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Yeah, this is part of the reason why vscodium exists.

Wow. Just like using ungoogled-chromium instead of chrome, lineage os instead of oem android, using vscodium instead of vscode is again justified. These decisions really are the ones that I'll never regret.

In addition, using the word microslop instead of microsoft is again justified, too.


One thing that makes me wonder is that there are 4 security issues raised and all of them were automatically commented and closed by some bot called `pl-ghost` [1][2][3][4]. In the end, only this one [4] properly handled, and all bot comments are deleted. You can see the bot comments in another report [5], which is more informative than the OP one.

[1] https://github.com/Lightning-AI/pytorch-lightning/issues/216...

[2] https://github.com/Lightning-AI/pytorch-lightning/issues/216...

[3] https://github.com/Lightning-AI/pytorch-lightning/issues/216...

[4] https://github.com/Lightning-AI/pytorch-lightning/issues/216...

[5] https://socket.dev/blog/lightning-pypi-package-compromised


Andy from Lightning here. Yeah, the PyPi credentials were stolen through the compromised pl-ghost bot account. The attacker used this account to create a new actions workflow, which was ran and parsed out secrets for PyPi. After releasing the package, the attacker then used that account to troll us a bit with those comments.

Although the performance claim of 8b dense matching 32b moe is somewhat questionable, thank you granite team for releasing small dense LLMs.

Thank you Qwen team. Small DENSE LLMs shapes the future of local LLM users.

When Qwen 3.5 27b released, I didn't really understand why linear attention is used instead of full attention because of the performance degradation and problems introduced with extra (linear) operators. After doing some tests, I found that with llama.cpp and IQ4_XS quant, the model and BF16 cache of the whole 262k context just fit on 32GB vram, which is impossible with full attention. In contrast, with gemma 4 31b IQ4_XS quant I have to use Q8_0 cache to fit 262k context on the vram, which is a little annoying (no offenses, thank you gemma team, too).

From benchmarks, 3.5->3.6 upgrade is about agent things. I hope future upgrades fix some problems I found, e.g., output repetitiveness in long conversations and knowledge broadness.


Check out Fig. 6 in this paper, it shows the comparison between the proposed method and pytorch native FSDP offload method.


No open weights.

Besides, I'm old enough to recall that META has trained a version of LLAMA 4 specifically for LM arena elo benchmaxxing and PR things, and proceeded to release a different version of LLAMA 4.


Thank you Gemma team for releasing small dense VLM(s).

The elo ranking [1] is too good to be true. I don't know why gemma-4-26b-a4b performs better than gemma-4-31b.

Also waiting for more bugfixes in llama.cpp, sglang and vllm to do proper evaluations.

[1] https://arena.ai/leaderboard/text/expert?license=open-source


Please no.

If you want to install APKs directly on Android phones selling in China, you'll face even more draconian restrictions imposed by both Chinese OEMs and Chinese government, e.g., cannot install telegram [1], cannot install VPNs [2], called by local police station after installing VPNs [3], and so on. And you do not have the freedom to even talk about these restrictions freely without getting sued or censored.

[1] https://xcancel.com/whyyoutouzhele/status/168915238841261670...

[2] https://xcancel.com/whyyoutouzhele/status/197843066556268971...

[3] https://xcancel.com/whyyoutouzhele/status/170299205759627676...


Yeah, let's hold Google accountable. Is there a way to practice anti-trust laws?


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