371–380 of 464 posts
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what do you object to about it? I don't see an issue with referring to "the corpus of human knowledge". "Corpus" pretty much just means the "collection of".
Human knowledge != Reddit/Twitter/Wikipedia
> In recent times I had to learn Rust, Go, TypeScript, WASM, Java and C# for various projects, and I wouldn't delegate this learning effort to an AI, even if it saved me time. Either/or fallacy. There exist a varied set of ways to engage with the technology. You can read reference material and ask for summarization. You can use language models to challenge your own understanding. Are people really this clueless? (Yes…
Let me help you remove the beam from your own eye first: this comment leaves me with the impression that your writing isn’t great.
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So are people. People are trained on existing data and learn to reproduce known solutions. They also take this to the meta level—a scientist or engineer is trained on methods for approaching new problems which have yielded success in the past. AI does this too. I’m not sure there is actually a distinction here..
Of course there is. Humans can pattern match as a means to save time. LLM pattern match as the only mode of communication and “thought”. Humans are also not as susceptible to context poisoning, unlike llms.
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> But it's important to realize that AI coding is itself a skill that you can develop. It's not just , pick the best tool and let it go. Managing prompts and managing context has a much higher skill ceiling than many people realize No, it's not. It's something you can pick in a few minutes (or an hour if you're using more advanced tooling, mostly spending it setting things up). But it's not like GDB or using UNIX as…
> No, it's not. It's something you can pick in a few minutes (or an hour if you're using more advanced tooling, mostly spending it setting things up). But it's not like GDB or using UNIX as a IDE where you need a whole book to just get started. The skill floor is something you can pick up in a few minutes and find it useful, yes. I have been spending dedicated effort toward finding the skill ceiling and haven't found…
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> It is absolutely true, and AI cannot think, reason, comprehend anything it has not seen before. The amazing thing about LLMs is that we still don’t know how (or why) they work! Yes, they’re magic mirrors that regurgitate the corpus of human knowledge. But as it turns out, most human knowledge is already regurgitation (see: the patent system). Novelty is rare, and LLMs have an incredible ability to pattern match and…
> The amazing thing about LLMs is that we still don’t know how (or why) they work! You may want to take a course in machine learning and read a few papers.
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what do you object to about it? I don't see an issue with referring to "the corpus of human knowledge". "Corpus" pretty much just means the "collection of".
Human knowledge != Reddit/Twitter/Wikipedia
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Is it a skill worth learning though? How much does the output quality improve? How transferable is it across models and tools of today, and of the future? From what I see of AI programming tools today, I highly doubt the skills developed are going to transfer to tools we'll see even a year from now.
Maybe this is yet another application of the bitter lesson. It's not worth learning complex processes for partnering with AI models, because any productivity gains will pale in comparison to the performance improvement from future generations.
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This isn't about being AGI or not, and it's not "out of thin air". Modern implementations of LLMs can "do research" by performing searches (whose results are fed into the context), or in many code editors/plugins, the editor will index the project codebase/docs and feed relevant parts into the context. My guess is they either were using the LLM from a code editor, or one of the many LLMs that do web searches automati…
Yeah, doing web searches could be called research but thats not what we are talking bout. Read the parent of the parent. Its about being able to answer questions thats not in its training data. People are talking about LLMs making scientific discoveries that humans haven't. A ridiculous take. Its not possible and with the current state of tech never will be. I know what LLMs are trained on. Thats not the topic of con…
> Another common argument I've heard is that Generative AI is helpful when you need to write code in a language or technology you are not familiar with. To me this also makes little sense. I'm not sure I get this one. When I'm learning new tech I almost always have questions. I used to google them. If I couldn't find an answer I might try posting on stack overflow. Sometimes as I'm typing the question their search wo…
Sort of. The process of working through the question is what drives learning. If you just receive the answer with zero effort, you are explicitly bypassing the brain's learning mechanism.
There's huge difference between your workflow and fully Agentic AIs though.
Asking an AI for the answer in the way you describe isn't exactly zero effort. You need to formulate the question and mold the prompt to get your response, and integrate the response back into the project. And in doing so you're learning! So YOUR workflow has learning built in, because you actually use your brain before and after the prompt.
But not so with vibe coding and Agentic LLMs. When you hit submit and get the tokens automatically dumped into your files, there is no learning happening. Considering AI agents are effectively trying to remove any pre-work (ie automating prompt eng) and post-work (ie automating debugging, integrating), we can see Agentic AI as explicitly anti-learning.
Here's my recent vibe coding anecdote to back this up. I was working on an app for an e-ink tablet dashboard and the tech stack of least resistance was C++ with QT SDK and their QML markup language with embedded javascript. Yikes, lots of unfamiliar tech. So I tossed the entire problem at Claude and vibe coded my way to a working application. It works! But could I write a C++/QT/QML app again today - absolutely not. I learned almost nothing. But I got working software!
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> ChatGPT and Gemini literally only know the answer because they read StackOverflow. Stack Overflow only exists because they have visitors. I know this isn't true because I work on an API that has no answers on stackoverflow (too new), nor does it have answers anywhere else. Yet, the AI seems to able to accurately answer many questions about it. To be honest I've been somewhat shocked at this.
It is absolutely true, and AI cannot think, reason, comprehend anything it has not seen before. If you're getting answers, it has seen it elsewhere, or it is literally dumb, statistical luck. That doesn't mean it knows the answer . That means it guessed or hallucinated correctly. Guessing isn't knowing. edit: people seem to be missing my point, so let me rephrase. Of course AIs don't think, but that wasn't what I was…
" cannot think, reason, comprehend anything it has not seen before. If you're getting answers, it has seen it elsewhere, or it is literally dumb, statistical luck."