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enhances AI agents with persistent memory, mimicking human recall by retaining important information while allowing outdated facts to fade, requiring only two installation commands and no infrastructure setup.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.