Cloudflare is now returning RFC 9457-compliant structured Markdown and JSON error payloads to AI agents, replacing verbose HTML error pages with machine-readable instructions. This significantly reduces payload size and token usage – by over 98% in measured tests – which is crucial for cost-effective AI agent operation. The new responses include actionable guidance, allowing agents to understand *why* an error occurred and *how* to proceed, whether that means retrying with backoff, escalating the issue, or stopping altogether.
This is a network-wide change, automatically available without any site owner configuration.
- `Accept: text/markdown` returns a yaml header and human readable markdown
- `Accept: application/json` returns JSON
- `Accept: application/problem+json` returns JSON with the `application/problem+json` content type.
This is a network-wide change, automatically available without any site owner configuration.
- `Accept: text/markdown` returns a yaml header and human readable markdown
- `Accept: application/json` returns JSON
- `Accept: application/problem+json` returns JSON with the `application/problem+json` content type.
A library for working with prompt templates locally or on the Hugging Face Hub. It aims to provide a standardized way of sharing and using prompt templates, with a focus on interoperability and modularity.
yq is a command-line tool that processes YAML, XML, and TOML files by converting them to JSON and using jq for further processing. It supports various options for preserving YAML tags and styles, and can be used as a module for in-place edits.
The article discusses the use of jq, xq, and yq tools for processing JSON, XML, and YAML data from the command line. It includes examples of how to use these tools to extract and manipulate data, as well as converting GPX files to CSV format.
Introduction to Pkl, a programming language designed for generating configuration files.
Addresses limitations of static languages and general-purpose languages for configuration purposes.
Provides safety by catching validation errors before deployment.
Scales from simple to complex use-cases.
Enhanced with capabilities borrowed from general-purpose languages.
Familiar syntax and easy learning curve.
Built-in validation using type annotations.
Ability to publish packages and import them as dependencies in a project.
Language bindings for Swift, Go, Java, and Kotlin.
Editor support for IntelliJ, VS Code, and Neovim.
Addresses limitations of static languages and general-purpose languages for configuration purposes.
Provides safety by catching validation errors before deployment.
Scales from simple to complex use-cases.
Enhanced with capabilities borrowed from general-purpose languages.
Familiar syntax and easy learning curve.
Built-in validation using type annotations.
Ability to publish packages and import them as dependencies in a project.
Language bindings for Swift, Go, Java, and Kotlin.
Editor support for IntelliJ, VS Code, and Neovim.
Creating a JSON Schema for existing YAML values
We can infer a schema from existing YAML values and use it as a starting point when writing a new schema. The steps are:
Convert your values YAML file to JSON on https://www.json2yaml.com/
Paste the JSON on https://www.jsonschema.net/ and click on "Infer Schema"
Paste the schema into the values.schema.json file
W
We can infer a schema from existing YAML values and use it as a starting point when writing a new schema. The steps are:
Convert your values YAML file to JSON on https://www.json2yaml.com/
Paste the JSON on https://www.jsonschema.net/ and click on "Infer Schema"
Paste the schema into the values.schema.json file
W
