There is a hack for that: write the comments before and/or as you write the code. When things are still unclear, weird.
Of course do a final pass on them to ensure that they are correct and useful in the end.
This is one example of document as-you-go, instead of doing it after "the work" is done. I find it generally leads to better outcomes. Doing documenting only at the end is in many ways the worst way to do it.
Norway is currently building very little new power generation at all, unfortunately. NIMBYism is blocking onshore wind, ecological concerns blocking hydro. This has already started hurting, and is projected to get worse. Before taking any AI datacenters into account.
The wealth tax in itself probably does not explain the issues you see. They are more likely related to the global increases in interests.
Now the recently introduced exit tax has some serious problems, in that is has triggered and exodus both of genuinely rich people, and of prospective founders who can easily get rich on paper and have massively disproportionate tax effects. That law is very stupid - especially that unrealized gains are taxable 100% at shares market rate.
Education is free. Whether one considers the social support to be UBI is a matter of debate, I guess, but right now it would leave you decidedly poor/low-class.
Print in TPU? There is a limit to how soft one can get, and the finish is not so great (stringing). But fantastic for very tough semi-soft functional objects. Ninjaflex Cheetah and Armadillo are my favorites, but generic TPU also works. Direct drive extruder recommended, though I manage on my cheapo Bowden fed Ender 3.
If enough people believe it is true and act on this belief, it will become true (to a larger degree, at least). There is a massive shift of the direction of money happening right now, away from workers into "AI" (LLM based systems). The more of that shift can be produced now, the stronger the AI companies will be in the future. Because they need massive capital investments in compute, engineering investments in efficiency, and to reach economies of scale, to be able to really compete with human-based workforce.
Yeah it's partly a "this is the inevitable winning team" - join before it is too late. The earlier the better! There are some parallels to how MLM and ponzi schemes are sold.
Another aspect is for distraction. OpenAI for example have had periods where they talked a lot on existential risk (humanity is doomed due to AI overloads, yadda yadda). This can serve as a very useful distraction to avoid talk about more plausible risks in the foreseeable future - such as massive concentrations of wealth, increased inequality, large amounts of unemployment, large scale personalized psychological manipulation, etc.
One of the ways it can work is that people may reject the most extreme case (say end of humans beings in control), but accept some milder version (most jobs will be done by AI, or most jobs will be AI assisted). The fear of an extreme can cause people to rationalize the "milder" outcome - independently of whether there are good arguments for that outcome, or even whether the outcome is desirable, or better than status quo.
The investor class are not dependent on wages, so their livelihoods are not at stake. Same with big corporate partners, they are hoping to improve competitiveness by having fewer employees and CEO take a bonus for that.
Regular users in fear of their jobs may act on that, in hope that they can reskill and transition by being AI savvy.
I do not agree with the argument they make, but I understand what they are are playing at, and that unfortunately it can be effective.
They could be be playing into such a narrative, even though they have different motivations.
Certainly Altman has had a few moments where he is saying "this stuff is getting so good it is society-scale dangerous". His motivations are to create FOMO with investors and users, and to try to form the regulatory landscape in their favor.
I do not think they are aiming to be cashflow positive now. That might not be possible. Though if it is within range they might want to go for it. Because the stamina needed to win this race is going to be immense. Especially since OpenAI is going hard for scaling up via investor funding, and Google can afford to loose/invest a couple billions annually by diverting from their main revenue sources.
A realistic business plan would be to burn cash for many years (potentially more than a decade), and bank on being able to decrease costs and increase revenue over that time. Investors will be funding that journey.
So it is way too early to tell whether the business plan is unsustainable. For sure the unit economics are going to be different in 5 and 10 years.
Right now is very tough though- since it is basically all early adopter power user types, which spend a lot of compute. Later one probably can expect more casuals, maybe even a significant amount of "gym users" that pay but basically never uses the service. Though OpenAI is currently stronger for casuals, I suspect.
Over the next decade, hardware costs will go down a lot. But they have go find a way to stay alive (and competitive) until then.
That just gives your team another thing to do, trying to police a ToS clause. And one would have to define abuse somehow, which is quite tricky. There are a lot of guite legitimate but expensive ways to use such a general thing as LLM compute.
Investors love MRR/ARR, so I do not think we will see that as the main option anytime soon.
That said, you can use the Claude API to get usage-based billing.
Would have loved to get some more insights. Cost estimates, before and after, for example. But also if any architectural changes where needed, or what kind of other challenges and learnings they got from the migration.
ChatGPT and Claude do the same. And I have noticed that model performance can often degrade a lot before such a hard limit. So even when not hitting the hard limit, splitting out to a new session can be useful.
Context management is the new prompt engineering...
Agreed. I chose gevent over asyncio for a backend in 2019, and we still using it. Works pretty well. No plans to phase out gevent just yet.
Though the community has clearly centered on asyncio by now. So if I were to start a new backend today, it would reluctantly be asyncio. Unfortunately...
Some percentage will not comply. Either in bad faith or just lazyness/incompetence/accident/whatever. But as long as that number is relatively low and a decent chunk pays "their dues", it is not really a problem.
Why is "AI" (current LLM based systems) a danger on the level comparable to nukes?
Not saying that it is not, just would like to understand your reasoning.
I agree with your reasoning. But the conclusion seems to be throwing the baby out with the bathwater?
The same line of thought can be used for any (new) tool, say a calculator, a computer or the internet. Shouldn't we try to find responsible ways of adopting LLMs, that empower the majority?
I like the idea. But it is missing something to actually get money out of companies, I think?
Because even when there are people in a company that are nominally willing to pay, there is usually so much friction/hassle to actually get money out of a company - that it most often ends up not happening for open source. Unless there is something that "forces" them.
One nice thing about GitHub sponsorship is that there is only one bill for the sponsor, and one can support NN projects/creators there. I think it is even bundled with the regular Github invoice?
I would only say that they have become maintainers if they consistently do so, over time. Including helping out in areas which is not directly useful to them. And thinking about the whole picture, not just individual features and bugs. And also putting in the time when it is needed, even though other obligations are pulling at them.
Of course it is somewhat of a continiuum. And it always starts with being a contributor. So that is on the right path.
Another very good way to lie is to set up the framing such that any interpretation of any fact skews in your desired direction. Including which things are to be considered important/relevant, what kind of argument is considered valid/not. Done well, people might not even pick up that there is lying/misdirection involved. Rig the game.
That expert systems were not financially viable in the 80-90ies does not mean that LLM-as-modern-expert-systems cannot be now.
The addressable market for such solutions has expanded enormously since then, thanks to PCs, servers, and mobile phones becoming ubiquitous. The opportunity is likely 1000x (or more) what is was back in those days - which shifts the equation a lot as to what is viable or not.
BTW, have you tried out your challenges with a LLM? I expect them to be tricky to answer direct. But the systems are getting quite good at code synthesis which seems suitable. And I even see some MCP implementations for using constraint solvers as tools.