# Apr 26, 2026

- **Replace IBM Quantum back end with /dev/urandom**  
  - The **patch** to `projecteleven.py` replaces the IBM Quantum backend with `/dev/urandom`, demonstrating that **classical noise** can recover private keys at rates indistinguishable from quantum hardware, challenging the validity of the original quantum claim.

- **Databases Were Not Designed for This**  
  - **Agentic AI systems** disrupt traditional database assumptions by issuing unpredictable queries, necessitating a reevaluation of database architecture to ensure safety and reliability.

- **DeepSeek-V4 on Day 0: From Fast Inference to Verified RL with SGLang and Miles**  
  - **DeepSeek-V4** introduces **Day-0 support** for both inference and reinforcement learning (RL) training, leveraging a hybrid sparse-attention architecture and advanced features like **ShadowRadix** and **HiSparse** for enhanced performance.

- **Why do only big ML labs dominate widely-used models despite many open-source pretrained models smaller labs could do RL on?**  
  - **Dominance of major ML labs** like GPT and Claude stems from their superior **reinforcement learning from human feedback (RLHF)**, which enhances pretrained models beyond mere scale, making them more effective in real-world applications.

- **How Visual-Language-Action (VLA) Models Work**  
  - **Visual-Language-Action (VLA) models** integrate perception, reasoning, and control, enabling robots to interpret visual and linguistic inputs to perform complex tasks like folding clothes or identifying objects.

- **Show HN: AI memory with biological decay (52% recall)**  
  - **YourMemory** enhances AI agents with **persistent memory**, mimicking human recall by retaining important information while allowing outdated facts to fade, requiring only two installation commands and no infrastructure setup.

- **Going from 3B/7B dense to Nemotron 3 Nano (hybrid Mamba-MoE) for multi-task reasoning — what changes in the fine-tuning playbook?**  
  - **Transitioning to Nemotron 3 Nano** offers a **30B-A3B hybrid architecture** that enhances multi-task reasoning capabilities, leveraging **Mamba-2 layers** for state-aware processing across longer contexts.

- **How would you build an automated commentary engine for daily trade attribution at scale?**  
  - **Automated commentary engines** for trade attribution must balance **deterministic precision** with **dynamic natural language generation**, ensuring accurate analysis of thousands of trades while avoiding data hallucination from LLMs.