Comments URL: https://news.ycombinator.com/item?id=50184897

Points: 4

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Comments URL: https://news.ycombinator.com/item?id=49955626

Points: 6

# Comments: 9

Comments URL: https://news.ycombinator.com/item?id=49953606

Points: 1

# Comments: 2

After putting my iPhone in lockdown mode > disconnecting my ATT fiber PON (leaving the router on) > going to WiFi settings in iOS > disabling Connectivity Assist and crucially > “Reset Data Usage”, iOS informed me that my certificate expired in May of 2025. The “common name” was set to ‘dsldevice.domain_not_set.invalid’, which lead me to the above link.

Comments URL: https://news.ycombinator.com/item?id=49795325

Points: 1

# Comments: 0

What's neat about "safety evaluations" in LLM parlance is apparently encountering any novel information constitutes a "safety event" that can result in new reinforcement learning data... This anthropic 2022 paper that basically describes how it's a perpetual information siphoning machine with a cute little graphic https://arxiv.org/html/2212.08073 - they publish their "constitution". OpenAI has a "model spec" they somewhat regularly update that I suspect is their equivalent process. I suspect we've all been unwittingly advancing their models capabilities..

This Dec 2025 Google paper "A Practical Guide to Generating Synthetic Data With Differential Privacy" spells it out pretty clearly - the focus is on "privacy" - nothing about protecting the user's IP or unique knowledge/ insights.. https://arxiv.org/html/2512.03238v1

In OpenAI's case it seems to essentially generating synthetic training tuples from {prompt, chain-of-thought, answer} or scores on the chain of thought for reinforcement learning.

It would appear opting out of "Improve the model for everyone" didn't actually mean what we thought it meant.

I suspect anything you make in these, the thinking traces or "safety evaluations" of the content allows whatever you build to become RL or eventual pre-training data.

A .github/CODEOWNERS file seems to help when it’s going down that path, but I don’t like to indulge it..

An open weight tool call auto-reviewer, has all sorts of achievable scaling curve milestones.

I maintain my own fork of Codex for "fun". Whenever I look at the sandboxing churn they're doing every release, as someone who used to work at Microsoft on Windows, my reaction is usually: https://c.tenor.com/vTzzhTiypwQAAAAC/tenor.gif

But I’m reminded of ~2008 and the rise of “the cloud” as a marketing term that seemed to me to be a cover for dropping an expectation of rich clients, increasing a companies margins around subscriptions that would chip away at local ownership.

Then I got offput by the zealotry and absolutism around “true FoSS”, told myself I was young and moved on.

And really, a lot of subscription models I kind of can appreciate/ tolerate. Might be irksome but whatever, I get that software is expensive to make and it’s not fair in 2026 to value a yearly upgrade of Photoshop at $200. The capricious UI changes to things that’ve worked for 20 years and they take away say the classic color swatches altogether - silly and dumb.

I can use another professionally necessary tool I pay $200/ mo for, Codex, to whip up a classic swatch plugin.

Is that $200 a fair price for my token usage? I think an extremely heavy month I might’ve used a billion tokens?

But that right there is the problem. They have no idea what, specifically, profitability looks like and are going to be pulling endless levers for … I genuinely have no idea how long - at least through 2030/2032 if we tea leaves their debt obligations?

I don’t want to think about any of that. At all. I don’t want to spend time evaluating model preference and degradation and updating the nuances of how I “speak” to an AI because there’s some mystery backend experiment running on the output I use to produce functional outputs — ie the actual products I get paid to build/ maintain.

AI’s something between a tool and coworking companion, and the capricious “personality” changes due to playing with poorly understood and knobs and levers at the inference level - is maddening. To that end, I want a box in the corner I can point to and know exactly the quality of outputs that no one but myself modifies.