# May 27, 2025

Daily

- **Most leading chatbots routinely exaggerate science findings**  
  Up to 73% of leading chatbots produce inaccurate summaries of scientific findings, often exaggerating claims and misrepresenting the original texts, as revealed by a study from Utrecht University and Western University.

- **Lossless video compression using Bloom filters**  
  The **new_bloom_filter_repo** introduces a novel approach to **lossless video compression** using **Rational Bloom Filters**, which enhance traditional Bloom filters by allowing non-integer hash function counts for improved efficiency in data representation.

- **TSMC bets on unorthodox optical tech**  
  TSMC's innovative use of MicroLED-based interconnects aims to enhance energy efficiency in AI data centers, potentially revolutionizing data transmission methods.

- **Mistral Agents API**  
  The **Mistral Agents API** enhances AI capabilities by integrating **persistent memory**, **code execution**, and **web search**, enabling agents to perform complex tasks and maintain context across interactions.

- **Revisiting the Algorithm That Changed Horse Race Betting**  
  Bill Benter's horse betting algorithm, which generated over **$1 billion** in profits, utilizes a **multinomial logit model** to estimate winning probabilities, refined through decades of data and modern coding techniques.

- **World's first petahertz transistor at ambient conditions**  
  Researchers at the University of Arizona have developed the **world's first petahertz-speed phototransistor**, capable of operating in ambient conditions, which could enable computers to process data over **1,000 times faster** than current technology.

- **Outcome-Based Reinforcement Learning to Predict the Future**  
  Outcome-based reinforcement learning (RL) can achieve frontier-scale accuracy in forecasting by adapting algorithms like **Group-Relative Policy Optimisation (GRPO)** and **ReMax**, effectively handling binary, delayed, and noisy rewards in real-world applications.

- **Launch HN: Relace (YC W23) – Models for fast and reliable codegen**  
  **Relace** offers a **Fast Apply model** that merges code snippets at **4300 tokens per second**, significantly reducing merge errors compared to competitors like Sonnet and Llama, while also saving **~40% on Claude 4 output tokens**.

- **Show HN: My LLM CLI tool can run tools now, from Python code or plugins**  
  **LLM 0.26 introduces the ability for large language models to run tools directly in the terminal**, allowing integration with models from OpenAI, Anthropic, and others through a Python function interface and CLI tool.

- **[R] AutoThink: Adaptive reasoning technique that improves local LLM performance by 43% on GPQA-Diamond**  
  **AutoThink** enhances local model reasoning by **43%** on GPQA-Diamond through adaptive resource allocation, dynamically adjusting thinking time based on query complexity.

- **[P] Evolving Text Compression Algorithms by Mutating Code with LLMs**  
  **Evolving text compression algorithms** through **mutations** with LLMs achieved a **compression ratio of 1.85**, significantly improving from an initial ratio of 1.03 over 30 generations.

- **Grammars of Formal Uncertainty**  
  **LLMs** exhibit a significant **accuracy variance** in automated reasoning, with performance ranging from **+34.8%** on logical tasks to **-44.5%** on factual tasks, highlighting the need for careful evaluation of their outputs.

- **[R] Panda: A pretrained forecast model for universal representation of chaotic dynamics**  
  **Panda** is a **pretrained model** that forecasts chaotic dynamics, trained on a dataset of **20,000 chaotic systems**, showcasing its ability to perform zero-shot predictions on real-world data without prior exposure.

- **Demonstrating end-to-end scientific discovery with Robin: a multi-agent system**  
  **Robin**, a **multi-agent system**, has successfully automated the entire scientific discovery process, leading to the identification of **ripasudil** as a novel treatment for **dry age-related macular degeneration (dAMD)**, showcasing the potential of AI in drug discovery.

- **Show HN: Maestro – A Framework to Orchestrate and Ground Competing AI Models**  
  **Maestro** is a framework designed to orchestrate multiple large language models (LLMs) in parallel, allowing them to **argue and mix outputs** while preserving dissent, rather than selecting a single "best" response.