This article critiques the notion of "Perfect Recall" in AI, suggesting it limits meaningful interactions. Instead, it draws parallels to NPCs in role-playing games, emphasizing the importance of associative memory. The author advocates for blending logical systems with narrative-driven decision-making to enhance human-machine engagement.

This article explores the possibility of an impending technological singularity, pinpointing February 10, 2026, as a significant date. By analyzing five key metrics of AI progress, the author builds a model to anticipate when rapid advancements may reach a critical tipping point.

On January 14, 2026, a significant and sudden drop in global telnet traffic was recorded, suggesting a possible configuration change in internet routing. This article explores the correlation between this decline and the subsequent discovery of a critical vulnerability in a widely used telnet service, probing the implications for security.

An automated scanning pipeline uncovered 287 Chrome extensions that secretly collect users' browsing history, affecting around 37 million installations. These findings raise concerns about user privacy and the potential misuse of collected data by various actors, including data brokers and less-known developers.

This article presents a framework for founders considering open-source strategies for their tech companies. It emphasizes that open-source should be a deliberate decision based on user needs and market characteristics, not just a trend. Key insights include understanding user-contributor dynamics and the importance of governance in successful open-source projects.

After layoffs at The Washington Post reduced its tech reporting team, a former columnist reflects on the importance of independent tech journalism. With a focus on consumer rights and accountability, he aims to continue exploring critical technology issues through a new Substack newsletter.

The internet is evolving, moving away from traditional webpage navigation to instant responses via AI interfaces like ChatGPT. This shift not only changes how users access information but also how brands present their products. Embracing AI-driven components could redefine user experiences in the coming years.

Anthropic is taking proactive steps to manage rising electricity costs from its expanding data centers. By covering infrastructure expenses, procuring new power, and investing in local communities, the company aims to mitigate consumer price impacts while fostering responsible AI development and supporting broader energy initiatives.

As AI agents transition to real-world applications, their memory needs vary significantly. This article explores the limitations of existing architectures and introduces the Context File System, a solution that allows agents to retain successful procedures for efficient operations, enabling greater reliability and reduced costs in enterprise environments.

This article explores the concept of Markdownlang, an innovative programming language where documentation and code merge. It discusses the implications of AI integration in programming, highlighting potential changes in how we view software creation and emphasizing user-friendly structured outputs as key to future programming environments.

This article explores the evolving landscape of software development, emphasizing that while coding has become increasingly easy and cheap, successful projects rely on effective human-technology collaboration. It reflects on past challenges in hiring and team dynamics while considering the implications of new technologies and their impact on team processes.

Carnegie Mellon University researchers will present 156 papers at the NeurIPS 2025 conference, covering diverse topics like neural networks for tactile processing, improvements in classification techniques, and language model evaluation. These studies highlight advancements in AI and machine learning, with potential implications for various applications.

DermFM-Zero is a novel vision-language model designed for dermatology, demonstrating strong performance in zero-shot clinical collaboration and concept discovery. Trained on over 4 million data points, it enhances diagnostic accuracy for general practitioners and outperforms specialists in skin cancer assessments, while maintaining interpretable and robust outputs.

This article explores rotary positional embeddings (RoPE) in long-context transformers, presenting a new perspective on their function through signal processing theory. It establishes crucial theoretical bounds on RoPE parameters and reveals implications for model performance, highlighting the balance needed for effective long-context processing.

This article introduces the Predictable Perception system for autonomous vehicles, which enhances DNN inference by identifying and prioritizing critical frames and regions of interest. The system significantly improves perception predictability, processing efficiency, and detection accuracy while maintaining cost-effectiveness compared to traditional methods.