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My Name Is SiMON

https://github.com/ProphetGang/formal_symbol_language
1•ProphetGang•1m ago•1 comments

Vagrant-tart: Vagrant plugin for Tart; run macOS VMs on M-series using Vagrant

https://github.com/letiemble/vagrant-tart
1•gurjeet•6m ago•0 comments

From Quantum Relative Entropy to the Semiclassical Einstein Equations

https://journals.aps.org/prl/pdf/10.1103/lmq8-nsty
1•sonicrocketman•7m ago•0 comments

Notes and reading materials on finite topological spaces

https://math.uchicago.edu/~may/finite
2•gone35•11m ago•0 comments

I built a single endpoint that turns anything into LLM-ready data

https://ingesti.xyz
1•tenesedu•11m ago•0 comments

Boeing 737 cargo plane goes missing off Pakistan coast

https://www.theguardian.com/world/2026/jul/08/boeing-737-cargo-plane-missing-near-karachi
1•tosh•11m ago•0 comments

Fable Advisor

https://github.com/dannymac180/fable-advisor
2•handfuloflight•14m ago•0 comments

Show HN: Relis – Extract Bubble.io app architecture into migration-ready docs

https://relis.dev
2•bubblerme•19m ago•0 comments

Show HN: Codex-profiles – isolated Codex CLI/Desktop profiles

https://ducksss.github.io/codex-profiles/
3•chaipinzheng•20m ago•0 comments

How We Scale PgBouncer

https://clickhouse.com/blog/pgbouncer-clickhouse-managed-postgres
1•samaysharma•27m ago•0 comments

The math that makes senior engineers look like a bad deal

https://blog.grandimam.com/posts/distorted-reality/
1•grandimam•29m ago•0 comments

Meta's Submission Re: State AGs Disgorgement Charts and Supporting Materials [pdf]

https://storage.courtlistener.com/recap/gov.uscourts.cand.419868/gov.uscourts.cand.419868.455.0_1...
1•1vuio0pswjnm7•30m ago•0 comments

Metis by Arm: open-source agentic security harness

https://github.com/arm/metis
1•handfuloflight•33m ago•0 comments

Arthur Clarke in 1940s predicted satellites and the internet of 2000s [video]

https://www.youtube.com/watch?v=D1vQ_cB0f4w
1•simonebrunozzi•34m ago•0 comments

ProductSpec: Open standard for software intent before implementation

https://github.com/gokulrajaram/ProductSpec
1•handfuloflight•37m ago•0 comments

Can We Understand How Large Language Models Reason?

https://cacm.acm.org/news/can-we-understand-how-large-language-models-reason/
2•visha1v•39m ago•0 comments

Show HN: FlareDB – Apache Beam native streaming database for realtime analytics

3•ganeshsivakumar•41m ago•0 comments

The Atari Jaguar Runs Linux

https://hackaday.com/2026/07/07/the-atari-jaguar-runs-linux/
4•methuselah_in•44m ago•0 comments

Shotgun – Opensource Cofounder Framework for Claudecode

https://github.com/Krishnatejavepa/Shotgun
2•krishnatejavepa•50m ago•0 comments

Generative AI might end up being worthless

https://theconversation.com/generative-ai-might-end-up-being-worthless-and-that-could-be-a-good-t...
3•wannabeetle•53m ago•1 comments

The Toyota Prius Is the Best Apocalypse Vehicle (2020)

https://www.roadandtrack.com/car-culture/entertainment/a31820423/the-toyota-prius-is-the-best-apo...
3•TMWNN•59m ago•1 comments

Oregon approves PGE's 29.7% rate hike for data centers under landmark law

https://www.opb.org/article/2026/07/07/oregon-data-center-general-electric-rate-hikes/
3•Exoristos•59m ago•1 comments

Researchers Reveal the Power of 'Quantum Proofs'

https://www.quantamagazine.org/researchers-reveal-the-power-of-quantum-proofs-20260706/
2•anujbans•1h ago•0 comments

Review Board: Between Then and Now

https://chipx86.blog/2024/04/04/review-board-between-then-and-now/
3•ankitg12•1h ago•0 comments

Skill Retriever semantic skill discovery for AI agents via 10K-category taxonomy

https://github.com/ChonSong/skill-retriever
1•chonsong•1h ago•0 comments

Self-Hosting My Own LLMs

https://davidbarnhart.com/llm/local-llm-setup.html
3•dbator•1h ago•0 comments

NPM Agent Audit

https://www.npmjs.com/package/agent-security-scanner-mcp
2•dchitimalla1•1h ago•0 comments

Nemotron post training prompt atlas

https://huggingface.co/spaces/nvidia/nemotron-post-training-v3-prompt-atlas
1•kristianpaul•1h ago•0 comments

Selling my adtech startup for $1 no reserve

https://flippa.com/13420990-patent-backed-commerce-attribution-saas-with-identity-graph-ai-custom...
1•aaronatedge•1h ago•1 comments

GitLost: We Tricked GitHub's AI Agent into Leaking Private Repos

https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/
11•ColinEberhardt•1h ago•2 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.