π xAI - Grok Build is Now Open Source
https://x.ai//news/grok-build-open-source
π xAI - Automations in Grok
https://x.ai//news/grok-automations
https://x.ai//news/grok-build-open-source
π xAI - Automations in Grok
https://x.ai//news/grok-automations
x.ai
Grok Build is Now Open Source
Explore the harness behind our coding agent and TUI.
π° Claude Blog - Working at the frontier: How Cursor knew Claude Fable 5 was ready for the hardest 1% of problems
https://claude.com/blog/working-at-the-frontier-cursor
π° Claude Blog - Zero risk isn't the job: a CISO's guide to agentic AI
https://claude.com/blog/ciso-guide-to-agentic-ai
π° Claude Blog - How Anthropic runs large-scale code migrations with Claude Code
https://claude.com/blog/ai-code-migration
π° Claude Blog - Working with Claude Fable 5 in Claude Cowork
https://claude.com/blog/working-with-claude-fable-5-in-claude-cowork
π° Claude Blog - Working at the frontier: Why Base44 trusts Claude Fable 5 with their most challenging engineering work
https://claude.com/blog/working-at-the-frontier-why-base44-trusts-claude-fable-5-with-their-most-challenging-engineering-work
https://claude.com/blog/working-at-the-frontier-cursor
π° Claude Blog - Zero risk isn't the job: a CISO's guide to agentic AI
https://claude.com/blog/ciso-guide-to-agentic-ai
π° Claude Blog - How Anthropic runs large-scale code migrations with Claude Code
https://claude.com/blog/ai-code-migration
π° Claude Blog - Working with Claude Fable 5 in Claude Cowork
https://claude.com/blog/working-with-claude-fable-5-in-claude-cowork
π° Claude Blog - Working at the frontier: Why Base44 trusts Claude Fable 5 with their most challenging engineering work
https://claude.com/blog/working-at-the-frontier-why-base44-trusts-claude-fable-5-with-their-most-challenging-engineering-work
Claude
How Cursor knew Claude Fable 5 was ready for the hardest 1% of problems | Claude by Anthropic
How Anthropic's Claude Fable 5 beat CursorBench and expanded what's possible for Cursor and agentic coding.
π [GitHub Releases] turboderp-org/exllamav3 - 1.1.0
https://github.com/turboderp-org/exllamav3/releases/tag/v1.1.0
https://github.com/turboderp-org/exllamav3/releases/tag/v1.1.0
GitHub
Release 1.1.0 Β· turboderp-org/exllamav3
An optimized quantization and inference library for running LLMs locally on modern consumer-class GPUs - Release 1.1.0 Β· turboderp-org/exllamav3
π [HF Models] openbmb - MiniCPM-RobotTrack
https://huggingface.co/openbmb/MiniCPM-RobotTrack
π [HF Models] openbmb - MiniCPM-RobotManip
https://huggingface.co/openbmb/MiniCPM-RobotManip
https://huggingface.co/openbmb/MiniCPM-RobotTrack
π [HF Models] openbmb - MiniCPM-RobotManip
https://huggingface.co/openbmb/MiniCPM-RobotManip
huggingface.co
openbmb/MiniCPM-RobotTrack Β· Hugging Face
Weβre on a journey to advance and democratize artificial intelligence through open source and open science.
π [GitHub Releases] PygmalionAI/aphrodite-engine - v0.22.0
https://github.com/dphnAI/sonar/releases/tag/v0.22.0
https://github.com/dphnAI/sonar/releases/tag/v0.22.0
GitHub
Release v0.22.0 Β· dphnAI/sonar
What's Changed
feat: add native Metal support by @AlpinDale in #1668
chore: optimize metal backend performance by @AlpinDale in #1669
perf: optimize GDN performance on Metal by @AlpinDale in #...
feat: add native Metal support by @AlpinDale in #1668
chore: optimize metal backend performance by @AlpinDale in #1669
perf: optimize GDN performance on Metal by @AlpinDale in #...
