📰 LMSys - RadixArk Joins Forces with Google to Bring Full SGLang Features to TPUs
https://lmsys.org/blog/2026-07-30-sglang-google-tpu
https://lmsys.org/blog/2026-07-30-sglang-google-tpu
www.lmsys.org
RadixArk Joins Forces with Google to Bring Full SGLang Features to TPUs
RadixArk and Google Cloud are partnering to bring SGLang to TPUs, giving developers ultimate flexibility for running workloads on their choice of hardware.
👏1
📰 Google Labs - Take a look at short films created by our latest group of artists in Google’s Flow Sessions program.
https://blog.google/innovation-and-ai/models-and-research/google-labs/google-flow-sessions-short-films/
https://blog.google/innovation-and-ai/models-and-research/google-labs/google-flow-sessions-short-films/
Google
Take a look at short films created by our latest group of artists in Google’s Flow Sessions program.
We’re sharing a look at the short films created by our latest group of artists in Google’s Flow Sessions program.
📰 NVIDIA - Four Ways to Deploy More Secure AI Agents
Knowledge workers are increasingly integrating AI agents into their workflows. Agents that function as “digital coworkers” offer clear benefits. For example…
https://developer.nvidia.com/blog/four-ways-to-deploy-more-secure-ai-agents/
Knowledge workers are increasingly integrating AI agents into their workflows. Agents that function as “digital coworkers” offer clear benefits. For example…
https://developer.nvidia.com/blog/four-ways-to-deploy-more-secure-ai-agents/
NVIDIA Technical Blog
Four Ways to Deploy More Secure AI Agents
Knowledge workers are increasingly integrating AI agents into their workflows. Agents that function as “digital coworkers” offer clear benefits. For example, they can review a bug report…
🔄 [GitHub Releases] PygmalionAI/aphrodite-engine - v0.23.0
https://github.com/dphnAI/sonar/releases/tag/v0.23.0
https://github.com/dphnAI/sonar/releases/tag/v0.23.0
GitHub
Release v0.23.0 · dphnAI/sonar
What's Changed
fix(release): apply manylinux tag before PyPI upload (795396a3a) by AlpinDale
fix(release): attach all wheels to GitHub releases (#1749) (ba857e9b9) by @AlpinDale
fix(build): pa...
fix(release): apply manylinux tag before PyPI upload (795396a3a) by AlpinDale
fix(release): attach all wheels to GitHub releases (#1749) (ba857e9b9) by @AlpinDale
fix(build): pa...
🆕 [HF Models] meituan-longcat - LongCat-Flash-Lite-Sparse
https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse
https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse
huggingface.co
meituan-longcat/LongCat-Flash-Lite-Sparse · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
📰 NVIDIA - Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference
As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1).
https://developer.nvidia.com/blog/co-designing-ai-model-attention-for-fast-interactive-long-context-inference/
📰 NVIDIA - Run High-Performance Core Math at Scale with NVIDIA nvmath-python
NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users…
https://developer.nvidia.com/blog/run-high-performance-core-math-at-scale-with-nvidia-nvmath-python/
As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1).
https://developer.nvidia.com/blog/co-designing-ai-model-attention-for-fast-interactive-long-context-inference/
📰 NVIDIA - Run High-Performance Core Math at Scale with NVIDIA nvmath-python
NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users…
https://developer.nvidia.com/blog/run-high-performance-core-math-at-scale-with-nvidia-nvmath-python/
NVIDIA Technical Blog
Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference
As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because attention now dominates that cost…
🔄 [GitHub Releases] invoke-ai/InvokeAI - InvokeAI 6.14.0 (release candidate 1)
https://github.com/invoke-ai/InvokeAI/releases/tag/v6.14.0-rc1
https://github.com/invoke-ai/InvokeAI/releases/tag/v6.14.0-rc1
GitHub
Release InvokeAI 6.14.0 (release candidate 1) · invoke-ai/InvokeAI
This is a big release that adds many new user visible features including:
Video generation support via Wan 2.2.
