skysight-inc/HackerNews huggingface dataset:
I've wondered if the impression isn't so much caused by the answers themselves but a trust level has formed from its behavior. A core appeal of LLM bots is they're instructed to be non-judgemental and ever-helpful, so in most scenarios one won't 'butt heads' with them.

This obviously has some real positives for learning (if the responses could be accurate) since you're more likely to continue eagerly the less ashamed you are asking 'dumb' questions to more rapidly gain an understanding of something (anonymous accounts online similarly facilitate shame-free question asking).

I wonder though if it might be having an effect where people begin to prefer AI over humans since with real humans, whether online or IRL, there's no consistency of response—people could be cranky, disinterested in responding or criticize someone for even asking.

So the more one interacts with an ever-willing, ever-pleasant, non-judgemental, human-esque answer machine—even if it's hallucinating things—I could see how it could become more of a go-to and even a trust begin to grow from familiarity and its generally polite ability to listen.
A project management tool/kanban board app. Similar to Trello, except Linear is way nicer to work with in my opinion.
That seems overly pessimistic (or perhaps overly optimistic about code that does not panic). Panics mostly exist to guard code from entering into such unrecoverable states, eg writing past the end of an array.
what a vapid pointless article
Air has a bit over 400 parts per million CO2. Extracting CO2 from air in meaningful amounts involves pumping a lot of air.

The preferred source of CO2 for most carbon capture schemes is the exhaust fumes of those burning fossil fuels. Basically how this works is that you burn some dead dinosaur juice/lumps/gas, you capture (a fraction of) the CO2, use lots of energy to turn that into synthetic dinosaur juice/gas, and when you burn that, 100% of the CO2 ends up in the atmosphere. That's right, 100% CO2 comes from fossil deposits and at the end of the process 100% of it ends up in the air.
a stackoverflow-like website for math already exists.[0]
but i agree with the general sentiment of most people that the actual solution should somehow be easily accessible. i will work on this bit.

thank you for your feedback!

[0]: https://math.stackexchange.com/
People can be quite tribal. Especially the ones who haven't taken the time to learn what the other tribe is up to and why.
The actual problem is a little more difficult - clearly fuel is often taxed, but the question is how much tax should be paid on which fuels, in which industries and in which countries.

Should an American putting fuel into their hummer pay the same level of duties on a litre of diesel as someone putting fuel in their tractor in Uganda, and how do we get all the countries to agree what the acceptable rate is?
You need multiple color wax and a photo of the pattern for that to work.
> Is there really no lower hanging fruit?

I would be interested in lower handing fruits that does not for 1/4 of the population to change. If we change consumption / spending habits that will also propagate.

Personally, I think politicians are doing a really good job in trying to balance things.

Eg. It should be super easy to increase taxes on one-family detached houses and cars. This would almost immediately lead to reduced emissions – so this fruit is almost on the ground.

However, the truth is that people don't want to live in apartments and take public transport.
It is not entirely different because the classic Autopilot was migrated from radar to vision-only and that surely was done by sharing some code. However the classic autopilot is outdated by 4 years. But my point was not about Autopilot vs. FSD but about the fact that the C++ control code is often rather stupid and unlikely to be as robust as end-to-end learning.
> 56k has that peculiar "ding" in the handshake

Depends which 56K “standard” you are using. There were two before v90 standardised things, they had different handshake modifications for negotiating the faster link, and v90 was different again. Some ISPs supported only one of the earlier “standards” for some time after v90 (most v90 modems were firmware switchable between it and one of the older methods or were soft-modems where that switch was in the driver).
My anecdotal example: Italian living in Catalonia (for a few years now, but moved as an adult), I speak both Catalan and Spanish with no Italian accent and what's even "worse", I speak Italian with Spanish accent ^^;

I know these languages are all very similar, so that helped a lot for sure, but the 12 years rule is definitely not absolute.
What is this “C programmer” you speak of?

