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AI-Assisted Programming

73 summarised stories about AI-Assisted Programming, each linking back to the original source. Browse all topics →

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Tuesday, 30 June 2026

ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration

Hugging Face 3 months ago 10

Researchers released ScarfBench, an open benchmark for evaluating AI agents on Java framework migration tasks across Spring, Jakarta EE, and Quarkus. The benchmark contains 34 applications, 204 migration tasks, and approximately 151,000 lines of code, with success measured by whether applications build, deploy, and preserve behavior. Current frontier AI agents achieve less than 10% behavioral success on the benchmark, revealing that dependency management across configuration, infrastructure, and runtime environments—rather than code translation itself—is the primary migration challenge.

You Don't Know Jack About Formal Verification

queue.acm.org 3 months ago 50

AI tools are lowering the cost of formal verification in software development by automating proof-writing, which previously required expensive specialist expertise. Developers can now mathematically verify that complex business rules work correctly without prohibitive expenses. This shift enables broader adoption of formal verification techniques across software projects.

Astryx

GitHub 3 months ago 47

Meta open-sourced Astryx, a design system developed internally over eight years that powers 13,000+ apps, featuring 150+ React components with full customization and dark mode support. The system includes a CLI, comprehensive theming via CSS custom properties, and was deliberately designed so that both human developers and AI assistants can build with identical tooling and APIs. Teams can now adopt Astryx without vendor lock-in, overriding styles with their existing CSS approach and customizing components through theme configuration rather than forking source code.

Working With AI: A Concrete Example

htmx.org 3 months ago 30

A developer debugged a parsing regression in Hyperscript with Claude's help, where AI excelled at root-cause analysis but proposed suboptimal fixes. The developer ultimately solved it by using the existing "follows" infrastructure to prevent the 'as' keyword from being parsed as a conversion expression during fetch commands. The experience demonstrates that effective AI collaboration requires domain expertise and healthy skepticism rather than blind acceptance of suggestions.

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