> My two favourite hypothetical questions regarding this used to be:
> If I'm running Codex and one of my API keys accidentally gets consumed in the context, what are the chances that someone else might ask for an API key in the future and get mine back? (I asked someone at OpenAI once and they called this the "regurgitation" problem and assured me that they take great pains to prevent that... but wouldn't describe how.)
> If I brainstorm with ChatGPT about potential new directions for my company, what's the chance that information might be exposed to a competitor in six months' time who asks "what might company X plan to do next"?
> My new preferred hypothetical for this is:
> If I use ChatGPT to help me partially solve a Millennium Prize problem, what are the chances that my work will influence training such that a later model helps someone else solve it first?
They wouldn't appear in weights but could be added to the context. My conversations regularly go "regarding your Java problem"... which was a separate item in the history from earlier. As long as I only see these (and nobody else sees mine), it can be helpful.
fwiw I found Astra to be faster than Sol w/ both on medium reasoning for simple agentic coding
difficult to compare though because for more open ended, complex tasks Sol might miss something that Astra notices and then Sol might yield a cheaper but worse outcome
Same experience here, but I have some strange feeling.
Sol I'm used to working a month ago doesn't feel the same I'm using today, slower and less accurate. My gut feeling is that they quantize previous models to prioritize new ones and, who nows, make the new one look better.
Up to July I was using mostly anthropic models and the feeling was the same, so much so that I was able to predict every model release 1 or 2 days before public announcements.
Anthropic releases models as open weights + more information about how they do training and alignment
reply