Anyone who’s been doing this for long enough is a polyglot.
> Of course, it's not on any of the four streaming services I pay for (I'm outside the US so ymmv). And why? Why is the studio hoarding access to this film that probably stopped making them money decades ago when they could just lease out the streaming rights and make something of it?

It will be available to any of those streaming services that the studio deals to take if they want to.

But those services buy x number of titles for y amount of money and your film is very obscure and would not get watched enough to justify a streaming service selecting it.

It is trivially available for an individual transaction, where the economics of making it available make more sense (but not infinite sense, listing/encoding/ingesting etc are not free, and people assume even the most obscure film generates more revenue than it actually does).
I don't think ML is harder than other technical and scientific fields. Being a good software developer, a good software scientist, a good mathematician, a good astronomer, a good physicist is hard.

And of course, there are hard fields outside of science. Being good at any kind of art is hard.

Just because some people believe they are good at something (because Dunning Krueger effect) doesn't mean that field is easier than ML. They assume ML is harder because they happen to know a few bits in a particular field and nothing about ML. Then, the same kind of people will learn some superficial bits on ML and think it's easy.

Any human endeavor done properly means time, sweat, critical thinking, determination. And lots of it.
> Is there really no lower hanging fruit?

I mean, you talk like this is the only project of this sort. There's _lots_ of lower hanging fruit, but it is being picked. See electric cars, forestry, biodiesel and a million other things.

Even without any carbon reduction, this would be worth doing, though, and there were charities who worked in this even before carbon credits were a thing. Household air pollution kills 3.8 million people a year (over half of the deaths from _all_ air pollution!), and it's mostly associated with cookstoves.

> The fastest growing type of offset on the global carbon market subsidizes the distribution of efficient cookstoves in developing countries to reduce greenhouse gas emissions

'fastest growing' can be quite deceptive, if the fast-growing thing is relatively small.

Matt Levine's newsletter occasionally covers the carbon credits market; it gets _weird_.
Accessibility Errors Found in 2023
I'd like to add that it wasn't obvious there was a whole ultimate guide. I think brining in some of that content on the homepage would let people discover more use cases for the command.
I was wondering if dead code elimination also removes the debug symbols related to the dead code?

The ~ 4 MiB of debug symbols the article talks about are for the whole libstd or just the portions that actually ended up in the binary?

I think the new default makes sense but I'd love to have the option to build a lean but debuggable release binary with just the needed symbols.
I don't want to see stoves. Like ICEs, they should be left in the past, for healthier and more efficient solutions.
> I got this: https://imgur.com/a/tEqprGq

Are you sure you asked ChatGPT4 to draw the schematic for a simple buck converter? I'm asking because that looks like a near perfect pre-Schneider coencarbulator-control oblidisk transistor, ingeniously aligned for use with theta arrays!

I'm quite out of date with the latest VX work, but it looks impressive enough that I think you should ask the VX community [0] in case any of them may use this design to get improved delta readings.

[0] https://old.reddit.com/r/VXJunkies/
I still don't get it, could someone smarter than me explain?

helloWorld : '{IO, Exception} ()
helloWorld _ = printLine "Hello World"

The example above is followed by explanation "{IO, Exception} indicates which abilities the program needs to do I/O and throw exceptions." Well, which abilities does it need then? No idea.
Let's draw a paralel. It's algebra hard? No if you want to solve simple equations. Yes if you want to achieve masters hip.
If your data is small and your needs simple, don't forget about the old school scripts like http://www.javascriptkit.com/script/script2/jse/index.shtml
I think you're part right in that I don't think they will make any backwards incompatible changes. But they could still make things a lot better in two simple ways:

* Fix error reporting. Nobody is doing anything that relies on the current error reporting anyway because it's near useless.

