CalvusRex

joined 2 months ago
[–] CalvusRex@lemmy.world 4 points 1 day ago

@queerlilhayseed@piefed.blahaj.zone Thanks for the positive response! You basically named the two hurdles that ate most of my development time on my projects system.

On the counting/nondeterminism thing: the trick was giving up on the LLM as a "knower" entirely and treating it as nothing more than a reader. So if I want to know how many times grass shows up in some sprawling Tolkien-esque passage, I'm not asking the model "how many times does grass appear" and hoping for the best. I use it to pull out and tag the relevant sentences into a structured database, and then a dumb, boring, deterministic Python script does the actual counting. Temperature's pinned at 0, and I force it to hand back verbatim quotes with citations instead of letting it synthesize a summary. That alone killed most of the hallucination problem.

The "systemic wrongness" side (bias, basically the model just inventing stuff) came down to hybrid search, meaning keyword/BM25 alongside vector embeddings. Pure semantic search will absolutely whiff on a weirdly named character or a specific term just because it doesn't "feel" semantically close to the query, so having the keyword layer as a backstop matters a lot. And I'm strict about grounding: no direct source quote from retrieval means the system says "I don't know" instead of guessing. It's not allowed to fill gaps with vibes.

Your instinct about cataloging prompts was right on too. I keep a golden dataset of queries I run every time I touch the system, and rather than some vague overall correctness score, I bucket failures by type: was it a retrieval failure or a synthesis failure. That distinction alone tells you whether the fix is in your chunking strategy or your prompting, instead of just flailing at both.

Honestly the tools have come a long way since you last looked at this stuff, and I think it's mostly a philosophy shift. People stopped treating LLMs like databases and started treating them like interfaces sitting in front of one.

 

The Long Century is a history site focused on the United States from 1850 to 1900, a period of upheaval, conflict, and rapid change. The site looks at the Civil War, Reconstruction, westward expansion, labor unrest, politics, industrial growth, and the social pressures that shaped the country in the late nineteenth century.

The goal is to make this era readable without flattening it. These articles are meant to be long-form, direct, and grounded in the people and events that made the period. Some pieces focus on major turning points. Others look at smaller subjects, forgotten figures, or details that are usually left out of the standard summary.

 

Most people I know will read this and have a blank stare. For me, this is kind of what keeps me going.

My degree is in History. I'm getting enrolled to work on my MA in History. I am also considering a side business that involves my passion for history, especially the American Civil War, and technology. These books you see here are just a sample of books I will have in a system called RAG. Retrieval-Augmented Generation (RAG) system grounds Large Language Models (LLMs) in external data. Instead of relying solely on an AI's training data, RAG retrieves relevant documents (like company policies or recent files, or in this case, details in these books), appends them to your prompt, and directs the AI model to generate a highly accurate, fact-based response.

It will be a TON of work creating, but that is part of the enjoyment. Yep... I'm an old nerd. 🤣🤓

[–] CalvusRex@lemmy.world 32 points 6 days ago (3 children)

10 years ago I would have said maybe. Now? Practicly everything that you run on Windows will run, in some form or another, on Linux. Even the hard to crack gaming is now becoming a non issue. There really is no point in Windows any longer.

 

I like the #smallweb the way it is. Quiet, a little rough around the edges, not trying to sell me anything every five seconds. It reminds me of how the internet used to feel before everything got optimized and polished into the same shape.

That said, there are a few things it could use without losing that spirit.

First, better ways to read. Not fancier, just smoother. If I’m following a handful of gemlogs, a couple Mastodon folks, maybe a Lemmy thread here and there, I shouldn’t have to juggle three different apps and a dozen tabs. Give me something simple that pulls it all together and lets me just sit and read. No ads, no tracking, no friction.

Second, a little more durability. Too many good sites just disappear. I get it, people move on, but it would be nice if there were easier ways to mirror or archive things so the good writing doesn’t vanish overnight. The small web has a memory problem.

Third, discovery that doesn’t feel like an algorithm breathing down your neck. I don’t want “recommended for you.” I want “here’s what someone else thought was interesting.” Old-school blogrolls, human-curated lists, maybe even random links that actually surprise you. Let people point to things they care about, not what performs well.

And maybe this is just me, but a bit more cross-connection wouldn’t hurt. Gemini, Gopher, the web, the #fediverse all feel like neighboring towns that don’t always have good roads between them. You can get there, but it takes effort. Smoother bridges would go a long way.

None of this needs to be big or complicated. In fact, it shouldn’t be. The whole point is to keep things human-scale. But a few small improvements could make it easier to stick around, read more, and maybe even contribute something back without feeling like you need to build a whole platform just to say your piece.

That’s really all I want out of it. A place that’s simple, a little more connected, and worth coming back to at the end of the day.