methodical

While this is true, something to keep in mind is that the liquidity of the market is finite. The market may be able to hold SPCX up currently but adding an additional $100b (and much more as the lockup periods end) plus whatever OpenAI ends up offering might be too much for a lot of these pensions and the other buyers of these shares can manage. They will naturally get included in the indices as they become able to but the question is whether or not all 6 trillion of collective market cap they're attempting to offload will be able to be held up with time. My working theory is that they're aware of this fact to and it's been part of the reason they've been rushing to the door with these IPOs and non-standard lockup periods.

Social media site for devs getting hug-of-death'd by HN... the irony!

Every single post along these lines gets some comment about how it's acceptable/understandable because they've had such a massive influx of usage but I'm sorry at this point it's completely unacceptable for a service which acts as such a vital part of so many organization's workflows to have this level of downtime.

Given I've never worked at an organization at such scale, but there have been several other companies who have experienced large influxes of usage and been able to scale WAY more gracefully than github. It's not even like these outages are some delayed worker assignment due to higher supply than demand, it's just complete outages that make deployment impossible.

Simply unacceptable, hopefully this one doesn't take the NN hours it did last time to resolve.

Bloomberg's moat is the messaging service within it, not it's aggregation of data

Those are not LLMs

I'm unsure what SpaceX's weighting would be in QQQ but with Tesla being <3.54% weighting it would take both companies being 0s within a year to offset the cost in taxes from reweighting...

Candidly, while I understand the need for some amount of redundancy, I'm curious what this level of redundancy adds in terms of complexity to the system of a whole and whether or not that complexity-add almost outweighs the higher redundancy. I'm sure NASA has calculated the trade off, but I'd be curious to see the thoughts behind that.

I feel in a similar vein when learning of certain aircraft accidents over the years, where it feels like the redundancy of certain systems and the complexity it adds has been the indirect cause of accidents instead of preventing them. I suppose there's not really a way to quantify the accidents that it's prevent to be able to compare them directly.

To be fair, delineating between benevolent and malevolent pen-testing and cybersecurity purposes is practically impossible since the only difference is the user's intentions. I am entirely unsurprised (and would expect) that as models improve the amount to which widely available models will be prohibited from cybersecurity purposes will only increase.

Not to say I see this as the right approach, in theory the two forces would balance each other out as both white hats and black hats would have access to the same technology, but I can understand the hesitancy from Anthropic and others.

Basically every single old bug report I've ever seen is essentially a red-herring that is usually not able to be reproduced anymore after N years and takes away time from focusing on newer and more solvable issues. I don't see the issue with removing that noise if it's no longer being reported, but to each their own I suppose.

While I fundamentally agree with the basis of compute getting cheaper by the year, I think a missed consideration here is the fact that these models are also requiring exponentially more compute with each iteration to train, in a way that arguably has outscaled the advances in compute.

Whether a generalized and broadly usable model will be able to trained within some N multiple of our current compute availability allowing the price to come down with iterative compute advances is yet to be seen. With the current race to the top in terms of SOTA models and increasingly iteratively smaller improvements on previous generations, I have a feeling the scaling need for compute will outpace the improvements in our hardware architecture, and that's if Moore's law even holds as we start to reach the bounds of physics and not engineering.

However as it stands today, essentially none of these providers are profitable so it's really a question of whether that disconnect will come within their current runway or not and they'll be required to increase their price point to stay alive and/or raise more capital. It's pure conjecture either way.

Circularly passing around tens to hundreds of billions of dollars for things which don't exist and may never exist to fund a technology that hasn't A. lived up to the hype they've marketed and B. proven any strategy to breakeven is fundamentally not that much different than the way in which Enron strategically boasted their revenue numbers by passing the money between shell corporations that their CFO created.

The main difference of course being that these are actual companies as opposed to just entities intently designed to inflate the apparent financials. While it seems like that difference means this situation is perfectly fine as compared with the fraudulent case of Enron, the net effect is still the same; these companies are posting crazy quarter over quarter revenue growth, sending their stock prices to crazy highs and P/E multiples, while the insiders are cashing out to the tunes of hundreds of millions of dollars.

I don't really see how exactly you're trying to make the argument that it may or may not be a bubble, it objectively meets the definition of a bubble in the traditional economic sense (when an asset's market price surges significantly above its intrinsic value, driven by speculative behavior rather than fundamental factors). These companies are massively overvalued on the speculative value of AI, despite AI having not yet shown much economic viability for actual profit (not just revenue).

Worse yet, it's not just one company with inflated numbers, it's pretty much the entire top end of the market. To compare it to the dot com bubble wouldn't be a stretch, it'd basically be apples to apples as far as I see it.

Watching along, will be interesting to hear how much they leave for the paper they seem to be releasing alongside this conference

Ditto. If anything, trying to add it into an existing codebase via JSDoc has only really been a detriment via being a massive time sink. It might have caught maybe 4-5 bugs in the code but none that presented a large enough issue to warrant the time investment. If you're starting from scratch with TS instead of JSDoc, it might be worth it, but even on the best of days trying to figure out typing oddities from library typings being wrong and such have only really added headache. As always YMMV

Ditto- I have a feeling the investors in his latest 2.3 quintillion dollar series Z round wouldn't be as happy if he'd have tweeted "there is a wall"