ποΈ Weekly GitHub Activity
π¦ llama.cpp
β Release: b9966 β b10068
β 102 commits
- Added support for Hunyuan 3 (hy_v3) with MTP speculative decoding #25395 #25641
- Added support for Minimax2 Eagle3 speculative decoding 259ae1d
- Added support for BitNetForCausalLM GGUF conversion #25769
- Implemented GGML_OP_LIGHTNING_INDEXER for DeepSeek V3.2/V4 on CPU and CUDA #24231 #25545
- Added fused hyper-connection ops for DeepSeek V4 to reduce graph splits #25585 #25702
- Added CUDA Virtual Devices support and enabled CUDA graphs on Volta and Turing architectures #25228 #25749
- Added Flash Attention via oneDNN graph API for SYCL on Intel Battlemage #25222
- Optimized CUDA MoE gate/up activation quantization, improving prefill times on RTX 5090 and Blackwell #25441
- Added auto-download of DeepSeek-Flash and Eagle3 speculative decoding sidecars from Hugging Face #25811
- Server now supports CORS configuration options and accepts null sampling parameters to request defaults #25655 #25538
- Added KleidiAI SME2 f32 kernel and improved hardware-specific kernel dispatch #24414 #25478
- Fixed CUDA crash when querying memory on devices with no available memory #25157
- Fixed Tensor Parallel execution for Phi3, Bert, Plamo2/3, and ChatGLM #25536
- Fixed quantization crash on DeepSeek-V4 i32 routing tables #25787
π All changes | Latest release
π¨ stable-diffusion.cpp
β Release: master-775-b5d8120 β master-782-b290693
β 7 commits
- Support for AnimateDiff SD 1.5 motion modules v2 and v3 #1784 with img2video capabilities via the --init-img parameter #1789
- Support for ADetailer #1785
- Support for PiD 1.5 #1790
- Configurable reference image processing for edit models #1780
- Fixed cross attention and output projection token protection for Anima LoRAs #1786
π All changes | Latest release
π€ Fresh models trending on HuggingFace:
thinkingmachines/Inkling β‘1060
OpenMOSS-Team/MOSS-VL-Realtime β‘76
ai-sage/GigaAM-Multilingual β‘56
nineninesix/diamond-1.0 β‘43
ai-sage/GigaChat3.1-Audio-10B-A1.8B β‘38
rzgar/Bernini-R-S2V β‘37
acvlab/ABot-World-0-5B-LF β‘29
fal/ideogram-v4-instant β‘27
InternScience/Agents-A1-4B β‘26
sensenova/SenseNova-U1-8B-MoT-Infographic-V3 β‘26
OpenMOSS-Team/MOSS-VL-Instruct-0708 β‘23
fal/ideogram-v4-fast β‘23
t-tech/T-Search β‘23
GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking β‘23
mente-ai/uyu-2-28B β‘20
OpenMOSS-Team/MOSS-VL-Base-0708 β‘17
yijunwang2/krea2-outpaint β‘17
yijunwang2/krea2-reid β‘15
π¦ llama.cpp
β Release: b9966 β b10068
β 102 commits
- Added support for Hunyuan 3 (hy_v3) with MTP speculative decoding #25395 #25641
- Added support for Minimax2 Eagle3 speculative decoding 259ae1d
- Added support for BitNetForCausalLM GGUF conversion #25769
- Implemented GGML_OP_LIGHTNING_INDEXER for DeepSeek V3.2/V4 on CPU and CUDA #24231 #25545
- Added fused hyper-connection ops for DeepSeek V4 to reduce graph splits #25585 #25702
- Added CUDA Virtual Devices support and enabled CUDA graphs on Volta and Turing architectures #25228 #25749
- Added Flash Attention via oneDNN graph API for SYCL on Intel Battlemage #25222
- Optimized CUDA MoE gate/up activation quantization, improving prefill times on RTX 5090 and Blackwell #25441
- Added auto-download of DeepSeek-Flash and Eagle3 speculative decoding sidecars from Hugging Face #25811
- Server now supports CORS configuration options and accepts null sampling parameters to request defaults #25655 #25538
- Added KleidiAI SME2 f32 kernel and improved hardware-specific kernel dispatch #24414 #25478
- Fixed CUDA crash when querying memory on devices with no available memory #25157
- Fixed Tensor Parallel execution for Phi3, Bert, Plamo2/3, and ChatGLM #25536
- Fixed quantization crash on DeepSeek-V4 i32 routing tables #25787
π All changes | Latest release
π¨ stable-diffusion.cpp
β Release: master-775-b5d8120 β master-782-b290693
β 7 commits
- Support for AnimateDiff SD 1.5 motion modules v2 and v3 #1784 with img2video capabilities via the --init-img parameter #1789
- Support for ADetailer #1785
- Support for PiD 1.5 #1790
- Configurable reference image processing for edit models #1780
- Fixed cross attention and output projection token protection for Anima LoRAs #1786
π All changes | Latest release
π€ Fresh models trending on HuggingFace:
thinkingmachines/Inkling β‘1060
OpenMOSS-Team/MOSS-VL-Realtime β‘76
ai-sage/GigaAM-Multilingual β‘56
nineninesix/diamond-1.0 β‘43
ai-sage/GigaChat3.1-Audio-10B-A1.8B β‘38
rzgar/Bernini-R-S2V β‘37
acvlab/ABot-World-0-5B-LF β‘29
fal/ideogram-v4-instant β‘27
InternScience/Agents-A1-4B β‘26
sensenova/SenseNova-U1-8B-MoT-Infographic-V3 β‘26
OpenMOSS-Team/MOSS-VL-Instruct-0708 β‘23
fal/ideogram-v4-fast β‘23
t-tech/T-Search β‘23
GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking β‘23
mente-ai/uyu-2-28B β‘20
OpenMOSS-Team/MOSS-VL-Base-0708 β‘17
yijunwang2/krea2-outpaint β‘17
yijunwang2/krea2-reid β‘15
GitHub
model: add Hy3 (hy_v3) support with MTP speculative decoding by satindergrewal Β· Pull Request #25395 Β· ggml-org/llama.cpp
Overview
Adds support for Tencent's Hy3 (hy_v3 / HYV3ForCausalLM, 299B MoE, 80 layers + 1 MTP layer), including its multi-token-prediction head as a draft-mtp speculative target. Addresses ...
