I think a "reversed" south-at-the-top is a good idea to at least be aware of. I don't think it makes sense to reverse it since it just flips the problem, but it is (very slightly?) unlucky for the southern hemisphere.
Maybe this is silly: what about zero-width spaces or other hidden characters? Add them in post-processing, and the response quality doesn't degrade (though rendering etc. might be wonky).
Yes, it is silly. Stripping these is about as trivial as removing "this post was written by Claude" appended in plaintext. You could make a clipboard monitor that does this as soon as you CTRL+C, it's a 1-shot prompt. Not to mention that these wonky Unicode chars will break in every other program.
Stripping Anthropic's watermarking, however, is more difficult - probably about 2 prompts.
But your average joe won't "setup custom clipboard monitor to auto clip out whitespace". That's why Gemini still include its logo into generated picture even if it's trivial to crop it out
Ok, sounds like a watermark of “this post was written by Claude” is sufficient then. Or don’t add any at all - the average joe is that Canadian politician whose speech included “here is a more natural-flowing version of that section that sounds more like legislative speech rather than a series of short points.”
I don't hate it if it's text I asked for, in which case it's also easier to glance through looking for the points relevant to the topic.
I hate it if the assumption is that I'm communicating with a human, and I then get hit with a wall of text that I have to decipher the context behind. I'd rather read the prompt then.
Loaded it on a pixel phone, saw a list of countries with no context about what picking one meant, saw a map but didn't understand how to interact with it, and that was about it.
Hi thanks for your feedback and also sorry for confusion.
The goal is basically to to leave your opponent with no valid neighboring countries to choose on their turn. I will think about how I can improve the onboarding.
Stan and PyMC beat Noise at the thing they’re built for, fitting a posterior to lots of continuous data with their HMC/NUTS samplers, and NumPy beats it at raw array crunching. Conditioning in Noise is rejection-based, so it works great for a handful of discrete observations but becomes useless for ten thousand continuous measurements, and there is no stateful simulation yet (no Markov chains yet). Where Noise wins when you have a probability question and you wanna know the answer without much hassle.
So use Noise for the whiteboard stage of a problem, when you want to run the math you just wrote, and move to Stan or PyMC when you need a real posterior, or to NumPy and JAX when you need to go to production.
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