Not exactly, because n commits will have been squashed into one, so making the edit would lose the reference to the originals which should have been squashed with other commits
See but you can break the commit back apart by doing a git rebase interactive, selecting that squashed commit as the commit to edit, then doing a git reset HEAD~1 then recommit it in pieces, then git rebase --continue
Then git rebase -i a second time and then remove the commit that you don't want to be included
You can also do a git rebase -i and mark a commit as edit and git will stop at that commit in the rebase to allow you to stop, look around, and make changes.. I think that is what you are looking for
I want to say that you've piqued my interest, but honestly I'm not sure I can set aside my bias. I deal with enough wrong code already as it is that my co-workers write, so I don't know if having yet another one giving me bad code suggestions adds much, but I appreciate you putting in the work showing everything.
ok first dumb question, is the block of code that you had below this line
Given a query of “write a json schema to represent a comment thread on a social media site like reddit”, it’ll do this bit of reasoning:
Was this an actual output from an LLM or a hypothetical example that you wrote up? It's not quite clear to me. It's a lot of output but I don't want to insult you if you wrote all that yourself
First, each comment has an ID, author info, content, timestamps, votes, and replies. The replies are nested comments, forming a tree structure. So the schema should allow for nested objects within each comment's replies.
I ask because I really want to nitpick the hell out of this design decision:
First, each comment has an ID, author info, content, timestamps, votes, and replies. The replies are nested comments, forming a tree structure. So the schema should allow for nested objects within each comment's replies.
Adding the replies as full items, that is going to absolutely murder performance. A better scheme would be for replies to be a list/array of IDs or URLs, or a URL to an API call that enumerates all the replies, instead of enumerating all the items and embedding them directly. That is going to absolutely kill performance. Depending on the implementation, you could easily be doing the classic N+1 query that a lot of web applications fall for.
But then again at this point I'm arguing with an LLM which is generating absolutely dogshit code.
Just wanted to say that I'm holding on to this post to read it carefully since you put a lot of work into it. Haven't had the time to give it the attention it deserves. Not to argue, so don't think I'm winding up a rebuttal, I just haven't been able to give it the time it deserves
You seem to think that the way these things work is by just simply pulling up chunks of existing code from a big db
Sorry, that is not what I think. It's just that surely there was something very similar enough to your JSON to get the prediction to come up with something that looks similar enough. It's very annoying having to discuss an LLMs intricate details of how it works and then get nitpicked on a concept that I don't think I was saying
For example, just the other day I used it to come up with a SQL table schema based on some sample input JSON data
How likely is it that this JSON structure and corresponding database schema is somewhere in the (immense) training data. Was it novel? New?
Like I just have continual questions about an LLM doing 3NF a priori on novel data.
Like if we just outright say that LLMs are just a better Google or a better IntelliSense that can fetch you existing data that it has seen (which, given that it's basically the entire Internet, across probably the entire existence of the Internet that has been crawled by crawlers and the Internet archive, which is a boggling amount) instead of dressing it up as coming up with NEW AND ENTIRELY NOVEL code like the hype keeps saying, then I'd be less of a detractor
Second, it’s already quite good at doing real world tasks, and saves me a ton of time writing boilerplate when coding.
So, that's another thing that I wonder about. All these LLMs are doing is automating boilerplate code, and frankly that's not really innovative. "Programming via stack overflow" was a joke that has been in use for nearly two decades now (shudder) and all the LLM is doing is saving you the ALT+TAB between SO and your text editor, no?
If you're doing a TODO app in Angular or NextJS I'm sure you get tons of boilerplate.
But what about when it comes to novel, original work? How much does that help? I mean really how much savings do you get, and how useful was it?
Honestly even your idea of having an LLM "update" a curriculum just makes me annoyed. Why does everyone automatically give authority to an LLM on perhaps one of the most important societal functions, instead of trusting teachers to do their job, with the decades of experience that they have in teaching?
Is this what we want? AI generated slop for teaching the next generation because it'll get it done in a day?
I think the difference between generative AI and AI as a whole needs to be made here.
Does it? I would not consider LLMs to be AI and it's unfortunate that a marketing gimmick has been turned into a colloquialism to the point where they have stolen the term AI and renamed the original pre-OpenAI concept to "AGI".
Regardless, the point I am making is that the current hype surrounding LLMs is unwarranted.
The level of data that machine learning systems can analyze provides a usage far beyond the problems that are certainly inherent to generative AI.
But here's where I disagree. I think Machine Learning at least has a relatively modest marketing pitch, where you feed it data and based on some training and inputs (yes this is indeed similar to an LLM) and you will get a reasonable estimation of whatever you are seeking as an output, based on historical data. Nobody is running around claiming that my monitoring system that has some ML in it for CPU and temperature and load averages is suddenly going to turn into God like Sam Altman and all these other wackos want to happen with LLMs.
I would argue that if your goal is to get an output that is the statistical mean for a given input, then sure an LLM will generate a set of outputs that statistically go together. It just happens that you throw enough data at it and waste a small country's annual energy consumption then of course you'll get something statistically similar. Congrats. You did it.
I'm not so sure about that. Your analysis correctly identifies that it is being used in the West for nefarious purposes, but I honestly think even on the technical merits it is a flawed technology and a waste. DeepSeek is more efficient, yes, but it is still a flawed technology that I do not believe they should be using
I think blocking the instance that Dessalines is on would just be considered too gauche. It's easier for them to just ignore them or maybe they just block the communities with the new features that lemmy implemented instead of defederating
Don't know what to tell you. Been doing it that way since 2011