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I moved away from mac because of the OS and couldn't be happier. The hardware may be great but non-Apple hardware is fine too, and Linux is significantly better experience than MacOS these days.

No idea, and I don't think anyone does, hence the prediction that it won't happen anytime soon. Either we need perfect simulations (seems almost impossible) for training at scale or fundamental algorithmic breakthroughs in learning from very sparse data that we can collect in the real world.

I fail to understand why 888 KiB matters if it's just a wrapper around a cloud api.

I'd love to see what is being achieved by these massive parallel agent approaches. If it's so much more productive, where is all the great software that's being built with it? What is the OP building?

Most of what I'm seeing is AI influencers promoting their shovels.

It's the same as it has always been, the only difference is that you are being bombarded with these issues because you are terminally online and use social media while previously you were blissfully ignorant. The solution is to touch grass and stop worrying about things outside of your control.

Not anytime soon. The big leaps in LLMs don't really carry over to robotics much, aside from some computer vision stuff. I'd say we're still 10+ years out from robots cooking for you. Maybe we'll get some kind of dedicated 'cooking machines' if there's money in it, but not humanoid cooks walking around your kitchen. And even that's being pretty optimistic because right now there's no clear way to get there, and we'd need some real fundamental breakthroughs to make it happen.

"Humans are now writing code in strict specification language so that AI agents have completely context and don't mistakes. This specification language is called C' and has led to a whopping 20% reduction of code. 1000 of C++ code can be expressed in no more than 800 lines of specification C' code written by humans"

Does that mean more issues will show up soon?

https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing is a good starting point. You start seeing patterns at some point.

But more important than the writing style, there is no interesting content here. It's all generic statements and platitudes with a bunch of generated links.

I don't know either what the solution is other than human verification, but nobody wants that. Perhaps the times of semi-anonymous online communities are over and the best you can do now is follow real people you trust that can filter content for you.

How is this highly upvoted and on the front page? It's so clearly at least 50% AI written slop, probably closer to 95%. Wow HN these days... this site is dying, completely overrun by bots.

It can schedule stuff and run in a loop, so it's like claude combined with cron. Truly amazing technology.

Where is the detail? Examples? Something concrete? I don't think it is, but it does read like LLM generated content marketing. Lots of generic statements everyone knows. Yes, dev environments are helpful. Have been for 20 years. Yes, context and rules are important for agents. Surprise.

TLDR "look we use AI at Stripe too, come work here"

It depends. If you have an LLM that uses reasoning the explanation for why decisions are made can often be found in the reasoning token output. So if the agent later has access to that context it could see why a decision was made.

If you search you can easily find sites to buy aged HN accounts, lots of them. Just like reddit accounts.

Can someone explain to me what AGI means? What is the concrete technical definition? How do we know it is achieved?

You're not the only one. I've worked quite a bit with Minio, Arrow, and Spark, and I don't really understand the point the article is trying to make or how it's related to Minio. It's either badly explained or just a fluff piece throwing together a bunch of technologies.

To me, the article says "columnar formats that don't require deserialization are good, oh, and you can use Minio to store data!"

I typically only look for upvoted content. Prefix your searches with "site:reddit.com", "site:news.ycombinator.com" or "site:stackoverflow.com" - The social/human filter is quite a good one in my experience and it gets rid of all the Medium-like personal branding fluff.

I had the opposite experience. The early TF versions were difficult to use in that they required a lot of boilerplate code to do simple things, but at least there was no hidden complexity. I knew exactly what my code did and what was going on under the hood. When I use today's high-level opaque TF libraries I have no idea what's going on. It's much harder to debug subtle problems. The workflow went wrong "Damn, I need to write 200 lines of code to do this simple thing" to "I need to spend 1 hour looking through library documentations, gotchas, deprecation issues and TF-internal code to figure out which function to call with what parameters and check if it actually does exactly what I need" - I much prefer the former.

Having barriers of entry is not always a bad thing - it forces people to learn and understand concepts instead of blindly following and copying and pasting code from a Medium article and praying that it works.

But I agree with you that there are many different use cases. Those people who want to do high-level work (I have some images, just give me a classifier) shouldn't need to deal with that complexity. IMO the big mistake was trying to merge all these different use cases into one framework. Let's hope JAX doesn't go down the same route.

I'm skeptical of JAX. It feels good right now, but when the first TF beta version came out it was very much like that too - clean, simple, minimal, and just a better version of Theano. Then the "crossing the chasm" effort started and everyone at Google wanted to be part of it, making TF the big complex mess it is today. It's a great example of Conway's Law. I'm not convinced the same won't happen to JAX as it catches on.

PyTorch has already stood the test of time and proven that its development is led by a competent team.

> I think the most dangerous distractions are the ones that feel productive but don’t actually work toward your goals. For example, browsing hacker news.

Time to set up my emacs config to manage my life and stop wasting time.