Adds support for Tencent's Hy3 (hy_v3 / HYV3ForCausalLM, 299B MoE, 80 layers + 1 MTP layer), including its multi-token-prediction head as a draft-mtp speculative target. Addresses ...
π [HF Models] nvidia - Cosmos3-Edge
https://huggingface.co/nvidia/Cosmos3-Edge
π [HF Models] nvidia - Cosmos3-Super-Text2Image-4Step
https://huggingface.co/nvidia/Cosmos3-Super-Text2Image-4Step
π [HF Models] nvidia - Cosmos3-Super-Image2Video-4Step
https://huggingface.co/nvidia/Cosmos3-Super-Image2Video-4Step
π [HF Models] nvidia - Cosmos3-Edge-Policy-DROID
https://huggingface.co/nvidia/Cosmos3-Edge-Policy-DROID
https://huggingface.co/nvidia/Cosmos3-Edge
π [HF Models] nvidia - Cosmos3-Super-Text2Image-4Step
https://huggingface.co/nvidia/Cosmos3-Super-Text2Image-4Step
π [HF Models] nvidia - Cosmos3-Super-Image2Video-4Step
https://huggingface.co/nvidia/Cosmos3-Super-Image2Video-4Step
π [HF Models] nvidia - Cosmos3-Edge-Policy-DROID
https://huggingface.co/nvidia/Cosmos3-Edge-Policy-DROID
huggingface.co
nvidia/Cosmos3-Edge Β· Hugging Face
Weβre on a journey to advance and democratize artificial intelligence through open source and open science.
π° Google AI Blog - Run Ray on TPU, Part 1: The foundations
Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google's accelerators using the familiar Ray task-and-actor APIs. To handle the strict networking requirement of keeping multi-host TPU "slices" together over their Inter-Chip Interconnect (ICI), the KubeRay Operator on GKE automatically provisions and labels the underlying hardware layout. Ray Core utilizes these labels via its slice_placement_group() primitive to atomically reserve complete slices, allowing developers to deploy jobs through KubeRay, Ray Train, or Ray Serve simply by declaring a hardware topology (like "4x4") without writing custom placement code.
https://developers.googleblog.com/en/run-ray-on-tpu-part-1-the-foundations/
Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google's accelerators using the familiar Ray task-and-actor APIs. To handle the strict networking requirement of keeping multi-host TPU "slices" together over their Inter-Chip Interconnect (ICI), the KubeRay Operator on GKE automatically provisions and labels the underlying hardware layout. Ray Core utilizes these labels via its slice_placement_group() primitive to atomically reserve complete slices, allowing developers to deploy jobs through KubeRay, Ray Train, or Ray Serve simply by declaring a hardware topology (like "4x4") without writing custom placement code.
https://developers.googleblog.com/en/run-ray-on-tpu-part-1-the-foundations/
Googleblog
Google for Developers Blog - News about Web, Mobile, AI and Cloud
Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google's accelerators using the familiar Ray task-and-actor APIs. To handle the strict networking requirement of keeping multiβ¦
π° OpenAI - Safety and alignment in an era of long-horizon models
https://openai.com/index/safety-alignment-long-horizon-models
https://openai.com/index/safety-alignment-long-horizon-models
OpenAI
Safety and alignment in an era of long-horizon models
OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards through iterative deployment.
π° HuggingFace - Grabette: an open system to record robot-manipulation data
https://huggingface.co/blog/grabette
https://huggingface.co/blog/grabette
huggingface.co
Grabette: an open system to record robot-manipulation data
Weβre on a journey to advance and democratize artificial intelligence through open source and open science.
