AI-assisted programming has moved far beyond autocomplete. Large language models are now capable of editing entire codebases, coordinating long-running tasks, and collaborating across multiple systems. As these capabilities mature, the core challenge in software development is shifting away from writing code and toward orchestrating work, managing context, and maintaining shared understanding across fleets of agents.

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Python 3.14 is here and continues Python’s evolution toward greater performance, scalability, and usability. The new release formally supports free-threaded, no-GIL mode, introduces template string literals, and implements deferred evaluation of type annotations. It also includes new debugging and profiling tools, along with many other features. Łukasz Langa is the CPython Developer in Residence at

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Engineering teams often build microservices as their systems grow, but over time this can lead to a fragmented ecosystem with scattered data access patterns, duplicated business logic, and an uneven developer experience. A unified data graph with a consistent execution layer helps address these challenges by centralizing schema, simplifying how teams compose functionality, and reducing

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SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Starlink’s rapid rollout of free, high-speed in-flight internet, Tesla’s move to deprecate Autopilot in favor of full self-driving, and

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AI coding agents are rapidly reshaping how software is built, reviewed, and maintained. As large language model capabilities continue to increase, the bottleneck in software development is shifting away from code generation toward planning, review, deployment, and coordination. This shift is driving a new class of agentic systems that operate inside constrained environments, reason over

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Engineering teams around the world are building AI-focused applications or integrating AI features into existing products. The AI development ecosystem is maturing, which is accelerating how quickly these applications can be prototyped. However, taking AI applications to production remains a notoriously complex process. Modern AI stacks demand LLMs, embeddings, vector search, observability, new caching layers,

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Package management sits at the foundation of modern software development, quietly powering nearly every software project in the world. Tools like npm and Yarn have long been the core of the JavaScript ecosystem, enabling developers to install, update, and share code with ease. But as projects grow larger and the ecosystem more complex, this older

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WebAssembly, or WASM, has grown from a low-level compilation target for C and C++ into one of the most influential technologies in modern computing. It now powers browser applications, edge compute platforms, embedded systems, and a growing ecosystem of languages targeting a portable and secure execution model. Andreas Rossberg is a programming languages researcher and

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