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AI Code Generation

133 summarised stories about AI Code Generation, each linking back to the original source. Browse all topics →

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Friday, 31 July 2026

DeepSeek Upgrades DeepSeek-V4-Flash-0731 with Major Agentic and Coding Gains

MarkTechPost 2 months ago 55 ● 6 sources

DeepSeek released DeepSeek-V4-Flash-0731 on July 31, 2026, improving agentic and coding performance through re-post-training while keeping the 284B-parameter architecture unchanged. The API version costs $0.14 per million input tokens on cache miss and $0.28 per million output tokens, roughly one-third of the V4-Pro price. The model now natively supports the Responses API format and is optimized for code tasks, making it accessible to startups and developers without large GPU budgets.

2x, not 10x: coding with LLMs in 2026

obryant.dev 2 months ago 33

A software engineer reflects on LLM coding productivity gains as of mid-2026, estimating a 2x improvement rather than 10x because models can reliably iterate toward concrete goals but struggle with subjective questions like code maintainability and documentation quality. The author's analysis suggests LLMs work well in automated feedback loops for objectively verifiable tasks, meaning further model improvements alone may yield diminishing returns. Future productivity gains will likely come from better tooling and workflows around current model capabilities rather than from model performance improvements alone.

JetBrains Open-Sources KotlinLLM: Smart Macros That Generate Kotlin Source Code at Runtime and Hot-Reload It Through JDI

MarkTechPost 2 months ago 57

JetBrains Research released KotlinLLM, an IntelliJ IDEA plugin that generates Kotlin code at runtime through LLM-powered "Smart macros" and hot-reloads it via Java Debug Interface. Testing on a Spring Petclinic project showed 24 of 24 application scenarios completed with 100% hot-reload success and roughly 1% runtime overhead. Developers can now commit generated Kotlin source code without runtime LLM dependencies, enabling faster iteration on type conversions and mock implementations while avoiding repeated inference costs for covered scenarios.

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