π° Google Model Cards - Gemini 3.6 Flash
https://deepmind.google/models/model-cards/gemini-3-6-flash/
π° Google Model Cards - Gemini 3.5 Flash-Lite
https://deepmind.google/models/model-cards/gemini-3-5-flash-lite/
https://deepmind.google/models/model-cards/gemini-3-6-flash/
π° Google Model Cards - Gemini 3.5 Flash-Lite
https://deepmind.google/models/model-cards/gemini-3-5-flash-lite/
Google DeepMind
Gemini 3.6 Flash - Model Card
π [HF Models] nvidia - NV-JEPA-DNA-HyenaDNA
https://huggingface.co/nvidia/NV-JEPA-DNA-HyenaDNA
π [HF Models] nvidia - NV-JEPA-DNA-NTv3
https://huggingface.co/nvidia/NV-JEPA-DNA-NTv3
π [HF Models] nvidia - NV-JEPA-DNA-DNABERT2
https://huggingface.co/nvidia/NV-JEPA-DNA-DNABERT2
https://huggingface.co/nvidia/NV-JEPA-DNA-HyenaDNA
π [HF Models] nvidia - NV-JEPA-DNA-NTv3
https://huggingface.co/nvidia/NV-JEPA-DNA-NTv3
π [HF Models] nvidia - NV-JEPA-DNA-DNABERT2
https://huggingface.co/nvidia/NV-JEPA-DNA-DNABERT2
huggingface.co
nvidia/NV-JEPA-DNA-HyenaDNA Β· Hugging Face
Weβre on a journey to advance and democratize artificial intelligence through open source and open science.
π° Google Gemma Blog - Scaling Agentic RL: High-Throughput Agentic Training with Tunix
Tunix is Googleβs new JAX-native post-training library designed to eliminate TPU idling bottlenecks when training multi-turn, tool-using LLM reasoning agents. It maximizes hardware throughput by combining highly concurrent, asynchronous rollouts with a decoupled producer-consumer pipeline, ensuring the trainer is constantly fed even while agents wait on network I/O or environment steps. Additionally, Tunix provides plug-and-play abstractions and continuous macro-level profiling, allowing developers to easily integrate custom open-source environments and optimize complex distributed workflows without massive code rewrites.
https://developers.googleblog.com/en/scaling-agentic-rl-high-throughput-agentic-training-with-tunix/
Tunix is Googleβs new JAX-native post-training library designed to eliminate TPU idling bottlenecks when training multi-turn, tool-using LLM reasoning agents. It maximizes hardware throughput by combining highly concurrent, asynchronous rollouts with a decoupled producer-consumer pipeline, ensuring the trainer is constantly fed even while agents wait on network I/O or environment steps. Additionally, Tunix provides plug-and-play abstractions and continuous macro-level profiling, allowing developers to easily integrate custom open-source environments and optimize complex distributed workflows without massive code rewrites.
https://developers.googleblog.com/en/scaling-agentic-rl-high-throughput-agentic-training-with-tunix/
Googleblog
Google for Developers Blog - News about Web, Mobile, AI and Cloud
Optimize agentic RL training with Tunix, Googleβs JAX-native library. Eliminate TPU idling with async rollouts and easily plug in custom OSS environments.
π° Claude Blog - How Anthropic secures its AI-native software development lifecycle
https://claude.com/blog/how-anthropic-secures-its-ai-native-software-development-lifecycle
π° Claude Blog - How Datadog built a βuniversal machine toolβ for Claude Code
https://claude.com/blog/how-datadog-built-a-universal-machine-tool-for-claude-code
π° Claude Blog - Working at the frontier: How Rakuten builds agents overnight with Claude Fable 5
https://claude.com/blog/working-at-the-frontier-rakuten
https://claude.com/blog/how-anthropic-secures-its-ai-native-software-development-lifecycle
π° Claude Blog - How Datadog built a βuniversal machine toolβ for Claude Code
https://claude.com/blog/how-datadog-built-a-universal-machine-tool-for-claude-code
π° Claude Blog - Working at the frontier: How Rakuten builds agents overnight with Claude Fable 5
https://claude.com/blog/working-at-the-frontier-rakuten
Claude
How Anthropic secures its AI-native software development lifecycle | Claude by Anthropic
Anthropic Deputy CISO Jason Clinton details how the Security Engineering team secures an AI-native SDLC where AI authors 80% of merged code.
π° HuggingFace - The State of Simulation for Physical AI: An Overview
https://huggingface.co/blog/nvidia/state-of-simulation-for-physical-ai
https://huggingface.co/blog/nvidia/state-of-simulation-for-physical-ai
huggingface.co
The State of Simulation for Physical AI: An Overview
A Blog post by NVIDIA on Hugging Face
β€1