# Jul 22, 2026

## Daily Updates

- **Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber**  
  **Gemini 3.6 Flash** enhances efficiency and quality, achieving **17%** fewer output tokens than its predecessor, 3.5 Flash, while also reducing costs to **$1.50/1M input tokens** and **$7.50/1M output tokens**.

- **Kimi K3 Is Competitive with Fable; Kimi K3 and Fable Is SoTA**  
  **Kimi K3** competes effectively with **Fable**, achieving **93% accuracy** on over 1,000 tasks, while their combination creates a **state-of-the-art (SoTA)** solution that outperforms either model alone.

- **Judge approves $1.5B Anthropic settlement for pirated books used to train Claude**  
  A **federal judge** has approved a **$1.5 billion** settlement requiring Anthropic to compensate authors approximately **$3,000 per book** for using pirated works to train its Claude chatbot, marking a significant legal precedent in AI copyright cases.

- **Laguna S 2.1**  
  **Laguna S 2.1** is a **118B parameter Mixture-of-Experts model** that excels in long-horizon tasks, achieving a **70.2% score on Terminal-Bench 2.1**, outperforming larger models in its class.

- **GigaToken: ~1000x faster Language model tokenization**  
  **Gigatoken** is a **drop-in replacement** tokenizer that achieves speeds of **~1000x faster** than HuggingFace's tokenizers, enabling text data processing at **GB/s** rates across various CPU architectures.

- **Gemini last models: temperature, top_p, and top_k are deprecated and ignored**  
  **Gemini 3.6 Flash** and **3.5 Flash-Lite** models are now available, offering enhanced performance for complex tasks and lower costs, with **3.6 Flash** priced at **$1.50/1M input tokens** and **$7.50/1M output tokens**.

- **Can a MUD evaluate LLMs? A $99 proof of concept**  
  **CrucibleBench** introduces a benchmark for AI behavior in a **persistent MUD**, where **NPCs remember** actions and trust accumulates, allowing for nuanced evaluation over 50 turns with hidden social objectives.

- **Kimi K3: second only to Fable 5 on AA-Briefcase**  
  **Kimi K3**, with 2.8T parameters, scores 1543 on AA-Briefcase, outperforming models like GPT-5.6 and Opus 4.8, but incurs a high operational cost of $10.57 per task.

- **I Built a Private Genomics Study with Stoffel MPC**  
  **Stoffel MPC** enables genomic studies without collecting raw DNA, allowing participants to contribute data securely through a decentralized model that maintains privacy while computing aggregate statistics.

- **Built for Vera Rubin, NVIDIA Spectrum-6 Arrives in Gigascale AI Factories**  
  **NVIDIA Spectrum-6** is a **102.4-terabit-per-second Ethernet switch** that doubles the capacity of its predecessor, designed to enhance the performance and efficiency of **gigascale AI factories** by connecting vast numbers of GPUs and CPUs.

- **Atomically Thin Materials Significantly Shrink Qubits**  
  **MIT researchers** have demonstrated that using **2D materials** like hexagonal boron nitride in capacitors can significantly reduce the size of **qubits**, enhancing the scalability of quantum processors.

- **Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber**  
  **Gemini 3.6 Flash** enhances efficiency by reducing output token usage by **17%** compared to its predecessor, 3.5 Flash, while also lowering costs to **$1.50/1M input tokens** and **$7.50/1M output tokens**.

- **NVIDIA Vera Rubin Driving Performance Per Watt, Lowest Token Cost for Partners Worldwide**  
  **NVIDIA's Vera Rubin platform** achieves **benchmark leadership** in performance per watt and offers the **lowest token costs** for partners like CoreWeave and Google Cloud, enhancing AI infrastructure efficiency globally.

- **NVIDIA Open Sources First GPU-Accelerated Medical Physics Simulation Framework**  
  **NVIDIA's new Medical Physics Simulation framework** enables developers to create reusable simulation environments for healthcare robotics, significantly enhancing the training process for surgical robots by simulating complex anatomical interactions and device behaviors.

- **The State of Simulation for Physical AI: An Overview**  
  **Simulation** is essential for training physical AI systems, as it allows for the generation of vast amounts of photorealistic data, overcoming the limitations of real-world data collection which is often slow and costly.