Gregaros

Insane. No. Mathematicians will not produce >2 "high SNR" papers per month as their rate calculated over any reasonable time scale; but their maximum per month? Very much the case that they will complete linked papers/dependencies/series of work/etc, spend a month in editing across multiple nearly-complete submissions, and so on.

And in mathematics, at least, priority on results is determined by the first time it appears out in the wild; for most, this is the arXiv.

Nobody is going to accept waiting a month on something that they fear may be scooped, costing them years of work and thought.

This affectively makes the arXiv obsolete for much of the purpose it has heretofore been put to.

You did not distill Fable. Relevantly, what you did provides no evidence contrary to the parent’s assertion that Moonshot did not have time to distill Fable.

Onlyfans. Arguably, already began long before AI.

> In this blog post I will give my personal view on the recent counterexamples to the unit distance conjecture and sum-product conjecture over the reals (see [90] and [52] respectively). My goal is to sketch the constructions and try and give some intuition as to where they came from and why they work. *My main target audience is the me-of-a-month-ago*

Thomas Bloom is a world-class mathematician working at the frontier of his field. He explicitly did not write this for you, and your stopping reading it is fine.

They should define this, but after having read the entire article I think it’s clear they mean “frameworks for evaluating the output of an agent” rather than what first might come to mind as “LLM evals”.

Their thesis is that even when the eval is useless for correctness of a single agentic action in production, it allows you to choose between two agents by cross-comparing in a large aggregated collection of tasks. Effectively: you can tune your agentic parameters.

Nothing new to the idea that taking many samples and averaging can work when a single datapoint doesn’t. Presumably this is part of a conversation in which we’re lacking context.

The poorest of the poor, subsistence farmers are barely producing enough to feed themselves; they trade and barter the little bit they can manage but it is not much and has little impact that goes beyond a tiny village-level radius. Nobody is displacing that because nobody needs to compete with that.

whitepages vs yellowpages

> Mozilla uses the term "vulnerability" for even sec-high, even though they say right below that it doesn't mean the same thing as a practical exploit.

That’s not evident in what you pastedat all.

What you pasted says

> sec-critical and sec-high are assigned to vulnerabilities that can be triggered with normal user behavior […] We make no technical difference between these […] sec-critical bugs are reserved for issues that are publicly disclosed or known to be exploited in the wild.

> sec-low is assigned to bugs that are annoying but far from causing user harm (e.g, a safe crash).

From this one infers that the "180 were sec-high" bugs found are actually exploitsble but known to have been found in the wild, and are NOT mere annoying bugs.

The difference between 180 and 270 does nothing to deflate the signicance, or lack there of, of the implication re: Mythos.

> This also seems like a win for society, if there is some sort of pattern with ai helping with crimes.

That fails to recognize the tradeoff between freedom and security. Society suffers if we, for instance, lock everyone up, despite the reduction that would have in crimes. The balance between the two cannot be ignored to justify outcomes, though it is American tradition to value liberty over security when the two come in conflict.

Still very interesting timing to ban third party harnesses, given the proximity to the Claude Code leak …

I _don’t_ think that empathy has anything to do with it though.

Behaviour modification yes, but that is “stop talking so critically”. Or “don’t be so harsh” or “give this person special treatment”. WHEN to do that might be key here—perhaps the colleague’s husband has cancer, or their child missed school 3 days this week with the flu, or their project wasn’t productionalized/their new to the role/etc—and so a blanket “don’t talk so harshly” isn’t called for—instead what is really desired is social calibration.

But instead it seems everyone is getting caught up on the literal interpretation of this figure of speech instead.

Impossible to say what was behind any specific request, but what is generally meant by “Have a little emapathy” and its kin is : “Stop criticizingjudging/etc. or communicating with the individual being discussed that sharply, because we feel the individual has good reasons/a good excuse/a good justification for sympathy and/or some leniency here.”

*

  On many occasions, I have been told to “be more empathetic.”

  When I ask why, I typically get this reaction:  

  This is a ridiculous question. I am not going to answer it because it is so ridiculous.

  Empathy is the right thing to do! You should feel bad for that person. We’re humans, after all.

  These explanations never really helped.

*

Even after reading this, I am not sure the author really gets what is behind the request.

