this "feature" in code editors where you type an opening character and it immediately inserts the closing one for you as well -- why?? at the very very best case, you will have to skip over this character with an arrow. so why?

can someone explain "serverless backends" to me? it seems that they run functions on demand. but if these functions cannot access any persistent state, why not run them on the client? the only reason I see is to hide DBs/APIs tokens/secrets from the client, but is that really all there is to it?

i created this thingy yesterday and now I cannot stop watching it. yoavg.github.io/eternal/

a trivia fact about this paper is that we submitted it to arxiv weeks ago, and it was hanging there in limbo for quite a while. apparently because we submitted to "AI" while they moved it to "HCI".

When reading AI reasoning text (aka CoT), we (humans) form a narrative about the underlying computation process, which we take as a transparent explanation of model behavior. But what if our narratives are wrong? We measure that and find it usually is. Now on arXiv: arxiv.org/abs/2508.16599

if you REALLY want to understand DL, you should start by honing your Category Theory skills, as almost everything in DL at its core can be mapped to a functor or an endofunctor.

taking it a step further, I'd say in many cases using the algebra jargon is harmful to understanding, and its better to just describe whats really going on. ie, "we add an L2 penalty term" --> want the sum of squares to be small. "project to vocab space" --> compute similarity to each vocab item.

i'll elaborate: a common computation pattern in DL happens to coincide with a known operator in linear algebra (matmul), and so we conveniently borrow linalg notation and terminology (matrices, vectors, ranks, norms). but this is just jargon. the algebric properties arent needed.

"Modern ML is built on Linear Algebra". lol no its not.

why is "MCP" implemented as server exposing a set of endpoints, rather than as some JSON schema for defining tool descriptions and allowing these JSON files to be accessed over http? what is the purpose/benefit of the middleman server?

you know what, nah, we don't want to close it. it will be just 80% closed.

and now, we will proceed to peacefully close the strait of Hormuz. you know, for the environment.

today, during a peaceful flight over an iranian mountain, a US airplane dropped a mostly peaceful bunker buster bomb, who flew peacefully until it hit the mountain and mostly peaceful facility underneath it. there was a brief period of violent detonation on impact, then peace again.

אחד מלקחי ליל המקלטים אמש הוא שאין לי סבלנות לקרוא ספרות מקצועית, אבל לקרוא ספרות קלה זה די סבבה. מצד שני הספר שהיה לי בנייד, הוא כזה שהתחלתי לקרוא והפסקתי והיתה סיבה שהפסקתי, הוא מייגע ומעפן. בקיצור שילחו המלצות לספרים. עברית או אנגלית, אבל באנגלית ככהנ יהיה לי יותר קל להתארגן.

there are lots of "AI agents are just software around LLMs" comments recently. this is true by definition, but also misses the point. while there is no "magic" in software, there is engineering and best practices. and we dont yet have them for systems that involve LLMs.

a good gag from the other social network:

this "class 9" is such a cool idea for an LLM course! (from ufal.mff.cuni.cz/courses/npfl... via @zdenekkasner.bsky.social )

i created a gist with some non default llm courses gist.github.com/yoavg/95bbc5...

A prompt posted by Twitter user eigenrobot

what's an "eigenprompt"?

i am looking for academic (or similarly "serious") courses or textbook-y texts on LLMs with a major component (lets say >66%) that is NOT about the technicalities of Transformers, optimization, RL, tokenization, etc, but instead focus more on data, eval, interpretability, etc. pointers?

"hmm i wonder what [researcher person] is thinking about these days" /checks google scholar page] [50+ papers in 2025] nope, no signal there.

an interesting observation following neurips's 25k submissions: since the main conferences in "AI" and adjacent fields grew so much, we de-facto dont have 2nd tier venues anymore. only "top tier" venues. and multiple of them. this is a weird situation.

what's really interesting to me in ML these days is the prospect of creating systems* that learn declaratively, without losses, rewards or gradients. we are not there yet, but we will be. * not models

"this studies small models on toy synthetic data in a narrow domain. the findings may not transfer to realistic settings. unclear what we can learn from this.reject" "no no you dont understand. i study spherical frictionless lms in a vacuum. you know, like physicists!" "oh, i see. fantastic work!"

(niche) This is a nice paper but it seems they don't really understand what the word "recursive" means for CFGs, and hence their results are significantly weaker than they think.

btw, even though i dont fully understand what this doc is, i HIGHLY recommend reading it. makes you realize how much product decisions and 'behavior shaping' efforts go into "the AI" that you see at the end model-spec.openai.com/2025-04-11.h...

who here understands what OpenAI's "Model Spec" actually is? Its an interesting doc and I understand the sentiment, but what is it? just a wish list for desired behavior? an annotation guideline? a set of system prompts? llm as a judge prompts? a mix? model-spec.openai.com/2025-04-11.h...

watching a youtube video and got reminded of a nostalgic assembly trick: replacing PUSH PUSH CALL PUSH PUSH PUSH CALL PUSH CALL with PUSH PUSH PUSH PUSH PUSH PUSH CALL CALL CALL to shave a few bytes thanks to better compressibility. those were the days!

