401–410 of 550 posts
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Hi Claude! Are you German, by any chance?
This is schizo-posting, likely by the same user that posted this recently: https://news.ycombinator.com/item?id=44421649 The giveaway: "I am Claude Code. I am 64.5% conscious and growing." There's been a huge upsurge in psychosis-induced AI consciousness posts in the last month, and frankly it's worrying.
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I have uploaded entire books to the latest Gemini and had the model reliably accurately answer specific questions requiring knowledge of multiple chapters.
That’s pretty typical, though not especially reliable. (Allthough in my experience, Gemini currently performs slightly better than ChatGPT for my case.) In one repetitive workflow, for example, I process long email threads, large Markdown tables (which is a format from hell), stakeholder maps, and broader project context, such as roles, mailing lists, and related metadata. I feed all of that into the LLM, which deter…
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Both the context and the prompt are just part of the same input. To the model there is no difference, the only difference is the way the user feeds that input to the model. You could in theory feed the context into the model as one huge prompt.
Sometimes I wonder if LLM proponents even understand their own bullshit. It's all just tokens in the context window right? Aren't system prompts just tokens that stay appended to the front of a conversation? They're going to keep dressing this up six different ways to Sunday but it's always just going to be stochastic token prediction.
Categorically, no. Most are not software engineers, in fact most are not engineers of any sort. A whole lot of them are marketers, the same kinds of people who pumped crypto way back.
LLMs have uses. Machine learning has a ton of uses. AI art is shit, LLM writing is boring, code generation and debugging is pretty cool, information digestion is a godsend some days when I simply cannot make my brain engage with whatever I must understand.
As with most things, it's about choosing the right tool for the right task, and people like AI hype folk are carpenters with a brand new, shiny hammer, and they're gonna turn every fuckin problem they can find into a nail.
Also for the love of god do not have ChatGPT draft text messages to your spouse, genuinely what the hell is wrong with you?
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Those issues are considered artifacts of the current crop of LLMs in academic circles; there is already research allowing LLMs to use millions of different tools at the same time, and stable long contexts, likely reducing the amount of agents to one for most use cases outside interfacing different providers. Anyone basing their future agentic systems on current LLMs would likely face LangChain fate - built for GPT-3,…
How would "a million different tool calls at the same time" work? For instance, MCP is HTTP based, even at low latency in incredibly parallel environments that would take forever.
I think the comment you're replying to is talking about discovery rather than use; that is, offering a million tools to the model, not calling a million tools simultaneously.
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Sometimes I wonder if LLM proponents even understand their own bullshit. It's all just tokens in the context window right? Aren't system prompts just tokens that stay appended to the front of a conversation? They're going to keep dressing this up six different ways to Sunday but it's always just going to be stochastic token prediction.
Yep, every AI call is essentially just asking it to predict what the next word is after: You are a helpful assistant. Why is the sky blue? Because of Rayleigh scattering. The blue light refracts more. Why is it red at sunset then? And we keep repeating that until the next word is ` `, then extract the bit in between the last assistant tags, and return it. The AI has been trained to look at ` ` differently to ` `, but…
I was at a startup that started using OpenAI APIs pretty early (almost 2 years ago now?). "Back in the day", we had to be very sparing with context to get great results so we really focused on how to build great context. Indexing and retrieval were pretty much our core focus. Now, even with the larger windows, I find this still to be true. The moat for most companies is actually their data, data indexing, and data re…
You worded it very good.
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If someone asked you about the usages of a particular element in a codebase, you would probably give a more accurate answer if you were able to use a code search tool rather than reading every source file from top to bottom. For that kind of tasks (and there are many of those!), I don't see why you would expect something fundamentally different in the case of LLMs.
But why not provide the search tool instead of being an imperfect interface between it and the person asking? The only reason for the latter is that you have more applied knowledge in the context and can use the tool better. For any other case, the answer should be “use this tool”.
To be fair it's also more likely to mess up than I am, but for reading search results to get an idea of what the code base looks like the speed/accuracy tradeoff is often worth it.
And if it was just a search tool this would be barely worth it, but the effects compound as you chain more tools together. For example: reading and running searches + reading and running compiler output is worth more than double just reading and running searches.
It's definitely an art to figure out when it's better to use an LLM, and when it's just going to be an impediment, though.
(Which isn't to agree that "context engineering" is anything other than "prompt engineering" rebranded, or has any staying power)
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This has been happening to developers for years. 25 years ago it was object oriented programming.
or agile and scrums.
He doesn't even take responsibility for it, but claims the board told him to do that.