Developer builds Polish-keyword programming language, gains deep Ruby insights

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A Ruby developer spent 18 months building AlexScript, a fully featured interpreted scripting language whose keywords are written in Polish, his first language. The project grew from a weekend experiment into a language with a standard library, async/await support, a debugger, and a self-hosted web framework. Building the interpreter revealed several Ruby performance nuances, including that Ruby's throw/catch is far more efficient than raise/rescue for non-local exits like function returns. The developer also discovered that scanning UTF-8 strings character by character causes accidentally quadratic performance, fixable by using getbyte and byteslice for O(1) byte access. A unified method dispatch table — rather than separate registries for native and user-defined methods — further simplified the interpreter's architecture and improved lookup efficiency.

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Go developer builds linter to enforce file-level visibility in flat packages

A Go developer has released declscope, an open-source linter designed to enforce file-scoped access boundaries within Go's flat package structure. Go conventionally offers only two visibility levels — exported and unexported — with no native file-scope option, meaning any unexported symbol is accessible across all files in a package. The tool addresses a growing concern that AI coding agents, finding unexported helpers in scope, call or modify them freely since such cross-file conventions exist only as informal comments rather than compiler-enforced rules. The developer noted that even explicit instructions in AI configuration files failed to reliably prevent boundary violations, prompting a machine-checkable solution. declscope can be installed via standard Go tooling and flags violations when declarations are used outside their intended file scope, providing agents and reviewers with clear repair guidance.

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ProgrammingDEV Community ·

AI Agents Can Use Far More Energy Than Single Prompts, Researcher Finds

Climate scientist Zeke Hausfather analyzed the energy consumption of AI agentic workflows, finding they can use significantly more electricity than standard one-off chatbot prompts. Unlike a simple question-and-answer exchange, AI agents repeatedly re-process their entire accumulated context with each step — such as running commands, reading files, or calling tools — driving up computational costs. Hausfather estimated his own daily AI agent usage consumed between 1.2 and 5.9 kWh, with visible output accounting for just 0.4 percent of total tokens processed. The concern is not one user's consumption in isolation, but the aggregate energy demand when many users run agentic workflows simultaneously. While an exact global climate impact remains uncertain, the directional finding is clear: agentic AI systems can carry a substantially larger energy footprint than most users assume.

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ProgrammingDEV Community ·

ECC Agent Harness Runs in Cursor Without Context Bloat Using Modular Setup

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ProgrammingDEV Community ·

Why Saving a Container Snapshot Is the Wrong Way to Preserve an AI Agent's Work

A developer building an AI agent workbench found that the intuitive solution of saving work by committing a container filesystem snapshot creates more problems than it solves. Snapshots capture unintended debris such as build caches, failed install logs, and temp files, making it impossible to distinguish useful state from noise over time. They also risk baking secrets and API tokens into reusable artifacts, turning disposable credentials into a permanent security liability. Instead of preserving the container's history, the developer argues for capturing only the declared intent: the base image, named packages, network profile, and granted directories. This minimal, human-readable recipe — called a Workshop — acts as a reproducible blueprint rather than a frozen snapshot of accidental state.

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