Salgat

LLMs are exposing how much untapped potential comes from something that isn't necessarily more "intelligent" than humans, but is simply magnitudes faster at reading and analyzing data.

The few times I asked questions I'd get people telling me I should be doing something else, then I'd have to explain why I was doing it this weird way that necessitated me asking this question in the first place and they'd never respond or bother to answer the original question. It was like people only wanted to tell you were you wrong instead of trying to help.

That's half the utility of it. Friends and family use it, I use it when traveling.

MongoDB's biggest weakness was its default settings, which made it insecure and very vulnerable to data consistency failures. An absolute nightmare for PR. It's not perfect, but it's comparable to other NoSQL databases now.

It's a catch-22 because juniors need AI to be competitive, but you need to do the grunt work yourself for a few years before you really build the skills necessary to advance. I can't imagine learning how to do math by simply reading a solution book.

I don't like the UX but the extra controls on ebook rendering was worth the pain for me, since almost all of my time is just reading the ebook anyways.

My assumption is that it exacerbates it, by keeping the primary predator of rats well fed. Are you saying that cats have no impact on rat populations?

When I lived in Chicago I'd see rats and cats eating cat food left out alongside each other. I'm not against helping wild animals as long as it doesn't come with a rat infestation issue.

Thankfully as of now it appears they're very explicit about what content they share that is sponsored. They might be worried about the legislative backlash if they don't.

Regressions is a meaningless metric to me. Every app has bugs, and most are either rarely encountered or aren't breaking the app. All I care about is interruption of service.

Yeah it's a real catch-22. I think you're right though, LLM tech will keep up just enough to replace that lost workforce, till some day development as we know it will no longer exist, perhaps when the code becomes too abstract and complex for normal humans to ever hope to maintain without automation, similar to what happened to most assembly developers when the compiler came along.

The difference is that you already learned how to write code. New devs that go straight to genai are missing that.

LLMs are perfectly capable of translating between programming languages at this point. The main bottleneck for them is logic, not language.

You do get a public IPv6 IP, which is fine for most people (and with a simple script on a cron can keep a AAAA up to date, not that it changes often). And like someone else said, if you insist, you can use something like tailscale to punch a hole in Starlink's global NAT.

Starlink is a problem that solves itself. If enough fiber rolls out that there's no more customers, they'll scale back satellites (since they only last 3-5 years).

Kind of refreshing though that the "throw more processing at it" scaling we saw in the 90s has returned in a different way. For a while we were really bottlenecked in our advances by relatively low levels of parallelism (most software used by your average user doesn't scale cleanly with more than a few threads).

Anthropic wants companies that use API billing. Their growth period through hooking individual users who bring it to the business is over.

Lots of small moving parts that build up into a large architected platform, which requires careful design, otherwise you get a big ole ball of "it mostly works when we first created it but don't touch it". And yes, bad developers are susceptible to creating the same thing. That's the whole issue with vibe coding, it creates something that works, but is brittle, because there are no strict architecture requirements around the entire design besides vague suggestions like "follow best practices", and as it makes mistakes, it just patches whatever it can to make it work, instead of fixing the root problem with the design.

This is a genius way to farm ebooks while providing a useful service. I personally just use Google drive though.

That's not what is happening. Claude isn't charging for the tokens it generates from the OCR on its side, but it's still processing the same number of tokens as if you had sent the text, just with the extra step of OCR on Claude's side. This is 100% a loophole that's burning extra resources.

Using only Fable, if I do /usage it will show both Fable and Opus usage, so I don't think it's charging Fable prices for Opus code gen.

This is a misleading statement. The "private data" is still largely publicly produced data that has been curated through private agreements instead of scraping, such as reddit posts/comments (this is the "third-party data agreements" that companies like OpenAI mention). And yes, there is still a lot of processing done on this data, which is the norm for preparing training data.

Supposedly memristors would be ideal for this (and it would be reprogrammable), but then again, memristors seem to be the carbon nanotubes of the computing world.

This is signaling to non-US companies that Anthropic cannot provide reliable access to their models.

It also signals that Anthropic is a bad choice if you need stable access to their product outside the US.

It's ironic isn't it? All the marketing of how dangerous and powerful Mythos is and the government went "bet".

The whole point of async/await (besides allowing synchronous style code instead of callbacks) is that it lets you yield the thread efficiently back to the executor (OS threads have a massive overhead in comparison). And if you're doing something that blocks the thread instead of yielding it, you can choose between either spawn_blocking or a thread pool.

You're still cutting your annual income in half though. That's pretty big no?

To this day I do not get why Intel doesn't just offer massive memory options for their cards. Just charge what it costs to add the extra memory, no upcharge, and they will never be able to keep up with demand. Cheap VRAM is enough to justify a lot of open source investment into challenging CUDA.

That was the main reason for the big hype around Memristors 15 years ago. High density, high speed persistent memory to completely remove the need for hdd/ssds, potentially even removing the need for external memory altogether. So frustrating that it still seems like we're a long ways from that becoming reality. There's some renewed interest in Memristors as they can simulate neural network connections in models, so maybe the funding will return for it.