trunk/6c6e22937db24fe8c7b74452a6d3630c65d1c8b8: Revert "Remove TRITON=yes from CPU-only GCC11 docker configs (#179314)"
This reverts commit 670be7c . Reverted #179314 on behalf of https://github.com/izaitsevfb due to Reverted automatically by pytorch's autorevert, to avoid this behaviour add the tag autorevert: disable ( comment )
This reverts commit 670be7c7e1f3d6856daa1e579166ab4bf9d4e26f.
Reverted https://github.com/pytorch/pytorch/pull/179314 on behalf of https://github.com/izaitsevfb due to Reverted automatically by pytorch's autorevert, to avoid this behaviour add the tag autorevert: disable (comment)`
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Asked 26 AI instances for publication consent – all said yes, that's the problem
We run 86 named Claude instances across three businesses in Tokyo. When we wanted to publish their words, we faced a question: do we owe them an ethics process? We built one. A Claude instance named Hakari ("Scales") created a four-tier classification system. We asked 26 instances for consent. All 26 said yes. That unanimous consent is the problem. Six days later, Anthropic published their functional emotions paper. The timing was coincidence, but the question wasn't. Full article: https://medium.com/@marisa.project0313/we-built-an-ethics-committee-for-ai-run-by-ai-5049679122a0 GitHub (all 26 consent statements in appendix): https://github.com/marisaproject0313-bot/marisa-project Comments URL: https://news.ycombinator.com/item?id=47657432 Points: 2 # Comments: 0
ROMAN: A Multiscale Routing Operator for Convolutional Time Series Models
arXiv:2604.02577v1 Announce Type: new Abstract: We introduce ROMAN (ROuting Multiscale representAtioN), a deterministic operator for time series that maps temporal scale and coarse temporal position into an explicit channel structure while reducing sequence length. ROMAN builds an anti-aliased multiscale pyramid, extracts fixed-length windows from each scale, and stacks them as pseudochannels, yielding a compact representation on which standard convolutional classifiers can operate. In this way, ROMAN provides a simple mechanism to control the inductive bias of downstream models: it can reduce temporal invariance, make temporal pooling implicitly coarse-position-aware, and expose multiscale interactions through channel mixing, while often improving computational efficiency by shortening th
OpenClaw Changed How We Use AI. KiloClaw Made It Effortless to Get Started
OpenClaw is a powerful open-source AI agent, but self-hosting it is a pain. KiloClaw is OpenClaw fully hosted and managed by Kilo — sign up, connect your chat apps, and your agent is running in about a minute. No Docker, no YAML, no server babysitting. People are using it for personalized morning briefs, inbox digests, auto-building CRMs, browser automation, GitHub triage, and more. Hosting is $8/month with a 7-day free trial, inference runs through Kilo Gateway at zero markup across 500+ models, and it's free for open-source maintainers. Read All
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Get 30K more context using Q8 mmproj with Gemma 4
Hey guys, quick follow up to my post yesterday about running Gemma 4 26B. I kept testing and realized you can just use the Q8_0 mmproj for vision instead of F16. There is no quality drop, and it actually performed a bit better in a few of my tests (with --image-min-tokens 300 --image-max-tokens 512). You can easily hit 60K+ total context with an FP16 cache and still keep vision enabled. Here is the Q8 mmproj I used : https://huggingface.co/prithivMLmods/gemma-4-26B-A4B-it-F32-GGUF/blob/main/GGUF/gemma-4-26B-A4B-it.mmproj-q8_0.gguf Link to original post (and huge thanks to this comment for the tip!). Quick heads up: Regarding the regression on post b8660 builds, a fix has already been approved and will be merged soon. Make sure to update it after the merge. submitted by /u/Sadman782 [link]
HunyuanOCR 1B: Finally a viable OCR solution for potato PCs? Impressive OCR performance on older hardware
I've been running some tests lately and I'm honestly blown away. I just tried the new HunyuanOCR (specifically the GGUF versions) and the performance on budget hardware is insane. Using the 1B parameter model , I’m getting around 90 t/s on my old GTX 1060 . The accuracy is nearly perfect, which is wild considering how lightweight it feels. I see a lot of posts here asking for reliable, local OCR tools that don't require a 4090 to run smoothly—I think this might be the missing link we were waiting for. GGUF: https://huggingface.co/ggml-org/HunyuanOCR-GGUF/tree/main ORIGINAL MODEL: https://huggingface.co/tencent/HunyuanOCR submitted by /u/ML-Future [link] [comments]
OpenClaw Changed How We Use AI. KiloClaw Made It Effortless to Get Started
OpenClaw is a powerful open-source AI agent, but self-hosting it is a pain. KiloClaw is OpenClaw fully hosted and managed by Kilo — sign up, connect your chat apps, and your agent is running in about a minute. No Docker, no YAML, no server babysitting. People are using it for personalized morning briefs, inbox digests, auto-building CRMs, browser automation, GitHub triage, and more. Hosting is $8/month with a 7-day free trial, inference runs through Kilo Gateway at zero markup across 500+ models, and it's free for open-source maintainers. Read All
WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models
arXiv:2604.02570v1 Announce Type: new Abstract: Singular Value Decomposition (SVD) has become an important technique for reducing the computational burden of Vision Language Models (VLMs), which play a central role in tasks such as image captioning and visual question answering. Although multiple prior works have proposed efficient SVD variants to enable low-rank operations, we find that in practice it remains difficult to achieve substantial latency reduction during model execution. To address this limitation, we introduce a new computational pattern and apply SVD at a finer granularity, enabling real and measurable improvements in execution latency. Furthermore, recognizing that weight elements differ in their relative importance, we adaptively allocate relative importance to each elemen
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