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Linkstream
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Various links I find interesting. Mostly hardcore tech :) // by @oleksandr_now. See @notatky for the personal stuff
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avoid excess complexity and noise at all costs. mvp is not "all features of shitty quality" but "minimum features still decent quality".
or "hypothesis validation code and the commitment to rewrite"
https://minds.md/zakirullin/cognitive
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for something completely different:
first 2178 books of the Ritman Library are now digitized & online, safe from the natural disasters

https://embassyofthefreemind.com/en/library/online-catalogue/?mode=gallery&view=horizontal&sort=random%7B1517048201764%7D%20asc&page=1&fq%5B%5D=search_s_digitized_publication:%22Ja%22&reverse=0
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CERN Animal Shelter for Computer Mice
https://computer-animal-shelter.web.cern.ch/index.shtml
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very interesting!
i wonder if the approach used by the TRM/HRM models can be adapted from ARC-AGI back to other reasoning benchmarks usually done on LMs

https://alexiajm.github.io/2025/09/29/tiny_recursive_models.html
aaargh i should've wrote this paper!! it was intuitively obvious to me but then life happens >_<

tldr: LLM sampler is such a powerful prior that with the right sampler (MCMC, in this case), you can even use base models as reasoning models.
without supervised fine-tuning or RL.

this was completely ignored by ppl pilled with the Bitter Lesson mantra, but yes there still is a space for the right priors added or designed by hand!

obviously sampling with mcmc is very costly but you should compare the overall model feedback loop time that includes the posttrain, not just the sampling time

if eg topK sampling is assembly and Mirostat is COBOL (?) then MCMC sampling is like a Python in the space of samplers
https://aakaran.github.io/reasoning_with_sampling/
finally an article showing that people can perceive flickering and certain types of motion at least at 500hz

(it's kind of personal, i've been gaslighted like "hey you can't possibly see the difference" far too many times.
now at least when ppl don't believe me again I can send them this link)

https://www.nature.com/articles/srep07861
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interesting. small (321M not 300B!) and capable models aka reasoning cores are interesting both theoretically and practically
https://pleias.fr/blog/blogsynth-the-new-data-frontier