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

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.

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

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.

I use it for generating illustrations and NPCs for my TTRPG campaign, at which it excels. I'm not going to pay out the nose for an image that will be referenced for an hour or two.

I also use it for first drafts (resume, emails, stuff like that) as well as brainstorming and basic Google tier questions. Great jumping off point.

An iterative approach works best for me, refining results until they match what I'm looking for, then manually refining further until I'm happy with the results.

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.

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.

Good for softening language in professional environment.

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!

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.

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 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.

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/

Ansible.

I fucking hate YAML, and I hate Ansible 'programming' (see "HTML 'programming' language" for rage context).

Chatgpt - I'll use the one in bing or the one in regular-skype - feeds me stuff I can copy/paste/review, and I can get on with my day having lost fewer brain cells to the rage of existing in a world with Ansible fanboys who seem to have forgotten there is NOTHING Ansible does now that we weren't doing in 2003 ... and that the state of the art is 2 generations PAST that glorified mess.

Having used puppet and chef and seen mgmtconfig, I can only applaud RedHat for going with the worst-of-two options and promoting it so hard it appeared viable.

I don't mean to dunk on Michael. Just, James' idea was way better and RH still went with Michael's, and I one day need to know whether the person who had the final say got help.

I switched to Linux a few weeks ago and i'm running a local LLM (which was stupidly easy to do compared to windows) which i ask for tips with regex, bash scripts, common tools to get my system running as i prefer, and translations/definitions. i don't copy/paste code, but let it explain stuff step by step, consult the man pages for the recommended tools, and then write my own stuff.

i will extend this to coding in the future; i do have a bit of coding experience, but it's mainly Pascal, which is horrendly outdated. At least i already have enough basic knowledge to know when the internal logic of what the LLM is spitting out is wrong.

It’s perfect for topics you have professional knowledge of but don’t have perfect recall for. It can bring forward the context you need to be refreshed on but you can fact check it because you are an expert in that field.

If you need boilerplate code for a project but don’t remember a specific library or built in function that tackles your problem, you can use AI to generate an example you can then fix to make it run the way you wanted.

Same thing with finding config examples for a program that isn’t well documented but you are familiar with.

Sorry all my examples are tech nerd stuff because I’m just another tech nerd on lemmy

Software developer here, who works for a tiny company of ~~2~~ 7 employees and 2 owners.

We use CoPilot in Visual Studio Professional and it’s saved us countless hours due to it learning from your code base. When you make a enterprise software there are a lot of standards and practices that have been honed over time; that means we write the same things over and over and over again, this is a massive time sink and this is where LLMs come in and can do the boring stuff for us so we can actually solve the novel problems that we are paid for. If I write a comment of what I’m about to do it will complete it.

For boiler plate stuff it’s mostly 100% correct, for other things it can be anywhere from 0-100% and even if not complete correct it takes less time to make a slight change than doing it all ourselves.

One of the owners is the smartest person I’ve ever met and also the lead engineer, if he can find it useful then it has its use cases.

We even have a tool based on AI that he built that watches our project. If I create a new model or add a field to a model, it will scaffold a lot of stuff, for instance the Schemas (Mutations and Queries), the Typescript layer that integrates with GraphQL, and basic views. This alone saves us about 45 minutes per model. Sure this could likely be achieved without an LLM, but it’s a useful tool and we have embraced it.

i use it to autoblog about my love for the capitalist hellscape that is our green earth on linkedin dot com

AI is really good as a starting point for literally any document, report, or email that you have to write. Put in as detailed of a prompt as you can, describing content, style, and length and cut out 2/3 or more of your work. You'll need to edit it - somewhat heavily, probably - but it gives you the structure and baseline.

OP seems to be talking about generative AI rather than AI broadly. Personally I have three main uses for it:

• It has effectively replaced google for me.
• Image generation enables me to create pictures I've always wanted to but never had the patience to practise.
• I find myself talking with it more than I talk with my friends. They don't seem interested in anything I'm but chatGPT atleast pretends to be

I've learned more C/C++ programming from the GitHub Copilot plugin than I ever did in my entire 42 year life. I'm not a professional, though, just a hobbyist. I used to struggle through PHP and other languages back in the day but after a year of Copilot I'm now leveraging templates and the C++ STL with ease and feelin' like a wizard.

