Rebyte is an execution layer for enterprise agents. It runs agents in the cloud with durable execution, tools such as browsers, files, and compute, and policy controls over models, skills, MCP servers, and network access. Execution traces record model steps, tool calls, errors, tokens, and cost. The Agent API is compatible with the official OpenAI client and is described as model agnostic.
Meaning of is the personal blog of Kenny Bastani, covering open source software, graph databases, cloud native architectures, information theory, artificial intelligence, and machine learning. As of September 18, 2025, the blog is no longer maintained, with readers directed to the author's GitHub or LinkedIn profiles.
A Gary Marcus article on his Marcus on AI Substack asking when the GenAI bubble might burst and how that could happen within 12 months. It cites survey findings on generative AI and security, notes $50B in and $3B out, and argues that hallucinations remain and that the industry is built on hype.
A weekly news and interview podcast covering the Kubernetes community, including release discussions, projects, and AI policy topics. Episodes feature guests from the Kubernetes ecosystem and are hosted as numbered installments with accompanying show notes.
The caret package provides a set of functions in R intended to streamline creating predictive models. Its tools cover data splitting, pre-processing, feature selection, model tuning using resampling and variable importance estimation, and it offers a uniform interface and standardized common tasks.
A book offering a comprehensive introduction to central ideas underpinning deep learning, intended for newcomers to machine learning and those already experienced in the field. It covers concepts relating to contemporary architectures and techniques and includes a self-contained introduction to probability theory. Hardback, PDF eBook, and a free-to-use online version are listed as access options.
ML-Ops is a site about Machine Learning Operations. It covers motivation for MLOps, designing ML-powered software, the end-to-end ML workflow lifecycle, three levels of ML-based software, MLOps principles, CRISP-ML(Q), the MLOps Stack Canvas and ML model governance. It describes MLOps as aiming to unify the release cycle for machine learning and software.
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Meaning of is the personal blog of Kenny Bastani, covering open source software, graph databases, cloud native architectures, information theory, artificial intelligence, and machine learning. As of September 18, 2025, the blog is no longer maintained, with readers directed to the author's GitHub or LinkedIn profiles.
A Gary Marcus article on his Marcus on AI Substack asking when the GenAI bubble might burst and how that could happen within 12 months. It cites survey findings on generative AI and security, notes $50B in and $3B out, and argues that hallucinations remain and that the industry is built on hype.
A weekly news and interview podcast covering the Kubernetes community, including release discussions, projects, and AI policy topics. Episodes feature guests from the Kubernetes ecosystem and are hosted as numbered installments with accompanying show notes.
The caret package provides a set of functions in R intended to streamline creating predictive models. Its tools cover data splitting, pre-processing, feature selection, model tuning using resampling and variable importance estimation, and it offers a uniform interface and standardized common tasks.
A book offering a comprehensive introduction to central ideas underpinning deep learning, intended for newcomers to machine learning and those already experienced in the field. It covers concepts relating to contemporary architectures and techniques and includes a self-contained introduction to probability theory. Hardback, PDF eBook, and a free-to-use online version are listed as access options.
ML-Ops is a site about Machine Learning Operations. It covers motivation for MLOps, designing ML-powered software, the end-to-end ML workflow lifecycle, three levels of ML-based software, MLOps principles, CRISP-ML(Q), the MLOps Stack Canvas and ML model governance. It describes MLOps as aiming to unify the release cycle for machine learning and software.
