Last month NVIDIA released SOL-ExecBench , a new benchmark of 235 production CUDA kernels lifted from DeepSeek, Qwen, Gemma, and Kimi. We took several top-ranked AI-generated submissions and tried using them in production workloads. Many of them broke, sometimes in surprising ways. One of those kernels is the fused embedding-gradient + RMSNorm backward pass, which runs at the end of every transformer training step. We took the fastest submission on the benchmark for it, and dropped it into the training loop of a small transformer. The kernel had passed the benchmark's verifier with room to spare. But in our training run, the loss diverged and never recovered. We started debugging. Replace the dataset distribution with uniformly sampled tokens, the divergence vanishes. Swap SGD for AdamW, also vanishes. This is the worst kind of bug for research. Symptoms and masks both look exactly like "the idea didn't work". It's the type of bug that can make researchers spend a long time debugging without knowing what's at fault: the dataset? the research idea? the architecture? or the implementation itself? Turns out, the actual bug is that the embedding-gradient half of the kernel accumulates in bf16 instead of fp32. Embedding backward sums many small gradient contributions into each token's row of the embedding matrix. With uniform random tokens the contributions spread evenly and bf16 precision is enough. In real text, a handful of token IDs end up with thousands of contributions: the small ones round to zero against the growing accumulator, and the high-frequency rows drift. AdamW's per-parameter normalization absorbs the resulting multiplicative bias, so under AdamW the same drift is invisible in the loss. The other broken submissions had different bug shapes (all interesting). More examples in our blogpost . submitted by /u/laginimaineb [link] [留言]

I am wondering how are businesses integrating AI while protecting their data? submitted by /u/pappugulal [link] [留言]

These WIRED-tested computer speakers, from stereo speakers to surround sound, will suit any budget.

What comes to mind is you have some code that you want other ppl to be able to interact with, then you want to store data somewhere so you provision a database, then you need somewhere to host that code so you spin up a server then plop your code into that server and make it publicly accessible. I get that there are other parts like networking, security, RBAC/permissions, Linux commands, scalability/maintainability, API, cloud infrastructure, version and change management. But does it basically boil down to those three things: Code Database Server Thanks submitted by /u/throwaway0134hdj [link] [留言]

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AI email client built for focus. Runs locally on your Mac Discussion | Link

Secure way to share apps built with AI Agents Discussion | Link

As Cognition reaches $492 million in annualized revenue run rate, it more than doubled its valuation in eight months, it says.

AI can now realistically simulate massive crowds and public events. The scary part isn’t the quality anymore. It’s how quickly people are discovering creative ways to use it. Reality online is about to get very confusing. 💀 submitted by /u/Old_Establishment287 [link] [留言]

A calmer way to visualize & plan across timezones Discussion | Link

The company I work for was bought up and in the new company, one of my responsibilities will be developing our website. The issue is that the website was built by an external agency. And almost any change to the website have to be implemented by them. And of course they charge for every change. They own both the backend and frontend side of the website. I can basically only add text and images. I wonder what exactly I should advise my new boss(whom really doesn’t like it and wants to make several changes). Is it really an option to move the site to a different hosting plattform and build another theme? Which seems like last resort. And not even sure if that is really feasible either. Hiring a developer would probably be more costly and I am decent with front end, but not enough experience with backend. So don’t think I can do it myself. Should I just cave and downgrade my position? Asking for higher access will probably be rejected. This is how they make money. Hopefully this is the correct sub, and someone have some insights and experience! submitted by /u/craigularperson [link] [留言]

I'm working on a project that translates any language into English. So far, I've tried NMT models like NLLB, MADLAD, and SeamlessM4T v2. The main issue is that they struggle with proper nouns such as: - names - places - dates - organizations I also tried LLMs like Gemma 4, Qwen 3 4B, and Aya Tiny Global, but the issue still persists. The LLMs sometimes partially translate or modify entity names as well. I even tried NER masking / placeholder replacement before translation, but multilingual NER itself becomes a bottleneck. Most NER models only work reliably for a limited set of languages, while my dataset contains 100+ languages, including many low-resource ones. How do production systems usually handle this problem? Are there better multilingual translation models, multilingual NER approaches, or decoding techniques for preserving entities properly? Requirements: - Support for 100+ languages - Runs locally on an RTX GPU - Model size under 7B - English is always the target language. submitted by /u/Illustrious_Age_2792 [link] [留言]

Advances in adapters mean you could ditch Wi-Fi for better performance.

Starship is grounded until SpaceX finds out why the first V3 booster failed during its first test flight.