abixb

That's not what I meant at all. I understand no one would want 1:1 text search, and part of the reason why Google won in the 90s was because of PageRank. All I was saying was that basing a 'search engine' on a CoT LLM is very inefficient.

>LLMs designed and deployed properly are infinitely better at this than keyword search engines are, despite being less predictable.

In what way? How are LLMs more efficient and 'infinitely' better at this than what Google had before AI overviews? If anything, I find that search results have gotten worse.

Yes, I'm aware. I meant that deteministic systems should be at the core (PageRank was deterministic), alongside other contextual clues and knowledge graphs -- things Google did before this AI overview crap.

What pisses me off is that you can't turn it off. Like, fine, you may bury and dark pattern the crap out of the disable toggle, but at least give users the choice to opt-out. And no, UDM=14 is not a toggle.

Google is just scared. Google has been scared since November 30, 2022.

Text search is fundamentally a deterministic problem, and squeezing it through a heavy non-deterministic, stochastic engine -- especially through a CoT LLM -- is inefficient from a first-principles algorithm design pov. Also, Google did great search before AI overviews using probabilistic linking and knowledge graphs.

I've been using DuckDuckGo's NoAI version as a fallback, and I've had great results so far; brings me back to the web I remembered growing up -- personal blogs, random forum discussions, and whatnot -- with just a fraction of the page bloat.

I say all this as a HUGE fan of Gemini, Gemma, and Antigravity.

By the way things are going in the industry, I think Gemini 4 Carbon's lead will only persist for the half-life of Francium-223.

> Other numbers are largely unaffected, but anyone using '4' for their certificate key is urged to upgrade to '5.'

lol

This reaction always gets me:

“One of eMudhra’s certificate authority private keys is the number 4. Not a number that starts with 4, or the number 4 followed by a few hundred random digits, just, 4… Another one is 56, and another is 115. I know, because I recovered all of them from their public certificates on my laptop just now, and it took less than a second,” wrote Helme.

Security researcher Scott Helme was among those who recovered the keys. This is a hall-of-fame level fail.

As someone who's well-versed with the "face-saving" aspects of Indian culture, I can state with clarity that the statement they put out is completely in character.

Folks in India — especially the boomer class — are trepidated by how they're perceived over actual substance and quality of their work (exceptions exist, of course).

Edit: Sudhir Kakar's (an Indian psychoanalyst) work on Indian identity (The Inner World, Intimate Relations) helped me decode my own origin culture's internal logic. Highly recommend it for people of South Asian diaspora.

It was a "grading the AI 2027 report" video -- https://www.youtube.com/watch?v=k-qAht4w9Rg

The fear will again be if the HybridAgent model is being faithful to pseudo text generated, even if you can read it out partially.

Also, I just finished watching a video on AI 2027 scenario. I don't know if AI companies and researchers are using it as a cautious warning or as a target to hit (or if it's genuinely inevitable).

Yes, thinking in latent space might be the ultimate efficiency hack for CoT LxMs, but going down that path will (near certainly) mean we'll lost control, for one simple reason — humanity will have no way to pause and diagnose.

It'll be like a moody chimp with a button to the dead hand system (Systema Perimeter)

I have a question, and perhaps some of the AI/ML infrastructure experts here could answer: Mistral says, "ML4 was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral’s own datacenters in Europe."

If a 1T params model trained on ~4k NVIDIA GB GPUs could almost match the performance of Kimi's K3 (which is on par with top closed source models of OpenAI/Anthropic) while beating/exceeding other leading SOTA models from top Chinese labs, what are we (in the US) even building these super massive data centers for? Just to churn through more backpropagation reps more quickly?

SpaceXAI's Colossus supercluster in Memphis and Colossus 2 in Memphis/Mississippi (Southaven) are supposed to run into hundreds of thousands to a million GPUs. MSFT's Fairwater GPUs are supposed to have hundreds of thousands as well. So, 3800 GB GPUs are an absolute drop in the bucket. I don't understand the strategy of hyperscalers here, especially with edge inference hardware only getting better from here on (Apple, and all).

Distillation explains some of the advances, but doesn't that mean hyperscalers have a ton of deadweight wrt GPUs sitting on their balance sheets? Will all these GPUs be used for inference once a SOTA model's training checkpoint/batch is done? It's bonkers to me.

Imagine going back in time to late-80s/early-90s, and vividly describing all the tools we have now to folks back then -- coding agents, loops, autonomous multi-hour agentic workflows, automated testing, and PRs, and whatnot. Wonder what their reaction would be.

I sometimes look at my pre-2024 workflows and feel like I'm looking at a caveman's toolchain. Absolutely bonkers as to how quickly we humans adapt to things.

Also, neat bit of trivia: Borland was originally named MIT ("Market In Time"). The OG MIT in Cambridge, MA, sent a trademark letter and they then changed their name to Borland, which was supposedly a client of theirs.

You won't be around to be surprised, not as a human at least. /s

I remember making a comment here on HN a couple weeks ago.[0]

One of the things that was promised in 2024 was a "deflationary spiral" for physical goods and the economy in general because of insane efficiencies that AI would supposedly bring. I believed that whole-heartedly and I was a huge AI fanboi even as my artists friends and Lib Arts friends were complaining.

We're in almost October 2026 now and they're nowhere in sight. We have a bunch of unreliable agents that do clerical and design work here and there, but where are all the industrial productivity boosts? If AI were so good, large industries and factories should be adopting them as a never-before-seen scale. Where are they?

