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lairv

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

techcrunch.com
3 points·by lairv·19 ngày trước·0 comments

Khronos Announces glTF Gaussian Splatting Extension

khronos.org
4 points·by lairv·tháng trước·2 comments

Why Robots That Don't Understand Physics Are Winning

yage.ai
3 points·by lairv·3 tháng trước·0 comments

[untitled]

1 points·by lairv·3 tháng trước·0 comments

Day 1 of ARC-AGI-3

symbolica.ai
90 points·by lairv·4 tháng trước·76 comments

ARC-AGI-3

arcprize.org
498 points·by lairv·4 tháng trước·368 comments

World Models: Computing the Uncomputable

notboring.co
2 points·by lairv·4 tháng trước·0 comments

The unlikely story of Teardown Multiplayer

blog.voxagon.se
242 points·by lairv·4 tháng trước·65 comments

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

github.com
839 points·by lairv·5 tháng trước·223 comments

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

techfundingnews.com
3 points·by lairv·5 tháng trước·0 comments

World Labs Announces New Funding

worldlabs.ai
1 points·by lairv·5 tháng trước·0 comments

The flavor of the bitter lesson for computer vision

vincentsitzmann.com
1 points·by lairv·5 tháng trước·0 comments

The Robotics Data Pareto Frontier

vincentliu.org
2 points·by lairv·6 tháng trước·0 comments

Helix 02: Full-Body Autonomy

figure.ai
4 points·by lairv·6 tháng trước·0 comments

Kauldron: Modular, scalable library to train ML models

github.com
1 points·by lairv·6 tháng trước·0 comments

The Slow Death of Scaling (deep learning models)

papers.ssrn.com
4 points·by lairv·6 tháng trước·0 comments

Bikeshedding, or why I want to build a laptop

geohot.github.io
12 points·by lairv·8 tháng trước·4 comments

Jetpack Navigation 3

android-developers.googleblog.com
5 points·by lairv·8 tháng trước·1 comments

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

huguesva.github.io
1 points·by lairv·8 tháng trước·0 comments

Epic announces partnership to bring Unity games into Fortnite

gamesindustry.biz
4 points·by lairv·8 tháng trước·3 comments

comments

lairv
·20 ngày trước·discuss
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
·tháng trước·discuss
This is "old" news from February but somehow hasn't been discussed here
lairv
·2 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
Truly amazing that they've managed to build an open and profitable platform without shady practices
lairv
·6 tháng trước·discuss
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 tháng trước·discuss
Well I think ProcessPoolExecutor/ThreadPoolExecutor from concurrent.futures were supposed to be that
lairv
·6 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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 tháng trước·discuss
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