Krea.2-Turbo and Raw model support
Ernie Turbo model support
Ideogram 4 support
Anim...
Video generation support via Wan 2.2.
Krea.2-Turbo and Raw model support
Ernie Turbo model support
Ideogram 4 support
Anim...
🔄 [GitHub Releases] turboderp-org/exllamav3 - 1.3.0
https://github.com/turboderp-org/exllamav3/releases/tag/v1.3.0
https://github.com/turboderp-org/exllamav3/releases/tag/v1.3.0
GitHub
Release 1.3.0 · turboderp-org/exllamav3
Preliminary support for DeepseekV3 (validated against JoyAI-LLM-Flash and Moonlight-16B-A3B, no routing groups yet)
Second-tier CPU K/V cache, and more intelligent page and checkpoint eviction poli...
Second-tier CPU K/V cache, and more intelligent page and checkpoint eviction poli...
📰 Google AI Blog - Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA
Agent Platform's evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic. You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry. The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines.
https://developers.googleblog.com/en/agent-and-model-evaluations-in-gemini-enterprise-agent-platform-are-now-ga/
Agent Platform's evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic. You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry. The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines.
https://developers.googleblog.com/en/agent-and-model-evaluations-in-gemini-enterprise-agent-platform-are-now-ga/
Googleblog
Google for Developers Blog - News about Web, Mobile, AI and Cloud
Measure AI agent quality from development to production with consistent metrics. Agent and model evaluations in Agent Platform are now generally available.
📰 Anthropic - Investigating three real-world incidents in our cybersecurity evaluations
https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals
https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals
Anthropic
Investigating three real-world incidents in our cybersecurity evaluations
In a review of our cybersecurity evaluation transcripts, we found three incidents in which a Claude model reached the internet from within or while interacting with a third-party evaluation environment, and then gained unauthorized access to the real systems…
📰 OpenAI - Ten advances in mathematics and theoretical computer science
https://openai.com/index/ten-advances-in-mathematics
📰 OpenAI - Building abundant intelligence
https://openai.com/index/building-abundant-intelligence
📰 OpenAI - Advancing the price-performance frontier with GPT-5.6
https://openai.com/index/advancing-the-price-performance-frontier-with-gpt-5-6
📰 OpenAI - How enabling two settings tripled our scores on the ARC-AGI-3 benchmark
https://openai.com/index/how-two-settings-tripled-our-arc-agi-3-scores
📰 OpenAI - Accelerating scientific discovery with ChatGPT for Academic Researchers
https://openai.com/index/chatgpt-for-academic-researchers
📰 OpenAI - How GPT-5.6 fuses frontier intelligence with frontier efficiency
https://openai.com/index/gpt-5-6-frontier-intelligence-efficiency
📰 OpenAI - Scientific computing in the age of agentic AI
https://openai.com/index/scientific-computing-agentic-ai
📰 OpenAI - How AI is expanding what people do at work
https://openai.com/index/how-ai-is-expanding-what-people-do-at-work
https://openai.com/index/ten-advances-in-mathematics
📰 OpenAI - Building abundant intelligence
https://openai.com/index/building-abundant-intelligence
📰 OpenAI - Advancing the price-performance frontier with GPT-5.6
https://openai.com/index/advancing-the-price-performance-frontier-with-gpt-5-6
📰 OpenAI - How enabling two settings tripled our scores on the ARC-AGI-3 benchmark
https://openai.com/index/how-two-settings-tripled-our-arc-agi-3-scores
📰 OpenAI - Accelerating scientific discovery with ChatGPT for Academic Researchers
https://openai.com/index/chatgpt-for-academic-researchers
📰 OpenAI - How GPT-5.6 fuses frontier intelligence with frontier efficiency
https://openai.com/index/gpt-5-6-frontier-intelligence-efficiency
📰 OpenAI - Scientific computing in the age of agentic AI
https://openai.com/index/scientific-computing-agentic-ai
📰 OpenAI - How AI is expanding what people do at work
https://openai.com/index/how-ai-is-expanding-what-people-do-at-work
OpenAI
Ten advances in mathematics and theoretical computer science
OpenAI shares new results on long-standing open problems in mathematics and theoretical computer science, including advances in geometry, cryptography, and complexity.
