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The great software IP writedown

https://martinalderson.com/posts/the-great-software-ip-writedown/
1•martinald•53s ago•0 comments

Project Detroit: From Foreign Libraries to Foreign Language Runtimes

https://inside.java/2026/11/04/devoxxbelgium-project-detroit/
1•ejboy•3m ago•0 comments

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1•andsoitis•5m ago•1 comments

The Overton Window: How Unthinkable Ideas Become the Norm

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Super Fast Linux Photo Viewer

https://licasa.pics/
2•UncleJo7•9m ago•0 comments

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'Starlog' Magazine Returns with First Cover in 17 Years

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1•rdmuser•16m ago•0 comments

U.S. Cleared Nvidia Chips to Chinese Firms with Empty Offices and Troubling Ties

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https://github.com/ozkl/ruxis
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Piezoelectric toothbrush reduces plaque

https://www.science.org/doi/10.1126/sciadv.aeg5744
1•Gaishan•27m ago•0 comments

Ask HN: What are you working on? (November 2026)

9•david927•32m ago•5 comments

Surnames

https://dogdogfish.com/blog/2026/11/08/surnames/
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Microseason.app

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Future startup ideas, excavated from science fiction

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Welcome to Boulder Ruby

https://boulder-ruby.org/
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Nvidia launches investigation into CEO's affair with suspected Chinese spy

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There Is No Place in a Functional Society for Elon Musk

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6•HotGarbage•46m ago•0 comments

UK Government 'looking seriously' at banning under-16s from Vinted and eBay

https://www.bbc.co.uk/news/articles/cmzxjvpqrjxro
4•Lio•46m ago•3 comments

Making profile-guided optimization reproducible

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Mstein – Ich klau den RAM

https://www.youtube.com/watch?v=8kd67EQaieU
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Nvidia's Jensen Huang admits to affair with Chinese spy

https://www.ft.com/content/1397e2f4-d7f5-4cbb-a4d9-04ecf661d1aa
9•zaki_01•49m ago•1 comments

You can no longer (un-)star archived GitHub repositories

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2•mmsc•51m ago•0 comments

Map of Science

https://sciencemap.eto.tech/
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Save Our Screen: Generated Art for Samsung Frame TVs

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Ask HN: Anyone here does cold email?

1•dontoni•53m ago•0 comments

Meta's Charm gadget carries CEO Zuckerberg's big AI ambitions

https://tech.yahoo.com/ai/meta-ai/articles/metas-charm-gadget-carries-ceo-043955979.html
1•gmays•54m ago•0 comments

What Anthropic's IPO Filing Says About the Future of A.I

https://www.nytimes.com/2026/10/30/opinion/anthropic-ipo-ai.html
3•edward•55m 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.