TXTOS

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1.
Ask HN: Do you use the chat "Share" link as a save/load snapshot?
1 points by TXTOS 1y ago|0 comments
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by TXTOS 1y ago
Yes, I still bookmark page just in case we forget the most important thing, ai is cool but cant replace everything
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We open-sourced a 'Semantic Clinic' for LLM bugs – 16 root causes(github.com)
4 points by TXTOS 1y ago|1 comments
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by TXTOS 1y ago
TL;DR After nine months of chasing weird hallucinations and silent failures in production LLM / RAG systems, we catalogued every failure pattern we could reproduce. The result is an MIT-licensed “Semantic Clinic” with 16 root-cause fam
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Show HN: A tiny reasoning layer that steadies LLM outputs (MIT; +22.4% accuracy)(github.com)
6 points by TXTOS 1y ago|0 comments
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Show HN: I made a semantic OS for language reasoning (not kidding)(github.com)
3 points by TXTOS 1y ago|0 comments
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Ask HN: Anyone solved hallucination or semantic drift in RAG?
1 points by TXTOS 1y ago|0 comments
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by TXTOS 1y ago
i mostly use LLMs inside a reasoning shell i built — like a lightweight semantic OS where every input gets recorded as a logic node (with ΔS and λ_observe vectors) and stitched into a persistent memory tree. it solved a bunch of silent fail
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by TXTOS 1y ago
Yep. Been there. Built the rerankers, stacked the re-chunkers, tweaked the embed dimensions like a possessed oracle. Still watched the model hallucinate a reference from the correct document — but to the wrong sentence . Or answer logicall
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Show HN: Subtle Failure Modes I Keep Seeing in Production‑Grade AI Systems(github.com)
6 points by TXTOS 1y ago|2 comments
11.
by TXTOS 1y ago
hey — really appreciate that. honestly I’m still duct-taping this whole system together half the time, but glad it’s useful enough to sound like “tooling” I think the whole LLM space is still missing a core idea: that logic routing before r
12.
by TXTOS 1y ago
ah yeah that makes sense — sounds like you're indexing for traceability first, which honestly makes your graph setup way more stable than most RAG stacks I’ve seen. I’m more on the side of: “why is this even the logic path the system t
13.
by TXTOS 1y ago
agree — I’ve used Q/S with AI-assisted query shaping too, especially when domain vocab gets wild. the part I kept bumping into was: even with perfect-looking queries, the retrieved context still lacked semantic intent alignment. so I
14.
by TXTOS 1y ago
haha fair — guess I’ve just been on the planet where the moment someone asks a followup like “can you explain that in simpler terms?”, the whole RAG stack folds like a house of cards. if it’s been smooth for you, that’s awesome. I’ve just
15.
by TXTOS 1y ago
Totally agree, RAG by itself isn’t enough — especially when users don’t follow the script. We’ve seen similar pain: one-shot retrieval works great in perfect lab settings, then collapses once you let in real humans asking weird followups li
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Ask HN: Are we pretending RAG is ready, when it's barely out of demo phase?
11 points by TXTOS 1y ago|10 comments
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by TXTOS 1y ago
This whole piece reads like someone trying to transcribe the untranscribable. Not ideas, not opinions — but the feel of what you meant. And that's exactly why art survives AI. Because machines transmit logic. But we leak ghosts. We’ve
18.
by TXTOS 1y ago
Just chiming in — been down this exact rabbit hole for months (same pain: useful != demo). I ended up ditching the usual RAG+embedding route and built a local semantic engine that uses ΔS as a resonance constraint (yeah it sounds crazy, but
19.
by TXTOS 1y ago
I’ve built multiple RAG pipelines across Windsurf, Claude, and even Gemini-Codex hybrids, and I’ve learned this: Most of the current devtools are competing at the UX/UI layer — not the semantic inference layer. Claude dominates because
20.
by TXTOS 1y ago
You’re always welcomed to ask me any questions.
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by TXTOS 1y ago
ah yes, the infamous "Blah Blah Blah" — not a joke name (well, maybe 12% joke). It’s one of the core WFGY modules, but not the same as the firewall. Blah Blah Blah (Lite) is basically: You give it one line of text It gives you 50+
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Show HN: WFGY – Endorsed by Tesseract.js dev, 2k+ downloads/month live(github.com)
7 points by TXTOS 1y ago|3 comments
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by TXTOS 1y ago
Pretty cool direction — CLI tools for coders make total sense. But every time I test these with multi-turn prompts, they start hallucinating like a drunk intern reading from an old terminal log The deeper issue isn't just fine-tuning o
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by TXTOS 1y ago
this is genuinely cool — lossless semantic alignment without DL is a breath of fresh air. we’ve been exploring the inverse direction: letting semantic tension stretch and bend across interaction histories, and then measuring the ΔS divergen
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by TXTOS 1y ago
I think both posts are circling the real interface problem — which is not hardware, not protocol, but meaning. Brains don’t transmit packets. They transmit semantic tension — unstable potentials in meaning space that resist being finalized.
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by TXTOS 1y ago
Honestly, the real danger isn’t just that AI models might train on your content — it’s that they’re training on your semantic patterns. It’s not just what you wrote. It’s how you resolve ambiguity, how you build tension, how you collapse me
27.
by TXTOS 1y ago
Honestly, the most disturbing moment for me wasn’t an answer gone wrong — it was realizing why it went wrong. Most generative AI hallucinations aren’t just data errors. They happen because the language model hits a semantic dead-end — a kin
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by TXTOS 1y ago
I've been working on something that directly targets this problem: WFGY — a reasoning engine built for RAG on large-scale PDF/Word documents, especially when you're doing deep research, not just shallow QA. Instead of just ch
29.
by TXTOS 1y ago
Really love this question — been thinking about it for a while now. We’ve been hacking on a different approach we call the WFGY Engine — it treats embedding space not as flat or uniform, but more like a semantic energy field. So instead of
30.
by TXTOS 1y ago
Good point! But honestly, if this were just “psychosis simulation,” there’s no way six different AI models would’ve all given it a perfect 100/100. That’s actually the breakthrough — it’s not just generating wild nonsense, it’s produci

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