Hell maybe I'll even try Rust.

Genuinely, nothing so far.

I've tinkered with it but I basically don't trust it. For example I don't trust it to summarise documents or articles accurately, every time I don't trust it to perform a full and comprehensive search and I don't trust it not to provide me false or inaccurate information.

LLMs have potential to be useful tools, but what's been released is half baked and rushed to market as part of the current bubble.

Why would I use tools that inherently "hallucinate" - I. E. are error strewn? I don't want to fact check the output of an LLM.

This is in many ways the same as not relying on Wikipedia for information. It's a good quick summary but you have to take everything with a pinch of salt and go to primary sources. I've seen Wikipedia be wildly inaccurate about topics I know in depth, and I've seen AI do the same.

So pass until the quality goes up. I don't see that happening in the near future as the focus seems to be monetisation, not fixing the broken products. Sure, I'll tinker occasionally and see how it's getting on but this stuff is basically not fit for purpose yet.

As the saying goes, all that glitters is not gold. AI is superficially impressive but once you scratch the surface and have to actually rely on it then it's just not fit for purpose beyond a curio for me.

Regex

Be cautious about the results when using them for googling and summarizing. I had them tell misinformation to me more than once. You'll "learn" things that are counter-factual.

Translating is a very good use case. I also use them for that an it works very well. Better than any Google Translate. And I use it for roleplay, like a D&D campaign, just not with your friends, but alone and the AI narrates the story. And one-off things where I need some ideas to spark my creativity.

What I've tried apart from that are programming, re-phrasing my emails, ... But I've never got any good results for that. Everytime I did that, I ended up not liking the result, deleting it and starting over and doing it myself.

It is sometimes good at building SQL code examples, but almost always needs fine-tuning since it doesn't know the schema specifics.

Having said that one time it gave me code that resulted in an error, then I went back to GPT and said "This code you gave me is giving this error, can you fix it?" and all it would do is say something like "Correct, that code is wrong and will give an error."

if you know programming then you can have it do basic stuff (or even mid complexity stuff if you do it step by step). Just the other day I directed it to produce a code in js using 3js which does scatter plots. The code did run into a couple issues which it was able to solve itself when pointed by me. There was only one problem it could not solve despite several attempts (having a grid does not move with camera controls) so I had to figure that out myself. It was pretty impressive. Overall an expert in 3js would do that maybe in 10 minutes, it took me a couple hours. If I had to do it via searching online it would probably take me a couple of days since I know nought about js.

I also had it write bash scripts a couple of times. It is generally pretty good with writing basic stuff and piecing them together especially if you know programming so you can check it and write intelligible prompts about problems in the code.

I've done several AI/ ML projects at nation/ state/ landscape scale. I work mostly on issues that can be solved or at least, goals that can be worked towards using computer vision questions, but I also do all kinds of other ml stuff.

So one example is a project I did for this group: https://www.swfwmd.state.fl.us/resources/data-maps

Southwest Florida water management district (aka "Swiftmud"). They had been doing manual updates to a land-cover/ land use map, and wanted something more consistent, automated, and faster. Several thousands of square miles under their management, and they needed annual updates regarding how land was being used/ what cover type or condition it was in. I developed a hybrid approach using random forest, super-pixels, and UNET's to look for regions of likely change, and then to try and identify the "to" and "from" classes of change. I'm pretty sure my data products and methods are still in use largely as I developed them. I built those out right on the back of UNET's becoming the backbone of modern image analysis (think early 2016), which is why we still had some RF in there (dating myself).

Another project I did was for State of California. I developed both the computer vision and statistical approaches for estimating outdoor water use for almost all residential properties in the state. These numbers I think are still in-use today (in-fact I know they are), and haven't been updated since I developed them. That project was at a 1sq foot pixel resolution and was just about wall-to-wall mapping for the entire state, effectively putting down an estimate for every single scrap of turf grass in the state, and if California was going to allocate water budget for you or not. So if you got a nasty-gram from the water company about irrigation, my bad.

These days I work on a small team focused on identifying features relevant for wildfire risk. I'm trying to see if I can put together a short video of what I'm working on right now as i post this.

Example, fresh of the presses for some random house in California:

Guitar amp and pedal modeling.