* Add a slight templating change that means "after this parses as valid YAML, expand this bit, and check that the expansion is itself valid YAML before merging it in" with options to either replace the node, or merge in adjacent (the latter to insert in lists etc). You can do that without backwards incompatible changes by making a syntax change that still uses the go {{ ... }} blocks, but that starts with a directive they can make simply expand to a new template processing directive in the first pass. Then just add a second pass that operates on a parse tree (I've just written a template expansion mechanism that works on json/yaml parse trees, in fact; if we didn't need Helm charts primarily for distribution to partners that I don't want to make use a custom deployment tool, I'd be tempted to replace our Helm charts with an expansion of that.

Better error reporting and being able to avoid the incessant "| indent .." blocks and ensuring the output either generates valid yaml or can "contain" the error report to the generated sub-block would make it so much easier to use.
I don't think the EU has ever had net-neutrality as you'd call it in the US. The infrastructure and internet service are separated in most places which has similar outcomes. (and mobile data is wicked cheap for other reasons)

Most notably, this means a bunch of stuff gets "zero-rated" (e.g. free unlimited facebook/whatsapp with any phone plan, but other data is limited), which was explicitly called out as anti-competitive when the fight was going on about net neutrality in the states.
The rise and fall of the standard user interface
> It only works for you as you accept lowering the bar of that would pass as an "app".

I asked it to create an app, it created the app I wanted. That’s not “lowering the bar”. You’re just redefining words to prop up your incorrect claims.
The examples are lot more consistent and longer than other techniques we have seen before. Legs are not sliding on the floor as much as they do with other models. On the other hand, human faces didn't look good. e.g. the mona lisa smiling.

To me this looks like the first good video generation model.

EDIT: Just noticed its by Google, NVM, will never be released publicly.
In an idealized world, with infinite time and no financial constraints. In the real world, clients get screwed over all the time because they can't afford to pay lawyers that much.

It's a shame that ChatGPT isn't trustworthy, because if it were, it could really help reduce the cost of legal representation and create a fair legal system.
Build your own Phone Verification Service
> So, a huge project is set up to change the lifestyle (food preparation) of almost a third of the world population. To remove at most 2% of greenhouse gas emissions?

And improve health outcomes, these things are bad for people breathing nearby - especially when used in small areas.

You have actually cut out

> contributing to 2 to 3 million premature deaths annually

from the sentence!
My guess would be that they are chary of phones being used as spam relays.
It will only get better, based on what? A true believer just knows.
It sounds closer when it's sped up.
Ooo. I do not know that. … many decades too late.
> For longevity and cost? The rest of the market can't come close.

The Cybertruck was supposed to be a vehicle costing 40k with a range of 500 miles. It's a vehicle costing 100k with a range of 300 miles.

Reality distortion field in effect.
A naive implementation would be small, but a correct implementation (with some concrete definition of "correct") will need a sizable data table. I know this because I wrote that part of code [1] and somehow it is still in the std even though other algorithms now exist [2].

[1] https://github.com/rust-lang/rust/pull/24612

[2] https://github.com/rust-lang/rust/issues/52811
Was this via gene transfer?

Any idea how far off we are from overwriting entire genes with known perfect copies?

And if we can do that, then how much longer before we can iteratively overwrite segments until we've overwritten the entire genome in every cell with a perfect copy (derived by merging the sequences of many of our mutated cells)?
Usually agriculture is very subsidied everywhere yes because countries want cheap food for stability which is totally understandable.

There are nasty side effects to that though, the first one being that these subsidies usually are based directly or indirectly on the land size which made less efficient food products like meat much more subsidized than anything else.

We'll have to bring some more rational approach there if we want to improve the land use for climate change.

And then there's also the utility of the fuel used as you mentioned, there should be a difference of treatment between tractors and private jets indeed. But I don't think it needs to be too precise, cutting the worst offenders and taxing otherwise should be good enough.
> I’d be interested in understanding why the compilers never panned out but have never seen a good writeup on that. Or why people thought the compilers would be able to succeed in the first place at the mission.