[0] https://news.ycombinator.com/item?id=49735506

What bothers me about this whole AI tokenomics situation is the lack of transparency. OpenAI and Anthropic have to perhaps be the most opaque companies in existence wrt their offerings. There's like a thousand variables that they can change on the backend at the push of a button which can wildly swing API spends within the same model (partly also due to the non-deterministic nature of LxMs, but still), and there's no objective way to measure them other than vibes.

When the regulations do arrive, I think they should really focus on AI companies and API providers being more transparent wrt how they're billing their customers. Because right now, it's a totally vibes-dependent and a mess.

> At the end of the day, I need to maintain my personal understanding of the BUSINESS use cases + decisions so that I can make judgements that AI would never be able to make.

Exactly. There's no point having a model equivalent to a trillion Einsteins at superhuman speed if you can't verify and judge the outputs of the models for your business, else it might just be as useful as comparing water displacement capacity of Niagara falls to your bathtub.

These tools have to be human centered. AI brained tech bros believe that singularity is a good thing — no it isn't. We don't want a world where normal laws and rules and regulations breakdown -- chaos is the word.

"Vastly better" in what ways? Benchmarks? You know Benchmarks can be optimized for and benchmaxxed for, right?

I'd wager on the second scenario. Anyone who's been paying attention to the industry knows that most of the 'gains' have come from test-time compute and architecting harnesses in novel ways. In my estimation, capability increases from "pre-training" alone died early last year, and we're now probably seeing test-time and other benchmark hacks approaching their limit as well.

If you zoomed back to late-2024, people in the industry were predicting how we'd have AGI by now and the economy would've already 'taken off' with massive productivity growth and ushering in of great prosperity ('deflationary spiral'). Where is it? Where is the productivity growth? Where is the deflationary spiral?

To be fair, models have gotten better in jagged ways, but reliability is far from usable, especially in long duration tasks, and there has been no effort by the AI companies to address the human brain's bandwidth bottleneck -- they hit the gas like there's no tomorrow and we have enormously capable but jaggedly intelligent multi-modal models with agentic capabilities that are only as effective as the human using it. This whole thing has become a giant mess.

Guessing Opus 5 burned through kilowatts of thinking tokens to output, "powerhouse of a cell."

I remember Logan Kilpatrick from Google talking about how AGI will be more of an experience with a product than raw 'intelligence' of a model. [0] No one outside of the nerd community cares about benchmarks or any of the other crap. They just want their tech to work so they could go do other IRL things.

[0] https://www.reddit.com/r/singularity/comments/1ldkkth/google...

> you should really question your worldview a bit.

Yeah, I still struggle with it. I have a diverse friend circle and sometimes I forget that most of them don't have the same information diet that I do (or don't even care about tech the way I do). Maybe I should be more like them and just chill? I don't know.

I think Meta's strategy is to capture the 'normie-tier' of AI users. I know most of us here on HN track model releases quite frequently and discuss every parameter weight out of them, but most of the world is just... oblivious?

I was discussing latest in tech with an accounting friend out of curiosity, and I kept talking about tiers of GPT-5.6 (Sol vs Terra vs Luna) and asked which one she used on the desktop 'Work' app, and she responded with, "just ChatGPT, what is Sol?."

I then realized that most people just stick with whatever default they're provided with, and it's a lot of them. So, us serial HN users and commenters are the extreme minority, and I'm sure millions of people will gobble up this Muse agent from Meta as if it's some sort of an innovative cutting-edge way to use the internet by Meta alone.

Curse of knowledge and all. [0]

[0] https://en.wikipedia.org/wiki/Curse_of_knowledge

The couldn't even get the bar chart right, iirc. [0]

[0] https://www.reddit.com/r/singularity/comments/1mk8tm8/gpt5_c...

I think models using these harnesses were also RLHF'd hard on responding to looping instructions and following through on goals. Older models were tuned for basic chat responses.

>When we released ARC 3, I got asked, "when do you think a frontier model will saturate it?", and I answered "in about a year, though it depends on how much it gets explicitly targeted"

You're treating an off-hand comment by an ARC 3 researcher as some sort of a precise AI capability acceleration benchmark. Can we leave casual anecdotes (even from researchers) out of the discussions please?

>I think that the harnesses and context management is really where the rubber meets the road, and the real gains are happening there.

True. So we did hit a wall with pure scaling alone, though no lab would admit it. It's crazy to see how harness switchout results in such vast delta in benchmark scores.

It's "harnessmaxxing" all the way down. AI benchmark scene is exhibit A for Goodhart's law.

I want to take a step back: So, this is GPT-6 -- the natural number version release comparable to GPT-4 and GPT-5 from the past few years. The ARC-AGI-3 score is obviously impressive at 99.9% (we'll need to wait for more details on how they used the response API harness on GPT-6 Astra, wrt reasoning retention and compaction), but every other benchmarks seems to be a relatively modest improvement, comparable with any of the 'point' updates from AI labs.

If this is truly AGI (subject to one's definition of AGI still), then this is a very boring release of an AGI model. No video announcement, no presser, just a blog post (with some Twitter promo vids)?

As others mentioned, I'm starting to think OpenAI was under immense pressure to deliver an 'AGI' model for certain contractual reasons, but I never expected GPT-6 release to be this mundane and banal.

I like Google's strategy here. These new Flash models of late (Flash 3.6, 3.7 and now 3.8) have obviously been distilled from a much larger unreleased model (Gemini 3.5 Pro, iirc from the rumors).

One aspect of model releases that don't get discussed as much are the cache invalidation (changes in underlying architecture, weights, or tokenizers); I assess Google seems to be squeezing the maximum out of the last 'Pro' version they released with 3.1 back in February.

Small models cataching up with their bigger siblings are fantastic news.

Username checks out. /s