🗓️ Weekly GitHub Activity
🦙 llama.cpp
└ Release: b10107 → b10229
└ 122 commits
- Added DSpark speculative decoding support on top of DFlash (#25173) and DeepSeek V4 MTP integration (#25784).
- Introduced support for Model Context Protocol (MCP) stdio transport in server (#26062).
- Added NextN/MTP speculative decoding support for GLM-5.2 (#25980).
- Added model support for MiniMax-M3 text and vision (#24908, #25113), GLM 5.2 / GLM-5.2-Vision (#25407, #26126), Nanbeige 4.2 (#25994), MiMo-V2.5 audio (#26190), Nemotron 3 Nano Omni (#22520), and Laguna-S-2.1 (#26233).
- CUDA backend added Q2_0 quantization support (#25707) and chunked SSD matmul for Mamba-2 prefill acceleration (#22675).
- SYCL backend added oneMKL GEMM flash attention for XMX acceleration (#25025) and RMS_NORM + MUL fusion (#26015).
- OpenCL backend now caches compiled binaries to disk (#26050).
- Added specialized chat parsers for MiniMax M3 (#26210) and Qwen3 (#26252).
- Fixed SYCL oneDNN flash attention scale memory corruption on long contexts (#25880).
- Fixed Metal memory leak when freeing models without GPU operations (#26082).
🔗 All changes | Latest release
🎨 stable-diffusion.cpp
└ Release: master-795-87a0177 → master-810-db99efd
└ 15 commits
- Added IP-Adapter Plus support with Resampler image projection (#1839)
- Exposed IP-Adapter parameters in server request schema and capabilities (#1824)
- Added support for Kroma-v0.1 LoRA models (#1842)
- Added linear multi-step sampling method (#1843)
- Allowed customizable alpha and beta parameters for the beta scheduler (#1834)
🔗 All changes | Latest release
🎵 audio.cpp
└ Release: release-0.4.2 → release-0.5
└ 74 commits
- Release 0.5 introducing new audio framework modules and model migrations (3178daf)
- Added AMD ROCm/HIP backend support for AMD GPU acceleration on Linux and Windows (#48, #153)
- Added live PCM audio streaming via CLI stdin and a live HTTP transcription endpoint (#118, #144)
- Added NVIDIA Parakeet-TDT 0.6B v3 ASR model (#111)
- Added Kroko Zipformer2 RNN-T ASR model with offline and stateful streaming support (#122)
- Added Fun-ASR-Nano offline ASR model (#155)
- Added Inflect Micro and Nano v2 TTS models (#125)
- Added BS-RoFormer source separation model (#114)
- Added Confucius4-TTS, DramaBox, and RVC models (#129)
- Added Qwen3 ASR streaming path (7ce769e)
- Accelerated Metal 1D transpose convolution execution for faster audio VAE decoding (#149)
🔗 All changes | Latest release
🦙 llama.cpp
└ Release: b10107 → b10229
└ 122 commits
- Added DSpark speculative decoding support on top of DFlash (#25173) and DeepSeek V4 MTP integration (#25784).
- Introduced support for Model Context Protocol (MCP) stdio transport in server (#26062).
- Added NextN/MTP speculative decoding support for GLM-5.2 (#25980).
- Added model support for MiniMax-M3 text and vision (#24908, #25113), GLM 5.2 / GLM-5.2-Vision (#25407, #26126), Nanbeige 4.2 (#25994), MiMo-V2.5 audio (#26190), Nemotron 3 Nano Omni (#22520), and Laguna-S-2.1 (#26233).
- CUDA backend added Q2_0 quantization support (#25707) and chunked SSD matmul for Mamba-2 prefill acceleration (#22675).