It's a fundamentally impossible ask.

Compilers are being asked to look at a program (perhaps watch it run a sample set) and guess the bias of each branch to construct a most-likely 'trace' path through the program, and then generate STATIC code for that path.

But programs (and their branches) are not statically biased! So it simply doesn't work out for general-purpose codes.

However, programs are fairly predictable, which means a branch predictor can dynamically learn the program path and regurgitate it on command. And if the program changes phases, the branch predictor can re-learn the new program path very quickly.

Now if you wanted to couple a VLIW design with a dynamically re-executing compiler (dynamic binary translation), then sure, that can be made to work.
> Not when you look at their margins.

Look at BMWs and Mercedes margins. They're quite comparable with Tesla margins.

Let's see Tesla margins once they grow to try to match that insane market valuation that only makes sense if they sell 10+ million cars per year, most of which will have to be cars costing 20k€ per year, not 40k+€ per year.
Its not weird. A factual analysis doesn't do attribution if the facts don't support it.
If the user doesn't understand their tools maybe they should delegate to someone that does before they rely on the results

Especially if they're a lawyer! Llmao
Interesting idea, but isn't code addressable already in most languages?

We call them modules/libraries and we pip/npm install them from Github and you can keep track of changes/versions/PRs.
> It's always capped to 720p on Chrome/Win11.

Netflix appear to be testing 1080p in Chrome for Windows, and support it in Chrome for Mac now.
I maintain two projects that I use daily for both work and personal stuff, that have attracted a modest, but appreciable amount of contributions. In both cases, the codebases are relatively small (500-1k sloc), and laser-focused on doing exactly one thing well.

I'm very grateful for every contribution, no matter how small - people have found bugs, fixed real problems, done cleanups. The hardest part is telling someone that a feature/idea does not have a place in this project. I think the general emphasis on minimalism tends to help here - I've never had to deal with any drama.

In terms of workload, again - the minimalist design and extremely clear goals have helped so much. I got trapped by that once before - I volunteered to build an internal automation tool (that saved someone else from doing like 1h/d of work), but literally couldn't spare 1h/mo to maintain it; the cause of the maintenance burden was an influx of changes in the APIs of the external services it integrated. So now I'm much more careful about volunteering to maintain integrations with external tools; in case of these two projects, the targets are SSH and ZFS - both have extremely stable interfaces.

In both cases it was absolutely worth it to publish and (very lightly) promote the projects; since these are "devops" tools that theoretically have unlimited potential for causing great harm, having any response at all helped reassure me that the code I'm running against production infrastructure has fewer unknown bugs. https://i.pinimg.com/474x/2f/e0/87/2fe08785e8eb112cada6da789...

The projects: <https://github.com/rollcat/judo>; <https://github.com/rollcat/zfs-autosnap>.
I'm not sure if disbarring is appropriate here, but then again I'm no legal professional.

I don't know what the appropriate response would be to a lawyer lying to the court and making up facts. I don't think something as bad as making up lawsuits even happens in normal legal proceedings. I'd presume fines and other types of punishment, depending on if the lawyer is stupid enough to lie about using ChatGPT like in the American case.

The person who made the mistake of hiring this lawyer will probably have grounds to sue them for malpractice, especially if they end up losing this case. I know I'd want my money back if my lawyer didn't even bother to read the paperwork they were filing.

This lawyer will now have "lawyer lied to the court" show up the moment you Google their name. I think that, plus a hefty fine, is more than enough punishment. Whether or not their future clients will trust them after this is up to them.
"""That's one of those irregular verbs, isn't it? I give confidential security briefings. You leak. He has been charged under section 2a of the Official Secrets Act.""" - Yes Minister.

To rephrase for the subject: We're only human, mistakes are to be expected; They are a idiots, mistakes are to be expected; that thing is just a glorified calculator, mistakes are to be expected.

