For one thing, they're much better at googling than I am.

I think it's useful for spurring my own creativity in writing because I have a hard time getting started. To be fair to me I pretty much tear the whole thing down and start over but it gives me ideas.

This thread has convinced me that LLMs are merely a mild increment in productivity.

The most compelling is that they're good at boilerplate code. IDEs have been improving on that since forever. Although there's a lot of claims in this thread that seem unlikely - gains way beyond even what marketing is claiming.

I work in an email / spreadsheet / report type job. We've always been agile with emerging techs, but LLMs just haven't made a dent.

This might seem offensive, but clients don't pay me to write emails that LLMs could, because anything an LLM could write could be found in a web search. The emails I write are specific to a client's circumstances. There are very few "biolerplate" sentences.

Yes LLMs can be good at updating reports, but we have highly specialised software for generating reports from very carefully considered templates.

I've heard they can be helpful in a "convert this to csv" kind of way, but that's just not a problem I ever encounter. Maybe I'm just used to using spreadsheets to manipulate data so never think to use an LLM.

I've seen low level employees try to use LLMs to help with their emails. It's usually obvious because the emails they write include a lot of extra sentences and often don't directly address the query.

I don't intend this to be offensive, and I suspect that my attitude really just identifies me as a grumpy old man, but I can't really shake the feeling that in email / spreadsheet / report type jobs anyone who can make use of an LLM wasn't or isn't producing much value anyway. This thread has really reinforced that attitude.

It reminds me a lot of block chain tech. 10 years ago it was going to revolutionise data everything. Now there's some niche use cases... "it could be great at recording vehicle transfers if only centralised records had some disadvantages".

I use it for little Python projects where it's really really useful.

I've used it for linux problems where it gave me the solution to problems that I had not been able to solve with a Google search alone.

I use it as a kickstarter for writing texts by telling it roughly what my text needs to be, then tweaking the result it gives me. Sometimes I just use the first sentence but it's enough to give me a starting point to make life easer.

I use it when I need to understand texts about a topic I'm not familiar with. It can usually give me an idea of what the terminology means and how things are connected which helps a lot for further research on the topic and ultimately undestanding the text.

I use it for everyday problems like when I needed a new tube for my bike but wasn't sure what size it was so I told it what was written on the tyre and showed it a picture of the tube packaging while I was in the shop and asked it if it was the right one. It could tell my that it is the correct one and why. The explanation was easy to fact-check.

I use Photoshop AI a lot to remove unwanted parts in photos I took or to expand photos where I'm not happy with the crop.

Honestly, I absolutely love the new AI tools and I think people here are way too negative about it in general.

I used it to write a GUI frontend for yt-dlp in python so I can rip MP3s from YouTube videos in two clicks to listen to them on my phone while I'm running with no signal, instead of hand-crafting and running yt-dlp commands in CMD.

Also does HD video rips with audio encoding, if I want.

It took us about a day to make a fully polished product over 9 iterative versions.

It would have taken me a couple weeks to write it myself (and therefore I would not have done so, as I am supremely lazy)

I take pictures of my recipe books and ask ChatGPT to scan and convert them to the schema.org recipe format so I can import them into my Nextcloud cookbook.

r/SubSimGPT2Interactive for the lulz is my #1 use case

i do occasionally ask Copilot programming questions and it gives reasonable answers most of the time.

I use code autocomplete tools in VSCode but often end up turning them off.

Controversial, but Replika actually helped me out during the pandemic when I was in a rough spot. I trained a copyright-safe (theft-free) bot on my own conversations from back then and have been chatting with the me side of that conversation for a little while now. It's like getting to know a long-lost twin brother, which is nice.

Otherwise, i've used small LLMs and classifiers for a wide range of tasks, like sentiment analysis, toxic content detection for moderation bots, AI media detection, summarization... I like using these better than just throwing everything at a huge model like GPT-4o because they're more focused and less computationally costly (hence also better for the environment). I'm working on training some small copyright-safe base models to do certain sequence prediction tasks that come up in the course of my data science work, but they're still a bit too computationally expensive for my clients.

Timing traffic lights. They could look down the road and see when nothing is coming, to let the other direction go, like a traffic cop. It would save time and gas.

