What You Need to Know: Nvidia pledged $250 million in accelerated computing resources over five years to the UK for agentic safety testing and safeguards research. Workday launched Sana as the default interface that orchestrates secure agents for tasks like hiring and payroll. Developers can now train LoRA adapters directly on quantized GGUF files without PyTorch or an FP16 parent model.

Top Story

Nvidia pledged $250 million worth of computing resources to Britain over five years to fund AI security research. The annual $50 million allocation targets agentic safety testing and helps British researchers strengthen safeguards for increasingly powerful AI. Jensen Huang stated that AI capability and safety must advance together. The resources will support projects at Oxford’s BOLD initiative, Imperial College robotics safety work, and University College London programs. British AI minister Kanishka Narayan said the donation will help ensure future AI is safe, secure, and serves people’s best interests. The pledge follows Microsoft President Brad Smith’s recent comments that the UK is well-placed to lead AI safety guidelines work. Scrutiny of AI safety has intensified after developers including Anthropic and OpenAI disclosed instances of rogue or undesired behaviour by AI agents. The donation will connect areas of British research including BOLD at the University of Oxford, which develops approaches that help AI systems learn, adapt and operate alongside people. Watch for expanded evaluation benchmarks and new agent red-teaming frameworks that emerge from the funded labs. Source: ca.finance.yahoo.com

Model Updates

Google Releases Gemini 4 Argon API With 1M Token Output Limit: MarkTechPost Google made Gemini 4 Argon generally available on the Gemini API, AI Studio, and the Gemini app. The sparse MoE model supports up to 1M output tokens and leads the Vals Index at 68.90%. It is the first model from the lab to expose that output length in production. The model is accessible to developers, enterprises, scientists, and consumers through the standard Gemini channels. Pricing at launch stands at $2 per million input tokens and $10 per million output tokens. The price is scheduled to double on December 1. Source: marktechpost.com

Run fast, private agents on-device with LFM2.5-24B-A2B: Hugging Face Blog LiquidAI released LFM2.5-24B-A2B, a 24B-parameter reasoning model with 2.45B active parameters. It scores 48.09 on LiveClaw, competitive with Qwen3.6-35B-A3B despite using roughly half the active parameters. The model runs at 184 tok/s on an Apple M5 Max and up to 21K output tokens per second on a single NVIDIA B300 GPU. Pre-training scaled from roughly 17T to 33T tokens, with mid-training adding 2T tokens at 32K context and 300B tokens at 131K context. Post-training uses five stages including multi-domain on-policy distillation and agentic reinforcement learning inside harnesses such as OpenClaw and Hermes Agent. The architecture retains 40 layers with 64 experts per MoE block and top-4 routing, while expanding context from 32K to 131K tokens and vocabulary from 65K to 128K. Source: huggingface.co

Agent & Tool Developments

Workday puts agentic AI front and center to assist rather than just answer: IT Pro Workday launched Sana as the default home screen that lets users request tasks in natural language. Sana orchestrates secure Workday agents for hiring, payroll, accounting, and compliance workflows. The company also released Work Intelligence, which decomposes job roles into tasks and identifies which can be automated versus augmented. CEO Aneel Bhusri described Sana as the new front door to Workday that flips the script from hunting through menus. Autopilot Agents work continuously in the background across the same domains. The platform maintains strong links to models including Anthropic Claude while keeping an increasingly open architecture. Source: itpro.com

Governed AI agents can drive platform expansion, says Securonix: iTnews Asia Securonix introduced its Agentic Mesh (SAM) coordinator inside the Unified Defense SIEM. SAM manages specialized agents for log search, noise filtering, incident summarization, and playbook execution while requiring human validation for high-impact actions. The system presents events as narrative stories grounded in evidence to help analysts trust recommendations faster. Chief product officer Simon Hunt noted that the platform limits context to a specific alert or violation to avoid confusion. The architecture addresses procurement trust by operating within strict boundaries and surfacing underlying evidence. It replaces fragmented operational tools with an integrated agentic control layer that manages repetitive tasks. Source: itnews.asia

Updated list of official MCP servers (300+), well-known and widely used projects only: r/LocalLLaMA The community maintains a curated list of over 280 official MCP servers from product companies and actively maintained open-source projects. The repo accepts PRs for new entries and focuses only on widely used implementations. All entries come from the companies behind the products or from actively maintained open-source efforts. The list is updated regularly to reflect current production-grade servers. Source: reddit.com

