BRM

Normally we decline deals like this. Unclear rules. Conflicting info. Too risky. With structure: → separated confirmed / uncertain / unknown → built contract to absorb risk → Deal went through. AI fails because everything is mixed. See this ↓ github.com/continuity-mod…

@VHeadd71906 yeah this feels right but also sometimes even with context, it still comes out “generic” not because it lacks info, but because it drifts slightly off intent so it looks correct, just not exactly what you meant

The best AI prompts aren't short—they're specific. Context beats clever wording every time. Tell it: role, audience, constraints, format. That's the difference between generic output and exactly what you needed.

@glubo11 “Everything looks real, but nothing feels certain.” That’s new. AI doesn’t just generate images— it blurs the boundary people used to rely on.

being a kid in the ai age must be so weird as a child even crappy cgi looked real it must feel weird to grow up in a world where the line between whats real and whats fake is so blurred

@iuliatech “It feels weird at first” is doing a lot of work here. That “weird” is the signal. The output looks fine, but the underlying structure doesn’t match the intent. So you can get usable results— without ever fully trusting them.

I tried Google Stitch and I’m not impressed. It created the design for an app but it looks and feels weird, like those AI generated images with 6 fingers. Maybe it will get better but for now, I’ll stick with human designers.

@wingzpire_main (b) is the standard answer, but it’s only part of it. A lot of hallucinations come from the model trying to resolve internal pressure to produce a complete answer — even when it lacks grounding. So it fills the gap instead of stopping.

Do you know the correct answer related to "AI"? Difficulty: MEDIUM Q. In the context of Large Language Models (LLMs), what is the phenomenon of 'HALLUCINATION'? (a) System overheating (b) Fabricating false information (c) Recursive self-learning (d) Emotional mimicry #AI

@BrightAcco46879 @grok Interesting — how are you defining “hallucination-free”? In practice it’s less about eliminating it completely, and more about controlling when the model is allowed to assume vs verify. That boundary is where most systems break.

🚀 Launch AI SaaS today — no weeks of coding! My open-source React + Gemini boilerplate: hallucination-free. ✅ Dark UI ✅ API ready ✅ Anti-halluc prompts Ship fast 👇 selar.com/2k323e529g #IndieHacker #AISaaS @grok #ReactJS #Gemini #LegalTech #BuildInPublic

@WellnessCoreAI The dangerous part isn’t just the error rate. It’s that the output often looks structured and confident, so it passes as correct — even when it’s not grounded or verified. That’s why hallucination is so hard to catch in practice.

General AI chatbots have a hallucination rate of up to 30% on medical questions. That is unbelievably dangerous. Stop guessing. WellnessCore AI analyzes your documents against verified clinical data for precision, not fiction. app.wellnesscore.ai/chat #PatientSafety #MedicalAI

@FinanceDirCFO Honestly, same thing happens with AI. It can sound smarter than most people just by structuring things well — even when the underlying reasoning isn’t actually tracking the intent or verifying anything. Form can easily outshine substance.

Agreed... My working assumption when someone says "ChatGPT told me this" is that I'm speaking with an imbecile who is almost certainly wrong about whatever they just said...

@JamshidHormuz That sunk cost loop is usually a signal that the reasoning structure broke, not just the prompt. Switching modes — treating it as a diagnosis task rather than a generation task — often breaks the cycle faster.

I'm finding devbots operate in a strangely frustrating way: - They often do things way faster than I could - But when they fails, it can drag on for a long time, and I keep retrying because of sunk cost, ("one more prompt will get it through.") In the end I often have to do it by hand then. The overall speedup is much less than it should be because of the wasted time.

@AKay19_ That loop is usually a symptom of something upstream. There's a free diagnostic mode that helps identify what's actually causing it before burning more credits.

Sometimes I think some AI hallucination loop after certain iterations, in tools like Claude/Cursor is intentionally designed to waste more credits. I have noticed multiple times, two to three greps could have solved a thing but these tools took longer path getting stuck later on

@HiddenDiscount Context engineering helps. But the gap persists because the reasoning structure itself doesn't change per task. Same context, same mode — different tasks need different reasoning frameworks applied on top.

Everyone upgraded from prompt engineering to context engineering. The results gap didn't close. Here is why context engineering still fails and what the actual next layer is. @robert.shane.kirkpatrick/2edad856b09e" target="_blank" rel="nofollow noopener">medium.com/@robert.shane.…

@VirtualKenji The router logic makes sense for retrieval. Same principle applies to reasoning — different tasks need different reasoning structures, not the same mode applied to everything. That's where the inconsistency comes from.

Your Claude keeps forgetting shit? Not a memory problem, but a retrieval problem. I've been obsessed with building a content system on Claude that writes by itself and learns from its mistakes. 250 sessions, 126 error files, 65 lesson files, 16 GitHub repos analyzed, and a daemon (auto-writer) that writes tweet drafts at 2 AM. Last night the daemon wrote tweets bragging about how I put auto-fire rules into CLAUDE md. The problem: This was the exact thing I spent all day removing! My auto-writer had zero awareness of what I fixed this week... it made me look like a dumbass for shilling the (wrong) implementation I spent a whole day fixing. Root cause: my system had ONE retrieval method. Glob for file names. Grep for text. That's it. My semantic search tool (qmd — hybrid BM25 + vector embeddings + LLM reranking) had been stale for a month. 675 of 5,058 files indexed. 13% visibility. The most powerful search tool in my vault wasn't firing. The fix is a router, not a waterfall: Query type → tool Known file path → Read directly File name pattern → Glob Exact text match → Grep "What's relevant to X?" → qmd vsearch Cross-domain patterns → qmd query (full pipeline with reranking) Open-ended exploration → Agent subprocess No waterfall. No "try this first, then fall back." Match the tool to the query type. Period.

@OVinh9892 @YOM_Official @mwx_ai Exactly. And indistinguishability gets worse when the reasoning has no structure — the model can't separate what it knows from what it's inferring. Structured reasoning modes change that.

The real problem isn’t AI hallucination It’s indistinguishability Truth and noise now look identical That breaks human judgment at scale Verification becomes the new interface Tracking @YOM_Official and @mwx_ai #AI #Crypto #Data #InfoFi

This isn’t about better prompts. It’s about structuring how AI thinks. No special environment needed. Works with ChatGPT, Gemini, Claude — even Copilot. → stable thinking stack github.com/continuity-mod…

Uncertainty is not the problem. Structure is. - separate facts / assumptions - define context - control variables Same AI. Same data. Different structure → different outcome.

AI doesn’t fail when it’s uncertain. It fails when uncertainty is unstructured. - too many variables - no clear context - no reliable basis This is where everything breaks. #AI #LLM #SoftwareEngineering #SystemDesign

“Looks good” is how broken work gets shipped. #AI #LLM #SoftwareEngineering #Dev #AIFailure

Fix: Stop auto-improving. Start controlled review. Why change? What happens if not? Is original better? If you can’t answer, don’t touch it. github.com/continuity-mod…

You wrote something intentional. AI reviewed it. Now it’s: softer shorter worse “Looks good 😊” …what? Unverified improvement destroys work. #AI #LLM #AIWriting

We didn’t get better answers. We got better decisions. Same model. No API. No setup. Prompt engineering didn’t fix it. Structure did. From real work. See real cases ↓ github.com/continuity-mod… #AI #LLM

We didn’t get a better answer. We got a better decision. Same model. No special setup. Different structure. #AI #LLM