Ask HN: What are you actually using LLMs for in production?
49 comments
·June 28, 2025petercooper
Analyzing firehoses of data. RSS feeds, releases, stuff like that. My job involves curating information and while I still do that process by hand, LLMs make my net larger and help me find more signals. This means hallucinations or mistakes aren't a big deal, since it all ends up with me anyway. I'm quite bullish on using LLMs as extra eyes, rather than as extra hands where they can run into trouble.
captainbland
Is cost a major consideration for you here? Like if you're dealing with firehose data which I'm assuming is fairly high throughput, do you see an incentive for potentially switching to a more specific NLP classifier model rather than sticking with generative LLMs? Or is it that this is good enough/the ROI of switching isn't attractive? Or is the generative aspect adding something else here?
simonw
If you do the calculations against the cheapest available models (GPT-4.1-nano and Gemini 1.5 Flash 8B and Amazon Nova Micro for example - I have a table on https://www.llm-prices.com/ ) it is shockingly inexpensive to process even really large volumes of text.
$20 could cover half a billion tokens with those models! That's a lot of firehose.
actinium226
We have a prompt that takes a job description and categorizes it based on whether it's an individual contributor role, manager, leadership, or executive, and also tags it based on whether it's software, mechanical, etc.
We scrape job sites and use that prompt to create tags which are then searchable by users in our interface.
It was a bit surprising to see how Karpathy described software 3.0 in his recent presentation because that's exactly what we're doing with that prompt.
Vegenoid
Can you elaborate on what makes this “software 3.0”? I didn’t really understand what the distinction was in Karpathy’s talk, and felt like I needed a more concrete example. What you describe sounds cool, but I still feel like I’m not understanding what makes it “3.0”. I’m not trying to criticize, I really am trying to understand this concept.
adobrawy
In other words, are you using LLM as a text classifier?
blindriver
This is what I'm using it for as well, it's really simple to use for text classification of any sort.
jerpint
Are there currently services (or any demand for) a text classifier that you fine tune on your own data that is tiny and you can own forever? Like use a ChatGPT + synthetic data to fine tune a nanoBERT type of model
yamalight
Built vaporlens.app in my free time using LLMs (specifically gemini, first 2.0-flash, recently moved to 2.5-flash).
It processes Steam game reviews and provides one page summary of what people thing about the game. Have been gradually improving it and adding some features from community feedback. Has been good fun.
polishdude20
I usually find that if a game is rated overwhelmingly positive, I'm gonna like it. The moment it's just mostly positive, it doesn't stay as a favorite for me.
yamalight
Those games are usually brilliant - but those are very rare. Like "once in a few years" kind of rare IMO. While that is a valid approach, I play way more than that haha!
What I found interesting with Vaporlens is that it surfaces things that people think about the game - and if you find games where you like all the positives and don't mind largest negatives (because those are very often very subjective) - you're in a for a pretty good time.
It's also quite amusing to me that using fairly basic vector similarity on points text resulted in a pretty decent "similar games" section :D
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intermerda
Mostly for understanding existing code base and making changes to it. There are tons of unnecessary abstractions and indirections in it so it takes a long time for me to follow that chain. Writing Splunk queries is another use.
People use it to generate meeting notes. I don't like it and don't use it.
ArneVogel
I am using it for FisherLoop [1] to translate text/extract vocabulary/generate example sentences in different languages. I found it pretty reliable for longer paragraphs. For one sentence translations it lacks context and I have to manually edit sometimes. I tried adding more context like the paragraph before and after, but then I found it wouldn't follow the instructions and only translate the paragraph I wanted but also the context, which I found no good way to prevent. So now I manually verify, but it saves me still ~98% of the work.
GarnetFloride
We've been encouraged to use LLMs for brainstorming blog posts. The actual posts it generates are usually not good but gives us something to talk about so we can write something better. And doing SEO to posts. It seems to do that pretty well.
rootsofallevil
> Beyond the obvious chatbots and coding copilots, curious what people are actually shipping with LLMs.
We're delivering confusion and thanks to LLMs we're 30% more efficient doing it
tibbar
Internal research assistants. Essentially 'deep research' hooked up to the internal data lake, knowledge bases, etc. It takes some iterations to make a tool like this actually effective, but once you've fixed the top N common roadblocks, it just sorta works. Modern (last 6 months) of models are amazing.
If all you've built is RAG apps up to this point, I highly recommend playing with some LLM-in-a-loop-with-tools reasoning agents. Totally new playing field.
jabroni_salad
One of my clients is doing m&a like crazy and we are now using it to help with directory merging. Every HR and IT department does things a little differently and we want to match them to our predefined roles for app licensing and access control.
You used to either budget for data entry or just graft directories in a really ugly way. The forest used to know about 12000 unique access roles and now there are only around 170.
perk
Several things! But my favourite use-case works surprisingly well.
I have a js-to-video service (open source sdk, WIP) [1] with the classic "editor to the left - preview on the right" scenario.
To help write the template code I have a simple prompt input + api that takes the llms-full.txt [2] + code + instructions and gives me back updated code.
It's more "write this stuff for me" than vibe-coding, as it isn't conversational for now.
I've not been bullish on ai coding so far, but this "hybrid" solution is perfect for this particular use-case IMHO.
[1] https://js2video.com/play [2] https://js2video.com/llms-full.txt
miketery
I built a SQL agent with detailed database context and a set of tools. It’s been a huge lift for me and the team in generating rather complex queries that would take non trivial time to construct, even if using cursor or ChatGPT.
dartharva
I'm in the process of building one too. Handing off SQL queries to LLMs feels like a no-brainer.
alonsonic
I created an agent to scan niche independent cinemas and create a repository of everything playing in my city. I have an LLM heavy workflow to scrape, clean, classify and validate the data. It can handle any page I throw at it with ease. Very accurate as well, less than 5% errors right now.
Beyond the obvious chatbots and coding copilots, curious what people are actually shipping with LLMs. Internal tools? Customer-facing features? Any economically useful agents out there in the wild?