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90 articles
This article explains the dual costs of AI web retrieval—retrieval charges and model processing—and introduces TinyFish, a system that makes AI search effectively free by optimizing these costs through intelligent caching and reinforcement learning.
This explainer explores how Reinforcement Learning from Human Feedback (RLHF) is used to train AI models for cybersecurity research, using the example of Cantina's open-source model <em>apex-flash-1</em>.
Learn how to use Kyutai's Voice of Reason speech-native model to solve spoken math problems with 77.1% accuracy using reinforcement learning.
Learn how to implement a simplified version of Google DeepMind's Dream-RSI technique, which allows AI agents to improve by analyzing past attempts and generating new strategies without expensive recomputation.
Google Research's Retrieve-for-Train (R4T) framework accelerates search results using reinforcement learning and diffusion models, achieving 12× to 20× faster query fan-out than traditional methods.
This article explains model misalignment in AI systems, how OpenAI is addressing it through a new disclosure framework, and why transparency is important for AI safety.
Learn about SWE-2, a new AI coding model developed by Cognition that performs nearly as well as top models but at a much lower cost, thanks to post-training with Kimi K3.
Google's open-sourced Mantis toolkit enables AI coding agents to autonomously find, reproduce, and patch software vulnerabilities across different programming environments. This represents a significant advancement in AI-driven cybersecurity automation.
CUA-Lite is a new open platform from UC Berkeley that unifies sandboxes, data, evaluation, and reinforcement learning for computer-use agents, enabling more efficient and standardized AI agent development.
This article explains the technical concepts behind Hugging Face's Microduck robot, including reinforcement learning, sim-to-real pipelines, and the role of MuJoCo and ONNX in robotics AI.
Learn what IBM's new Granite 4.2 AI models are, how they work, and why they matter for real-world tasks like coding and problem-solving.
This explainer explores agentic AI - autonomous systems capable of planning, executing, and adapting to complex environments. Learn how these multi-agent systems work, why they matter for businesses, and why only 15% of organizations have achieved scaled adoption.
