Byte Pair Encoding [1] will be different for different languages. Application of the per-language BPEs to the input text will produce encodings with different lengths.
It naturally takes care of common prefixes and suffixes.
It is easy and fast to apply using radix tree or with finite automata. Even without radix tree, it is possible to have processing speed in the range of hundredths of thousands of bytes per second.
34 bits of key growth resulted in resource usage growth slightly more than 2 (30 GPU-years vs 13.5 GPU-years).
Thus, it appears, that ~585 GPU years can factor 1024 bit RSA. 2.2^((1024-896)/34)=19.5, expected growth of resources' usage compared to 896 bits factorization, multiplying it by 30 GPU years for 896 bits gives about 585 GPU-years.
This will cost about $20M with Cognition AI setup.
What is you are writing, what is the problem domain? What does need tens of thousands of lines of code written each day?
Because 100 lines of (debugged, reviewed) code per day is a good speed for seasoned software engineer. I assume that you can produce more than 100 lines of something per day as a prompt.
So, what is the problem domain that requires one to write several thousands of lines of code per day?
My comment was to match productivity of human programmer to claimed productivity of their use of LLM.
If they write 2 (two) lines of code, the combined output of them with LLM, as I read it now, is 20K SLOC. This is yearly output of human programmer.
It is very much possible to write 100 lines of (debugged, reviewed) code per day for human. And these 100 lines of code would result, if we apply their LLM multiplication factor, in 50 (fifty) man-years of work!
Even if they write very little code, say, one line per month, after a year of work they will get result of about 6 (six) years of human programmer. SQLite is 155K SLOC, about 8 man-years.
And that what raised my question: what is the problem domain that requires so much code?
> all were able to find every problem planted there, with fairly little steering, and no spoilers.
Given that LLMs prefer output of LLMs (of the same LLM and of others) [1], can it be the case that they generate "hard challenges" from the manifold of challenges solvable by (other) LLMs?
My son got caught trying to fix Windows installation on my wife work computer with bootable Linux Mint USB stick.
"Digital Naive," yes, of course.
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