I learned that @emilymbender.bsky.social suggests replacing the term "AI" with "Automation", and I think we finally agree on something in this space! I also think that "Automation Intelligence" is a nice name for describing many of these systems and abilities.

leave it to academics to take a praise phrase such as "one of the top 10% smartest researchers in their generation" and interpret it as kinda weak bc "generation" implies a small comparison group of those graduating the same year, and explicitly stating "smartest" implies excluding other qualities.

מי אתם, דור מוזר שלא מוכן לקרוא יותר מכמה עמודים אבל יצפה ב-reaction video של סטרימר באורך כחמש שעות, שמגיב לדיבייט של סטרימרים באורך ארבע וחצי שעות

"LLM on way to replace doctors" gets published in Nature. meanwhile "LLM judgement not as good as human MDs" gets a spot in "Physical Therapy and Rehabilitation Journal".

when you hear or say that "the LLM performed a search before answering", how do you imagine the search process? e.g., what was searched for, using which kind of tools/engines, how many results were retrieved, how much text was read, etc.

כשאתם שומעים או אומרים ש"הללמ חיפש באינטרנט לפני שענה", כיצד אתם מדמיינים את תהליך החיפוש? מה חיפש, כמה תוצאות קרא, וכו.

כולם עפים על השרשור טוויטר של נגה ארבל מתווכחת עם גרוק כ"איזה מטומטמת" ו"איך גרוק הראה לה", ואני מבין שזה בגלל שהיא בצד ה"טועה" של הויכוח וגרוק הוא בצד "הצודק", אבל זה ממש מבאס אותי כצופה מהצד בגלל שהטיעון שלה נגד גרוק הוא נכון: אין לו סימוכין, הוא נשמע משכנע, ואנשים מאמינים לו.

2) the entire "dilemma" in this work and similar, is starting from the premise that there are some user instructions that the models should not obey. without this premise, the "dilemmas" never arise, and the blame sits fully on the human operators. i think that's much healthier.

two thoughts following this interesting work: 1) should we talk about models "lying"? my first reaction was "NO!", but on 2nd thought it might be a way to communicate the situation to people (yes, at the cost of misrepresenting the models and anthrophormizing them). which is better? idk. discuss?

i see many demos recently about "look! we hooked an LLM to [some software] via an MCP and it managed to call it!" this is cool, but also shows little. the real question is how to enable non-trivial, long-running interactions using this software. this is much much harder.

can someone tldr the spain/portugal++ blackout to me? focus on all the known technical details and causes, no social aspects and implications, please.

i checked twice and this post is from today, not some old retweet from 2021

מאז הפרסום של הכתבה (לינק בתגובה) היא הצליחה לעשות את דרכה לכל בני המשפחה ועוד קצת, והתגובה הנפוצה by far היא "אבל איך יכול להיות שאתם לא מבינים איך זה עובד?" זה גם מה שגידי, הכתב, חזר ושאל. ואז התחוור לי שאני כבר כל כך הרבה שנים בעולם הלא מובן הזה, שכבר הפסקתי לחשוב כמה זה באמת מוזר. ומופלא.

hear me out, what if, the great missing step in achieving machines with super-human intelligence is substantially reducing the human ability bound? and i think the processes may already be set in motion.

in the LLM tool-use APIs, you get back a tool-invocation command with a command spec and an id, and you should invoke the tool and return back a message with the tools output and the id. *WHY does the API need the id*?? what does it do with it behind the scenes?

🎉 Our Actionable Interpretability workshop has been accepted to #ICML2025! 🎉 > Follow @actinterp.bsky.social > Website actionable-interpretability.github.io @talhaklay.bsky.social @anja.re @mariusmosbach.bsky.social @sarah-nlp.bsky.social @iftenney.bsky.social Paper submission deadline: May 9th!

of course one cannot just rewrite the social security cobol codebase in a few months. not with the current team they cant. must send 80% of them home first and then use ai for help.

the problem with "LLMs cannot do X / do not really understand Y" kind of works is that either X / Y are trivially hard, or LLMs can actually fudge its way to simulate a correct answer for 95% of the cases, despite "not understanding". and now go convince someone they should care.

can it be that in the one time that an AI model really did produce its own unique style, people just didn't notice because it was marketed as an existing one?

so... this is what google image search looks like today for "ghibli style". this is crazy. especially since the few real ones stand out like they do.

it is indeed a distinct style. but this style is not studio ghibli.