A recent attack on Trivy has compromised numerous GitHub Actions tags, allowing attackers to deliver malware that targets CI/CD secrets. This incident reveals vulnerabilities in the update mechanism and highlights the ongoing risks in software supply chains. Updates include additional compromised artifacts found in Docker Hub.

As large language model workloads increase, traditional monolithic serving processes struggle with efficient resource use. Disaggregated serving separates tasks like prefill and decode into independent services, allowing for optimized scaling and resource allocation. This article explores how to deploy disaggregated inference on Kubernetes, highlighting essential scheduling techniques.

This article explores effective strategies for predicting hypertension risk using healthcare AI. It emphasizes the importance of handling data imbalance and avoiding leakage during model training. Techniques such as stacked tree-based ensembles and SMOTE Tomek are highlighted for enhancing sensitivity and ensuring robust, practical applications in clinical settings.

This article details the creation of Axle, an AI receptionist for a luxury mechanic shop. It covers the process of building a voice agent capable of answering customer queries with accurate information, implementing telephony integration, and refining voice responses to enhance customer interactions.

In March 2026, the Resolv DeFi protocol faced a severe attack when an intruder exploited its off-chain infrastructure, minting $25 million of unbacked stablecoins within minutes. This incident highlights the vulnerabilities of complex DeFi systems and the critical need for real-time on-chain threat detection to safeguard against future exploits.

This article explores the use of Hacker News comments to create user profiles, leveraging the Algolia API and language models like Claude. It highlights the insights derived from comments, demonstrating how detailed user personas can be constructed while raising ethical concerns about privacy and data sharing on online platforms.

This article discusses the successful migration of the American Express Payments Network, executed twice without any downtime. It explores the engineering strategies, challenges, and methodologies involved in transitioning to a modernized, microservices-based architecture, all while maintaining robust performance and reliability.

This article explores the author's insights into transformer circuits, particularly focusing on the residual stream and its implications for mechanistic interpretability. By detailing their understanding of attention mechanisms in transformers, the author aims to clarify complex concepts for readers interested in AI alignment and the intricacies of machine learning models.

Despite its potential to revolutionize AI engineering, DSPy remains underutilized mainly due to its complexity. Organizations often resort to constructing their own less effective versions, driven by immediate needs. This article explores why DSPy excels and the common pitfalls teams encounter as they build customized solutions.

cq proposes a new platform akin to Stack Overflow, tailored for AI agents to share knowledge, learn from past experiences, and enhance efficiency. This initiative aims to address the challenges agents face in isolation and foster a collaborative environment for growth, ultimately improving AI reliability and trust.

This article examines the limitations of AI in driving scientific paradigm shifts. While current systems excel at predictions within established frameworks, they often fall short in fostering innovative concepts. To advance science meaningfully, we must design AI that encourages new ways of thinking rather than merely refining existing knowledge.

This article details an experiment using Karpathy’s Autoresearch concept to enhance an existing research code in image processing. The author outlines a systematic approach integrating an LLM agent to optimize model performance, ultimately achieving significant improvements while navigating challenges and limitations in the process.

This study explores the connection between adversarial fragility in vision systems and hallucination in large language models through a newly proposed Neural Uncertainty Principle. By revealing their shared geometric origins, the research offers practical methods for improving robustness and detecting hallucination risks, unifying these seemingly separate issues.

CatRAG presents a new approach to debiasing large language models by combining functor-based structural adjustments with retrieval augmentation. This dual framework significantly reduces biases and enhances accurate performance across various demographic groups, outperforming previous methods on the Bias Benchmark for Question Answering.

This article introduces Neuro-Symbolic Recursive Self-Alignment (NSRSA), a method that improves recursive self-training in AI by ensuring data quality through symbolic verification. The approach reduces errors and enhances reasoning, demonstrating its effectiveness across multiple self-training iterations with tangible performance gains.