medium is not the message, but certainly is an attractor of the messages of a certain kind
https://open.substack.com/pub/reinventscience/p/revive-the-republic-of-letters

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

TLDR: to exercise agency you need to understand what's around you and the rules of the game, both explicit and implicit ones

https://arxiv.org/abs/2506.01622

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

beautiful and sad

https://open.substack.com/pub/sorhed/p/are-diamonds-forever

CERN Animal Shelter for Computer Mice
https://computer-animal-shelter.web.cern.ch/index.shtml

chat apps will become history
your artificial limbs and senses should feel real
https://www.nature.com/articles/s44287-025-00218-x.epdf

Physical labs are the next frontier and the ultimate data source for the future AIs.

Grounded in real experiments and observations, not interpretations from the web and the books but genuinely /new/ data.

Watch this space.
https://x.com/liamfedus/status/1973055380193431965?s=46

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

TLDR: full stack chatgpt (training, inference, etc etc) in one 8K LOC repo
https://github.com/karpathy/nanochat/discussions/1

attributing this to AI models and ignoring the "first using the simulation" part is totally unfair to the simulation developers.
Still cool!
https://blog.google/technology/ai/google-gemma-ai-cancer-therapy-discovery/

DIY 2 billion frames per second camera, with SIMPLE explanation of how it works. Really.
https://www.youtube.com/watch?v=o4TdHrMi6do

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/

turns out, human memory is quite editable (at least it's possible to vary the brightness of individual memories)
https://www.nature.com/articles/s41588-025-02368-y

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

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

https://x.com/lundukejournal/status/1988346904581726501?s=46 no silver bullet for security

More paranoia for the paranoid out there ^_^

Timers are a reliable side channel for communicating between containers on the Linux machine, via /proc/self/ns/time.

https://h4x0r.org/funreliable/

Claim: Isotropic Gaussian Regularization for latent representations in the world models is mathematically optimal

What's illustrated:
-- Adopting the isotropic gaussian regularization replaces stop-grad, teacher-student, EMA and various other adhoc tricks
-- Improves model training stability
-- SOTA quality on 10+ datasets and 50+ architectures
https://arxiv.org/abs/2511.08544, https://github.com/rbalestr-lab/lejepa

Realized that privacy is important but not enough. And "assistants" are def not the answer either.

https://open.substack.com/pub/cortex/p/gentian-the-second-wind?r=1clcn&utm_campaign=post&utm_medium=telegram

lol perfect timing 😅 4h later Pavel Durov announced Cocoon: https://news.1rj.ru/str/durov/462

my 2c: it is a fine business, sadly only a small part of what Anima, Cortex and the minds need.
Gentian proxy is still required, etc etc.
They do acknowledge the limitations of RA-TLS and their model in general though, which is commendable.