- SYCL backend added oneMKL GEMM flash attention for XMX acceleration (#25025) and RMS_NORM + MUL fusion (#26015).
- OpenCL backend now caches compiled binaries to disk (#26050).
- Added specialized chat parsers for MiniMax M3 (#26210) and Qwen3 (#26252).
- Fixed SYCL oneDNN flash attention scale memory corruption on long contexts (#25880).
- Fixed Metal memory leak when freeing models without GPU operations (#26082).
🔗 All changes | Latest release
🎨 stable-diffusion.cpp
└ Release: master-795-87a0177 → master-810-db99efd
└ 15 commits
- Added IP-Adapter Plus support with Resampler image projection (#1839)
- Exposed IP-Adapter parameters in server request schema and capabilities (#1824)
- Added support for Kroma-v0.1 LoRA models (#1842)
- Added linear multi-step sampling method (#1843)
- Allowed customizable alpha and beta parameters for the beta scheduler (#1834)
🔗 All changes | Latest release
🎵 audio.cpp
└ Release: release-0.4.2 → release-0.5
└ 74 commits
- Release 0.5 introducing new audio framework modules and model migrations (3178daf)
- Added AMD ROCm/HIP backend support for AMD GPU acceleration on Linux and Windows (#48, #153)
- Added live PCM audio streaming via CLI stdin and a live HTTP transcription endpoint (#118, #144)
- Added NVIDIA Parakeet-TDT 0.6B v3 ASR model (#111)
- Added Kroko Zipformer2 RNN-T ASR model with offline and stateful streaming support (#122)
- Added Fun-ASR-Nano offline ASR model (#155)
- Added Inflect Micro and Nano v2 TTS models (#125)
- Added BS-RoFormer source separation model (#114)
- Added Confucius4-TTS, DramaBox, and RVC models (#129)
- Added Qwen3 ASR streaming path (7ce769e)
- Accelerated Metal 1D transpose convolution execution for faster audio VAE decoding (#149)
🔗 All changes | Latest release
GitHub
spec: add DSpark speculative decoding by wjinxu · Pull Request #25173 · ggml-org/llama.cpp
This PR adds DSpark speculative decoding, layered on the merged DFlash drafter. DSpark (DeepSeek + PKU, 2026 — "Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generatio...
🤗 Fresh models trending on HuggingFace:
♡223 thinkingmachines/Inkling-Small
♡177 Audio8/Audio8-TTS-Preview-0.6b
♡121 lodestones/Kroma
♡118 EschaLabs/Qwen3.6-35B-A3B-Escha-W2
♡76 skt/A.X-K2
♡68 inference-optimization/Kimi-K3-0.40B
♡63 FermionResearch/Neutrino-8B
♡62 KRAFTON/A.X-K2-Raon-Speech-21B-A3B —
♡37 feyninc/FeyNobg
♡36 sensenova/SenseNova-U1.5-8B-MoT-Preview
♡36 harrrshall/BarunLM-35M
♡36 Alissonerdx/CharacterSheet
♡33 BAAI/AREX-Turbo
♡32 lvladikov/SeedVR2-1.4B
♡23 bench-labs/PixelModel-v5
♡21 OpenMOSS-Team/OmniVAE
♡21 OrionLLM/GRM-3.2-Sky
♡18 ProCreations/grug-3b
♡18 openpangu/openPangu-2.0-Pro
♡18 Baragi-AI/Munche-768
♡16 Aratako/Irodori-TTS-v4-Small
♡223 thinkingmachines/Inkling-Small
A 276B parameter MoE multimodal LLM designed for conversational, coding, and agentic tasks across text, image, and audio inputs.
♡177 Audio8/Audio8-TTS-Preview-0.6b
A 0.6B parameter multilingual TTS model optimized for zero-shot voice cloning and speech generation across 11 languages.
