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RuntimeWire – Startup intelligence for the AI economy

https://runtimewire.com/
1•enjoyyourlife•2m ago•0 comments

A Manifesto for Preserving Content on the Web (2019)

https://jeffhuang.com/designed_to_last/
1•downbad_•3m ago•0 comments

Might doomscrolling cause our commitment muscles to atrophy?

https://chrisyeh.com/2026/07/commitment-is-declining-and-why-you-should-resist.html
1•chrisyeh•3m ago•1 comments

1,300 Beautiful Wildlife Illustrations from the 19th Century Now Restored

https://www.openculture.com/2026/07/explore-1300-beautiful-wildlife-illustrations-from-the-19th-c...
1•gslin•6m ago•0 comments

OpenAI and Guardian Media Group launch content partnership

https://openai.com/index/openai-and-guardian-media-group-launch-content-partnership/
1•wertyk•6m ago•0 comments

Linux kernel won't be anti-AI – Linus Torvalds

https://lore.kernel.org/all/CAHk-%3Dwi4zC%2BZe8e%2Bp3tMv8TtG_80KzsZ1syL9anBtmEh5Z40vg@mail.gmail....
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I Collect Blog Statistics, Respectfully

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Roman Empire GDP per Capita Map Shows That Romans Poorer Than Countries Today

https://brilliantmaps.com/roman-empire-gdp/
1•mathattack•11m ago•0 comments

Sony-Owned Crunchyroll Is Now Kneecapping Physical Anime Sales

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GitHub Copilot: your AI pair programmer (2021)

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Can AI build a jet engine? JARVIS Challenge tests AI copilots

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Open Source, Free Tier Capable Whispr Using Cloudflare AI

https://github.com/PrestigePvP/Voicebox
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Where Americans Thrive in Europe

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Fleet: Hierarchical Task-Based Abstraction for Megakernels on Multi-Die GPUs

https://arxiv.org/abs/2604.15379
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The Shape of Apps

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AI Coach for Creators

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Ask HN: Is consciousness fundamentally a process of preserving identity?

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You didn't live a life. You doomscrolled [video]

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No Space Like J-Space

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Is a "Phi-Zero" AI architecture viable for safe robotics?

https://github.com/GorrihmAI/fbai-nonconscious-ai/tree/main
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Running Gemma4 on Apple Neural Engine

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Reverse-engineering a forgotten 80s arcade game for the Taito SJ System

https://fippi.io/reverse-engineering-adventure-canoe/
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It may be impossible to make data centers pay 'fair share' of electricity costs

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2•derbOac•46m ago•1 comments

Ubuntu 26.04 LTS vs. Windows 11 vs. CachyOS Performance on a $5399 Laptop

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2•dcu•48m ago•0 comments

How to be grateful to someone – even when you don't want to

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1•1659447091•50m 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.