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Exa says its index serves 80B pages, targets Google scale in 2027

https://runtimewire.com/article/exa-search-index-80-billion-pages-google-scale-2027
1•ryanmerket•1m ago•0 comments

An AI-supervised remote exam went so badly that 58,000 students must retake it

https://arstechnica.com/culture/2026/08/an-ai-supervised-remote-exam-went-so-badly-that-58000-stu...
3•sbulaev•2m ago•0 comments

Incus 7.3 adds easy GPU virtualization

https://discuss.linuxcontainers.org/t/incus-7-3-has-been-released/27033
2•ropyeett•2m ago•0 comments

Arsenic, 133 times the legal limit being found in the water around Aughinish

https://www.irishpoliticsnewsletter.ie/p/aughinish-133-times-the-limit
1•IrishPoliticsNL•3m ago•0 comments

Codes, Chess and Kubrick: The Life of Jack Good – GCHQ

https://www.gchq.gov.uk/information/codes-chess-and-kubrick-life-jack-good
1•rbanffy•4m ago•0 comments

Windows XP 2002 for the Itanium: Unbridled rage

https://virtuallyfun.com/2026/08/03/windows-xp-2002-for-the-itanium-unbridled-rage/
2•jandeboevrie•5m ago•0 comments

Reverse Jevons Paradox

https://mht.wtf/post/jevons/
1•martinhath•5m ago•0 comments

Apple launches legal challenge against UK government demand to access data

https://www.theguardian.com/technology/2026/aug/03/apple-legal-challenge-uk-government-data-access
3•sega_sai•7m ago•1 comments

Rejourney Flutter Analytics in Beta: Session Replay for Flutter GPU and Impeller

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Stanford CS329A Self-Improving AI Agents – Part 1 [video]

https://www.youtube.com/watch?v=6YnLB0XbTnI
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Show HN: Use same crypto crime-fighting toolset as top law enforcement agencies

https://mcp.caudexcatena.com
1•caudena•14m ago•0 comments

Younger generations may be more cancer-prone

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Show HN: Leclaude – A little badge for your Claude Code projects

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Wall Street learns to love blockchain

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1•toomuchtodo•23m ago•1 comments

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Ask HN: Who is quitting? (August 2026)

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1•allenc•25m ago•1 comments

A pattern-recognition test for coding interviews, no signup

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Neural Amp Modeler (Nam) Architecture 2 (A2)

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1•arbayi•27m ago•0 comments

Influencers draw backlash for attending OpenAI's first luxury trip

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Existential Risk from AI: An Exposition for Mathematicians

https://alkjash.github.io/ai-risk/
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https://theinterposer.substack.com/p/telescope-to-kaleidoscope
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Base Power raises another $1B

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2•rbanffy•30m ago•0 comments

Do birds enjoy flying? Analysis of affect after flight in galah (E roseicapilla)

https://brill.com/view/journals/beh/aop/article-10.1163-1568539X-bja10370/article-10.1163-1568539...
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Testing ultra-thin display film technology in Shenzhen

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2•kaycebasques•31m 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.