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[HIP] Bump ROCm version to 7.2.1 ( #21066 ) Bump ROCm version on Linux from 7.2 to 7.2.1 Add gfx1102 target Delete LLVM workaround since ROCm 7.2.1 has fix for ROCm 7.2 perf regression ROCm/rocm-systems#2865 Co-authored-by: Sigbjørn Skjæret [email protected] macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
Q1 2026 Timelines Update
We’re mostly focused on research and writing for our next big scenario, but we’re also continuing to think about AI timelines and takeoff speeds, monitoring the evidence as it comes in, and adjusting our expectations accordingly. We’re tentatively planning on making quarterly updates to our timelines and takeoff forecasts. Since we published the AI Futures Model 3 months ago, we’ve updated towards shorter timelines. Daniel’s Automated Coder (AC) median has moved from late 2029 to mid 2028, and Eli’s forecast has moved a similar amount. The AC milestone is the point at which an AGI company would rather lay off all of their human software engineers than stop using AIs for software engineering. The reasons behind this change include: 1 We switched to METR Time Horizon version 1.1 . We include
I Built Consistent Hashing From Scratch in Go — Here's What I Learned
If you've ever added a server to a cache cluster and watched your database melt, you already know the problem consistent hashing solves. You just might not know it by name. I built a full implementation from scratch in Go to understand it deeply. This post walks through what I learned — the problem, the fix, and the gotchas nobody tells you about. The five-minute version You have 5 cache servers. You route keys with hash(key) % 5 . Life is good. Then traffic spikes and you add a 6th server. Now it's hash(key) % 6 . Sounds harmless, right? Here's what actually happens: Before: hash("user:1001") % 5 = 3 → Server C After: hash("user:1001") % 6 = 1 → Server A ← moved! That key was sitting happily on Server C. Now every client thinks it's on Server A, where it doesn't exist. Cache miss. The req
Debunking Myths on the National Security Impact of Warrants for U.S. Person Queries in 2026
Co-authored with Gene Schaerr, General Counsel at the Project on Privacy and Surveillance Accountability [PDF Version] Warrantless queries of Americans’ communications obtained via Section 702 of the Foreign Intelligence Surveillance Act (“FISA 702”) are antagonistic to the basic principle of the Fourth Amendment. Deliberately seeking to read Americans’ private communications – but without ever showing evidence [ ] The post Debunking Myths on the National Security Impact of Warrants for U.S. Person Queries in 2026 appeared first on Center for Democracy and Technology .
5 Best Test Management Tools in 2026 — Features, Pricing & Honest Comparison
Quick Answer The test management tool market in 2026 is crowded, but five platforms stand out: TestKase , Qase , TestRail , BrowserStack Test Management , and TestMu AI (formerly LambdaTest). The right choice depends on your team size, budget, and how much you value AI capabilities. If you want the short version: TestKase offers the most generous free tier and lowest per-seat pricing. Qase has the most mature marketplace. TestRail dominates enterprise. BrowserStack bundles test management into a broader platform. TestMu AI is rebranding aggressively with AI-first features. Top 3 Key Takeaways Pricing varies wildly. From free to $36+/user/month for similar core features. The difference at 20 users is over $7,000/year between the cheapest and most expensive options. AI is the new differentia
Microsoft shivs OpenAI with three new AI models for speech and images
About that partnership... Microsoft on Thursday unveiled public preview versions of three home-baked machine learning models focused on speech recognition, speech synthesis, and image generation.…
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ggml-webgpu: add vectorized flash attention ( #20709 ) naive vectorized version add vectorized flash attention update vec version remove unused path and shader remove unused helper functions add comments remove pad path ggml-webgpu: fix flash-attn vec nwg=1 path and tighten vec specialization change back to vec4 enable multi split enable vec path when: Q->ne[1] Q->ne[0] % 32 == 0 V->ne[0] % 4 == 0 K->type == f16 update flast_attn_vec_split.wgsl to reduce redundant workgroup barrier usage and use select enable vec path for q4 and q8 flash-attn vec nwg=1 fast path (skip tmp/reduce staging) use packed f16 K loads in flash-attn vec split use packed f16 K loads in flash-attn vec split on host side tune flash-attn vec f16 VEC_NE by head dim cleanup cleanup keep host side clean cleanup host side
OpenAI brings ChatGPT's Voice mode to CarPlay
