California; from what I see, kids do drink soda, including coke, a lot.

Really? Like where in Canada.

You are rarity/have sheltered kid well. ( Have met some families where kids never had fast food, sweets, like rod and todd Flanders in the Simpsons lol)

I'm on West coast, kids in elementary school, and half the bday parties in the lower grades families serving mini cans of coke for the kids (and juice with just as much sugar, pick your poison).

Goto any fast food joint/mcd and see 5yr olds with a soda.

I have traveled throughout Canada to east coast and kids love soda pop/coke too.

Not all kids like soda, many do. Not all parents allow their kids to regularly have side, but may give as a special treat.

More and more kids are drinking coffee due to Starbucks type drinks, but that's mostly just sugar.

Everyone calling this a copy is willfully overlooking that the major labs are nakedly copying each other AND smaller vendors constantly. Codex and Claude Code didn't just materialize at the same time. And frankly, they both copied Aider in the beginning.

Then there is reasoning itself.

Agent SDKs.

On and on.

This isn't a game of hide and seek. It's a race. The fact that Kimi is Chinese doesn't make their stride any more a copy than 90% of what the major labs are outputting these days.

To me all these coding tools look indistinguishable from each other. If you changed the logo on one I wouldn’t notice.

As a parent of 4 kids, I find Shorts to be morally reprehensible. I think the people working on them should honestly question their decision framework. Youtube is an international treasure. One of the greatest learning resources of all time. I want my children to have as broad of access to youtube as possible. I can't do that though, because they can go from watching a very interesting documentary or something fun like a Mark Rober video, to being zombified unwittingly in to watching videos of a woman putting on makeup and making pronouncements in to the video camera.

It's not just annoying. It's not just inconvenient. It is harmful.

I went ahead and turned my youtube history off, I only see my subscriptions and subscription shorts.

I get zero recommendations of new/related videos (It breaks the algorithm) and it can't remember at what point of a video I was when I left it, but it is a very cheap price to pay for the shorts brainrot

It’s so incredibly frustrating the effort required to keep the site/app from funnelling my kids into low effort slop.

I like to use it with my kids (under age of 6) to search and find interesting videos with them. The algorithm combined with the TV apps’s UX makes it such a frustrating experience.

There is so much potential there, but searching for things like space videos inevitably funnels me into AI generated slop and conspiracies. Searching for upcoming movie trailers funnels me into completely fake trailers. It’s clear the platform is an engagement engine first and a knowledge base second.

Block really did not come down from it's COVID/ZIRP era high # of employees as much as many other companies, and it's COVID era headcount growth was extremely rapid by any standard.

In some ways this isn't daring, future looking leadership... it's much more lazy leadership that took a while to adjust to market demands.

I went through the setup process for Openclaw. Near the end I felt like I had wrestled more with setting it up than I would have had to if I had just built it from the ground up. So I pointed Pi at Nanoclaw and asked it to review it and build me a minimal clone. It took a few minutes and I had the core of something that is easier to maintain (for me) than some unknown large and cumbersome system, or whatever Openclaw is.

To each their own.

One concern I have is API key management.

.env files or injecting secrets at startup via a secret manager still risks leaking keys.

I vaguely recall an implementation that substitutes secret placeholders with real secrets only during outgoing calls to approved domains which sounds better. However, you're still trusting an agent on your machine with command execution.

Funny enough ooenclaw is based on Pi.

I’m kind of curious what you do with it. I feel like the real value is integrating it with everything but then even if it’s nanoclaw or simpler majority of the worthy things are on the unsafe side.

Would love to hear your experience as I’m planning to do the exactly same.

The most interesting thing for me is that I built an extension for Pi that has it recognize when it does not know how to do something I am asking and it then attempts to make its own extension and/or skill to enable whatever that functionality is. Best example there is I just told it to make a todo list for me, and so it made a skill that uses a local file to track todos and follow up on them. I instructed it to make an LLM call to find the best suggested follow up timing to remind me.

So... the real value so far is I find it fun? It isn't the "life changing need to go make a tweet!!" level for me.

Pi is great so it's sad to see that it only gained momentum because some trash tool like openclaw uses it.

