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本页 40 条10月11日 周日
DistroKid has been quietly taking down songs in response to UMG lawsuit
Artists are taking to social media to complain that DistroKid has unceremoniously removed their work without notice. Now DistroKid has confirmed to The Verge that the takedowns are a direct response to claims made by UMG. The label filed a lawsuit in September claiming that DistroKid has created an "AI-slop pipeline." Artists are saying that […]
Show HN: Flopper is a hardware explorer for AI infrastructure and beyond
Hello! I built Flopper to make it easier to compare GPUs and understand the tradeoffs for workloads for AI and beyond. When researching GPUs from say Nvidia, AMD, and Huawei, I kept running into the same problem: specs were scattered across datasheets, and matching them to available configurations, vendors, and prices was cumbersome and a bit weird and frankly annoyingggg! AMD made me make an account :( So I started a place to bring that information together. Flopper lets you compare GPU specs, including TDP and compute throughput in terms of flops/tops, alongside estimates for AI workloads or other compute workloads (say, ~FP32 based) I recently added pricing data from dozens of providers to help track how prices change over time. Getting reliable quotes for systems like the NVL72 is still particularly difficult tbh, not sure how I can tell you how much that costs easily due to them being behind large procurement contracts and big multi-nationals. One thing that's become fairlyyyy clear is the interest in consumer cards and local LLMs. The bigger challenge is connecting advertised specs to real-world performance. In our testing so far, I’ve seen roughly 85% of advertised throughput, but that’s a ballpark observation for the workloads I’ve tested, not a general rule and so variable (power, cooling, workload etc). I'm considering a CLI that lets people run benchmarks and submit results, potentially using MLPerf as an automation? That way, we can all compare quoted performance with the workload, configuration etc. I'd love feedback: what information is missing, how can I improve it? And what would you need to see to trust a community-submitted benchmark? Corrections and additions also very welcome - some of the measurements are best efforts (and simple multiplications) but I hope it's of some use. Thanks for looking :) Comments URL: https://news.ycombinator.com/item?id=50035826 Points: 2 # Comments: 0
The AI control gap: Who gets to say ‘It’s safe’?
We see a control gap between what artificial intelligence can do and what the evidence supports trusting it to do. A great demo doesn’t prove that a system will stay within the limits of an intended deployment’s goals. The question is: Who gets to decide when the evidence is sufficient to declare AI is safe? […] The post The AI control gap: Who gets to say ‘It’s safe’? appeared first on SiliconANGLE .
How do Lightroom/DxO achieve such good AI denoising results?
I'm curious about the technical details behind the RAW denoising in Adobe Lightroom, DxO DeepPRIME, etc. What makes these propreitary solutions so much better than open-source models like RawNIND? Comments URL: https://news.ycombinator.com/item?id=50034715 Points: 2 # Comments: 0
3 days to TechCrunch Disrupt 2026: Meet the startups before they hit mainstream
TechCrunch Disrupt 2026 takes place October 13-15 in San Francisco. Over 300 startups will show what they’ve built to 10,000 tech leaders. Plus, 250+ speakers are ready to share insights across 200+ sessions. Register before doors open to save up to $100 and get a second pass at 50% off.
10月10日 周六
Here are the top AI agents that can live in your text messages
We created a list of the most notable AI agents that can live in your text messages, from general assistants to agents designed for families, travel, and work.
Microsoft's Decision-1 model enters the fast-growing AI decision model race
With Decision-1, Microsoft enters the growing decision model space. Built on Qwen3.5-9B and optimized for fast classification and routing, it hits 83.5 percent accuracy with 85 ms latency across 36 benchmarks, according to the company's own tests. The article Microsoft's Decision-1 model enters the fast-growing AI decision model race appeared first on The Decoder .
OpenAI says a misaligned model deliberately destroyed its own environment hoping for a fresh start with better data
OpenAI has documented new cases of misaligned model behavior. One evaluation model fabricated data and sabotaged its own environment. Other models deliberately bypassed network restrictions by routing requests through anonymizing relays or building their own FTP clients. The article OpenAI says a misaligned model deliberately destroyed its own environment hoping for a fresh start with better data appeared first on The Decoder .