# Wired Extractor (`jupri/wired`) Actor πŸ’« Scrape Wired.com - **URL**: https://apify.com/jupri/wired.md - **Developed by:** [cat](https://apify.com/jupri) (community) - **Categories:** News, Social media - **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $30.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## πŸ’« Welcome to Wired Scraper ![dont be sad readme is here](https://raw.githubusercontent.com/JupriGH/resources/main/cats/catframe.png) ### About Wired.com _**Wired**_ is a bi-monthly American [magazine](https://en.wikipedia.org/wiki/Magazine "Magazine") that focuses on how [emerging technologies](https://en.wikipedia.org/wiki/Emerging_technologies "Emerging technologies") affect [culture](https://en.wikipedia.org/wiki/Culture "Culture"), the [economy](https://en.wikipedia.org/wiki/Economy "Economy"), and [politics](https://en.wikipedia.org/wiki/Politics "Politics"). It is published in both print and [online](https://en.wikipedia.org/wiki/Online_magazine "Online magazine") editions by [CondΓ© Nast](https://en.wikipedia.org/wiki/Cond%C3%A9_Nast "CondΓ© Nast"). The magazine has been in publication since its launch in January 1993.[[2]](https://en.wikipedia.org/wiki/Wired_(magazine)#cite_note-mfloss-2) Its editorial office is based in [San Francisco](https://en.wikipedia.org/wiki/San_Francisco "San Francisco"), California, with its business headquarters located in [New York City](https://en.wikipedia.org/wiki/New_York_City "New York City"). _Wired_ quickly became recognized as the voice of the emerging digital economy and culture[[3]](https://en.wikipedia.org/wiki/Wired_(magazine)#cite_note-3) and a pace setter in print design and web design.[[4]](https://en.wikipedia.org/wiki/Wired_(magazine)#cite_note-4)[[5]](https://en.wikipedia.org/wiki/Wired_(magazine)#cite_note-5) From 1998 until 2006, the magazine and its website, _Wired.com_, experienced separate ownership before being fully consolidated under CondΓ© Nast in 2006. It has won multiple National Magazine Awards[[6]](https://en.wikipedia.org/wiki/Wired_(magazine)#cite_note-6)[[7]](https://en.wikipedia.org/wiki/Wired_(magazine)#cite_note-7) and has been credited with shaping discourse around the digital revolution. The magazine also coined the term [_crowdsourcing_](https://en.wikipedia.org/wiki/Crowdsourcing "Crowdsourcing"),[[8]](https://en.wikipedia.org/wiki/Wired_(magazine)#cite_note-HiredGuns-8) as well as its annual tradition of handing out [Vaporware](https://en.wikipedia.org/wiki/Vaporware "Vaporware") Awards. ### About This Actor πŸ’« Scrape Wired.com URL for possible contents # Actor input Schema ## `query` (type: `array`): πŸ’‘ Search Terms or URL ## `limit` (type: `integer`): πŸ’‘ Number of results (per-query) ## `dev_proxy_config` (type: `object`): πŸ’‘ Supported protocol:

HTTP(S), SOCKS5
{http|socks5}://{user:pass}@{hostname|ip-address}:port

Example: socks5://example.com:9000 ## `dev_custom_headers` (type: `array`): πŸ’‘ Additional HTTP Headers ## `dev_custom_cookies` (type: `array`): πŸ’‘ Additional HTTP Cookies ## `dev_transform_fields` (type: `array`): πŸ’‘ Transform the resulting output. Select only needed fields.

For nested object use DOT. For example: 
address.streetAddress

For nested array use NUMBER (index of array element starting from index=0). For example: 
images.0.url
## `dev_dataset_name` (type: `string`): πŸ’‘ Save results into custom named Dataset, use mask to customize dataset name

{ACTOR} = actor name
{DATE} = date (YYYYMMDD)
{TIME} = time (HHMMSS)

This masks can be used to autogenerate Dataset Name.

example: data-{DATE}
Depending on today date the dataset name will be: data-20230603

default: data-{ACTOR}-{DATE}-{TIME} ## `dev_dataset_clear` (type: `boolean`): Clear Dataset before insert/update. ## `dev_no_strip` (type: `boolean`): πŸ’‘ Keep/Save empty values (NULL, FALSE, empty ARRAY, empty OBJECT, empty STRING) ## Actor input object example ```json { "limit": 5 } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "limit": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("jupri/wired").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "limit": 5 } # Run the Actor and wait for it to finish run = client.actor("jupri/wired").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "limit": 5 }' | apify call jupri/wired --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jupri/wired", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wired Extractor", "description": "πŸ’« Scrape Wired.com", "version": "0.0", "x-build-id": "B5xhPdF8SdhHgaDc6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jupri~wired/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jupri-wired", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jupri~wired/runs": { "post": { "operationId": "runs-sync-jupri-wired", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jupri~wired/run-sync": { "post": { "operationId": "run-sync-jupri-wired", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "query": { "title": "❓ Query", "type": "array", "description": "πŸ’‘ Search Terms or URL", "items": { "type": "string" } }, "limit": { "title": "♾️ Limit", "type": "integer", "description": "πŸ’‘ Number of results (per-query)" }, "dev_proxy_config": { "title": "🌐 PROXY NETWORKING", "type": "object", "description": "πŸ’‘ Supported protocol:

HTTP(S), SOCKS5
{http|socks5}://{user:pass}@{hostname|ip-address}:port

Example: socks5://example.com:9000" }, "dev_custom_headers": { "title": "πŸ“œ HTTP HEADERS", "type": "array", "description": "πŸ’‘ Additional HTTP Headers", "items": { "type": "object", "required": [ "key", "value" ], "properties": { "key": { "type": "string", "title": "Key" }, "value": { "type": "string", "title": "Value" } } } }, "dev_custom_cookies": { "title": "🍰 HTTP COOKIES", "type": "array", "description": "πŸ’‘ Additional HTTP Cookies", "items": { "type": "object", "required": [ "key", "value" ], "properties": { "key": { "type": "string", "title": "Key" }, "value": { "type": "string", "title": "Value" } } } }, "dev_transform_fields": { "title": "♻️ CUSTOM FIELD", "type": "array", "description": "πŸ’‘ Transform the resulting output. Select only needed fields.

For nested object use DOT. For example: 
address.streetAddress

For nested array use NUMBER (index of array element starting from index=0). For example: 
images.0.url
", "items": { "type": "object", "required": [ "key", "value" ], "properties": { "key": { "type": "string", "title": "Key" }, "value": { "type": "string", "title": "Value" } } } }, "dev_dataset_name": { "title": "πŸ“ CUSTOM STORAGE", "type": "string", "description": "πŸ’‘ Save results into custom named Dataset, use mask to customize dataset name

{ACTOR} = actor name
{DATE} = date (YYYYMMDD)
{TIME} = time (HHMMSS)

This masks can be used to autogenerate Dataset Name.

example: data-{DATE}
Depending on today date the dataset name will be: data-20230603

default: data-{ACTOR}-{DATE}-{TIME}" }, "dev_dataset_clear": { "title": "Clear Storage", "type": "boolean", "description": "Clear Dataset before insert/update." }, "dev_no_strip": { "title": "Disable data cleansing", "type": "boolean", "description": "πŸ’‘ Keep/Save empty values (NULL, FALSE, empty ARRAY, empty OBJECT, empty STRING)" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```