dvt

[1] https://en.wikipedia.org/wiki/Orchestrated_objective_reducti...

Not sure why people are willing to believe this without actually fact checking. Yeah, Orange Man Bad, whatever, but come on. It's trivial to verify and trivially untrue.

And yeah, I guess if you dig deeper it's somewhat editorialized, but it's not literal Tianmen-tier censorship:

https://imgur.com/a/I0CqPcg

Been working professionally for ~15 years or so, and it's never been this bad. Other than "knowing someone" (i.e. a referral, and even that falls through routinely), not sure what the best way to snag a position is these days.

Fractal tool discovery: tool taxonomy where an agent can "drill deeper" to find what specific tool it's looking for. Helps if/when polluting context with a zillion (mostly unnecessary) tools.

Leveraging splay trees: this is my favorite data structure and I think relatively unused in the context of agents/harnesses. A lot of times, recently-used workflows/tool-chains will be used again, so having those at the top of the search hierarchy is an awesome optimization.

Virtual containerized notebooks: models working in sandboxed (WASI) Python notebooks is incredible. Even local models (if given enough time) will usually converge on a good solution. Being able to mount tools/resources/fs is again, imo quite untapped. Some problems here are running native things (thing numpy/pandas) in containers is a nightmare (or impossible).

Anyway, happy to see other folks seriously doing stuff in this space. If anyone wants to collaborate on anything don't hesitate to reach out :) I'm also actively looking for a job or some contract gigs.

Fun times ahead.

I think this would be likely comparable to a scheduled backup, so I think it would be an acceptable maintenance window. However, deterministic algorithms would likely beat re-training (or re-fine-tuning) the model. For example, one could analyze actual distributions or whatever (instead of assuming uniform), and then some plans would automatically be eliminated.

Imo a good thought experiment is to look at places that are hyper-optimized, like compilers. Would LLMs bring anything to the table (architecturally or performance-wise) to a piece of software that has been carefully crafted for decades? (Methinks no.)

The protocol will of course be `text -> text` for the typical LLM (though some new models are structured, as we saw yesterday with Jev). Even so, the user doesn't need to be exposed to the protocol (almost ever). It's not like I'm crafting a POST request to send this form.

There's a lot of room for window dressing, and I look at AI like I look at the touch-screen. It was a fun technical toy until people (mostly Apple) poured in a lot of serious effort into "ok, how do we make this pleasant to use, intuitive, and genuinely useful?"

I know this all sounds abstract. I've been mulling over it for the past year and it's very hard; and LLMs are super janky and inconsistent so it's 100% not trivial. So in some sense I understand why a lazy bottom-of-the-barrel "chat interface" has become the de facto standard.

This isn't necessarily true. I'm working on a local harness that doesn't do this and instead coerces everything to YAML (including tool calls) for better bucketing. Some models are indeed trained on the `<|tool_call>...<tool_call|>` token schema (or something similar—e.g. jinja), but it's vendor-specific and often times inconsistent (so you're constantly fixing calls or going back to the LLM).

Was trying to combine AI with generative storytelling with a card game. It was a fun experiment. To play, you'll have to get a friend to queue up at the same time.

I was referring to the "AI labs" here. Sam Altman himself conceded that OpenAI is losing money on the $200 subscription. Using open-weight/open-source models is indeed cheaper (and no reason for inference to be subsidized).

It's also clear that, as Tan indicates, open-weight models will be (and basically already are) just as good as frontier models. It's all about the harness, baby. We will have two main forks in the road, and two new industries created:

    - AI hardware (NVidia/Cerebras/etc.), the equivalent of Intel/AMD
    - AI software (harnesses, assistants, etc.) the equivalent of Microsoft/Apple

It's all theatre. OpenAI and Anthropic will most likely go bust—or, more realistically sold for parts—, and they absolutely should for stealing my (books I wrote, blog posts, etc.) and many others' intellectual property. We're reaching a point where models are becoming commodetized and I'm 100% convinced the next move will be a sort of "software layer" on top of these reasoning systems which will be the actual revolution. The model itself won't be that interesting anymore, it's all the work that goes around it that makes it worthwhile (kind of what computers and phones are today; chips are amazing, but the software is really the magic).

The only scary part is that the boomers in Congress might actually believe these nerds, but seeing how Big Tech approval ratings are grazing the levels of Big Tobacco in the 90s, I don't think we have much to worry about.

Yes, that is Tao's premise, I'm just not sure I buy it. Suppose an oracle existed which could answer any question truthfully. Let's ignore the mechanics of this for now, but it could say things like "the Riemann hypothesis is False" or whatever and we would take it as gospel.

Does this mean that we wouldn't have mathematicians or physicists or computer scientists or biologists anymore? I genuinely don't think so.

Navier-Stokes is a bit different (because there's a prize attached, so "scooping" matters), but almost all interesting problems don't have any prizes attached.

Other than Twitter engagement baiting (which people have done w.r.t. every other new hyped up thing, whether it's drop shipping, SaaS, crypto, whatever), I really don't see this day-to-day.

> Best friend suggested I ask ChatGPT whether it’s cheaper to renovate or knock down a house I bought specifically to renovate it, which I’ve told him on multiple occasions.

I don't understand what this anecdote has to do with AI, it sounds like your friend is just a bad listener.

What makes humans human is love, laughter, community, children, art, beauty. How are LLMs even remotely a threat to this? The piece is so hyperbolic, it's just hard to take seriously

I really feel that the anti-AI crowd is becoming a weird religion, kind of like the crypto NFT crowd was a few years ago. Most people that use AI are just like "yeah whatever, it does X, Y or Z, sometimes it sucks and I have to re-prompt it, it's pretty neat."

While the anti-AI crowd is like "I PLEDGE TO NEVER USE AI, HERE IS MY BLOOD OATH." Like, calm down. It's not that big of a deal. Some of these bullet points are just straight-up nonsense.

> I won’t read AI summaries as a substitute for reading the source material with my own damn eyes.

Author is... just defining what a summary is. Yeah, no one looks at summaries as if they're the original text. Ever. What is he even saying here?

Building products has nothing to do with technical problems, just the end result. This is in contrast with things like writing a library or coming up with a new algorithm, or doing research, or even writing a technical blog post, etc.

> or is it just pretty?

People have been sharing "just pretty" things on HN for decades. It might be interesting, thought-provoking, discussion-worthy, or whatever. Even if this wasn't vibe-coded, it wouldn't be some monumental technical achievement. Your point is absolutely moot.

And we've been using the Dewey Decimal System for like 150 years. Both are good systems. Kepter (a visual/gallery system) would break down when looking at more than a dozen files (or when files are very similar). I don't really think it's a good idea.

What we desperately need is a system-wide (or at least documents-wide) semantic/vector search, which is basically a weekend project and a $4.99 one-time purchase.

Honestly, the internet has made folks so lazy. It's very ironic that the most "revolutionary" act taken recently (January 6) was actually done by the far-right. Yeah: if you believe in something, you need to actually get out of the basement, grab a gun, and storm the Capitol. And if you take it all the way, a lot of people are going to die. That's how revolution works. Unless we're all LARPing?

But Americans/Westerners (left, right, center) are way too comfortable for a revolution, so for people that are, or have been, actual dissidents (think Arab Spring, or IRA, or ETA, or whatever), this is all just a big meme.

It's like saying that moving from manual-labor-intensive agrarian societies to industrial ones will make us weak and meek. In fact, it's had the opposite effect: humans, on average, are healthier and live longer than ever in human history.