A podcast exploring the history of computing, from early machines like the LINC and LGP-30 to more modern devices like the Keypact Micro-VIP and Friden Flexowriter, and the people who restore and preserve them.
A new study reveals that while current AI models excel at solving math problems, they struggle with the reasoning required for mathematical proofs, demonstrating a gap between pattern recognition and genuine mathematical understanding.
Abstract
Optimizing deep learning algorithms currently requires slow, manual derivation, potentially leaving much performance untapped. Methods like FlashAttention have achieved a ×6 performance improvement over native PyTorch by avoiding unnecessary data transfers, but required three iterations over three years to be developed. Automated compiled methods have consistently lagged behind. This paper extends Neural Circuit Diagrams for deep learning models to consider resource usage and the distribution of tasks across a GPU hierarchy. We show how diagrams can use simple relabellings to derive high-level streaming and tiling optimization strategies along with performance models. We show how this high- level performance model allows the effects of quantization and multi-level GPU hierarchies to be readily considered. We develop a methodology for representing intermediate-level pseudocode with diagrams, allowing hardware-aware algorithms to be derived step-by-step. Finally, we show how our methodology can be used to better understand existing techniques like FlashAttention. This work uses a theoretical framework to link assumptions about GPU behaviour to claims about performance. We aim to lay the groundwork for a scientific approach to GPU optimization where experiments can address clear hypotheses rather than post-hoc rationalizations.
This article introduces QuadTrees, a data structure for efficiently organizing and searching spatial data. It explains the concept, use cases (collision detection, map services, AI image upscaling), and provides a TypeScript implementation with basic point and rectangle classes.
Guidance on choosing the best AI model for GitHub Copilot projects, considering speed, depth, cost, and task complexity. Models discussed include GPT-4.1, GPT-4o, Claude 3.5 Sonnet, o4-mini, o3, Gemini 2.0 Flash, and GPT-4.5.
DeepMind is prioritizing readiness, proactive risk assessment, and collaboration with the wider AI community as they explore the frontiers of AGI, focusing on mitigating risks like misuse and misalignment.
This article details the creation of a simple, 50-line agent using Model Context Protocol (MCP) and Hugging Face's tools, demonstrating how easily agents can be built with modern LLMs that support function/tool calling.
- MCP Overview: MCP is a standard API for exposing tools that can be integrated with Large Language Models (LLMs).
- Implementation: The author explains how to implement a MCP client using TypeScript and the Hugging Face Inference Client. This client connects to MCP servers, retrieves tools, and integrates them into LLM inference.
- Tools: Tools are defined with a name, description, and parameters, and are passed to the LLM for function calling.
- Agent Design: An agent is essentially a while loop that alternates between tool calling and feeding tool results back into the LLM until a specific condition is met, such as two consecutive non-tool messages.
- Code Example: The article provides a concise 50-line TypeScript implementation of an agent, demonstrating the simplicity and power of MCP.
- Future Directions: The author suggests experimenting with different models and inference providers, as well as integrating local LLMs using frameworks like llama.cpp or LM Studio.
This article explores a framework for evaluating AI models for use with GitHub Copilot, considering factors like recentness, speed, accuracy, and how to test them within your workflow. It highlights the benefits of using different models for chat versus code completion, and reasoning models for complex tasks.
Researchers at HiddenLayer have developed a novel prompt injection technique that bypasses instruction hierarchy and safety guardrails across all major AI models, posing significant risks to AI safety and requiring additional security measures.
Docker is making it easier for developers to run and test AI Large Language Models (LLMs) on their PCs with the launch of Docker Model Runner, a new beta feature in Docker Desktop 4.40 for Apple silicon-powered Macs. It also integrates the Model Context Protocol (MCP) for streamlined connections between AI agents and data sources.
