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AI frontier model updates & open source LLM releases
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πŸ“° 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


πŸ“° Claude Blog - Zero risk isn't the job: a CISO's guide to agentic AI

https://claude.com/blog/ciso-guide-to-agentic-ai


πŸ“° Claude Blog - How Anthropic runs large-scale code migrations with Claude Code

https://claude.com/blog/ai-code-migration


πŸ“° Claude Blog - Working with Claude Fable 5 in Claude Cowork

https://claude.com/blog/working-with-claude-fable-5-in-claude-cowork


πŸ“° 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
πŸ—“οΈ Weekly GitHub Activity


πŸ¦™ 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

πŸ”— All changes | Latest release


🎨 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

πŸ”— All changes | Latest release


πŸ€— 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
πŸ“° HuggingFace - Introducing Cosmos 3 Edge


https://huggingface.co/blog/nvidia/cosmos3edge
πŸ“° 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/
πŸ“° Google Gemma Blog - Scaling Agentic RL: High-Throughput Agentic Training with Tunix
Tunix is Google’s 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/