In a surprise release , OpenAI has made ChatGPT's Voice mode available through Apple CarPlay. If you're running the latest version of both iOS and the ChatGPT app, and own a CarPlay-compatible vehicle, you can check out the experience. To get started, download all the necessary software, connect your iPhone to CarPlay and select "New voice chat" from ChatGPT. When the in-app text indicates ChatGPT is "listening," you can start a conversation. There are some notable limitations to using ChatGPT Voice with CarPlay. For one, OpenAI's chatbot can't control car functions. If you want to adjust the cabin temperature or skip tracks, you'll still need Siri for those tasks. Due to Apple's restrictions, you also can't start using ChatGPT through a wake word like you can Siri. For example, to resume
How social ideas get corrupt
I’ve noticed that sometimes there is an idea or framework that seems great to me, and I also know plenty of people who use it in a great and sensible way. Then I run into people online who say that “this idea is terrible and people use it in horrible ways”. When I ask why, they point to people applying the idea in ways that do indeed seem terrible - and in fact, applying it in ways that seem to me like the opposite of what the idea is actually saying. Of course, some people might think that I’m the one with the wrong and terrible version of the idea. I’m not making the claim that my interpretation is necessarily always the correct one. But I do think that there’s a principle like “every ~social idea [1] acquires a corrupted version”, and that the corruption tends to serve specific purposes
What Building AI Projects Taught Me Beyond the Prototype
Over time, I’ve built a few AI-heavy projects, and one thing has become very clear to me: Getting something to work once is exciting. Making it useful is a completely different challenge. Earlier, I used to think that once the model worked and the output looked good, the hard part was mostly done. But building more projects changed that pretty quickly. A prototype can prove that an idea is possible. It does not prove that the idea is actually useful. That difference matters a lot. A lot of AI projects look impressive in the first version. The demo works, the output feels smart, and everything seems promising. But once you start thinking beyond that first success, better questions show up. Will it still work when the input is messy? Will someone understand how to use it easily? Will the res
Gemma 4 released
Blog: https://deepmind.google/models/gemma/ Models: - Gemma4-2B: https://huggingface.co/google/gemma-4-E2B-it - Gemma4-4B: https://huggingface.co/google/gemma-4-E4B-it - Gemma4-26B-A4B: https://huggingface.co/google/gemma-4-26B-A4B-it - Gemma4-31B: https://huggingface.co/google/gemma-4-31B-it The GGUF versions can be found here: https://huggingface.co/collections/unsloth/gemma-4 https://preview.redd.it/j7c0107ewssg1.png?width=1552 format=png auto=webp s=1c47b1d9986c42a6cb1f81d73c142863586b1fd6 submitted by /u/garg-aayush [link] [comments]
Google releases Gemma 4, a family of open models built off of Gemini 3
When Google released Gemini 3 Pro at the end of last year, it was a significant step forward for the company's proprietary large language models. Now, the company is bringing some of the same technology and research that made those models possible to the open source community with the release of its new family of Gemma 4 open-weight models. Google is offering four different versions of Gemma 4, differentiated by the number of parameters on offer. For edge devices, including smartphones, the company has the 2-billion and 4-billion "Effective" models. For more powerful machines, there’s the 26-billion "Mixture of Experts" and 31-billion "Dense" systems. For the unfamiliar, parameters are the settings a large language model can tweak to generate an output. Typically, models with more paramete
Gemma 4 1B, 13B, and 27B spotted
[Gemma 4](INSET_PAPER_LINK) is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis. You can find all the original Gemma 4 checkpoints under the [Gemma 4]( https://huggingface.co/collections/google/gemma-4-release-67c6c6f89c4f76621268bb6d ) release. submitted by /u/TKGaming_11 [link] [comments]
Samsung's new Frame Pro and OLED TVs are now available to order
After sharing pricing and availability for its new Mini LED TVs in March, Samsung is ready to detail some of the other TVs it introduced at CES earlier this year. The 2026 versions of Samsung's The Frame Pro and OLED TVs are both available to order today – save for some notable exceptions – and they start for as little as $1,200. The Frame Pro was originally introduced in 2025 as the more premium version of Samsung's popular The Frame art TVs. The big advantage of stepping up to a Pro model over a normal Frame is you get a Neo QLED panel with better backlighting, and support for Samsung's Wireless One Connect box, which lets you avoid cluttering your TV with extra cables. The 2026 version doesn't really change that formula. You still get a glare-free QLED panel, a refresh rate of up 144Hz
it looks like it will be soon 💎💎💎💎
https://github.com/ggml-org/llama.cpp/pull/21309 (thanks rerri ) from HF https://github.com/huggingface/transformers/pull/45192 [Gemma 4](INSET_PAPER_LINK) is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis. this PR probably only applies to dense, so it must be separate for MoE submitted by /u/jacek2023 [link] [comments]