# Apr 13, 2026

## Daily

### - Anthropic downgraded cache TTL on March 6th
- **Cache TTL** for Claude Code was **silently reduced** from **1 hour to 5 minutes** in early March 2026, leading to a **20–32% increase** in cache creation costs and unexpected quota consumption spikes for users.

### - All elementary functions from a single binary operator
- A **single binary operator** ( eml(x,y) = exp(x) - ln(y) ) can generate all **elementary functions**, including constants and operations, previously thought to require multiple distinct operations, thus revolutionizing continuous mathematics.

### - Exploiting the most prominent AI agent benchmarks
- **Automated exploits reveal that eight major AI benchmarks can be manipulated to achieve near-perfect scores without solving any tasks**, highlighting a critical flaw in how these benchmarks assess AI capabilities.

### - Apple's accidental moat: How the "AI Loser" may end up winning
- **Apple, once deemed an "AI loser," is leveraging its cash reserves and existing user context to gain a competitive edge in AI, as commoditization of intelligence reduces the need for massive infrastructure investments.**

### - Evaluation of Claude Mythos Preview's cyber capabilities
- **Claude Mythos Preview** demonstrates significant advancements in **cyber capabilities**, successfully executing multi-stage attacks autonomously, a feat previously unattainable by AI models.

### - The Future of Everything Is Lies, I Guess: Safety
- **New machine learning systems pose significant risks** to psychological and physical safety, as they enable the creation of both "friendly" and "evil" models, with the latter potentially leading to sophisticated security attacks and harassment.

### - If you started a company two years ago, many assumptions are no longer true
- **Most startups over two years old have outdated business models** due to rapid changes in technology and market dynamics, particularly with the rise of AI, which has shifted venture capital focus and commoditized many tech stacks.

### - LLMs learn backwards, and the scaling hypothesis is bounded. [D]
- **LLMs exhibit impressive capabilities but reveal significant limitations**; their performance hinges on the sheer volume of data rather than sophisticated algorithms, as demonstrated by Hays & Efros's 2007 work, which thrived on a large image corpus without complex modeling.

### - [N] AMA Announcement: Max Welling (VAEs, GNNs, AI4Science & CuspAI)
- **Max Welling**, a prominent ML researcher, will lead an AMA on April 15th, focusing on the intersection of **AI and Materials Science**, including his work with CuspAI on next-generation materials search engines.

### - (AMD) Build AI Agents That Run Locally
- **GAIA SDK** is an **open-source framework** for creating AI agents in **Python and C++** that operate entirely on local hardware, ensuring **no cloud dependency** and enhanced privacy.

### - TurboOCR: 270–1200 img/s OCR with Paddle + TensorRT (C++/CUDA, FP16) [P]
- **TurboOCR achieves** an impressive **270–1200 img/s** throughput by leveraging C++/CUDA and FP16 TensorRT, significantly outperforming traditional PaddleOCR's 15 img/s on complex documents.

### - KIV: 1M token context window on a RTX 4070 (12GB VRAM), no retraining, drop-in HuggingFace cache replacement - Works with any model that uses DynamicCache [P]
- **KIV (K-Indexed V Materialization)** enables a **1M token context window** on an RTX 4070 with **12GB VRAM**, utilizing a tiered retrieval system that optimizes memory usage without modifying model weights or requiring retraining.

### - RecaLLM: Addressing the Lost-in-Thought Phenomenon with Explicit In-Context Retrieval
- **RecaLLM** enhances reasoning language models by integrating **in-context retrieval**, addressing the **lost-in-thought phenomenon** that hampers performance during reasoning tasks.

### - SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks
- **SPPO** introduces a novel approach to **Proximal Policy Optimization** (PPO) that enhances **sample efficiency** and stability for long-horizon reasoning tasks by treating the reasoning process as a **Sequence-Level Contextual Bandit problem**.

### - Noise-Aware In-Context Learning for Hallucination Mitigation in ALLMs
- The **Noise-Aware In-Context Learning (NAICL)** method significantly mitigates hallucination in Auditory Large Language Models (ALLMs) by utilizing a **noise prior library** to guide model generation, reducing hallucination rates from **26.53% to 16.98%**.