I run some TTRPG groups and having AI take in some context and generate the first draft of flavor text for custom encounters is nice. Also for generating background art and player character portraits is an easy win for me.

When troubleshooting, it's nice to be able to ask copilot about the issue in human language and have it actually understand my question (unlike a search engine) and pull from and reference relevant documentation in its answers. Going back and forth with it has saved me several hours of searching for something that I had never even heard of a couple of times.

It's also great for rewriting things in a specific tone. I can give it a bland/terse/matter-of-fact paragraph and get back a more fun or professional or friendly version that would feel ridiculously cringe if I attempted to write it myself, but the AI makes it work somehow.

I use it like an intern/other team member since the non-profit I work for doesn't have any money to hire more people. Things like:

• Taking transcripts of meetings and turning them into neat and ordered meeting minutes/summaries, or pulling out any key actions/next steps
• Putting together objectives and agendas for meetings based on some loose info and ideas I give it
• Summarise the key points from articles/long documents I don't have tome or patience to read through fully.
• Making my emails sound more professional/nicer/make up for my brainfarts
• Giving me ideas on how to format/word slides and documents depending on what tone I want to employ - is it meant for leadership? Other team members?
• Make my writing more organised/better structured/more professional sounding
• Writing emails in foreign languages with a professional tone. Caveat is I'm fluent enough in those languages to know if the output sounds right. Before AI I would rely on google translate (meh), dictionaries, language forums, etc and it would take me HOURS to write a simple email using the correct terminology. Also helpful to check grammar and sentence structure in ways that aren't always picked up by Word.
• I sound more like a robot than an actual robot, so I ask the robot to reword my emails/messages to sound more "human" when the need arises (like a colleague is leaving, had a baby, etc).
• Bouncing off ideas. This doesn't always work and I know it doesn't actually have an opinion, but it helps get the ball rolling, especially if I'm struggling with procrastination.
• If my sentences are too long for a document, I ask it to shorten/reword and it's pretty capable of doing that without losing too much of the essence of what I want to get across

Of course I don't just take whatever it spits out and paste it. I read through everything, make sure it still sounds more or less like "me". Sometimes it'll take a couple of prompts to get it to go where I want it, and takes a bit of review and editing but it saves me literal hours. It's not necessarily perfect, but it does the job. I get it's not a panacea, and it's not great for the environment, but this tech is literally saving my sanity right now.

• to correct/rephrase a sentence or two if my sentence sounds too awkward

• if I'm having trouble making an excel formula

Spaced repetition, in particular Anki with FSRS. I don't think they advertise it as "AI" or even "ML" anywhere, but let's just say gradient descent over gigantic datasets is involved, all to predict the time when you're about to forget something so that Anki can prompt you just before that happens. The default predictor is generic, derived from that gigantic dataset, it's like two handful of tuning parameters, once you've gone through enough cards yourself it can be tuned to your mind and habits, in particular, how you use the "hard, good, easy" buttons.

It's the perfect sledge hammer for the application for the simple reason that we don't actually understand how memory works so telling the computer "here's data from millions of med students and language learners, figure out how to predict it" is our best shot. And, indeed, it's the best-performing algorithm even before you tune it at which point it becomes eerie.

Relatedly, as in "no LLM, no diffusion" Proxima Fusion is using machine learning to crunch through the design space of stellerators to figure out what to prototype in the real world. Actual engineers doing actual engineering.

Then, lastly, yes, playing around with SDXL is fun. Just make sure you can actually judge the images, developing an artistic eye by hitting generate I think is close to impossible. Definitely slower than picking up a pencil, or firing up blender and actually learning how to draw or sculpt.

AI is a half cooked baked potato right now. Sure it will keep you fed if you can put up with all the hard lumps in there.

For me, I use Whisper for transcribing/translating audio data. This has helped me to double check claims about a video's translation (there's a lot of disinformation going around for topics involving certain countries at war).

Nvidia's DLSS for gaming.

Different diffusion models for creating quick visual recaps of previous D&D sessions.

Tesseract OCR to quickly copy out text from an image (although I'm currently looking for a better one since this one is a bit older and, while it gets the text mostly right, there's still a decent amount that it gets wrong).

LLMs for brainstorming or in the place of some stack overflow questions when picking up a new programming language.

