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search_facilityon Someone bought 30 WordPress plugins and planted a backdoor in all of them

But on the other hand, adding LLM with strong guards (not yet here but doable for popular attack vectors) into the human loop can drastically eliminate insider factor, imho.

search_facilityon Show HN: I built a tiny LLM to demystify how language models work

probably 8M params are too much even :)

search_facilityon Make macOS consistently bad unironically

Well, this is a business model, not a coincidence... They are in the battle of selling fresh hardware each and every year consistenly

search_facilityon Go hard on agents, not on your filesystem

Exactly!

search_facilityon A ngrok-style secure tunnel server written in Rust and Open Source

Interesting! Can it be used in Google Colab to open temporary access to python server? NGROK can be attached this way in 6 lines of code

search_facilityon You gotta think outside the hypercube

biology plays the role, certainly, but nature was trying to capture a model for 3D physical interactions first of all, physics first. And final choice of two 2D sensors is explicitly optimal and minimally effective for 3D - so it can not be similarly descriptive for 4D, just not fair to expect results on same level imho.

For meaningful 4D perception on similar level our body need three volumetric sensors, separated, to define volume with 4D direction

search_facilityon You gotta think outside the hypercube

Our 3D visualization relies havily on photons doing the heavy lifting of traversing 3D space in straight lines, people get, you know, accustomed to it. In fact how we see things is frozen by physics, not brains - they are just accommodated to reality

There are no such utility particles doing any heavy lifting in 4D, so nothing to accommodate to.

search_facilityon Why can't transformers learn multiplication?

Interesting research, but it is still fascinates me why AI devs of current SOTAs ignore possibility to add numbers as first-grade citizens to AI. like for example suggested here: https://huggingface.co/papers/2502.09741

clean separation matter, it’s really strange to force models to mimic numbers and math via incredibly unfit token-mangling stuff, imho

search_facilityon Gemma 3 270M: Compact model for hyper-efficient AI

Seems the team and working conditions worth mentioning it twice, nonetheless.

Good there are places to work with normal knowledge culture, without artificial overfitting to “corporate happiness” :)

search_facilityon A brief history of the absurdities of the Soviet Union

They definitely does not aware of soviet reality that “roof over head” usually is not in the place where human want to live, same with job. if student after university decided (not by student, by state distributing workforce) to go work at city on polar circle - that means that student will go live and work here, without sunlight for the rest of his life! not joking, personal story with soviet collapse as happy ending (moved to normal place after that)

search_facilityon The bitter lesson is coming for tokenization

turns out - no, by intuition they should do this for sure - but no.

UPD: Found the paper: - https://huggingface.co/papers/2502.09741 - https://fouriernumber.github.io/

in paper mentioned “number” is a single sort-of “token” with numeric value, so network dealing with numbers like real numbers, separately from char representation. All the math happens directly on “number value”. In majority of current models numbers are handled like sequences of chars

search_facilityon The bitter lesson is coming for tokenization

regarding “math with tokens”: There was paper with tokenization that has specific tokens for int numbers, where token value = number. model learned to work with numbers as numbers and with tokens for everything else... it was good at math. can’t find a link, was on hugginface papers