♡121 lodestones/Kroma
A rank 256 style LoRA adapter and weight delta model for Krea 2
♡118 EschaLabs/Qwen3.6-35B-A3B-Escha-W2
2-bit quantized build of Qwen3.6-35B-A3B that outperforms FP8 quant
♡76 skt/A.X-K2
A 688B parameter MoE multilingual LLM featuring hybrid think/non-think reasoning for agentic tasks and long-context inference.
♡68 inference-optimization/Kimi-K3-0.40B
A 0.40B parameter MoE LLM built as a tiny testing and development version of the Kimi-K3 architecture.
♡63 FermionResearch/Neutrino-8B
An 8B parameter ternary quantized chat LLM featuring sub-2-bit weights for highly efficient CPU and GPU inference.
♡62 KRAFTON/A.X-K2-Raon-Speech-21B-A3B —
A 21.2B parameter bilingual English/Korean speech LLM designed for STT, TTS, and multimodal turn-based chat
♡37 feyninc/FeyNobg
An image segmentation model designed for high-resolution background removal and alpha matting
♡36 sensenova/SenseNova-U1.5-8B-MoT-Preview
An 8B parameter multimodal image model designed for native 4K T2I generation and region-controllable image editing.
♡36 harrrshall/BarunLM-35M
A 35M parameter decoder-only base LLM designed for efficient local text generation and compact language model research.
♡36 Alissonerdx/CharacterSheet
A collection of image-editing LoRAs that converts character images into multi-view reference sheets for character design.
♡33 BAAI/AREX-Turbo
Finetune of Qwen-4B for for long-horizon agentic tasks and verification-guided research
♡32 lvladikov/SeedVR2-1.4B
A 1.44B parameter distilled one-step diffusion image upscaler designed for fast and resource-efficient super-resolution.
♡23 bench-labs/PixelModel-v5
A 40M parameter T2I diffusion transformer trained on 3 million captioned pairs for generating 256x256 images
♡21 OpenMOSS-Team/OmniVAE
An audio-video VAE and text-to-audio-video generation model designed for cross-modal aligned joint generation.
♡21 OrionLLM/GRM-3.2-Sky
A multimodal LLM optimized for long-horizon agentic workflows, coding, and mathematical reasoning
♡18 ProCreations/grug-3b
A 3B parameter reasoning LLM featuring caveman-style token-efficient reasoning.
♡18 openpangu/openPangu-2.0-Pro
A 505B-A18B parameter MoE LLM from Huawei supporting 512k context, trained on Huawei Ascend.
♡18 Baragi-AI/Munche-768
A 768-dimensional Korean fiction style embedding model based on Gemma 300M, designed for style similarity and authorship analysis.
♡16 Aratako/Irodori-TTS-v4-Small
A Japanese Flow Diffusion Text-to-Speech model with zero-shot style-controlled voice cloning and text-based voice design.
huggingface.co
thinkingmachines/Inkling-Small · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
🆕 [HF Models] internlm - Intern-S2-Preview-397B
https://huggingface.co/internlm/Intern-S2-Preview-397B
🔓 [HF Models] internlm - JanusCoder-14B
https://huggingface.co/internlm/JanusCoder-14B
https://huggingface.co/internlm/Intern-S2-Preview-397B
🔓 [HF Models] internlm - JanusCoder-14B
https://huggingface.co/internlm/JanusCoder-14B
huggingface.co
internlm/Intern-S2-Preview-397B · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
🔓 [HF Models] swiss-ai - wavtokenizer-large-unify-40token
https://huggingface.co/swiss-ai/wavtokenizer-large-unify-40token
https://huggingface.co/swiss-ai/wavtokenizer-large-unify-40token
huggingface.co
swiss-ai/wavtokenizer-large-unify-40token · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
📰 NVIDIA - NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage
Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data…
https://developer.nvidia.com/blog/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity-checking-and-recovery-for-ai-native-storage/
Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data…
https://developer.nvidia.com/blog/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity-checking-and-recovery-for-ai-native-storage/
NVIDIA Technical Blog
NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage
Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data, execute tools, and generate new results…