This article discusses the challenges and strategies involved in deprecating obsolete software systems. It emphasizes the importance of planning for deprecation from the start and the costs associated with maintaining outdated systems. The piece also touches on emotional resistance to deprecation and the need for careful management of the process.

This article discusses how Coinbase uses AI to enhance their engineering processes, allowing teams to manage production operations effectively. It highlights the benefits of AI in identifying issues, optimizing costs, and improving shipping speed while maintaining system resilience.

This article discusses the role of forward deployed engineers (FDEs) in software and AI companies, highlighting the difference between effective and ineffective deployment. It emphasizes the importance of leveraging FDEs to improve products rather than merely filling gaps that should be addressed with code.

The article discusses the misconceptions around operations (ops) in software development, arguing that ops is essential for efficient systems and shouldn’t be viewed negatively. It emphasizes the need for a clear distinction between development and operations roles, highlighting how both are vital for successful engineering outcomes.

The article discusses why large software systems are often poorly understood, even by their creators. It highlights the challenges of documenting complex features and the reliance on engineers' tacit knowledge to answer basic questions about the software's functionality. As software evolves, keeping track of these details becomes increasingly difficult.

The article explores the concept of the "reverse ivory tower" in software engineering, where decision-making occurs in a detached environment, leading to systems that prioritize internal coherence over user needs. It follows a new product engineer, Cassandra, as she navigates a complex platform and its bureaucratic processes, highlighting the disconnect between developers and real-world applications.

This article introduces SWE-Universe, a framework designed to automatically create verifiable software engineering environments from GitHub pull requests. It addresses issues like low production yield and high costs by using a custom-trained building agent that ensures reliable task generation. The framework scales to nearly a million environments and demonstrates effectiveness through reinforcement learning applications.

Guillermo Rauch discusses the advancements in AI's ability to write complex software, questioning whether these developments indicate true super-intelligence. He outlines specific challenges for AI to tackle, such as identifying security vulnerabilities and rewriting compilers, as benchmarks for assessing AI's capabilities in software engineering.

This article discusses how AI is reshaping software engineering, leading to a divide between high-performing and mediocre teams. It emphasizes that the real challenge lies in understanding user needs and making strategic decisions, rather than just coding. The author argues that those who adapt will thrive, while others risk becoming obsolete.

In 2026, coding will accelerate dramatically due to advanced AI tools, allowing developers to produce vastly more code. However, organizations must adapt their processes to handle this increased output effectively; otherwise, they risk bottlenecks in review and deployment. The future of software delivery will depend on optimizing the entire pipeline, not just the coding phase.

This article discusses how modern software products rely on a complex web of external dependencies, making supply chain risk a critical concern for product engineering teams. It emphasizes the need for trust verification and security measures to prevent compromises from third-party components. The framework SLSA is presented as a solution for establishing software integrity.

The article argues that we are entering a new phase in software development, likening it to the Cambrian explosion in biology. AI coding tools have advanced significantly, allowing rapid creation of software, but they still fall short in critical areas like system architecture and security. As a result, skilled engineers will be essential to manage the influx of new software and ensure quality.

This article explores the trend of startups emulating Palantir's model of embedding engineers within client organizations to deliver customized software solutions. It critiques the scalability of this approach, highlighting the unique factors that make Palantir effective and the challenges faced by other companies attempting to replicate its success.

The article explores the definition of an engineer and what engineering truly entails, especially in the context of advancing AI technology. It emphasizes that engineering is about taking the right actions in the right sequence to achieve various intentions, highlighting the importance of clarity in project goals and the art of sequencing tasks.

TRAE is an AI-driven platform designed to function as a highly efficient software engineer, capable of independently creating software solutions. It emphasizes a smoother user experience and faster response times, encouraging users to explore its capabilities.

The article discusses the challenges and opportunities in creating software for hardware engineering, emphasizing the need for tools that respect the complexity and technicality of hardware systems. It highlights the importance of understanding the engineering process and avoiding common pitfalls in software development to better serve hardware engineers. The author encourages a more thoughtful approach to software design that aligns with the realities of hardware engineering.