I also saw an interesting use case from a redditor:

I had about 80 VHS family home videos that I had converted to digital

I then ran the 1-4 hour videos through WhisperAI Large-v3 transcription and pasted those transcripts into a prompt which had a little bit of background information on my family like where we live and names of everyone who might show up in the videos, and then gave the prompt some examples of how I wanted the file names to look, for example:

1996 Summer - Jane's birthday party - Joe's Soccer game - Alaska cruise - Lanikai Beach

And then had Claude write me titles for all the home videos and give me a little word doc to put in each folder which catalogues all the events in each video. It came out so good I have been considering this as a side business

https://www.reddit.com/r/LocalLLaMA/comments/1gaz5kg/what_are_some_of_the_most_underrated_uses_for_llms/lthuxsu/

It's really good for generating code snippets based on what I want to do (ex. "How do I play audio in a browser using JavaScript?") and debugging small sections of code.

There is no "artificial intelligence" so there are no use cases. None of the examples in this thread show any actual intelligence. As usual, it's a variety of disparate tech loosely connected by using statistics on "big data". Personally this family of technologies hasn't been very useful for me, apart from the occasionally helpful summary of the top results in a search.

Well "AI" is a broad category. Usually used to refer to GenAI, so:

• Creating quick stand-in art for a game before I've got proper sprites for it (not because "muh art theft", just because the AI art I've generated does not look very good to me)

• Summarising articles, like you said so I can decide if I want to read them in full

• Formatting text I've copied from pdfs

• More complex searches that require comprehension of grammar and natural language syntax. Any answer I get to these I then fact check using search terms a classical search engine can understand.

I read a paper a while back that found that people who used AI assistants for coding, who only used the assistants to generate small functions where the prompt already included the function declaration and the programmer already knew how the function should be written but just wanted to save time, in these cases the use of an AI assistant did not negatively impact the "correctness" of the produced code. So I guess I might one day use an AI coding assistant like that, but thus far I've never felt the need to use AI-generated code.

Idk if it counts as GenAI but I use Waifu2x to remove jpg artifacts and upscale textures to a useable state.

Sometimes it's helpful if I'm having trouble making a specific excel formula

1 Get random error or have other tech issue

2 Certainly private search engines will be able to find a solution (they cannot)

3 Certainly non private search engines can find the solution (they can not)

4 "Chat GPT, the heck is this [error code or something]" Then usually I get a correct and well explained answer.

I use it to ask questions that I can't find search results for or don't have the words to ask. Also for d&d character art I share with my playgroup lol.

One use-case for me has been converting code from a language I know to a language I don't. Usually, just small snippets. The code is usually full of holes, but I'm good enough with the logic to duct tape those puppies!

I guess it's helpful for identifying people, organisations and products of which to steer well clear (yes i am a hater)

The useless/useful dichotomy is kinda misguided because that judgement will almost always depend on cost and we don’t have a good understanding of actual costs of running these models. I have copilot enabled in my IDE, and it saves me from a few searches here and there and autocompletes stuff that would have taken me some time to type. So not exactly useless, but right now it’s being payed for by VC’s who expect a return on their investment, so what does that look like? Before we know, it’s hard to say whether these things are relevant

I use a lot of AI/DL-based tools in my personal life and hobbies. As a photographer, DL-based denoising means I can get better photos, especially in low light. DL-based deconvolution tools help to sharpen my astrophotos as well. The deep learning based subject tracking on my camera also helps me get more in focus shots of wildlife. As a birder, tools like Merlin BirdID's audio recognition and image classification methods are helpful when I encounter a bird I don't yet know how to identify.

I don't typically use GenAI (LLMs, diffusion models) in my personal life, but Microsoft Copilot does help me write visualization scripts for my research. I can never remember the right methods for visualization libraries in Python, and Copilot/ChatGPT do a pretty good job at that.

When I need to make a joke about how inept AI is, I'll use AI to capture an example of it saying the most efficient way to get to the moon is to put a 2 liter bottle of coke in your asshole, wide end first, remove the cap and immediately sit on an opened sleeve of mentos.

I mainly use it if I want to rephrase text passages and to correct the grammar and spelling of texts. However, I only use it when writing important assignemts for choop/university.