tveita

Most cryptographers would still recommend the hybrid over pure ML-KEM. This RFC (for pure MLKEM) is marked "recommended to implement = N". It is purely for settings where the implementors independently want to use pure ML-KEM for some reason.

That's exactly how it was with Dual_EC_DRBG.

E.g. https://www.schneier.com/essays/archives/2007/11/did_nsa_put...

   I don’t understand why the NSA was so insistent about including Dual_EC_DRBG in the standard. It makes no sense as a trap door: It’s public, and rather obvious. It makes no sense from an engineering perspective: It’s too slow for anyone to willingly use it. And it makes no sense from a backwards-compatibility perspective: Swapping one random-number generator for another is easy.
  
  My recommendation, if you’re in need of a random-number generator, is not to use Dual_EC_DRBG under any circumstances.

Except as we know now, after the standardization NSA was able to persuade/bribe vendors to implement it.

RSA is still a viable cryptography vendor, after accepting money to backdoor their product for paying customers. The standardization gave them a fig leaf of plausible deniability. Honest mistake, could happen to anyone, right? If they had needed to implement a "non-standard" backdoor, or if it had been officially struck from the standard, it would have been a lot harder to row away from.

It also gives you the option of serialising the RNG states directly instead of using the counter hack.

who put the Transformer into LLMs?

Google?

who invented neural networks

People like Geoffrey Hinton, who was notably at Google Brain from 2013 to 2023?

The people who say Google was ahead were paying attention long before you were.

I think https://jamesclear.com/great-speeches/finding-your-own-visio... is a better take.

What gives you an unique perspective and your own voice can be sticking to your thing for a long time and exploring your exact path more deeply than anyone else has. You don't need to take a million random stabs to become "improbable", and there's no reason that should lead to anything authentic.

Would love to read specific examples of "the same trick being used to dismiss health concerns about medical supplements or influence financial information provided by Google's AI about retirement", but the relevant link in the article currently goes to

file:///Users/GermaTW1/BBC%20Dropbox/Thomas%20Germain/A%20Downloads%20and%20Documents/2026/And%20there's%20evidence%20that%20AI%20tools%20are%20being%20manipulated%20on%20a%20wide%20scale.

I can get a really high hit rate by only searching for dumb trivial things I already know the answer to.

The point of LMGTFY is to land people on either the official documentation or a curated site like Stack Overflow. Google used to be able to do that reliably.

With the power of LLMs you can Google a standard library function and get an inaccurate summarisation of a Reddit discussion where neither side knows what they're talking about

Is there any project that showcases Mojo for running neural network models on the GPU - like ideally something like llama.cpp that could run one or more existing models to showcase the readability and performance?

"Can you lay tiles until I say stop, or until it's about $250 worth, whichever comes first"

"No, as one of the top tile layers in the country I can't do that, for your own protection. What if fifty elephants came and wanted to use your bathroom all at once? You'd feel pretty dumb having to reject them instead of me simply automatically adding $1 million to your bill"

Honestly I'd sometimes like a checkbox to ignore any results uploaded after, say 2023. Or else to only see 'verified non-AI' content. There is nothing I'd ever search Youtube for where an AI generated video would be an acceptable answer.

There are real examples of males and females being initially mistaken for different species, as well as for adults and juvenile forms.

e.g. https://en.wikipedia.org/wiki/Cetomimidae

  In early 2009, the Royal Society published an article detailing the discovery "that three families with greatly differing morphologies, Mirapinnidae (tapetails), Megalomycteridae (bignose fishes), and Cetomimidae (whalefishes), are larvae, males, and females, respectively, of a single-family, Cetomimidae."

Building harnesses does seem like a task that's particularly conductive to psychosis. I've wondered if it's because there is no push-back; almost anything you try will be "right" in that your changes appear to make things happen. "Oh look I added a carpenter and now it's walking around and making notes about the scaffolding" So you get to stay in your flow state without reassessing if the concepts you are forming are ultimately meaningful.

Although I think the post also self-diagnoses some factors that also help:

  With the Gas Town Mayor, you feel like you’re operating at a special level, a VIP, above all the workers. You are talking to someone important: the mayor of a factory the size of a town. You have access to someone with resources, someone who gets you, someone who appreciates how busy you are.

  Working with regular coding agents just doesn’t give you that special feeling.

https://en.wikipedia.org/wiki/Fill_line

Selling drinks in mislabeled containers should warrant a fraud report to your local consumer protection agency. A crowdsourcing app seems like the wrong tool here.

"video compression" by analogy only, what this claims to actually do is delta encode the values in each token from the previous token.

Interesting idea, but the results seem almost suspicious? even accounting for the extra bits used to store the 16-bit start value for each block - ~5% for k=64

The code does funky things, like the encoder updates the reference value for each encoded token, using the non-quantized value! [1] But the decoder just ignored all that. [2] how can this work?

[1] https://github.com/cenconq25/delta-compress-llm/commit/f185f...

[2] https://github.com/cenconq25/delta-compress-llm/commit/f185f...

Sure, for instance, if all of them go through an 1 hour AI interview, then you might find a better candidate, at the cost of 1000 man-hours of work. You hire that person, another company opens a position, gets 999 applicants, send them all their own AI interview, and so forth.

How much would better would your hire be considering that you managed to check all 1000 of them, rather than just 50?

Assume that candidate fitness is a number normally distributed around 0 (half of them obviously being negative), that both you and the AI can perfectly pick out the best candidate, and that you picked the 50 to interview completely at random. The average actually seems to be around 40% better? Suprisingly decent. Is that improvement worth 1000 man-hours?

