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#tool important:1
Cursor – Working with Context
Cursor – Working with Context
How to work with context in Cursor
Intent context defines what the user wants to get out of the model. For example, a system prompt usually serves as high-level instructions for how the user wants the model to behave. Most of the “prompting” done in Cursor is intent context. “Turn that button from blue to green” is an example of stated intent; it is prescriptive.
State context describes the state of the current world. Providing Cursor with error messages, console logs, images, and chunks of code are examples of context related to state. It is descriptive, not prescriptive.
Together, these two types of context work in harmony by describing the current state and desired future state, enabling Cursor to make useful coding suggestions.
AIAI#cursor#ai#llm#tool#documentation
·docs.cursor.com·
Cursor – Working with Context
Fast JSON, NDJSON and GeoJSON Parser and Generator
Fast JSON, NDJSON and GeoJSON Parser and Generator
A fast JSON parser, generator and validator which converts JSON, NDJSON (Newline Delimited JSON) and GeoJSON (Geographic JSON) data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from JSON. GeoJSON data is read in as simple features objects. This implementation wraps the yyjson C library which is available from .
RR#r#r-package#json#geojson#data#parser#generator#tool#r-development#r-package-devt
·coolbutuseless.github.io·
Fast JSON, NDJSON and GeoJSON Parser and Generator
SpeCrawler: Generating OpenAPI Specifications from API Documentation Using Large Language Models
SpeCrawler: Generating OpenAPI Specifications from API Documentation Using Large Language Models
In the digital era, the widespread use of APIs is evident. However, scalable utilization of APIs poses a challenge due to structure divergence observed in online API documentation. This underscores the need for automat…
APIsAPIs#api#api design#apis#system-design#ai#dev#tool#webdev#openapi#documentation#large language models
·ar5iv.labs.arxiv.org·
SpeCrawler: Generating OpenAPI Specifications from API Documentation Using Large Language Models
Prompt and empower your LLM, the tidy way
Prompt and empower your LLM, the tidy way
The tidyprompt package allows users to prompt and empower their large language models (LLMs) in a tidy way. It provides a framework to construct LLM prompts using tidyverse-inspired piping syntax, with a library of pre-built prompt wrappers and the option to build custom ones. Additionally, it supports structured LLM output extraction and validation, with automatic feedback and retries if necessary. Moreover, it enables specific LLM reasoning modes, autonomous R function calling for LLMs, and compatibility with any LLM provider.
RR#ai#dev#documentation#r-development#r-package#tool#large language models#framework#llm#api
·tjarkvandemerwe.github.io·
Prompt and empower your LLM, the tidy way