curious_cat_163

> At the root of it all is people. I care about people (their existence, their happiness, my connections to them, their connections to each other)

100% agree. That's what it is all about. That, and our time.

> and any use of LLMs is (in my mind) anti-people

Hard disagree. A lot of good can be done for the people with this tech. Blanket rejection of an entire category of technology has not worked out well for the disadvantaged communities in the past. We have plenty of history.

I have used pgvector on an Azure DB. It did a "reasonable" job. I am not an expert on HNSW vs PRISM but because it is all backward compatible, I am curious why they didn't just send a PR to the pgvector folks?

Like, I would love to learn more about the design process that led to a whole new thing. I am sure the decision was not taken lightly, but the README is very thin on the details for why anyone should go through the troubles above, at least in the making of it.

I recently started an AT Proto PDS.

> most people in bluesky moved to bluesky driven by hate and fear instead of love and curiosity

My personal experience has been a lot more balanced. But, I don't want to say that there is no hate and fear on ATProto...

Agreed.

Also, it is not a monolith. There are parts of the internet (arguably like HN) that are off the charts when it comes signal-to-noise ratio, for me personally.

> I'd skip M5 and M6 chips for LLM work and wait for a year for M7.

Please say more? Is it because it is a one-time cost, unlike a recurring subscription of Claude/Codex?

Looks like their auth is broken. Do they ever publish postmortems?

Ah, my bad. I may have misread it.

> It’s going to move us from the big iron era of AI to small portable brains. Nature has already proved it’s possible with 20 watts and very little heat generation.

Agree. I know enough about the vagaries of scientific progress to not put any money on any timeline but directionally, that's where we are headed.

> And I think the data center buildout will end in carnage.

Disagree. And this is quite the leap from the previous statement, btw. The carnage happens if the demand for general purpose GPU compute disappears and even then there are so many ways to salvage the asset.

I think the test above is about tool calling... That's how I read it. The issue here is known as "out of distribution detection" in the old-timey classification world.

I am not sure how a micro model will fundamentally solve it. Would love to understand what dannyw and team did there?

Thanks!

When is Gemma5 coming out? :-)

Boris is right about:

> The verification is probably the single most important thing that people do not get right, largely.

and so, so, so wrong about giving this prompt (for verification) and expecting that it succeeds at building a "good" app:

> I want you to run the Electron app in the Mac virtual machine, screenshot it, and then look pixel by pixel. Compare it to the Swift version. Don’t stop until you’re done.

Given he has let it rip for two weeks, I am assuming they have been post-training Claude for some version of this to be more _likely_ successful than not. However, IMO, the verification that you get from a visual comparison is shallow.

To state the obvious: there is a lot more than what meets the eye. But, I think, one could prompt a Fable/Opus 5 to actually go verify that "lot more"...

The question is: should one be imperative in asking for a specific types of verification (like a rubric) vs hoping that the Google/Anthropic/Open AI/Moonshot's post-training will take care of it.

I think, as things stand today, even with the best-in-class models today, I would be leaning more imperative. And it is not because I am an expert in SwiftUI or such. It is because I want to be able to say that _I_ (i.e. the human) verified that this thing works.

Fundamentally, we needed to decide, as a society, that should someone (anyone) have access to neural networks that can launch successful cyberattacks by discovering new zero-days and/or leveraging knowledge about recent zero-days and applying it in an adversarial way.

We de-facto settled on the answer being yes. These foundation models companies have access to such systems.

However, because the answer to the first question is yes, now we need to decide should everyone else be able to defend themselves with neural networks that are perhaps equally capable but are not sold by these foundation model companies as a product.

I think the answer to the second question has to be a yes, IMHO.

> The government should use procurement to create demand for portable, interoperable systems rather than permanent dependence on one API vendor.

Now, here is an idea that I have not heard before... and I think there is some merit to this. This is also the sort of thing that a state (looking at you CA, CO, IL, NY) could do, instead of just the federal government.

exactly. a ban or even any restriction that is "fixed" in time is a bad idea.

The frontier is evolving so fast: any regulatory regime that started with a map of capabilities that 2025 frontier had is obsolete. Same would be true next year. This just becomes a whackamole and a government which is well-meaning and has good reasons to regulate, will have trouble keeping up.

But we know this about regulations. Like there is a century of literature on how to do this for new tech. It always targets harm prevention first. I thought Demis had the right framework for that part. I don't know if I agree fully with a self-regulatory regime because can become problematic if you don't have the right people around the table.

I think condensing intelligence into smaller models is the way to go.

