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lairv

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投稿

General Intuition's $2.3B bet that video games can train AI agents

techcrunch.com
3 ポイント·投稿者 lairv·18 日前·0 コメント

Khronos Announces glTF Gaussian Splatting Extension

khronos.org
4 ポイント·投稿者 lairv·先月·2 コメント

Why Robots That Don't Understand Physics Are Winning

yage.ai
3 ポイント·投稿者 lairv·3 か月前·0 コメント

[untitled]

1 ポイント·投稿者 lairv·3 か月前·0 コメント

Day 1 of ARC-AGI-3

symbolica.ai
90 ポイント·投稿者 lairv·4 か月前·76 コメント

ARC-AGI-3

arcprize.org
498 ポイント·投稿者 lairv·4 か月前·368 コメント

World Models: Computing the Uncomputable

notboring.co
2 ポイント·投稿者 lairv·4 か月前·0 コメント

The unlikely story of Teardown Multiplayer

blog.voxagon.se
242 ポイント·投稿者 lairv·4 か月前·65 コメント

Ggml.ai joins Hugging Face to ensure the long-term progress of Local AI

github.com
839 ポイント·投稿者 lairv·5 か月前·223 コメント

Ex-DeepMind's David Silver Eyes $1B Fundraise for Ineffable Intelligence

techfundingnews.com
3 ポイント·投稿者 lairv·5 か月前·0 コメント

World Labs Announces New Funding

worldlabs.ai
1 ポイント·投稿者 lairv·5 か月前·0 コメント

The flavor of the bitter lesson for computer vision

vincentsitzmann.com
1 ポイント·投稿者 lairv·5 か月前·0 コメント

The Robotics Data Pareto Frontier

vincentliu.org
2 ポイント·投稿者 lairv·6 か月前·0 コメント

Helix 02: Full-Body Autonomy

figure.ai
4 ポイント·投稿者 lairv·6 か月前·0 コメント

Kauldron: Modular, scalable library to train ML models

github.com
1 ポイント·投稿者 lairv·6 か月前·0 コメント

The Slow Death of Scaling (deep learning models)

papers.ssrn.com
4 ポイント·投稿者 lairv·6 か月前·0 コメント

Bikeshedding, or why I want to build a laptop

geohot.github.io
12 ポイント·投稿者 lairv·8 か月前·4 コメント

Jetpack Navigation 3

android-developers.googleblog.com
5 ポイント·投稿者 lairv·8 か月前·1 コメント

Joint-Embedding vs. Reconstruction: When Should You Use Each?

huguesva.github.io
1 ポイント·投稿者 lairv·8 か月前·0 コメント

Epic announces partnership to bring Unity games into Fortnite

gamesindustry.biz
4 ポイント·投稿者 lairv·8 か月前·3 コメント

コメント

lairv
·19 日前·議論
Not exactly what you describe but this project [0] did it with cubes.

The cube is filled with splats. Each face reveals a different picture when viewed from a perpendicular angle

[0] https://www.3dpoint.art/
lairv
·先月·議論
This is "old" news from February but somehow hasn't been discussed here
lairv
·2 か月前·議論
For a space that supposedly had "no moat", the number of players still competing for frontier models seems to be shrinking pretty fast
lairv
·3 か月前·議論
Wikipedia says there's 14.8 billion videos currently uploaded to YouTube, it seems technically easy to index that amount of title+description?

The more likely explanation is that Google doesn't want YouTube to be crawled, which gives them a massive moat for AI training
lairv
·3 か月前·議論
Worth noting that most of youtube videos can no longer be discovered through search. Search results can now only be sorted by "Relevance" and "Popularity" while you used to be able to sort by release date

Search results are also non-exhaustive and biased towards recent videos as noted in this study https://arxiv.org/abs/2506.11727

Basically many videos can no longer be discovered if you don't have a url to the video or the channel, and the algorithm doesn't recommend it
lairv
·3 か月前·議論
If the model is truly on par with Opus 4.6/Gemini 3.1/GPT 5.4 (beyond benchmarks) this still puts MSL in the frontier lab category, which is no small feat given that they pretty much rebooted last year

Many labs aren't able to keep up with the frontier, xAI, Mistral
lairv
·4 か月前·議論
Note that this uses a harness so it doesn't qualify for the official ARC-AGI-3 leaderboard

According to the authors the harness isn't ARC-AGI specific though https://x.com/agenticasdk/status/2037335806264971461
lairv
·4 か月前·議論
Blank screen, and it's referenced in the official docs as potentially a Wayland issue https://opencode.ai/docs/troubleshooting/#linux-wayland--x11...

