https://help.openai.com/en/articles/20001530-getting-started...
188,310 karma · joined May 4, 2010
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https://help.openai.com/en/articles/20001530-getting-started...
Anthropic releases models as open weights + more information about how they do training and alignment
> 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?
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
and found Astra ~30% faster and at similar cost to Sol for the same outcome
https://x.com/__tosh/status/2096201900555170032
the token efficiency helps Astra even though sticker price is 2.5x that of Sol
sol is $4 / $20
how?
replit is already leading the way with free luna usage
and if you want to see what a simple harness implementation looks like you can just dive into the code and ask your favorite agent about it
I don't know any harness that solves 'Garbage Collection' in the way described here
(most harnesses accelerate context pollution and code base drift via instructions they embed into system prompts, tool descriptions and skills)
Herkermer Homolka in Congo (1995)
https://www.imdb.com/title/tt0112715/
RIP
> trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board
> trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board
I hope the future for DuckDB is still bright
on toy benches it made quite a few mistakes but was able to fix all of them on its own
(meaning more tokens, more turns, more tool calls — but same outcome as gpt 5.6 sol)
it's good for linux if there is more activity on that front
there is a reason why ubuntu and fedora became so popular
build your own framework, database, operating system, game engine etc
things that used to be infeasible (too hard, too big, …)
from There's Something About Mary
- increase energy for activation (motivation, habit, …)
- make obstacle lower (break it down, find an onramp, …)
sometimes one is easier than the other
other times working on both is what makes it happen