Another illusion is that these are absolute numbers. They're relative. When you're young, $100k is a lot of money. Enough to be happy and worry-free. Once you have that, you need $1M in cash to be happy and retire. Once you have $1M but realize everyone around you is making $1M/year to retire at $10M you suddenly need $10M. This will repeat itself, and you'll never have enough. You'll just become more miserable realizing how much more everyone else has. Yeah, platitudes. We all know this, right? Turns out, this cycle is totally unconscious and incredibly hard to break once you're in it and surrounded by such people. Get away while you can.

At least for me, this was one of the reasons why working at FAANG where everyone is striving for $$$ made me miserable. It gave me such a warped view of the world.

Not everyone is optimizing for making money. I left a Silicon Valley FAANG for another country more than five years ago, even though I was making ~3x there compared to what I made in the new place. Even with the higher living costs, that's still a lot more money.

I regret nothing. I'm so much happier here than I ever was in the Bay Area, and I doing this early in my life allowed me to have a lot of fun and stories to tell. Why would I "waste" my precious 20s and early 30s being miserable and retire at 35? Who even wants to retire? I wouldn't even know what to do with that money? Buy a house and sit in the garden the whole day? :) Not the life I wanted.

This whole "make a lot of $$$ and be miserable in your 20s to optimize for the future" is such a common thing I see in SV and HN as part of the narratives that these VCs are pushing. I think you have it all backwards. You can make money any time. You can never back to your 20s and 30s where you don't have health problems, are full of energy, and have an easy time making friends.

I'm not super familiar with prediction markets. Could someone explain how exactly these markets create initial liquidity, how they set the number of tradeable shares, etc?

I generally like next.js, but I have since built some things in Svelte [0] and prefer it for simple projects. Not dealing with JSX and complex React components is refreshing. It just works. There are some upsides to its static generation (with sapper) as well - it can create static pages for things that would need API routes in Next.

TLDR; For large projects that must make use of the React ecosystem I'd take Next. For smaller SPA projects I prefer svelte.

[0] https://svelte.dev/

I'm in the same camp. I'm quite interested since it mentions asset data as an example, but I have no idea what this does from looking at the landing page. Does someone have an end-to-end example? Since this stores arrays, is this kind of like Apache Arrow but with a persistence layer? Is this suited for large amounts (~1TB) of time series data?

Someone asked for a more nuanced perspective, so here we go.

For a lot of AI researchers, OpenAI has been a huge disappointment. We had hope that OpenAI would be the company to democratize AI with good open source work, transparency, no PR bullshit (aka DeepMind), and evangelism. That they would develop in the open, and perhaps even do research in the open. You know, kind of like the name says.

It all started out okay with their release of OpenAI Gym, tutorials, leaderboards, and competitions around that. That was when Karpathy was still there. Over time, many projects have become abandoned, poorly maintained, or just disappeared [1]. And many projects they promised never happened [2]. OpenAI became just another research lab obsessed with publishing papers in closed (!) journals, indistinguishable from Google AI, DeepMind, FAIR, MSR, and the many others.

There is nothing open or different about them. Most paper code is not published, and even when it is, it's just the typical poorly written and unmaintained research code that you see from other labs. None of their infrastructure is open source either, because it's needed to maintain their competitive advantage to train models and publish research papers. GPT-3 being offered as a paid API to a select number of people is latest joke in a long series of other jokes. All of this would be fine, if it was not for the name and branding of being a transparent and good-willed nonprofit company. It is just misleading and that rubs many people the wrong way, as if the whole "open" thing was just a PR stunt.

HuggingFace [0] these days is pretty much what OpenAI should have been, but only time will tell what happens.

[0] https://huggingface.co/

[1] https://www.reddit.com/r/MachineLearning/comments/aqwcyx/dis...

[2] https://github.com/openai/roboschool/issues/159

Yes, but that's a bad example because pretending to be a lawyer is hard. A better example would be gurus spreading nutrition recommendations that are not wrong per-se, but extremely simplified. Nutrition is a complex topic and individual differences make it hard to generalize. Let's say the information are so simplified that they are likely to hurt people who blindly follow them without doing further research. So, should this information be taken off air or not? I would say yes, and perhaps you would say no. In either case, I don't think the answer is quite as clear-cut.

You're right that this is an ungrounded accusation and I changed my comment.

For what it's worth, I know several people at competing labs who applied for access and didn't hear back. If you are doing science, aren't other scientists, especially those who are critical, the first ones that should get access?

I've seen multiple profiles just like that one tweeting amazing things about GPT-3. I didn't keep track, but [0] and [1] just turned up doing a quick Twitter search. There are a lot more.

It's quite suspicious that instead of giving access to AI researchers who have the ability to evaluate the model and may be skeptical, OpenAI has largely been giving access to Silicon Valley startup people and VCs who know very little about AI but say how game-changing it is. Perhaps it's just their network with extra incentives. Gwern being the only exception that comes to mind.

[0] https://twitter.com/arram/status/1281258647566217216

[1] https://twitter.com/mckaywrigley/status/1284110063498522624