# Jul 13, 2025

## News Highlights

- **ETH Zurich and EPFL to release a LLM developed on public infrastructure**  
  ETH Zurich and EPFL are set to release a fully open large language model (LLM) in late summer 2025, developed on the “Alps” supercomputer, emphasizing transparency and multilingual capabilities across over **1,000 languages**.

- **Amazon CEO says AI agents will soon reduce company's corporate workforce**  
  Amazon's CEO Andy Jassy predicts a significant reduction in the corporate workforce as the company increasingly adopts generative AI tools, aiming for efficiency gains in the coming years.

- **Kimi k2 largest open source SOTA model?**  
  Kimi K2 is a mixture-of-experts (MoE) language model with **32 billion activated parameters** and **1 trillion total parameters**, optimized for advanced reasoning and coding tasks using the **Muon optimizer** for stability during training.

- **The upcoming GPT-3 moment for RL**  
  Reinforcement learning (RL) is poised for a transformative shift akin to GPT-3, moving from narrow task fine-tuning to massive-scale training across diverse environments, which will enhance few-shot, task-agnostic capabilities.

- **Context Engineering Guide**  
  Context engineering redefines prompt engineering, emphasizing the design and optimization of instructions and context for LLMs to enhance task performance, particularly in complex AI workflows.

- **Hill Space: Neural nets that do perfect arithmetic (to 10⁻¹⁶ precision)**  
  Hill Space transforms neural network arithmetic by enabling calculation of optimal weights for discrete operations, leading to rapid convergence and high reliability in mathematical tasks.

- **Show HN: ArchGW – an intelligent edge and service proxy for agents**  
  ArchGW is an intelligent proxy server designed to streamline the development of AI agents by managing prompt handling, routing, and safety checks, thus reducing repetitive low-level coding tasks.

- **How to scale RL to 10^26 FLOPs**  
  Scaling reinforcement learning (RL) to 10^26 FLOPs requires a shift from traditional methods to leveraging next-token prediction on web data, enhancing reasoning capabilities beyond mere model size.

- **[R] I am building a framework for AI which will allow it to self learn and self evolve**  
  The **Neuro-Schema** framework aims to enable AI to learn, adapt, remember, and evolve autonomously, moving beyond traditional response mechanisms to a more dynamic interaction model.

- **[P] Hill Space: Neural networks that actually do perfect arithmetic (10⁻¹⁶ precision)**