The jsonrepair library is designed to repair invalid JSON documents by fixing common issues such as missing quotes, commas, and brackets, as well as handling special characters and formats. It supports both function and streaming API usage, making it suitable for various applications, including Node.js and command-line operations. The library can also process large documents efficiently and is available for installation via npm.

The pycti-mcp is an MCP server front-end for pycti designed to enhance data presentation from OpenCTI into a more consumable JSON format for LLM applications. It aims to provide better field naming, include contextual information, and reduce non-informative metadata, along with easy setup instructions for integration with tools like mcp-hub and VSCode. Additional guidance is provided for creating new tools and using existing lookup functionalities for observable, adversary, report, and indicator data.

A lightweight JSON parsing library written in C99, featuring approximately 150 lines of code and designed for zero memory allocations. It requires the user to handle number and string parsing, providing a simple example to load a rectangle from a JSON string into a struct. The library is free and released into the public domain.

C++26 introduces reflection capabilities, allowing compile-time processing of JSON data to create C++ objects. Using a simple example, the author explains how to parse a JSON file and transform it into a structured C++ object, highlighting the contributions of Dan Katz and the utility of the new reflection features in C++. The article also discusses the process of generalizing the parsing function to handle multiple key-value pairs in JSON objects.

JSON is no longer the fastest option for data serialization in web browsers, as recent benchmarks show that binary formats like Avro, Protobuf, and Bebop can outperform it. Factors such as improved internet speeds, the complexity of web applications, and user demand for responsiveness make deserialization performance increasingly important. After testing various libraries, the author concludes that while some binary encodings have advantages, careful consideration of benchmarks and use cases is crucial for selecting the right option.

The article discusses the functionality and improvements of JSON.stringify in JavaScript, including how it handles different data types and its performance optimizations. It highlights the significance of serialization in web development and provides examples of its usage. Furthermore, it addresses potential pitfalls and best practices for effective JSON serialization.

The article presents a novel approach to handling JSON data in web applications by introducing the concept of progressive JSON. This technique allows developers to progressively load and parse JSON, improving performance and user experience, especially in applications with large datasets. Additionally, it discusses the implications of this method on state management and data rendering.

Embracing a flexible approach to data storage, the article advocates for using PostgreSQL to store various types of data without overthinking their structure. It highlights the advantages of saving raw data in a database, allowing for easier modifications and queries over time, illustrated through examples like Java IDE indexing, Chinese character storage, and sensor data logging.

JSON Query Language is a lightweight and expandable library for querying JSON data, featuring over 50 functions and operators. It supports both text and JSON query formats, allows the creation of custom functions and operators, and provides error handling with detailed insights. Users can install it via npm for use in JavaScript and Python applications.

The article discusses memory optimizations in Pydantic when handling JSON data. It highlights techniques for reducing memory usage and improving performance, particularly in scenarios involving large datasets or high-throughput applications. Practical examples and benchmarks are provided to illustrate the benefits of using specific configurations and data types.

jsonriver is a lightweight JavaScript library designed for incremental JSON parsing from streams, allowing users to receive progressively complete data structures. It operates solely on standard JavaScript features, ensuring compatibility across environments, and mimics the behavior of JSON.parse while providing performance benefits in streaming scenarios. The library also includes specific behaviors for handling types and properties in JSON objects.

To facilitate local development with Redis on AKS, a standalone Redis deployment is recommended, contrasting with cluster mode used in production. The article outlines the prerequisites and provides a Helm command to set up Redis standalone with JSON and search modules, ensuring accessibility from outside the AKS environment. It also suggests configuring a load balancer for local access and integrating with Redis Insight for data management.

JSON Crack is an open-source tool designed for visualizing JSON data through interactive graphs, facilitating exploration, formatting, and validation. It includes features for converting data formats, generating schemas, and exporting visualizations, all while ensuring privacy through local data processing. The tool can be run locally with Node.js and Docker, with detailed setup instructions provided.

API developers must be aware of various HTTP edge cases that can lead to serious vulnerabilities and performance issues. The article discusses critical problems such as range header mishandling, content-type enforcement, and request smuggling, emphasizing the importance of proper configuration and validation in web applications.

The article discusses how to optimize the FDA's drug event dataset, which is stored as large, nested JSON files, by normalizing repeated fields, particularly pharm_class_epc. By extracting these values into a separate lookup table and using integer IDs, the author significantly improved query performance and reduced memory usage in DuckDB, transforming slow, resource-intensive queries into fast, efficient ones.

The article discusses the concept of Base64 encoding for JSON data, explaining its utility in data transmission and storage. It highlights how Base64 encoding can make binary data safe for transmission over media that are designed to deal with textual data and provides a brief overview of its implementation.

The article discusses the differences between importing and fetching JSON data in JavaScript, highlighting the implications of each method on performance and code maintainability. It provides insights into when to use each approach, considering factors like asynchronous behavior and module loading. The author emphasizes best practices for optimizing data handling in web applications.

Gemini Nano is set to be fully released for Chrome users by the end of the year, with key functionalities offered through the Prompt API. The article provides a guide on setting up the model, highlights its features and pitfalls, and suggests best practices for usage, including how to manage statefulness and import wrapper libraries in browser contexts.

JSON, while designed to be a universal data interchange format, reveals significant inconsistencies in its implementation across different programming languages. These variations can lead to issues such as precision loss in numbers, differing string encodings, and object key ordering problems, complicating data handling in multi-language systems. Developers are encouraged to establish conventions, validate schemas, and rigorously test compatibility to mitigate these challenges.

The article provides a comprehensive guide on JSON prompting for marketers, detailing its significance in enhancing marketing strategies and communication efficiency. It explores various techniques and best practices for effectively utilizing JSON prompts to engage audiences and streamline marketing efforts.

The article introduces JSON Query, a versatile and expandable query language designed for working with JSON data. It provides a playground, documentation, and function references, showcasing various examples of querying, filtering, sorting, and transforming JSON objects and arrays. Key functionalities include the ability to chain methods and utilize operators for diverse data manipulation tasks.

The article introduces Token-Oriented Object Notation (TOON), a compact data format designed to reduce token usage when passing structured data to Large Language Models (LLMs). TOON is more efficient than traditional formats like JSON and XML, achieving up to 60% fewer tokens while maintaining readability and structure, particularly for uniform complex objects. It combines features from YAML and CSV to optimize data representation for AI applications.