WhitneyLand

Say AI is used to identify and rewrite a single function that improves performance or fixes a bug, then the developer carefully reviews and tests it and submits a nice tight PR with all human communication.

So they don’t want that? They would just reject it?

If I’m understanding correctly, under the policy the higher performance function / bug free submission would be rejected and they could ask for a rewrite.

Should it then be rewritten from scratch, and clean room engineered so it doesn’t resemble the AI too much?

If it were a small amount of money, it wouldn’t be an issue. If the politics at stake were less important, it wouldn’t be an issue.

They’re not going to stop at immigration, look at other places in the world to see the future risk.

Sorry, I was a paying and satisfied customer, and now I’m out.

And on the other hand we do have evidence to the contrary. AI is not capable of writing a movie script that resonates with people like this. It’s not capable of doing what a stand-up comedian does.

People will argue if and when these gaps can be closed. I think they can be in principle, but it’s not going to happen anytime soon.

Do you mean that they may get away with less oscillators because of the decoder layer? Well there’s the rub isn’t it, the more work you have done by a software layer the less power you’ve proportionally saved by having it be done by physical computing.

But let’s spitball here what would you estimate would be needed in number of oscillators and interconnects for a 4K image?

But specifically what they’ve simulated here? I don’t see how that would ever work in real life scaled up to any kind of real size.

I’m not criticizing them for starting out small. Lots of things can be proven with small models. I’m saying in principle, I don’t see how this will work unless there’s some fundamentally new technique that is currently not known about. Maybe they have some secret idea but they haven’t shown it here.

You’ve got to wonder when you have an image generation demo why would you possibly have 64 x 64 pixel output as your demo?

If I’m understanding this properly to generate a 4K image, you need like 5 trillion point to point connections on the chip. Even if power use from the oscillators is zero that’s going to be an issue.

Reminds that I wish there was a modern way to do this for the words people speak and write online with. I want to literally know when people started putting literally twice in sentences.

Ngram seems is out of date a piece meal. Now Corpus seems like they try but UX terrible.

People review in different ways, sometimes before the commit or at a different cadence.

I get the concern, but I think it would be easy to tell if it’s responsible conclusively just by browsing around the code a bit. If it’s a mess, it’ll be obvious.

To let LLMs compose music I chose json for context efficiency, but this seems like it could be better choice, simple, efficient, already a real format.

https://github.com/whitneyland/riffmcp

It may not be necessary…a lot of the training pairs/data for this could probably be procedurally created via code.

Would be pretty fun to work on and see it come to life.

Honestly curious how many years before it can be one shotted in a coding harness with Fable.next by someone who’s not a linguistics expert.

Develop, test, and rank hypotheses about the phonetic values, morphology, grammar, and possible language family of Linear A using the full available corpus. Do not assume any decipherment is correct. Treat all candidate readings as hypotheses to be scored…”

For example for voice ChatGPT still uses a quantized gpt40 non-reasoning model that hallucinates pretty frequently. It also doesn’t do much automatic search for updated information and fact checking.

I usually don’t find I need high, usually DeepSeek v4 with medium reasoning is sufficient.

However if it’s important chat like brainstorming on complex topics I sometimes bump it up.

OpenAI has a new voice api that supports adjustable reasoning, but ChatGpt is not using it currently.

One problem has been ChatGpt/Claude apps don’t really do this well. They use weak and/or non-reasoning models for voice interaction and the UX is not optimized for hands free.

I wrote an iOS chatbot app mainly for this purpose for myself and family/friends. Allows starting/sending voice prompts with the action button so I never have to look at the screen. Supports any model at any reasoning level so conversations are not dumbed down. Added a video transcription tool so any model can “read” YouTube/Tiktok videos and chat about them. Great to discuss lectures on tech topics.

It takes slightly longer to use a reasoning model for voice interaction use but I prefer the intelligence. The latency can be minimized a few ways, bidirectional streaming helps. It’s TTS agnostic, I’ve got a few selectable providers and the output can be prompt styled “use a chill tone that’s not too eager”.

The difference is when creating each pixel, there’s no coordinate to look up, instead it’s using only a set of rules like Conway’s game of life.

But the rules come from a neural network trained on the image, so… it’s kind of memorizing enough information to effectively do the same thing as texture sampling, but using only local information.

I’m sure I’m missing something about how it works or what makes it interesting…

It’s a White House office run by Russell Vought, highly ideological maga institutionalist.

Frame level embedding it covering a lot, but can miss out on a lot of action related searches.

When GUI apps first made it easy for anyone to use 100 fonts and colors in a document, it did not make the quality of design go up. The tools became more powerful but the average quality of design went down because lots of people with less skill could suddenly participate.

Same issue here, two things are true at once. Models and agents are very powerful, and it makes it possible for less skilled or less conscientious developers to increase volume of low quality PRs.

This is what needs a solution with the PRs.

But saying his opinion hasn’t changed on this:

“the main and most important reason why GenAI tools do not work for me is that they do not make me any faster.”

It’s been a year and agents and models have improved dramatically.

I can see for some things it doesn’t make sense, but not using it at all because there’s nothing it can help with?

Devs who don’t use models at all are a dying breed and I think it won’t be long before he’ll be forced to concede the point.

The comparison is kind of a category error. One is a DNA editing technique and the others are deliver platforms. I recall the hype mostly being how revolutionary it could be, not comparing it on a timeline to specific technologies that are at different levels of the stack.

The timing of these price cut discussions says to me OpenAI has no imminent release that will be edging out Mythos/Fable.

If so the question becomes when can they do so, or is this possibly a turning point where Anthropic keeps the crown to themselves for the foreseeable future.

On one hand, industrial research is different from academic research. There’s no tenure and not the same level or presumption of academic freedom. Fair enough.

The problem is they specifically wanted to bathe in the glory of an ethical research team and all the benefits that come with that.

You can’t have it both ways.

- Reductionist analogies like how Microsoft Word is not conscious therefore AI is not.

- Dismissive in saying LLMs are not capable of moral reasoning. Maybe he meant agency or responsibility?

- Builds a case based on a Julius Caesar example without realizing its natural extension leads to a philosophical zombie which is not easy to disprove as consciousness.

- Seems to casually disregard non human sadness as a concern.

- Rolls out the stochastic parrot argument with a new coat of paint. This argument never made sense. Having a predictive element a part of the mechanism doesn’t rule anything out. Proper functionalist arguments tend to say what is required not what excludes.

Overall the article seems like a bit of a red herring. The premise is LLMs are not conscious, but most people don’t think they currently are so what’s the point of the claim?

The more interesting questions are whether artificial consciousness is fundamentally possible, and if so how far away are we and what pieces are still missing.

Iiuc water might compress ~50% at the right place in the Earth’s mantle, maybe just not looking much like liquid.

The part I hate though is that I’d bet none of the performance claims are based on int8.

Why do we care about bf16 benchmarks when no one will be using that with this model.

It sounds like marketing spin where the performance claims are based on BF16 and the “runs in 16GB” claim is on a totally different quantized version.

That result would say less about performance of languages than it would about competency of developers with a language.

I just don’t buy that a task could be assigned to two teams with comparable expertise and domain knowledge in Rust and Java, and have the Rust result be at a “huge” performance deficit.

No, don’t believe that was an apples to apples comparison.

To a lot of us it’s not clear that’s what’s happening. It’s speculation and one possibility.

It may also be a secondary consideration and not the primary gating factor.

Anthropic has had their missteps but it’s still plausible to take what they say at face value.