Also, “smaller” can mean many different things. The cost is not in storing the weights on disk.

It is in the power required to do the inference with the “active parameters”.

There is increasingly more evidence that those two can be decoupled and more power to those who are pushing on that lever!

Where is this thing? Would like to visit!

We are headed towards the “trough of disillusion” of this particular cycle.

Hey! Love the idea. I think a lot of skepticism here would be addressed if you discussed your plans to monetize. People just want to know how you will (eventually) make money in a way that is aligned with how they expect this to evolve.

That makes me sad. I will miss his voice. I loved how he interrupted his guests and kept them honest and on point. I loved the casual offer for tea/coffee at the end. I would love how it had this encore bit at the end, sometimes!

This podcast chose its listeners and kept it real. Thanks to everyone who makes it possible. Hope they get a fitting replacement for Melvyn and keep it going!

Give it time, it should [1] go down to where it belongs. :-)

[1] https://news.ycombinator.com/item?id=1781013

We don't really have very many open source models. We have "open weights" models. Ai2 is one of the very few labs that actually make their entire training/inference code AND datasets AND training run details public. So, that this investment is happening is a welcome step.

Congratulations to the team at Ai2!

> So what is final state here for us? Return to menial not-yet-automated work? And when this would be eventually automated, what's left? Plug our brains to personalized autogenerated worlds that are tailored to trigger related neuronal circuitry for producing ever increasing dopamine levels and finally burn our brains out (which is arguably already happening with tiktok-style leasure)? And how you are supposed to pay for that, if all work is automated? How economics of that is supposed to work?

Wow. What a picture! Here's an optimistic take, fwiw: Whenever we have had a paradigm shift in our ability to process information, we have grappled with it by shifting to higher-level tasks.

We tend to "invent" new work as we grapple with the technology. The job of a UX designer did not exist in 1970s (at least not as a separate category employing 1000s of people; now I want to be careful this is HN, so there might be someone on here who was doing that in the 70s!).

And there is capitalism -- if everyone has access to the best-in-class model, then no one has true edge in a competition. That is not a state that capitalism likes. The economics _will_ ultimately kick in. We just need this recent S-curve to settle for a bit.

That's an interesting way to look at it.

Aren't layoffs a version of that? Are we seeing any evidence that folks who have been let go from Intel have resulted in spin-offs and startups?

I know at least one person who went to work at Nvidia from Intel but that is neither of those things.

Oh, I don't know. Maybe build chips that do things 10x more efficiently and sell them a lower cost to compete?

It _is_ a hype bubble but it is also an S-curve. Intel has missed the AI boat so far, if they are trying to catch up, I would encourage them to try. Building marginally better x86 chips might not cut it anymore.

And, so what?

> It is fairly rare to see an ex-employee put a positive spin on their work experience.

I liked my jobs and bosses!

Maybe you'll want to try Techne: https://techne.app.

> Rather than fine-tuning models on a small number of environments, we expect the field will shift toward massive-scale training across thousands of diverse environments.

This is a great hypothesis for you to prove one way or the other.

> Doing this effectively will produce RL models with strong few-shot, task-agnostic abilities capable of quickly adapting to entirely new tasks.

I am not sure if I buy that, frankly. Even if you were to develop radically efficient means to create "effective and comprehensive" test suites that power replication training, it is not at all a given that it will translate to entirely new tasks. Yes, there is the bitter lesson and all that but we don't know if this is _the_ right hill to climb. Again, at best, this is a hypothesis.

> But achieving this will require training environments at a scale and diversity that dwarf anything currently available.

Yes. You should try it. Let us know if it works. All the best!

100% agreed. It is all about removing friction for me. Case in point: I would not have touched React in my previous career without the assist that LLMs now provide. The barrier to entry just _felt_ to be too large and one always has the instinct to stick with what one knows.

However, it is _fun_ to go over the barrier if it is chatting with a model to get a quick tutorial and produce working code for a prototype (for your specific needs) where the understanding that you just developed is applied. The alternative (without LLMs) is to first do the ground work of learning via tutorials in text/video form and then do the cognitive mapping of applying the learning to one's prototype. I would make a lot of mistakes that expert/intermediate React developers don't make on this path.

One could argue that it shortcuts some learning and perhaps the old way results in better retention. But, our field changes so fast... and when it remains static for too long, projects die. I think of all this as accelerant for progress in adoption of new ways of thinking about software and diffusing that more quickly across the developer population globally. Code is always fungible, anyway. The job is about all the other things that one needs to do besides coding.