# Hashnode Blog Translator (`srini047/hashnode-blog-translator`) Actor Translates Hashnode blog posts to the target language using Lingo.dev AI localization, preserving code blocks, images, and markdown structure - **URL**: https://apify.com/srini047/hashnode-blog-translator.md - **Developed by:** [Sriniketh J](https://apify.com/srini047) (community) - **Categories:** AI, Automation, SEO tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## 🗣️ Hashnode Blog Scrapper and Convertor ![Logo](./static/Apify%20Lingo%20Hashnode%20Blog%20Convertor.png) An Apify actor to extract and translate blog content from Hashnode, then store results in Apify storage. Built in Python and designed for quick local runs and Apify deployments. --- ### 🔎 What it does - 📝 Fetches blog posts from Hashnode - 🌐 Translates content using configured translation logic - 📦 Writes outputs to Apify `datasets` ### ⚙️ Configuration & Input - Example input file: ```json { "hashnodePublicationName": "srinikethj.hashnode.dev", "hashnodeSlugName": "send-sms-using-twilio-for-g-calendar-events-using-naas-template", "targetLocale": "de" } ```` - Hashnode API key: To generate the token, go to https://hashnode.com/settings/developer and click on "Generate New Token". - Lingo.dev API key: https://lingo.dev/en/cli/quick-start (Refer #Lingo.dev Engine) ### 🏗️ Working ![Flow Diagram](./static/Working.png) ### 📁 Output - Results are saved to Apify storage under `storage/datasets/default/` and `storage/key_value_stores/default/` during local runs - Modelled output types are in `src/models/output.py` - Example ouptut: ```json { "translated_title": "SMS mit Twilio für G-Calendar-Events über Naas Template versenden", "translated_markdown_content": "### Einführung in Naas.ai\n\nNaas (Notebooks as a Service) ist eine Low-Code-Open-Source-Daten- und KI-Plattform, die jedem, der mit Daten arbeitet (Analysten, Wissenschaftler und Ingenieure), ermöglicht, leistungsstarke Datenlösungen zu erstellen, die Automatisierung, Analytik und KI kombinieren – bequem aus ihren Jupyter-Notebooks heraus unter Nutzung der Leistungsfähigkeit von Low-Code-Formeln und Microservices.\n\nUm den Lebenszyklus einfacher und dennoch leistungsfähiger zu gestalten, können Sie aus einer Reihe vorgefertigter Vorlagen wählen, die eine bestimmte Aufgabe erfüllen und die Sie je nach Anwendungsfall frei anpassen können. Eine Liste aller verfügbaren Vorlagen (derzeit mehr als 600) finden Sie hier 👇\n\n[https://github.com/jupyter-naas/awesome-notebooks/](https://github.com/jupyter-naas/awesome-notebooks/)\n\nAlle diese Notebooks ...", "title": "Send SMS using Twilio for G-Calendar Events using Naas Template", "markdown_content": "### Introduction to Naas.ai\n\nNaas (Notebooks as a service) is a low-code open-source data & AI platform that empowers anyone working with data (analysts, scientists, and engineers) to create powerful data solutions combining automation, analytics, and AI from the comfort of their Jupyter notebooks utilizing the power of low-code formulas and microservices.\n\nTo make the life cycle simpler yet more powerful you can choose from a set of pre-built templates that perform a particular task and are free to modify them as per the use case. You could find a list of all the templates available (currently more than 600)👇\n\n[https://github.com/jupyter-naas/awesome-notebooks/](https://github.com/jupyter-naas/awesome-notebooks/)\n\nAll these notebooks ...", "target_language": "de", "detected_title_language": "en", "detected_content_language": "en" } ``` ### ✅ Notes - Use `apify validate-schema ./.actor/input_schema.json` before pushing if schema validation is required. - Keep credentials and API keys out of repo; use Apify secret store or environment variables. - If `target_language` and current content language matches, then don't process the request. # Actor input Schema ## `hashnodePublicationName` (type: `string`): The Hashnode publication host (e.g., 'tests.hashnode.dev') ## `hashnodeSlugName` (type: `string`): The slug of the blog post to translate (e.g., 'your-slug-name') ## `targetLocale` (type: `string`): The language code to translate the blog post ## Actor input object example ```json { "hashnodePublicationName": "srinikethj.hashnode.dev", "hashnodeSlugName": "send-sms-using-twilio-for-g-calendar-events-using-naas-template", "targetLocale": "de" } ``` # 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 = { "hashnodePublicationName": "srinikethj.hashnode.dev", "hashnodeSlugName": "send-sms-using-twilio-for-g-calendar-events-using-naas-template", "targetLocale": "de" }; // Run the Actor and wait for it to finish const run = await client.actor("srini047/hashnode-blog-translator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 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); }); // 📚 Want to learn more 📖? Go to → 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 = { "hashnodePublicationName": "srinikethj.hashnode.dev", "hashnodeSlugName": "send-sms-using-twilio-for-g-calendar-events-using-naas-template", "targetLocale": "de", } # Run the Actor and wait for it to finish run = client.actor("srini047/hashnode-blog-translator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "hashnodePublicationName": "srinikethj.hashnode.dev", "hashnodeSlugName": "send-sms-using-twilio-for-g-calendar-events-using-naas-template", "targetLocale": "de" }' | apify call srini047/hashnode-blog-translator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=srini047/hashnode-blog-translator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hashnode Blog Translator", "description": "Translates Hashnode blog posts to the target language using Lingo.dev AI localization, preserving code blocks, images, and markdown structure", "version": "0.1", "x-build-id": "eMchlgHH7ihHwbd6v" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/srini047~hashnode-blog-translator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-srini047-hashnode-blog-translator", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/srini047~hashnode-blog-translator/runs": { "post": { "operationId": "runs-sync-srini047-hashnode-blog-translator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/srini047~hashnode-blog-translator/run-sync": { "post": { "operationId": "run-sync-srini047-hashnode-blog-translator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "hashnodePublicationName", "hashnodeSlugName", "targetLocale" ], "properties": { "hashnodePublicationName": { "title": "Publication Host", "type": "string", "description": "The Hashnode publication host (e.g., 'tests.hashnode.dev')" }, "hashnodeSlugName": { "title": "Blog Post Slug", "type": "string", "description": "The slug of the blog post to translate (e.g., 'your-slug-name')" }, "targetLocale": { "title": "Target Language", "enum": [ "en", "es", "fr", "de", "it", "pt", "pt-BR", "ru", "zh", "zh-CN", "zh-TW", "ja", "ko", "ar", "hi", "nl", "pl", "tr", "sv", "da", "no", "fi", "cs", "el", "he", "th", "vi", "id", "uk", "ro", "bg", "hr", "sk" ], "type": "string", "description": "The language code to translate the blog post" } } }, "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 } } } } } } } } } } ```