(There's a short story I found I couldn't bring myself to finish, “Zero for Conduct” by Greg Egan, where the lead character is bullied by someone who has a similar disregard for her intelligence; I know one cannot use fiction to learn about reality, so I will instead say that this disregard of human intelligence by other humans happens a lot in real life too, the racism and xenophobia of βαρβαρίζω can still be found today in all the people who insist that ancient structures like the pyramids couldn't possibly have been built by the locals and therefore it must have been aliens).

> AI hype is just sales talk

But where does the hype end and the reality begin?

> I had to convince a customer the other day we cannot write whole apps with ChatGPT. As far as I know, not a single example exists of a full app written by ChatGPT. It just can't be done, because ChatGPT is not reasoning!!! It is not intelligent.

I will agree ChatGPT does indeed make incoherent solutions — one test project was making a game in JS, it (eventually) gave me a vector class with methods for multiply(scalar) etc., but then tried to use mul(scalar).

But ironically, I've also made a functioning (basic, but functioning) ChatGPT API interface… by bolting together the output of ChatGPT. I won't claim it's amazing or anything, because I'm an iOS developer and the thing "I" made is a web app, but it works well enough for my needs (just don't paste HTML into the query section, because I stopped adding to it when it was good enough for my needs and therefore only have a very basic solution to code being shown in the chat list, there's a lot of stuff that would be improved by using simple libraries but I didn't want to).

> I think it's best described as a word-calculator, or autocomplete on crack.

And that's the same error in the opposite direction.

If I understand right, GPT-3 is literally the complexity of a mid-sized rodent. Thus the metaphor I use is:

Imagine a ferret was generically modified to be immortal, had every sensory input removed except smell, which was wired up to a computer. The ferret and computer then spend 50,000 years going through 10% of all the text on the internet, where every sequence is tokenised and those tokens are turned into a pattern of olfactory nerves to stimulate, and the ferret is rewarded or punished based on how well it imagined the next token.

You're annoyed that this specific ferret's jokes are derivative, their code doesn't always compile, that they make mistakes when trying to solve algebraic problems, that their pecan pie recipe needs work, and that they make mistakes when translating Latin into Hindi.

I'm amazed the ferret can do any of these things.
I was extremely confused by this statement because well... I exist. I didn't start speaking until around 5 because of various health issues and I wouldn't say I was reasonable at it until I was a preteen, but I definitely acquired language, just extremely slowly.
I am curious what the differences are. Did they bet on some unstable features that since changed, did they run into some soundness fixes, or are there genuine regressions in the stability guarantees of the compiler that are pushing them in this direciton?
> There was a beautiful period before the fragmentation happened where there was good content on Netflix, discovery was still solid, and competitors hadn't caught up yet so lots of studios were licensing to Netflix. That is when I paid for it. But now every network has their own streaming service for $20/m and we don't subscribe to any of them.

"I am only willing to subscribe to you on terms where the company was having to borrow billions of dollars a year to stay afloat" does not make you very valuable as a customer, and you shouldn't be surprised nobody makes what you want on that basis.
From TFA, "the case was a high-net-worth family matter" so probably not an existential threat to anyone.
I wouldn't advise it, powerline technology is very flaky and even less reliable than WiFi. I have a Devolo G.hn powerline bridge and my parents as well, the one downstairs requires power-cycling about once a week (why they don't just put a watchdog timer in the unit eludes me).
That mirrors my experience. I've hardly ever met really experienced people that are hardstuck on language flaming. Programming languages are tools, with general and situational up and downsides.
Sure, a second agent can do fact and sanity checking with retrieval too.
> The main downside to factobatteries is the same as for e-fuels: high capital cost due to low equipment utilization.

shorter equipment life, and significantly reduced efficiency for things that require heat. A lot of processes are continuous, where the cost of stop/starting is much greater than absorbing the energy price differential. aluminium, smelting is a prime example. Yes you need kiloamps to smelt, but you also have to keep the alumina molten.