I didn't dig further

Seems like there's many github issues about this actually

https://github.com/anomalyco/opencode/issues/14336

https://github.com/anomalyco/opencode/issues/14636

https://github.com/anomalyco/opencode/issues/14335
lairv
·4 か月前·議論
This issue: https://github.com/anomalyco/opencode/issues/9505

And then the official docs: https://opencode.ai/docs/troubleshooting/#linux-wayland--x11...

> Linux: Wayland / X11 issues

> On Linux, some Wayland setups can cause blank windows or compositor errors.

> If you’re on Wayland and the app is blank/crashing, try launching with OC_ALLOW_WAYLAND=1.

> If that makes things worse, remove it and try launching under an X11 session instead.

OC_ALLOW_WAYLAND=1 didn't work for me (Ubuntu 24.04)

Suggesting to use a different display server to use a TUI (!!) seems a bit wild to me. I didn't put a lot of time into investigating this so maybe there is another reason than Wayland. Anyway I'm using Pi now
lairv
·4 か月前·議論
I tried to use it but OpenCode won't even open for me on Wayland (Ubuntu 24.04), whichever terminal emulator I use. I wasn't even aware TUI could have compatibility issues with Wayland
lairv
·4 か月前·議論
https://cims.nyu.edu/dynamic/news/1441/

This is just the official name of a chair at NYU. I'm not even sure Jacob T. Schwartz is more well known than Yann LeCun
lairv
·5 か月前·議論
Truly amazing that they've managed to build an open and profitable platform without shady practices
lairv
·6 か月前·議論
Default to "spawn" is definitely the right thing, it avoids many footguns

That said for PyTorch DataLoader specifically, switching from fork to spawn removes copy-on-write, which can significantly increase startup time and more importantly memory usage. It often requires non-trivial refactors, many training codebase aren't designed for this and will simply OOM. So in practice for this use case, I've found it more practical to just use pandas rather than doing a full refactor
lairv
·6 か月前·議論
Well I think ProcessPoolExecutor/ThreadPoolExecutor from concurrent.futures were supposed to be that
lairv
·6 か月前·議論
I would agree if not for the fact that polars is not compatible with Python multiprocessing when using the default fork method, the following script hangs forever (the pandas equivalent runs):

    import polars as pl
    from concurrent.futures import ProcessPoolExecutor

    pl.DataFrame({"a": [1,2,3], "b": [4,5,6]}).write_parquet("test.parquet")

    def read_parquet():
        x = pl.read_parquet("test.parquet")
        print(x.shape)

    with ProcessPoolExecutor() as executor:
        futures = [executor.submit(read_parquet) for _ in range(100)]
        r = [f.result() for f in futures]

Using thread pool or "spawn" start method works but it makes polars a pain to use inside e.g. PyTorch dataloader
lairv
·6 か月前·議論
NVIDIA stock tanked in 2025 when people learned that Google used TPUs to train Gemini, which everyone in the community knows since at least 2021. So I think it's very likely that NVIDIA stock could crash for non-rationale reasons

edit: 2025* not 2024
lairv
·7 か月前·議論
That's why I always disliked calling null the "billion dollar mistake", null and Options<T> are basically the same, the mistake is not checking it at compile time
lairv
·8 か月前·議論
Is there a solution to this exact problem, or to related notions (renewal equation etc.)? Anyway seems like nothing beats training on test
lairv
·8 か月前·議論
Out of curiosity, I gave it the latest project euler problem published on 11/16/2025, very likely out of the training data

Gemini thought for 5m10s before giving me a python snippet that produced the correct answer. The leaderboard says that the 3 fastest human to solve this problem took 14min, 20min and 1h14min respectively

Even thought I expect this sort of problem to very much be in the distribution of what the model has been RL-tuned to do, it's wild that frontier model can now solve in minutes what would take me days
lairv
·8 か月前·議論
For those curious here are the actual keywords (from https://docs.python.org/3/reference/lexical_analysis.html?ut... )

Hard Keywords:

False await else import pass None break except in raise True class finally is return and continue for lambda try as def from nonlocal while assert del global not with async elif if or yield

Soft Keywords:

match case _ type

I think nonlocal/global are the only hard keywords I now barely use, for the soft ones I rarely use pattern matching, so 5 seems like a good estimate