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Source channel @githubtrending · Post #15440 · Jan 27

#go#config#config_loader#configuration#configuration_file#configuration_management#etcd_client#go#golang#golang_package#s3_bucket#toml#viper#yaml koanf is a lightweight Go library to load config from files (JSON, YAML, TOML), env vars, flags, S3, Vault and more, merging them easily with dot-path keys like "app.server.port". Install core with `go get github.com/knadh/koanf/v2`, add providers/parsers as needed. It's a cleaner Viper alternative with fewer dependencies and better extensibility. This saves you time by simplifying config in apps, letting you override values flexibly without bloat or forced orders. https://github.com/knadh/koanf

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AI & Law

@ai_and_law · Post #341 · 06/28/2024, 07:04 AM

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Implementing Transparency in AI: A Step Forward Zuzanna Warso and Paul Keller from Open Future, alongside Maximilian Gahntz from Mozilla, have published a proposal to implement the EU AI Act’s training data transparency requirement for general-purpose AI (GPAI). Article 53 1(d) of the Act mandates GPAI model providers to publish detailed summaries of their training content, covering data sources and sets with narrative explanations. The proposed template emphasizes a comprehensive scope and sufficient technical detail to benefit both experts and laypeople. These summaries should list primary data collections, provide narrative explanations of other data sources, and clearly distinguish between 'data sources' (origins) and 'datasets' (processed data points). This transparency requirement aims to enhance accountability, enable research and scrutiny, and strengthen individuals' and organizations' ability to exercise their rights in the AI development process. #AI#Transparency#AIAct#DataGovernance#OpenFuture#Mozilla

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