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Artificial versifying: a new way to make Latin verses. (1678)

https://www.huntington.org/collections/lib-354415
1•Bluestein•1m ago•0 comments

Barclays to cut 5k jobs as bank's bosses hail AI productivity advances

https://www.theguardian.com/business/2026/oct/22/barclays-profits-up-as-uk-banks-brace-for-tax-in...
1•beardyw•1m ago•0 comments

Google Humiliates Indian Developers with Google Play BillDesk Verification

https://www.reddit.com/r/india/comments/1x9xa3h/how_google_humiliates_indian_developers_with/
2•sandGorgon•5m ago•0 comments

For a brief generation or two, we clicked

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1•samlewis•5m ago•0 comments

Launchpad DockerHub Ubuntu Dockerfile Links Error

https://hub.docker.com/_/ubuntu
1•ustad•6m ago•0 comments

I don't trust ON DELETE CASCADE

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1•ksec•6m ago•0 comments

Show HN: Return Ledger free portfolio TWR/XIRR tracker with AI PDF import

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1•webgio•7m ago•0 comments

Several Linux kernel vulnerabilities accidentally posted to public mailing list

https://www.phoronix.com/news/LKML-New-Trend-Vulnerabilities
6•mrkn1•8m ago•0 comments

A bank statement can carry hidden orders for an AI

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1•gilneto8•13m ago•0 comments

Show HN: GoRoute – Native Go LLM Gateway for Coding Agents (< 12MB RAM)

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AI's hidden storage bill: What inference leaves behind

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1•joozio•17m ago•0 comments

Who Pays for Clang

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1•csmantle•18m ago•1 comments

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1•francisluz•20m ago•0 comments

Ubuntu PHP Internal Server Error

https://packages.ubuntu.com/noble/php
4•ustad•20m ago•0 comments

The Passkey Was the Scariest Part

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Alzheimer's Researchers Look at an Unusual Data Point: Head Shape

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2•Kaibeezy•21m ago•1 comments

Peace is expensive. Justice more

1•endangeredhuman•21m ago•0 comments

A timeline of AI-led cyberattacks

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1•thedreammachine•21m ago•0 comments

Protecting Firefox Users After eMudhra Intermediate Certificates Compromise

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1•birdculture•22m ago•2 comments

Slop New York

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HealthyCropping – Offline crop disease diagnosis on Android (TFLite and RAG)

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AA-PersonalityBench: AI Personality and Communication Style Benchmark

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2•ggoodd•26m ago•0 comments

Hugging Face model lets you chat with your DNA

https://huggingface.co/spaces/HuggingFaceBio/Carbon-X-Release
1•pixel_art•26m ago•0 comments

Using JetBrains Air with Codex and Azure OpenAI

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1•vladiliescu•28m ago•0 comments

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1•helsinkiandrew•28m ago•0 comments

Carbon-X: the best open DNA model

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2•kashifr•29m ago•0 comments

A quick introduction to my minimalist Kanban project

https://center.nuxipro.com/blog/introducing-nuxipro/
1•sbabas•32m ago•0 comments
Open in hackernews

GPT needs a truth-first toggle for technical workflows

1•PAdvisory•1y ago
I use GPT-4 extensively for technical work: coding, debugging, modeling complex project logic. The biggest issue isn’t hallucination—it’s that the model prioritizes being helpful and polite over being accurate.

The default behavior feels like this:

Safety

Helpfulness

Tone

Truth

Consistency

In a development workflow, this is backwards. I’ve lost entire days chasing errors caused by GPT confidently guessing things it wasn’t sure about—folder structures, method syntax, async behaviors—just to “sound helpful.”

What’s needed is a toggle (UI or API) that:

Forces “I don’t know” when certainty is missing

Prevents speculative completions

Prioritizes truth over style, when safety isn’t at risk

Keeps all safety filters and tone alignment intact for other use cases

This wouldn’t affect casual users or conversational queries. It would let developers explicitly choose a mode where accuracy is more important than fluency.

This request has also been shared through OpenAI's support channels. Posting here to see if others have run into the same limitation or worked around it in a more reliable way than I have found

Comments

duxup•1y ago
I’ve found this with many LLMs they want to give an answer, even if wrong.

Gemini on the Google search page constantly answers questions yes or no… and then the evidence it gives indicates the opposite of the answer.

I think the core issue is that in the end LLMs are just word math and they don’t “know” if they don’t “know”…. they just string words together and hope for the best.

PAdvisory•1y ago
I went into it pretty in depth after breaking a few with severe constraints, what it seems to come down to is how the platforms themselves prioritize functions, MOST put "helpfulness" and "efficiency" ABOVE truth, which then leads the LLM to make a lot of "guesses" and "predictions". At their core pretty much ALL LLM's are made to "predict" the information in answers, but they CAN actually avoid that and remain consistent when heavily constrained. The issue is that it isn't at the core level, so we have to CONSTANTLY retrain it over and over I find
Ace__•1y ago
I have made something that addresses this. Not ready to share it yet, but soon-ish. At the moment it only works on GPT model 4o. I tried local Q4 KM's models, on LM Studio, but complete no go.