Results for “json-decoding”

Working effectively with JSON data structures.

jq - JSON Querying and Transformation

Use for writing, reviewing, debugging, or testing Python JSON serialization and deserialization with orjson, including bytes/text boundaries, datetimes, dataclasses, NumPy, custom default handlers, option flags, strictness, and web/file integration. Do not use for JSON Schema validation, format-preserving JSON edits, streaming I/O frameworks, or choosing a JSON library when orjson is not requested or present.

Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.

Shuffle repetitive JSON objects safely by validating schema consistency before randomising entries.

Provides guidance on new System.Text.Json APIs introduced in .NET 11, including typed JsonTypeInfo access and PascalCase naming policy.

Reduce per-token latency on Jetson vLLM servers by appending speculative decoding configuration, with guidance on when to enable and how to benchmark the improvement.

Detect, validate, and generate Schema.org structured data in JSON-LD format for SEO and rich results.

Centralized JSON validation for AGENT_SUCCESS_CRITERIA with defensive parsing and injection attack prevention (CVSS 8.2)

Query, filter, transform, and aggregate JSON data using jq in shell pipelines and scripts.

Query, filter, transform, and aggregate JSON data using jq, with practical patterns for shell pipelines and CLI integration.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Process JSON data from files or standard input using jq filters for extraction, filtering, and transformation.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Apply a jq-style filter to transform a JSON payload

LEMON v2.0 — The Degen Fader. Identifies historically reckless traders (DEGEN activity + CHOPPY consistency) on Hyperliquid, monitors their live positions, and counter-trades them when they're bleeding at high leverage. If a cluster of degens goes max-leverage long on a coin and starts losing, LEMON shorts it — betting on their inevitable liquidation cascade. DSL exit managed by plugin runtime via runtime.yaml.

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Reviews parsers, deserialization, uploads, archives, YAML/JSON/XML/pickle, paths, templates, SSRF, and unsafe loaders.

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections for visual canvases, mind maps, and flowcharts.

Detect, validate, and generate Schema.org structured data. JSON-LD format preferred. Use when user says "schema", "structured data", "rich results", "JSON-LD", or "markup".

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.

Decode and inspect a JWT (header, claims, expiry) without verifying signature

Context compression for YAMTAM — nén JSON/structured tool output trước khi vào LLM. Hiệu quả với JSON (50-72% tiết kiệm); text thuần cần bản [all].

Score raw leads as HOT/WARM/COOL based on config-driven weights from agency.config.json

Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.