10. Optimus Party Mode

no one did it like she does

Diffusion based image editing and personalization methods are expensive💰due to training, latent optimization or prompt-tuning🤷‍♂️. Introducing RF-Inversion🎯,the first efficient zero-shot inversion and editing framework for Flux🚀without training,optimization or prompt-tuning🧵⬇️

🎉At #ICASSP2024, I published a paper showing that synthetic data can effectively improve ASR models (arxiv.org/abs/2309.10707). At #EMNLP2024, we're presenting SYN2REAL to address synthetic-to-real gap in ASR! 🔗Paper: arxiv.org/abs/2406.02925 💡Project: farnhua.github.io/syn2real.githu…

google Japan is cooking in a way the West couldnt dream of

What to know about DeepSeek youtu.be/0eMzc-WnBfQ?si… In which we aim to understand MoE, o1, scaling, tech reporting, modern semiconductors, microeconomics, and international geopolitics.

🚀 Excited to share our work Jailbreaking with Universal Multi-Prompts accepted at #NAACL2025 Findings! 🎉 We propose JUMP, a universal jailbreak method for LLMs that optimizes multi-prompts for high transferability. 🔗Paper: arxiv.org/abs/2502.01154

omg 😳

x.com/i/article/1909…

AlphaEvolve by Deepmind and Text-based Search. The AlphaEvolve paper is an evolution (sorry!) of the FunSearch paper that appeared in Nature in 2023 with partially overlapping authors. In a nutshell, it seems to me its FunSearch with modern reasoning LLMs: A coding agent that

Hedgefund Manager says to “not pursue his career”…

🤹 New blog post! I write about our recent work on using hierarchical trees to enable sparse attention over irregular data (point clouds, meshes) - Erwin Transformer. blog: maxxxzdn.github.io/blog/erwin/ paper: arxiv.org/abs/2502.17019 Compressed version in the thread below:

new post. there's a lot in it. i suggest you check it out

Nature is Amazing ☘️ A bear made a "Bro, send me some too" gesture with its paw to a man feeding the bears at the zoo.

This kid caught a Vulture thinking it was a chicken.

Today, as part of our effort to make our platform transparent, we are open-sourcing the latest code used to recommend posts on the For You timeline. Our algorithm is always a work in progress. We will continue to refine our approach to surface the most relevant content to our

I told my parents that I’d like to drop out of my cs phd program at Stanford a few months back. They didn’t let me, we’re asian :) So I graduated and started Moonlake. Sharon Lee and I saw urgency, and opportunities. Excited to share that we raised a 28 million dollar

Walk with Robert Nishihara & I in NYC with 10% charge 🪫 as we talk through 5 key differences between 𝗟𝗟𝗠 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗩𝗦 𝗥𝗲𝗴𝘂𝗹𝗮𝗿 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 Let’s see how much we can get through before our mic dies! 🤣