had more suggestions that the AI couldn't think of but still couldn't help because the product was a piece of shit.
Or just look at Amazon Q. Ask it a basic AWS question and it'll just give you a bogus "sorry I can't help with that" answer where you just know that running over to chatgpt.com will actually give you a legitimate answer. Most AI "products" seem to be castrated versions of ChatGPT/Claude/Gemini.
That sort of overall garbage experience seems to be what is most frequently associated with AI. Basically, a futile attempt to replace low-wage employees that didn't end up delivering any value to anyone, especially since any company interested in eliminating employees just because "fuck it why not" without any real strategy probably has a busted-ass product to begin with.
Putting me on hold for 15 minutes would have been more effective at getting me to go away and no compute cycles would have been necessary.
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Everything where structured output is involved, from filling in forms based on medical interview transcripts / court proceedings / calls, to an augmented chatbot that can do things for you (think hotel reservations over the phone), to directly generating forms / dashboards / pages in your system.
LLMs make all sorts of classification problems vastly easier and cheaper to solve.
Of course, that isn't a "transformative AI product", just a regular old product that improves your boring old business metrics. Nothing to base a hype cycle on, sadly.
I have used LLMs for some simple text generation for what I’m going to call boilerplate, eg why $X is important at the start of a reference architecture. But maybe it saved me an hour or two in a topic I was already fairly familiar with. Not something I would have paid a meaningful sum for. I’m sure I could have searched and found an article on the topic.
Two off the top of my head:
There are a lot of tools in the sales space which fit your criteria.
LLMs in data pipelines enable all sorts of “before impossible” stuff. For example, this creates an event calendar for you based on emails you have received:
https://www.indexself.com/events/molly-pepper
(that’s mine, and is due a bugfix/update this week. message me if you want to try it with your own emails)
I have a couple more LLM-powered apps in the works, like next few weeks, that aren’t chat or code. I wouldn’t call them transformative, but they meet your other criteria, I think.
Is Cursor actually good though? I get so frustrated at how confidently it spews out the completely wrong approach.
When I ask it to spit out Svelte config files or something like that, I end up having to read the docs myself anyway because it can’t be trusted, for instance it will spew out tons of lines to configure every parameter as something that looks like the default when all it needs to do is follow the documentation that just uses defaults()
And it goes out of its way to “optimise” things that actually picks the wrong options versus the defaults which are fine.
> Perhaps the only reason Cursor is so good is because editing code is so similar to the basic function of an LLM without anything wrapped around it.
I think this is an illusion. Firstly, code generation is a big field - it includes code completion, generating entire functions, and even agenting coding and the newer vibe-coding tools which are mixes of all of these. Which of these is "the natural way LLMs work"?
Secondly, a ton of work goes into making LLMs good for programming. Lots of RLHF on it, lots of work on extracting code structure / RAG on codebases, many tools.
So, I think there are a few reasons that LLMs seem to work better on code:
1. A lot for work on it has been done, for many reasons, mostly monetary potential and that the people who build these systems are programmers.
2. We here tend to have a lot more familiarity with these tools (and this goes to your request above which I'll get to).
3. There are indeed many ways in which LLMs are a good fit for programming. This is a valid point, though I think it's dwarfed by the above.
Having said all that, to your request, I think there are a few products and/or areas that we can point to that are transformative:
1. Deep Research. I don't use it a lot personally (yet) - I have far more familiarity with the software tools, because I'm also a software developer. But I've heard from many people now that these are exceptional. And they are not just "thing wrappers on chat", IMO.
2. Anything to do with image/video creation and editing. It's arguable how much these count as part of the LLM revolution - the models that do these are often similar-ish in nature but geared towards images/videos. Still, the interaction with them often goes through natural language, so I definitely think these count. These are a huge category all on their own.
3. Again, not sure if these "count" in your estimate, but AlphaFold is, as I understand it, quite revolutionary. I don't know much about the model or the biology, so I'm trusting others that it's actually interesting. It is some of the same underlying architecture that makes up LLMs so I do think it counts, but again, maybe you want to only look at language-generating things specifically.
For all I know, this could be ML advertised AI, but I found various SQL query analyzers and index optimizers that are now frequently offered as part of managed platforms (e.g. Aiven, Google Cloud SQL) extremely helpful.
This challenge is a little unfair. Chat is an interface not an application.
Outside of coding, Google's NotebookLM is quite useful for analysing complex documentation - things like standards and complicated API specs.
But yes, an AI chatbot that can't actually take any actions is effectively just regurgitating documentation. I normally contact support because the thing I need help with is either not covered in documentation, or requires an intervention. If AI can't make interventions, it's just a fancy kind of search with an annoying interface.