artemisart

Official API doc says only max effort level is currently supported. https://platform.kimi.ai/docs/guide/kimi-k3-quickstart#think...

bwrap is builtin in claude too, activate with /sandbox command.

Hill climbing doesn't mean much but absolutely doesn't imply they cheat on benchmarks. They have more details here https://microsoft.ai/news/introducing-mai-thinking-1/ it seems to be "RL on everything".

Every US intelligence org probably has at least API access, but anything outside of the US? No chance.

I may be misunderstanding the question but that should be just decompressing gzip & compressing with something better like zstd (and saving the gzip options to compress it back), however it won't avoid compressing and decompressing gzip.

Does refactoring mean moving things around for people? Why don't you use your IDE for this, it already handles fixing imports (or use find-replace) and it's faster and deterministic.

Do you know about other security issues? If it's only about curl | sh it really isn't a problem, if the same website showed you a hash to check the file then the hash would be compromised at the same time as the file, and with a package manager you still end up executing code from the author that is free to download and execute anything else. Most package managers don't add security.

Why should we expect companies to be able to reuse the correct token if they can't coordinate on using a single domain in the first place?

Yes for parakeet, but only comparing benchmark results for canary. Whisper also has severe hallucinations on silence and noise and WhisperX helps a lot, it adds voice activity detection i.e. a model to detect when someone speaks, to filter the input before running whisper. https://github.com/m-bain/whisperX

Nvidia parakeet and canary are better and faster, here is a leaderboard: https://huggingface.co/spaces/hf-audio/open_asr_leaderboard

No, you never compute individual pixels because you never need to, and it's always faster to it in bulk (vectorization, memory access...) and so over an area you take the same number of pixels as input (or a little bit more with padding) and the blur will only increase significantly the compute.

This seems to be exclusive to Safari, I can't get it to work in Chrome either (and didn't know about the feature before right now, the discoverability is terrible).

I don't understand what's not optimized on 5090. If we're comparing with Apple chips or AMD Strix Halo yes you will have very different hardware + software support, no FP4 etc. but here everything is CUDA, Blackwell vs Blackwell, same FP4 structured sparsity, so I don't get how it would be honest to compare a quantized FP4 model on Spark with an unoptimized FP16 model on a 5090 ?

Ok then just to clarify: you can fit 4x larger models on the Spark vs 5090, not 17x.

That's very true and what's segmenting the market, but I don't understand why you're saying the 5090 supports only 12B model when it can go up to 50-60B (= a bit less than 64B to leave room for inference) as it supports FP4 as well.

The economics don't make sense, each video is stored ~ once (+ replication etc. but let's say O(1)) but viewed n times, so server-side upscaling on the fly is way too costly and currently not good enough client-side.

Then, once that is perfected, they will offer famous content creators the chance to sell their "image" to other creators, so less popular underpaid creators can record videos and change their appearance to those of famous ones, making each content creator a brand to be sold.

I'm frightened by how realistic this sounds.

Yes I don't understand how they can claim it's optimized for legibility when the base font does the inverse.

Gitless is this fork https://marketplace.visualstudio.com/items?itemName=maattdd.... it's not updated but still works well.

pyrefly is not tied to vscode? Also please try to be more considerate of people preferences, and pycharm is not strictly better. Remote dev on vscode is very convenient for me, should I go on the Internet saying that pycharm is trash? No

But it is, as long as the positional embedding are sufficient, i.e. use relative positional embeddings here.

But do you understand it will harm Hollywood? This is the economic, country scale equivalent of saying "fuck your movies", do you think the answer will be "oh sorry, I'll keep buying yours" or "fuck your movies too"?

ChatGPT free gets it right without reasoning mode (still explained some steps) https://chatgpt.com/share/6810bc66-5e78-8001-b984-e4f71ee423...

The first sentence of the introduction ends with "we introduce Dynamic-Length Float (DFloat11), a lossless compression framework that reduces LLM size by 30% while preserving outputs that are bit-for-bit identical to the original model" so yes it's lossless.

That was the joke.

There is absolutely some bloat in 100gb+ games but also come on, graphics are much better now compared to 15 years old games. Textures + video cinematic + models weight a lot.

Yes, the idea works and was explored with dreambooth/textual inversion for image diffusion models.

https://dreambooth.github.io/ https://textual-inversion.github.io/

This depends a lot on your domain, e.g. pypy is not compatible with pytorch or tensorflow so DL is out of the picture.

Thanks! I was waiting for your benchmarks. Do you plan to test haiku 3.5 too? It would be nice to show API prices needed to run the whole benchmark too to have a better idea of how many internal tokens o1 models consume.

Important precision: the async part is absolutely not python specific, but comes from CUDA, indeed for performance, and you will have to use cuda events too in C++ to properly time it.

For ONNX the runtimes I know of are synchronous as we don't do each operation individually but whole models at once, there is no need for async, the timings should be correct.