I think developer of pi and openclaw are friends, not sure if it matters but also Pi has its own small following. I agree with you it’s such an elegant piece of project with an awesome clean architecture. (Also see: oh my pi)

The real lesson is if you ignore security and data disasters agentic AI is easier than anyone expected.

Yeah it does seem a little fragile. Still battling with working out why it pegs CPU at 100% permanently on a VPS I tried using. Literally just from installing the base

I wonder how long npm/pip etc even makes sense.

Dependancies introduce unnecessary LOC and features which are, more and more, just written by LLMs themselves. It is easier to just write the necessary functionality directly. Whether that is more maintainable or not is a bit YMMV at this stage, but I would wager it is improving.

What a bizarre comment. Take something like NumPy - has a hard dependency on BLAS implementations where numerical correctness are highly valued for accuracy and require deep thinking for correct implementation as well as for performance. Written in a different language again for performance so again an LLM would have to implement all of those things. What’s the utility in burning energy to regenerate this all the time when implementations already exist?

What do supply chain attacks look like against one of these containers?

Interesting thought (I think recently more than ever it's a good idea to question assumptions) - but IMO abstractions are important as ever.

Maybe the smallest/most convenient packages (looking at you is-even) are obsolete, but meaningful packages still abstract a lot of complexity that IMO aren't easier to one-shot with an LLM

Concretely, when you use Django, underneath you have CPython, then C, then assembly, and finally machine code. I believe LLMs have been much better trained on each layer than going end-to-end.

The most popular modules downloaded off pip and npm are not singular simple functions and cannot easily be rewritten by an llm.

Scikit-learn

Pandas

Polars

This is like saying Wikipedia doesn't make sense because there's now Grokipedia

there are people (on Hacker News Dot Com, even) who believe this without a shred of shame or irony.

I consider packages over 100k download production-tested. Sure LLM can roll some by themselves but if many edge cases to appear, (which may already be handled by public packages) you will need to handle it.

Don't base anything on just download numbers, not only is it easily game-able, it's enough with like 3 small companies using a package and push commits individually and CI triggering on every new commit for that number to lose any sort of meaning.

Vanity metrics should not be used for engineering decisions.

At times I wonder why x tui coding agent was written in js/ts/python, why not use Go if it's mostly llm coded anyway? But that's mostly my frustration at having to wait for npm to install a thousand dependencies, instead of one executable plus some config files. There's also support libraries like terminal ui that differ in quality between platforms.

Funny because as a non-Go user, the few Go binaries I've used also installed a bunch of random stuff.

This can be fixed in npm if you publish pre-compiled binaries but that has its own problems.

>the few Go binaries I've used also installed a bunch of random stuff.

Same goes for rust. Sometime one package implicitly imports other in different version. And look of rustup tree to resolve the issue just doesn't seem very appealing.

Well you do need to vet dependencies and I wish there was a way to exclude purely vibe coded dependencies that no human reviewed but for well established libraries, I do trust well maintained and designed human developed libraries over AI slop.

Don't get me wrong, I'm not a luddite, I use claude code and cursor but the code generated by either of those is nowhere near what I'd call good maintainable code and I end up having to rewrite/refactor a big portion before it's in any halfway decent state.

That said with the most egregious packages like left-pad etc in nodejs world it was always a better idea to build your own instead of depending on that.

I've been copy-pasting small modules directly into my projects. That way I can look them over and see if they're OK and it saves me an install and possible future npm-jacking. There's a whole ton of small things that rarely need any maintenance, and if they do, they're small enough that I can fix myself. Worst case I paste in the new version (I press 'y' on github and paste the link at the top of the file so I can find it again)

As long as "don't roll your own crypto" is considered good advice, you'll have at least a few packages/libraries that'll need managing.

For a decent number of relatively pedestrian tasks though, I can see it.

LLMs are great at the roll you own crypto foot gun. They will tell you to remember all these things that are important, and then ignore their own tips.

Tokens are expensive and downloading is cheap. I think probably the opposite is true, really, and more packages will be written specifically for LLMs to use because their api uses fewer tokens.

It still takes a little bit of time for an LLM to rewrite all the software in existence from scratch.

That was already the case for a lot of things like is-even.

You have insane delusions about how capable LLMs are but even assuming its somehow true: downloading deps instead of hallucinating more code saves you on tokens

And your opinions on how average people use these tools are 100% accurate?

