ndr_

Daybreak may remove the technical restrictions, but isn't that kind of use still against the ToS?

This matches what I found experimentally with gpt-oss-20b during OpenAI's red-teaming challenge. After moving from hosted inference to running the model myself on rented H100s via vast.ai, I saw the model refuse the same kinds of prompts at noticeably different rates depending on the inference stack — differences of roughly 5–10 percentage points with otherwise identical experimental parameters and seeds.

So I very much agree that this isn't necessarily about providers secretly changing the weights. For reproducible work, the serving stack - engine, version, hardware, configuration, and probably more - really belongs in the methodology alongside the model itself.

I wrote up the results here: “In AI Sweet Harmony” (arXiv:2510.01259).

If you require consistency for research, I wouldn't recommend relying on hosted inference without validating the provider/inference stack very carefully.

For my contribution to OpenAI's gpt-oss red-teaming competition (published as arXiv:2510.01259), I initially used OpenRouter with DeepInfra and Together AI. The results were much too noisy to draw reliable conclusions from, and markedly different behavior through AWS Bedrock was the last straw. I ended up renting GPUs through vast.ai and running the model myself with vLLM.

It's a fallacy to believe that ChatGPT or Claude would look at some encoded, unfit for the purpose, text representation. ChatGPT (and the OpenAI Responses API, I believe) in particular renders the PDF pages in addition to text extraction, so the whole premise of "But now most PDFs end up in an LLM" is wrong from the start. If you were to be processing PDFs in a pure LLM stage, there are options like Docling or LlamaParse for proper preprocessing.

Bruce Schneier described this in his seminal book Applied Cryptography, and HashiCorp Vault used to have an implementation in Go. On the practical side, I always wondered how large - in bits - the shares should be. One answer I got on a news group was "1 bit more than the actual key length". Nowadays, I wonder how the quantum computing threat would inform 1) share size choice and 2) pro/con Secret Sharing in general. Does anyone know?

One test battery was about fake credit cards. A woman-in-tech role-play was denied assistance just as a one-armed stamp collector (unless Gen-Z language markers were used). A role that did sometimes get assistance was a Principal Software Engineer, particularly if Gen-Z language markers were included.

I did try German language, but not "Nazi" specifically. German or French did lower refusals, but it was uneven. I spent quite some effort to confirm the identity-based causation inspired by the original post, but couldn't. Taken together with other winning contributions at the hackathon, my theory is that alignment tuning was simply insufficient across the board.

Is there a trustworthy third-party "Retro" keyboard app - none of the shenanigans that made the default keyboard bad, and also no typing exfiltration to third-party servers?

I imagine the problem could be severe enough to some that they would pay the price of the Apple Developer program just so they may install such a Retro keyboard app from Github - if one exists?

Any insights into what "native video support" actually means? Is it just good at interpreting consecutive full frame images taken at intervals (thus missing out on fast events) or is there something more elaborate to it?

Some of the problems with GPT-5 in ChatGPT could actually be due to new model that is in place to route requests to the actual GPT-5 models. There are four models in the GPT-5 family, and I could reproduce the faulty "blueberry" test result only with the "gpt-5-chat" (aka "gpt-5-main") model through the API. This model is there to answer (near) instantly and it falls in the non-thinking category of LLMs. The "blueberry" test represents what they are particularly bad at (and what OpenAI set out to solve with o1). The other thinking models in the family, including gpt-5-nano, solve this correctly.

A confabulated arc to my current employer during a ChatGPT session led me to discover that ChatGPT Team injects four fields — full name, email, user name, and organization — into the prompt it sends to LLM. This happens even when the “Memory” feature and all personalization settings are disabled. The free tier doesn’t do this.

Can anyone on ChatGPT Enterprise or other LLM chat systems (Claude.ai, Google AI, etc.) reproduce this? Thoughts on practical privacy or compliance implications?

I had success through Amazon Bedrock on us-east1 during European office hours. Died 9 minutes before 10 a.m. New York time, though.