MLVC: Multi-platform Learned Video Codec for Real-World Deployment [P]

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1t2zy4h/torchnvenccompress_gpu_nvenc_silicon_as_a_pcie/"> <img alt="torch-nvenc-compress: GPU NVENC

arXiv:2606.28027v1 Announce Type: cross Abstract: Neural video codecs have surpassed classical codecs in coding efficiency but remain impractical for deployment due to

<!-- SC_OFF --><div class="md"><p>The modern ML (LLM) compiler stack is brutal. TVM is 500K+ lines of C++. PyTorch piles Dynamo, Inductor, and Triton on top of each other. I built

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1thkwgk/bytedance_released_an_open_source_model_that/"> <img alt="bytedance released an open source model

<!-- SC_OFF --><div class="md"><p>Running Llama-3-70B with long contexts and hitting HBM limits constantly. 128K context burns approx 40GB just for KV cache — half the H100 gone

<!-- SC_OFF --><div class="md"><p>I saw that Dreamina just launched Seedance 2.0 recently and I spent a few days testing it. Since I handle a lot of video content for my team, I

<!-- SC_OFF --><div class="md"><p>One thing we’ve been noticing lately is that a surprisingly large percentage of day-to-day AI workflows no longer seem to require frontier-scale

<!-- SC_OFF --><div class="md"><p>Until last week, generating an image inside Claude meant Claude wrote you a prompt. Then you copied it. Opened another tab. Pasted it into