71–80 of 185 posts
I'm not feeling it. Constantly pinging these yuge LLMs is not economic and not good for sensitive docs.
But don’t you think LLM pricing is heading toward zero? It seems to halve every six months. And on privacy, you can hope model providers won’t train on your data, (but there’s no guarantee)
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Almost as dull as being spoon-fed AI slop articles, yeah.
There's an idea - create a website which can accurately assess "Slop-o-Meter" for any link, kind of like what FakeSpot of old did for Amazon products with fake reviews.
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It truly is unfortunate. Thankfully most people seem to have an innate immune response to this kind of RLHF slop.
Unfortunately this can't be true, otherwise it wouldn't be a product of RLHF.
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That's fair, but how do you grep down to the right 100-200 documents from millions without semantic understanding? If someone asks "What's our supply chain exposure?" grep won't find documents discussing "vendor dependencies" or "sourcing risks." You could expand grep queries with synonyms, but now you're reimplementing query expansion, which is already part of modern RAG. And doing that intelligently means you're ba…
Classical search
The chunk, embed, similarity search method was just a way to get a decent classical search pipeline up and running with not too much effort.
Actually, let me be specific: everything from "The Rise of Retrieval-Augmented Generation" up to "The Fundamental Limitations of RAG for Complex Documents" is good and fine as given, then from "The Emergence of Agentic Search - A New Paradigm" to "The Claude Code Insight: Why Context Changes Everything" (okay, so the tone of these generated headings is cringey but not entirely beyond the pale) is also workable. Everything else should have been cut. The last four paragraphs are embarrassing and I really want to caution non-native English speakers: you may not intuitively pick up on the associations that your reader has built with this loudly LLM prose style, but they're closer to quotidian versions of the [NYT] delusion reporting than you likely mean to associate with your ideas.
[NYT]: https://www.nytimes.com/2025/08/08/technology/ai-chatbots-de...
This glosses over a fundamental scaling problem that undermines the entire argument. The author's main example is Claude Code searching through local codebases with grep and ripgrep, then extrapolates this to claim RAG is dead for all document retrieval. That's a massive logical leap. Grep works great when you have thousands of files on a local filesystem that you can scan in milliseconds. But most enterprise RAG use…
Appreciate the feedback. I’m not saying grep replaces RAG. The shift is that bigger context windows let LLMs just read whole files, so you don’t need the whole chunk/embed pipeline anymore. Grep is just a quick way to filter down candidates. From there the model can handle 100–200 full docs and jot notes into a markdown file to stay within context. That’s a very different workflow than classic RAG.
But on top of this I would also use AI to create semantic maps, like hierarchical structure of content, and put that table of contents in the context, let the AI explore it. This helps with information spread across documents/chapters. It provides a directory to access anything without RAG, by simply following links in a tree. Deep Research agents build this kind of schema while they operate across sources.
To explore this I built an graph MCP memory system where the agent can search both by RAG and text matching, and when it finds top-k nodes it can expand out by links. Writing a node implies having the relevant nodes first loaded up, and when generating the text, place contextual links embedded [1] like this. So simply writing a node also connects it to the graph in all the right points. This structure fits better with the kind of iterative work LLMs do.
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But don’t you think LLM pricing is heading toward zero? It seems to halve every six months. And on privacy, you can hope model providers won’t train on your data, (but there’s no guarantee)
I don't see how it can trend to zero when none of the vendors are profitable. Uber and doordash et. al. increased in price over time. The era of "free" LLM usage can't be permanent
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We don't want LLM-generated content on HN, but we also don't want a substantial portion of any thread being devoted to meta-discussion about whether a post is LLM-generated, and the merits of discussing whether a post is LLM-generated, etc. This all belongs in the generic tangent category that we're explicitly trying to avoid here.
If you suspect it, please use the the established approaches for reacting to inappropriate content: if it's bad content for HN, flag it; if it's a bad comment downvote it; and if there's evidence that it's LLM-generated, email us to point it out. We'll investigate it the same way we do when there are accusations of shilling etc, and we'll take the appropriate action. This way we can cut down on repetitive, generic tangents, and unfair accusations.