EvanWard97

Good points. Somehow typing latency might actually be better, lol.

V8 might just invent like 3 more execution engines though, 1 of which uses an external TPU (open source though!) to run code JITed to HVM (Higher Order Virtual Machine) that everyone is eventually compelled to adopt, one can't be too sure JS will lose. /s

20x more compute isn't much in terms of cryptographic security concerns, no? Ah but triple-DES was recently depreciated.

Definitely sounds right that we'd get an earlier, heavier emphasis on parallelism and hardware acceleration. I'm guessing the slower speed of causality also applies to propagation delay and memory latencies, so there wouldn't be new motivation for particular architectural decisions beyond "God please make this fast enough for our real-time control systems or human interaction needs".

If we got deep learning years or decades earlier, that also seems scary for AI existential risk, as we are just barely starting to figure out how the big inscrutable matrices work, and that's with the benefit of more time people have had to sound the alarm bells and attract talent and funding for AI interpretability research.

Sounds right to me. Without being able to rely on flashy visuals and low-latency so much, games would've had to be somewhat more strategic and intellectual to sell (although I imagine graphics would eventually catch up due to its fitness for parallel processing). Even if brain rotting visual spectacles were just pushed 7 years down the line, they still would probably have a more sophisticated flavor that might be cemented with time (e.g. this counterfactual TikTok might have given users much more direct control over their feed algorithm).

Regardless of where anyone thinks the maximas are in software trade-off space, developers are going to experiment shipping software at new points anyway, as markets at known points become saturated and exploration again becomes worthwhile in expectation.

The aspect of this collective optimization process that seems particularly helpful and tractable to focus on is ensuring that users know the risks and benefits of their various options.

I'm more interested in seeing web platforms point users to excellent, impartial 3rd party analyses of their options and their associated risks/benefits, rather than stomp out innovation that some people clearly think is worth trying.

I didn't realize this was Nature, my bad.

And of course I am the one who decides for myself if anything is worth thinking about more. I was simply trying to elicit the sort of information which would be relevant to this decision such as, "Here's a bit of evidence you might not be aware of that suggest this is more intractable than you may think. Also, considering the existentially pressing X and Y risks on the horizon, which better programmer productivity presumably wouldn't help, you may want to consider that your comparative advantage may be A or B."

I am not really interested in having people switch who don't want to and are perfectly happy with their language. I am proposing the r&d of a way to coordinate people switching who would like to switch languages, but only under certain conditions, such as if their would be jobs in it and sufficient expected growth in attention to the language's development.

Many programming languages would fit a project\team better if they had more community support. It's often worth putting up with worse-designed languages due to the existing libraries and documentation.

Also, the whole 'subjective\objective' distinction is tiresome and doesn't really help here. If we held a RCT where half of people started learning and developing in Julia, and the other half in Python, and they both only had the standard library and documentation, and we measured productivity, code performance, dev satisfaction, etc. and pehaps even had the developers eventually switch, we'd likely see data supporting the idea that one of the languages is overall favored by developers more than the other.

Congrats! There's been a lot of solid object level advice here...I'll just repeat some basic & meta advice in case anything resonates: - you can't lose it if you don't spend it (aside from the real small negative yield from inflation outpacing banks' savings interest rate).

- this is enough money to really provide a lot of investment opportunities, be extremely picky about a number of the first 'good' opportunities that come your way. There's a very good chance that you'll find better opportunities just by waiting a bit longer (where rate of return will make up for the opportunity cost of not investing).

- this is enough money that's it's probably worth reading at least a few books about investing and wealth management. You spend thousands of hours a year to otherwise earn $150k, it probably makes sense to spend at least 1/100th of that amount of time to really become informed about how to manage an additional $150k.

- the majority of non-profits and charities suck in terms of their impact/dollar efficiency. If you are actually trying to maximize your impact rather than donate to feel good (which is okay too! Just recognize when you are doing so), I'd hold off on donating to charities until you've done at least X hours of research per say, $1000k that you donate. Considering you make around $100 an hour, a few hours of research per $1000 donated probably isn't unreasonable. Additionally, the best charities aren't just 5 or 10x more impactful than the average ones, but probably 100's, 1000's, or even more times more impactful.

- don't ignore the peace of mind that a solid runway from a variety of uncorrelated, fairly liquid assets may provide. Regardless of what happens--a solar flare knocks out our electric grid for months, the US defaults, banks can't let you withdraw cash for whatever reason--you want to know that you'll be able to incentivize other's labor and buy goods from other people. Cash, gold, BTC/ETH/Monero/Zcash/etc. in cold storage wallets, and perhaps even other 'currencies' like common caliber bullets or cigarettes in a safe deposit box and/or a safe at home, might be worth storing $5k or even more in.

- consider using getguesstimate.com, www.causal.app, or at least Excel/Sheets to try to quantify the different risks and returns of all the options you are considering. The first two apps allow you to easily include uncertainty in your estimates of values, as well as do sensitivity analysis, which can help you decide which model inputs are probably most worth reducing your uncertainty about by researching them further.

- when in doubt about a spending decision, especially if you haven't exhaustively researched and thought about it, just wait a day and sleep it off. And if you don't feel good the next day, just wait again. For most people, it's too easy to spend money and too hard to save it. Don't be like most people.

`But those animals that do live at depth will clearly need some special adaptations, says Dr Jamieson.

"They'd have to do something clever inside their cells. If you imagine a cell is like a balloon - it's going to want to collapse under pressure. So, it will need some smart biochemistry to make sure it retains that sphere," the scientist explained.`

I don't understand how octopi would need special cellular adaptations for living at t those depths. So long as their cells do not require air cavities (fairly certain they don't), I can't see what the issue could be. Differential pressure can cause problems, but there's no delta-p when your cells are equally incompressible solids and liquids.

I hope that I'm wrong though and that this scientist isn't as mistaken as they sound.

- Far UVC lights (200 to ~222nm) such as Ushio's Care222 tech. This light destroys pathogens quickly while not seeming to damage human skin or eyes.

- FPGAs. I'm no computer engineer, but it seems like this tech is going to soon drastically increase our compute.

- Augur, among other prediction platforms. Beliefs will pay rent.

- Web Assembly, as noted elsewhere. One use case I haven't read yet here is distributed computing. BOINC via WASM could facilitate dozens more users to join the network.

- Decision-making software, particularly that which leverages random variable inputs and uses Monte Carlo methods, and helps elicit the most accurate predictions and preferences of the user.