ImprobableTruth

The current benefit is that chat has no quota.

Why would you accept it when the benchmark's ranking is obviously nonsense. It literally has muse spark 1.3 above 6 astra, 5.6 sol and fable 5. Anyone who has played with any of these models for any amount of time would immediately realize that this is total bunk.

That's the ankle. The actual knee is hidden in the feathers of the body.

Its "pure capabilities" are definitely worse than Fable, but I find codex has a much more pleasant style and is in comparison much more generous with its limits.

It's probably GLM 5.3 flash, so weaker but cheaper.

These exist and are used. The issue is that because they're so much smaller, they're also much worse, so they tend to have lots of false positives while still being easy to circumvent.

The fundamental issue is that for these systems bigger is essentially always better (if affordable). So if we can squash something like Kimi K3 down to run on a 'normal' system, that just incentivizes devs to increase the model size until once again we're at the limit of what can be run.

This is not what the bitter lesson is about. It's not "don't develop better methods, just scale", it's that those methods which scale best win. LeCun's work is fundamentally about devising a method that scales better with data than LLMs. Agree with him or not about the feasibility of it, but this is fundamentally still a bitter lesson-pilled mindset.

This is some bizarre victim inversion. The providers of closed models are the ones who are trying to use regulation to stop their open model competition, not the other way around.

Now, what if you order a construction crew for building your house? What if you also hire an architect to design the plan? And an overseer who manages everything?

Surely at some point you would stop saying "I built this house", even if you ordered and financed it?

Sure, but "10x faster, but only applies on small greenfield, throwaway projects" is a major caveat. In fact, there's a good chance this doesn't disprove the original blog post, you could be way faster on small projects but slower on 'real' projects.

>Also give it a chance - as you said "good" models have only been available very recently and you wouldn't expect everyone to start using them instantly.

But I'm not expecting everyone to have built something like that, but surely among millions of users someone should have, especially the people proclaiming insane productivity gains? There are no super impressive open source projects done using AI and all the companies boasting about how all their code is AI written now don't show much improvement either.

Opus 4.5/4.6 are what many people consider the first 'good' models and it's from last year/start of this year.

But fine, let's say everything before gpt 5.5 was unusable crap. Then there should still be projects that would normally have previously taken ~2 years done in just two months. Where are they?

>I just did a ~6 month project in ~2 weeks using a frontier model.

Claims like this are hard for me to take seriously because 'good' models have been available since the start of the year. So, if they really 10x one's productivity, then people should be able to have gotten done 5 years worth of work since then, but I've never actually seen anybody show any project like this.

This is a very strange mindset. Even if you want to treat everything as sort of billable hours this doesn't really make sense because the average boss's boss's isn't paid anywhere near 144x. If a SWE spends 100 hours to save their boss's boss one hour, they're wasting a ton of money.

Anthropic had to settle with authors because they literally pirated books! Their behavior regarding distillation is genuinely beyond parody.

Except it's not just a tool.

It's when a woodworker, musician or painter completely outsources their work and just marks what's wrong, sending those parts back. Yes, the final art piece might be the same, but the artist definitely uses less of their "soul".

Shared memory of the past meant reserving a part of the memory for the GPU, which could then not be used or accessed by the CPU. If the CPU wanted to access something, it had to copy it from the GPU's section of the memory to its own. Unified memory means both just fully share the same memory.

If that was the real reason, why wouldn't they just make it so that if you don't correctly use caching you use up more of your limit?

The quill and ink at least communicates that it's about writing. The new one is so abstract that when I first looked at it I had no idea what I was even looking at, it certainly doesn't communicate "this is like word" to me. Without comparison to the previous icon, how many people do you think would understand that the bottom line is intended to be a stroke drawn by the pen?

The key thing is that you teach multiplication tables in a structured, incremental manner. Yes, it's just rote memorization, but the structure makes it way easier. You don't just dump all tables on the student at once and start quizzing them until they get it.

Imo not being able to select a subset of intervals to train heavily limits how useful this is.

How is the raw Gemini 3 CoT accessed? Isn't it hidden?

They're not making money on inference alone because they blow ungodly amounts on R&D. Otherwise it'd be a very profitable business.

> These games are the starting point, but the bulk of the game is new puzzles combining mechanics from different games together

Seems like the puzzles are novel, but the mechanics are not?

An almost 50% price increase. Benchmarks look nice, but 50% more nice...?

Unfortunately not, this model is noticeably worse. I imagine horizon is either gpt 5 nano/mini.

Even near-perfect LLMs would benefit from the compiler optimizations that types allow.

However perfect LLMs would just replace compilers and programming languages above assembly completely.

This is the fault of sloppy language. In Lean, _proofs_ (equivalent to functions) and _proof objects/certificates_ (values) need to be distinguished. You can't compute proofs, only proof objects. In the above quote, replace "proof" with "certificate" and you'll see that it's a perfectly valid (if trivial - it essentially just applies a lemma) proof.

Caveat: Coercions exist in Lean, so subtypes actually can be used like the supertype, similar to other languages. This is done via essentially adding an implicit casting operation when such a usage is encountered.

I think the concept of a game DSL is cool, but it just feels so undercooked to me.

Like, I'm a huge fan of gradual typing, especially TypeScript's, but gdscript's is just so primitive. Not even to speak of something like intersection or union types, even something basic like an interfaces mechanism is missing. has_method is an awful substitute - in general way too much relies on strings, making even simple refactoring a headache and breaks autocompletion. Lots of things also just aren't typable e.g. because generics are missing, pushing one to Variant. These aren't deal breakers, especially for the small-ish projects I've done, but it just feels bad.

A 'fully realized' version of gdscript would probably be great, but as is I'm just really not very fond of it and progress currently isn't exactly happening at a rapid pace (which is of course understandable).

Also - and this is definitely a lot more subjective - but I find its C++ FFI pretty ugly, even for basic stuff like working with structs. In theory using gsdcript as glue and C++ for the more core things would be a great approach (like unreal with its blueprints), but in practice I just want to avoid it as much as possible.

What's the issue with the remix -> react-router transition? As far as I can tell it's just a branding thing.