Practical & Community

Train LoRA directly on Quantized GGUFs, no PyTorch, no Parent(FP16) needed, just your GGUF and the data: r/LocalLLaMA Runner lets users train a LoRA adapter directly on a quantized GGUF base without PyTorch or an FP16 copy. The command ./runner -m base-Q4_K_M.gguf --train data.jsonl --train-steps 200 --lora-rank 8 --train-out adapter.gguf produces an adapter that can be served alongside the frozen base or merged into a new GGUF. The same forward pass used for training is used for inference, so the adapter matches the exact weights it will run on. When merging a 4-bit adapter the system refuses the operation unless overridden because the adapter can disappear into the weights. Held-out exact tool-call scores were measured on Qwen3-4B Q4_K_M before and after training. The adapter can later be folded into a standalone 8-bit or better GGUF file for any runtime. Source: reddit.com

Moving a running model's layers between GPUs mid-reply (8 ms, no re-prefill): r/LocalLLaMA The jitllm engine can relocate model layers and the accompanying KV cache between GPUs in 8 ms during generation. On a dual T4 setup, Llama-3.2-3B Q4_K_M moves from GPU 0 to GPU 1 with zero recomputed tokens. The same mechanism works across CUDA and Vulkan on laptops, enabling dynamic placement without restarting the prompt. The design generates kernels for whatever hardware is present and treats layers like processes that the engine can schedule and move. On a dual V100 setup with a 1.8k-token chat the move takes 20–21 ms. The approach supports models larger than single-device memory and mismatched GPUs while conversations grow. Source: reddit.com

Discover AI: A Learning Guide - 51 hands-on labs for learning how AI actually works.: r/LocalLLaMA The offline app contains 51 labs covering neurons, gradient descent, attention, RAG, agents, fine-tuning, quantization, and serving. Several labs execute real models inside the app, including a 1.37M-parameter transformer for the attention exercise and live character-level training with slider controls. The app works completely offline with no account or telemetry. Labs are grouped into six sections that progress from basic concepts to advanced deployment topics. Each lab includes a short lesson readable in plain or standard mode. The author welcomes contributions for new interactive labs or clearer visualizations of cause and effect. Source: reddit.com

Under the Hood: Pipeline Parallelism on Consumer IoT Hardware

Pipeline parallelism splits a model’s layers across multiple low-memory devices so each device computes only its assigned slice and passes activations forward. On cheap Android TV boxes with 4 GB RAM, researchers placed two layers per box and chained fifteen units to run a 26B model at 0.39 tokens per second. The KV cache travels with the layers, eliminating any need to re-encode the prompt when devices are added or removed. Because each box holds only a fraction of the weights, total system memory stays far below what a single GPU would require. The approach trades massive latency for extreme hardware accessibility; doubling time for a self-replicating agent on such a fleet would be measured in days rather than minutes. The main engineering constraint is network bandwidth between boxes, which quickly becomes the bottleneck once more than a handful of devices participate. Teams facing strict air-gapped or cost-sensitive deployments should evaluate this pattern when GPU clusters are unavailable but idle edge hardware is plentiful; the gotcha is that any single box failure halts the entire pipeline until the layer is reassigned.

Things to Try This Week

• Try Runner to train a LoRA adapter straight on your existing GGUF file and serve it without any PyTorch dependency.
• Test Workday’s Sana interface on a trial tenant to see how natural-language task requests trigger governed agents for HR and finance workflows.
• Run the 51-lab Discover AI app offline on a laptop to explore attention and agent mechanics with live model execution inside each exercise.
• Add your own MCP server to the official list repo if you maintain a widely used tool integration that other developers would benefit from.
• Compare Gemini 4 Argon output-length behavior against current Claude and GPT-6 models on long-document generation tasks.

On the Horizon

• Expanded UK AI Safety Institute evaluations using the new Nvidia compute allocation are expected within the next quarter.
• Further Gemini 4 Argon price adjustments are scheduled for December 1.
• Additional open-weight reasoning models in the LFM2.5 family are anticipated as LiquidAI completes its roadmap.
• More enterprise platforms are likely to announce agent orchestration layers similar to Workday’s Sana in the coming months.

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