So attempt two here: maybe instead of each company sending candidates through an interview, there should be a common gatekeeper. All working age people take the same 1-hour AI interview, and the glorious overseer assigns them to the position they are best suited for.

(An actual answer here is you assess how important it is to get "the best candidate", and you interview enough people to get a reasonable approximation. The hour cost on your side is what keeps you honest. If wasting candidate time is free on your side, you're going to waste 500 man-hours of work for a 5% better result for you.)

I absolutely agree in principle, but I understand that the companies are also seeing a lot more applicants trying to skate past screening and interviews with AI assistance.

Connecting verified humans for a mutually respectful chat is a trust problem that companies like LinkedIn should be creating solutions for, instead of offering both sides automated shovels to shovel slop faster.

They are the ones who started using AI in the hiring process

Aren't you ignoring the reports of companies receiving thousands of ChatGPT-written resumes, bots sending applications, and interviews with applicants being live coached by AI?

This is a breakdown of trust on both sides.

Probably more like the long tail of software - software that was created for a particular purpose in a particular domain by a single person in the company who also happened to know programming - maybe just as Excel macros.

I strongly assume the long tail is shifting and expanding now and will eventually mostly be software for one-off purposes authored by people who don't know how to code, and probably have a poor understanding of how it actually works.

There's some odd choices here.

  - 90 days is a very long time to keep keys, I'd expect rotation maybe between 10 minutes and a day? I don't see any justification for this in the article.
  - There's no need to keep any private keys except the current signing key and maybe an upcoming key. Old keys should be deleted on rotation, not just left to eventually expire.
  - https://github.com/aaroncpina/Aaron.Pina.Blog.Article.08/blob/776e3b365d177ed3b779242181f0045cd6387b3f/Aaron.Pina.Blog.Article.08.Server/Program.cs#L70-L77 - You're not allowed to get a new token if you have a a token already? That's unworkable - what if you want to log in on a new device? Or what if the client fails to receive the token request after the server sends it, the classic snag with use-only-once tokens?
  - A fun thing about setting an expiry on the keys is that it makes them eligible for eviction with Redis' standard volatile-lru policy. You can configure this, but it would make me nervous.

I've definitely seen Opus go to town when asked to test a fairly simple builder. Possibly it inferred something about testing the "contract", and went on to test such properties as

  - none of the "final" fields have changed after calling each method
  - these two immutable objects we just confirmed differ on a property are not the same object

Elijah Newren, who wrote git's merge-ort (the default merge strategy), reviewed weave and said language-aware content merging is the right approach, that he's been asked about it enough times to be certain there's demand, and that our fallback-to-line-level strategy for unsupported languages is "a very reasonable way to tackle the problem." Taylor Blau from the Git team said he's "really impressed" and connected us with Elijah. The creator of libgit2 starred the repo. Martin von Zweigbergk (creator of jj) has also been excited about the direction.

Are any of these statements public, or is this all private communication?

We are also working with GitButler team to integrate it as a research feature.

Referring to this discussion, I assume: https://github.com/gitbutlerapp/gitbutler/discussions/12274

I'll wager that 95% of incitive and unhelpful comments aren't written by "bad faith actors" as you define them, but ordinary people carried away by emotions or mob sentiment.

Just a reminder that "this probably isn't worth replying to" should help a lot. But alas, it would directly reduce precious engangement.

Also, the generator will use a seeded randomizer so that, for local development, you work on the same dataset as others

Except that the generator script generates dates relative to time() ?

In GitHub, you have to switch tabs (which is slow and distracting) to go between the PR summary and the code.

As a case study of Github UI friction, take merging a Dependabot PR from the PRs tab, with code approval required before merges. By my count this takes 6 clicks, and none of them approach a 'snappy' response time.

This is for mostly trivial single-line diffs. The entire thing could be 1 click - a hover preview on the PR list, and an 'approve and merge' button.

(To list them out: Click PR, "Files changed", "Submit review", "Approve", "Submit Review", "Merge")

You could start by reading research on the topic instead of disregarding expert opinion based on your own gut feeling

E.g. https://www.anthropic.com/research/tracing-thoughts-language...

And LinkedIn is Microsoft as well...

IMO Microsoft is right at the nexus of opportunity for solving some of the the large _problems_ that AI introduces.

Employers and job seekers both need a way to verify that they are talking to real identified people that are willing to put in some effort beyond spamming AI or wasting your time on AI run filters. LinkedIn could help them.

Programmers need access to real human-verified code and projects they can trust, not low-effort slop that could be backdoored at any moment by people with unclear motives and provenance. Github could help.

etc. etc. for Office, Outlook ...

But instead they've decided to ride the slop waves, throw QA to the wind, and call every bird and stone "copilot".

The Copilot IntelliJ integration on the other hand is atrocious: https://plugins.jetbrains.com/plugin/17718-github-copilot--y...

I'm amazed that a company that's supposedly one of the big AI stocks seemingly won't spare a single QA position for a major development tool. It really validates Claude's CLI-first approach.

We might not be far from the first prompt worm

I think people's attitude would be better calibrated to reality if LLM providers were legally required to call their service "a random drunk guy on the subway"

E.g.

"A random drunk guy on the subway suggested that this wouldn't be a problem if we were running the latest SOL server version" "Huh, I guess that's worth testing"

Indeed, the Gas-Town token is down 97% from all-time high,

What else could possibly have happened? Surely every one put their money in with the express intention of participating in a pump and dump.

Not taking the money would have been the high road. I don't think basing the economy on gambling and scams is good for society. But who could realistically claim to be a 'victim' here?