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
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
> 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
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
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
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
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
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
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
Luma Labs https://lumalabs.ai/news/luma-open-physical-ai-lab
Runway https://runway.com/product/robotics