> The person in the article even laughs it off since she turns 65 soon and will then switch to Medicare.

You might be surprised how few Americans can ‘laugh off’ even a one-time payment increase of $2,300–let alone a monthly recurring one.

This is correct—I do not engage ethics only when it won’t cost me, nor take convenience into account when determining where my lines are. Perhaps I’m privileged to have that option.

_May_ be a case for extending out what has been explored by theory to cover more useful ground (or not, depending on whether real-world usecases like yours are too heterogenous for effective general techniques).

100%. Not sure why you’re downvoted here, there’s nothing controversial here even if you disagree with the framing.

I would go on to say that thisminteraction between ‘holes’ exposed by LLM expectations _and_ demonstrated museerbase interest _and_ expert input (by the devs’ decision to implement changes) is an ideal outcome that would not have occurred if each of the pieces were not in place to facilitate these interactions, and there’s probably something here to learn from and expand on in the age of LLMs altering user experiences.

Presumably the value in knowing "you need to sort a string in place and then discuss how a random forest gets trained" is that it impacts your answers - for instance, by allowing you to look this up before the interview while appearing to the interviewers to he operating unfer the dame conditions as the other candidates, who did not know to. Your performance then appears as a signal of broader inwoledge and capability than you possess - you have, as is the entire point here and which I should not need to spell out, gained an advantage over other candidates by virtue of the information which was intentionally leaked.

If the point of the interview were "answer those questions AND know enough to answer the follow up questions" _once told what to expect and prep_, they’d be sharing those questions with all candidates. If you feel that saying to the interviewers "by the way, I did know this because [X] told me they’d be here" wouldn’t impact outcomes, then great. If you feel you’d need to hide that, then you’re aware this involves dishonesty - and if you still struggle to see how that’s unethical, lets just make sure we never need to work together.

> If cheating means asking someone in the company you're interviewing for a peek at what will be asked then great. In my book that's using leverage.

In my book that is unambiguously unethical and should get the contact fired. I am shocked to see this approach promoted in such a blasé manner.

I thought where you were going with his was "that realized the best way to dispose of their nuclear waste was to dump it in the deep past." I’d read that novel.

Some further questions:

1. For tasks like autocomplete, keyword routing, or voice transcription, what would the latency and power savings look like on an ASIC vs. even a megakernel GPU setup? Would that justify a fixed-function approach in edge devices or embedded systems?

2. ASICs obviously kill retraining, but could we envision a hybrid setup where a base model is hardwired and a small, soft, learnable module (e.g., LoRA-style residual layers) runs on a general-purpose co-processor?

3. Would the transformer’s fixed topology lend itself to spatial reuse in ASIC design, or is the model’s size (e.g. GPT-3-class) still prohibitive without aggressive weight pruning or quantization?

Curious if anyone has thoughts on going even further: eschewing soft-ware based inference in favor of a purely ASIC approach to a static LLM. Cost benefits? Software level additional, fine-tuneable layers to allow a degree of improvement and flexibility? We are quickly approaching ‘good enough’ for some tasks—at what point does that mean we’re comfortable locking something in for the ~2-4 year lifespan of a device if there _were_ advantages offered by a hyper-specialized chip?

Really anyone that writes for a living. I have a referee report on a paper asking me to correct something to be an em-dash.

gtfo. Nobody voted Trump to _raise_ taxes.

Sorry, the implication is that this is far worse than Brexit, but that this is the US’s Global Exit, riffing off the strong negative connotations in te US. I saw it from on Bluesky, but that linked here https://theradicalfederalist.substack.com/p/the-neoreactiona... - so, being framed as much worse than Brexit.

I’ve been seeing people call this GEXIT, or global exit.

Surveillance relies on our passive consent. Choosing intentional digital absence isn’t merely personal privacy, but an act of political defiance.

This substack discusses tactics of digital non-cooperation, collective phone fasting, encryption normalization, and why archiving freely available LLMs locally might serve as “moral backup” against authoritarian rewrites of history.

Read something of a similar bent here https://theradicalfederalist.substack.com/p/the-regimes-next...

Any suggestions on where in the world will remain relatively stable?

Models don’t know their own weights, so I’m not sure what this means.