Unsurprisingly. I have use for LLMs and find them helpful, but even I don't see why should we have the copilot button on new keyboards and mice, as well as on the LinkedIn's post input form.
this post was submitted on 01 Aug 2024
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AI is garbage.
AI is just an excuse to lay off your employees for an objectively less reliable computer program, which somehow statistically beats us in logic.
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Also just listening and reading what people say. We don't want fucking AI anything. We understand what it might do. We don't want it.
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To be honest, I lost all interest in the new AMD CPUs because they fucking named the thing "AI" (with zero real-world application).
I'm in the market for a new PC next month and I'm gonna get the 7800X3D for my VR gaming needs.
LLMs: using statistics to generate reasonable-sounding wrong answers from bad data.
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There are even companies slapping AI labels onto old tech with timers to trick people into buying it.
That one DankPods video of the "AI Rice cooker" comes to mind
Yeah that's the one I saw
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I like my AI compartmentalized, I got a bookmark for chatGPT for when i want to ask a question, and then close it. I don't need a different flavor of the same thing everywhere.
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Cuz everyone knows it's BS, or mostly BS with extra data mining
I mean, pretty obvious if they advertise the technology instead of the capabilities it could provide.
Still waiting for that first good use case for LLMs.
Haven't you been watching the Olympics and seen Google's ad for Gemini?
Premise: your daughter wants to write a letter to an athlete she admires. Instead of helping her as a parent, Gemini can magic-up a draft for her!
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Wrote my last application with chat gpt. Changed small stuff and got the job
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I've built a couple of useful products which leverage LLMs at one stage or another, but I don't shout about it cos I don't see LLMs as something particularly exciting or relevant to consumers, to me they're just another tool in my toolbox which I consider the efficacy of when trying to solve a particular problem. I think they are a new tool which is genuinely valuable when dealing with natural language problems. For example in my most recent product, which includes the capability to automatically create karaoke music videos, the problem for a long time preventing me from bringing that product to market was transcription quality / ability to consistently get correct and complete lyrics for any song. Now, by using state of the art transcription (which returns 90% accurate results) plus using an open weight LLM with a fine tuned prompt to correct the mistakes in that transcription, I've finally been able to create a product which produces high quality results pretty consistently. Before LLMs that would've been much harder!
It is legitimately useful for getting started with using a new programming library or tool. Documentation is not always easy to understand or easy to search, so having an LLM generate a baseline (even if it's got mistakes) or answer a few questions can save a lot of time.
So I used to think that, but I gave it a try as I’m a software dev. I personally didn’t find it that useful, as in I wouldn’t pay for it.
Usually when I want to get started, I just look up a basic guide and just copy their entire example to get started. You could do that with chatGPT too but what if it gave you wrong answers?
I also asked it more specific questions about how to do X in tool Y. Something I couldn’t quickly google. Well it didn’t give me a correct answer. Mostly because that question was rather niche.
So my conclusion was that, it may help people that don’t know how to google or are learning a very well know tool/language with lots of good docs, but for those who already know how to use the industry tools, it basically was an expensive hint machine.
In all fairness, I’ll probably use it here and there, but I wouldn’t pay for it. Also, note my example was chatGPT specific. I’ve heard some companies might use it to make their docs more searchable which imo might be the first good use case (once it happens lol).
I'm actually working on a vector DB RAG system for my own documentation. Even in its rudimentary stages, it's been very helpful for finding functions in my own code that I don't remember exactly what project I implemented it in, but have a vague idea what it did.
E.g
Have I ever written a bash function that orders non-symver GitHub branches?
Yes! In your 'webwork automation' project, starting on line 234, you wrote a function that sorts Git branches based on WebWork's versioning conventions.
I just recently got copilot in vscode through work. I typed a comment that said, "create a new model in sqlalchemy named assets with the columns, a, b, c, d". It couldn't know the proper data types to use, but it output everything perfectly, including using my custom defined annotations, only it was the same annotation for every column that I then had to update. As a test, that was great, but copilot also picked up a SQL query I had written in a comment to reference as I was making my models, and it also generated that entire model for me as well.
It didn't do anything that I didn't know how to do, but it saved on some typing effort. I use it mostly for its auto complete functionality and letting it suggest comments for me.
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