frontpage.
newsnewestaskshowjobs

Open Source @Github

fp.

Carrier Pidge – iOS messaging that travels at 100mph

https://www.tomshardware.com/software/applications/rogue-ios-developer-creates-app-that-simulates...
1•jtwaleson•45s ago•0 comments

Dunx – NestJS architecture for Bun without reflect-metadata

https://petarzarkov.github.io/dunx/#/
1•flaery•2m ago•0 comments

A receipt printer exposed to the internet, inviting people to send it things

https://www.reddit.com/r/selfhosted/comments/1vpskku/i_put_my_receipt_printer_on_the_internet_sen...
1•FinnKuhn•4m ago•0 comments

Climbing Guide as a Shared Infrastructure

https://irz.fr/en/articles/openclimbing-open-guide-en/
2•zbycz•5m ago•0 comments

Android Strings XML Translation Online: The Tool That Preserves Your Variables

https://locafileai.com
1•phobopt•5m ago•0 comments

Show HN: HN Glance – a visual reader for Hacker News links and discussions

https://hnglance.com/top
2•valentinvieriu•7m ago•1 comments

We cut RAG costs 5x without losing quality

https://trpevski.com/blog/scaling-rag-chunking-reranking-and-cost-optimization/
1•dzugumot•7m ago•0 comments

Why Chinese Citizens Are More Optimistic About AI Than Americans

https://www.bloomberg.com/news/articles/2026-08-14/why-ai-optimism-is-so-much-higher-in-china-tha...
2•helsinkiandrew•8m ago•1 comments

A Generative Cramér-Rao Bound

https://arxiv.org/abs/2203.03695
1•hasley•9m ago•1 comments

AI Paper Trail: What does AI know about you?

https://proton.me/lumo/ai/ai-paper-trail
1•Gys•11m ago•1 comments

How to Invest in AI (2025)

https://reactionwheel.net/2025/09/how-to-invest-in-ai.html
1•rzk•11m ago•0 comments

Twitch Adds Opt-Out for Amazon AI Training After Enrolling All Creators

https://www.ghacks.net/2026/08/14/twitch-adds-opt-out-for-amazon-ai-training-after-enrolling-all-...
1•01-_-•11m ago•0 comments

Anthropic shares more details about how Claude's new watermarks will work

https://techcrunch.com/2026/08/15/anthropic-shares-more-details-about-how-claudes-new-watermarks-...
2•01-_-•12m ago•0 comments

Secrets live in six places and one of them is a DM

https://anthony.dev.profullstack.com/blog/009-post.html
1•buffer_overlord•12m ago•0 comments

A Taxonomy of Moats (2019)

https://reactionwheel.net/2019/09/a-taxonomy-of-moats.html
1•rzk•15m ago•0 comments

A diagnostic for the "be more proactive" performance review

https://medium.com/@mike.salari.dev/be-more-proactive-take-more-ownership-72a07c24232a
1•salari_dev•18m ago•0 comments

Show HN: Sol-Luna – adaptive Codex orchestration that can choose zero workers

https://github.com/mahadansar/sol-luna-orchestrator
1•mahadansar•18m ago•1 comments

The Experience Feedback Loop

https://peterbloem.nl/blog/experience-feedback
1•riskone•20m ago•0 comments

Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework [pdf]

https://15721.courses.cs.cmu.edu/spring2016/papers/p743-leis.pdf
1•ksec•21m ago•0 comments

Amiga OS (UAOS)

https://github.com/stevereaver/uaos
2•doener•24m ago•0 comments

Show HN: My queue model made in a game

1•Lluis_0x•25m ago•0 comments

TestFlight for Steam

https://userplay.io/
1•0xnyn•29m ago•0 comments

What if you could build your SaaS without paying upfront?

https://www.indiehackers.com/post/what-if-you-could-build-your-saas-without-paying-upfront-908423...
1•AutFlowStudio•30m ago•0 comments

Keep It Up – Cookie Clicker, for Uptime

https://simpleobservability.com/games/keep-it-up
1•khazit•32m ago•0 comments

Young people don't trust AI – or the billionaires that tell us we should love AI

https://www.techradar.com/pro/young-people-increasingly-dont-trust-ai-or-the-billionaires-that-ke...
2•ksec•34m ago•0 comments

RVA23 vs. ARMv9 a Small Experiment

https://gist.github.com/camel-cdr/3a7aed17e017e8cab675ad696c7d14af
2•birdculture•35m ago•0 comments

Schism Tracker

https://schismtracker.org/
2•Bluestein•38m ago•0 comments

Show HN: The Data Forge – Generate up to 100K rows of JSON/CSV/SQL test data

https://zlvox.com/tools/data-forge
1•zlvox•39m ago•0 comments

Show HN: Customhouse – deterministic MCP proxy that blocks agent exfiltration

https://github.com/vineetpant/customhouse
1•vineetpant•43m ago•1 comments

SSOG: Near linear Visual-Attention that doesn't score but steers

https://www.pisoni.ai/posts/ssog/
1•4rtemi5•45m 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.