If average people try vibecoding their dependencies, they’ll fail, simple as that. We’ve already seen how that looks with the “web browsers” that have recently been vibecoded.

There's a new web browser project today that's a heck of a lot more impressive than the previous ones - ~20,000 lines of dependency-free Rust (though it uses system libraries for image and text rendering), does a good job of the Hacker News homepage: https://news.ycombinator.com/item?id=46779522

Thanks for the heads up, that does look much more interesting.

I don't think it really affects the point discussed above for now, because we were discussing average users, and by definition, the first person to code a plausible web browser with an agent isn't an average user - unless of course that can be reliably replicated with any average user.

But on that note, the takeaways on the post you linked are relevant, because the author bucked a few trends to do this, and concluded among other things that "The human who drives the agent might matter more than how the agents work and are set up, the judge is still out on this one."

This will obviously change, but the areas that LLMs need to improve on here are ones they're notoriously weak on, so it could take a while.

at least 5% more accurate than average LLM

Docker desktop has a pretty nice sandbox feature that will also store your CC (and other) credentials, so you don't have to re-auth every time you create a new container.

Funnily enough, we shipped the Docker Desktop VM a decade ago now (experience report at https://dl.acm.org/doi/10.1145/3747525). The embedded VM in DD is much more stripped down than the one in Claude Cowork (its based on https://github.com/linuxkit/linuxkit), and its more specialised to container workloads rather than just using bubblewrap for sandboxing (system services run in their own isolated namespaces).

Given how many products seem to be using this shipping-Linux-as-a-library-VM trick these days, it's probably a good time for an open source project to step up to supply a more reusable way of assembling this layer into a proper Mac library...

This is one of those announcements that actually just excites me as a consumer. We give our children HomePods as their first device when they turn 8 years old (Apple Watch at 10 years, laptop at 12) and in the 6 years I have been buying them, they have not improved one ounce. My kids would like to listen to podcasts, get information, etc. All stuff that a voice conversation with Chatgpt or Gemini can do today, but Siri isn't just useless-- it's actually quite frustrating!

Siri still can't play an Apple Music album when there is a song of the same name.

Even "Play the album XY" leads to Siri only playing the single song. It's hilariously bad.

Or the even more frustrating:

Me: "Hey Siri, play <well known hit song from a studio album that sold 100m copies"

Siri: "OK, here's <correct song but a live version nobody ever listens to, or some equally obscure remix>"

Being these things are at their core probability machines, ... How? Why?

> Being these things are at their core probability machines, ... How? Why?

Is Siri a probability machine? I didn't think it was an LLM at all right now? I thought it was some horrendous tree of switch statements, hence the difficulty of improving it.

Apple search is comically bad, though. Type in some common feature or app, and it will yield the most obscure header file inside the build deps directory of some Xcode project you forgot existed.

It’s absolutely insane that you can’t say “Siri, play my audiobook” and it play the last audiobook you listened to. Like, come on.

Or when you are driving, someone sends a yes-no question where the answer is no.

Siri: Would you like to answer?

Me: Yes

Siri: ...

Me: No + more words

Siri: Ok (shuts off)

Not exactly the same, but kinda: my gen 1 Google Home just got Gemini and it finally delivers on the promise of like 10 years ago! Brought new life to the thing beyond playing music, setting timers, and occasionally asking really basic questions

It remains to be seen what the existing HomePods will support. There’s been a HomePod hardware update in the pipeline for quite some time, and it appears like they are waiting for the new Siri to be ready.

it's not going to help them. For Siri to be really useful it wouldn't need deep system integration and an external model is not going to provide that. People don't believe me when I said it about Apple Intelligence with open AI

Thats what you get for buying into one ecosystem and sticking with it. All that stuff has been available on Alexa for a decade.

You can tell just from the title alone.

I am currently employing a consultant for something. It's something I don't want to do myself and they are doing what I need, but it's so painfully obvious they are just vanilla ChatGPTing everything it's almost funny at this point.

How can you tell from title alone? I am clueless.

The median age for MAID recipients is over 77.

Both can be true, I think.

50% is not a vast majority, so that's a red herring

50% can be a massive majority if there is a plethora of opinions