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Learn Anything from HN - Get Top Resource Recommendations from Hacker News
https://n8nworkflows.xyz/workflows/learn-anything-from-hn---get-top-resource-recommendations-from-hacker-news-2697
# Learn Anything from HN - Get Top Resource Recommendations from Hacker News
### 1. Workflow Overview
This workflow automates the discovery and delivery of top community-recommended learning resources on any user-specified topic by leveraging Hacker News discussions. It is designed for learners who want curated, high-quality educational content without manually sifting through forums.
The workflow is logically divided into the following blocks:
- **1.1 Input Reception:** Captures the user’s learning topic and email via a web form.
- **1.2 Hacker News Data Retrieval:** Searches Hacker News for relevant "Ask HN" posts and fetches their top-level comments.
- **1.3 Data Aggregation and AI Analysis:** Aggregates comments into a single text blob and uses a Large Language Model (Google Gemini) to analyze and extract top learning resources.
- **1.4 Formatting and Delivery:** Converts the AI output from Markdown to HTML and sends a personalized email to the user with the curated resource list.
- **1.5 Workflow Completion:** Marks the workflow as finished.
---
### 2. Block-by-Block Analysis
#### 2.1 Input Reception
- **Overview:**
This block collects the topic the user wants to learn and their email address through a simple web form.
- **Nodes Involved:**
- GetTopicFromToLearn
- **Node Details:**
- **GetTopicFromToLearn**
- Type: Form Trigger
- Role: Entry point that triggers the workflow when a user submits the form.
- Configuration:
- Form path: `/learn`
- Button label: "Submit"
- Form title: "What do You want to learn ?"
- Fields:
- "I want to learn" (text input, placeholder: "Python, DevOps, Ai, or just about anything")
- "What's your email ?" (email input, required)
- Response: Displays "We'll shortly send you an email with top recommendations." after submission.
- Inputs: HTTP request from form submission
- Outputs: JSON object containing user inputs, passed downstream
- Edge Cases:
- Missing or invalid email input (form validation handles this)
- Empty or vague topic input may reduce search relevance
#### 2.2 Hacker News Data Retrieval
- **Overview:**
Searches Hacker News for "Ask HN" posts matching the user’s topic and retrieves the IDs of their top-level comments.
- **Nodes Involved:**
- SearchAskHN
- SplitOutChildrenIDs
- FindHNComments
- **Node Details:**
- **SearchAskHN**
- Type: Hacker News node
- Role: Searches Hacker News for posts tagged "ask_hn" containing the user’s topic keyword.
- Configuration:
- Limit: 150 posts
- Tags: ["ask_hn"]
- Keyword: dynamically set to user input from "I want to learn" field
- Inputs: User topic from form trigger
- Outputs: List of posts matching criteria
- Edge Cases:
- No matching posts found (empty output)
- API rate limits or downtime
- **SplitOutChildrenIDs**
- Type: Split Out
- Role: Extracts and splits the `children` field (comment IDs) from each post to process comments individually.
- Configuration: Field to split out: `children`
- Inputs: Posts from SearchAskHN
- Outputs: Individual comment IDs
- Edge Cases:
- Posts without comments (empty children array)
- **FindHNComments**
- Type: HTTP Request
- Role: Fetches the JSON data for each comment ID from Hacker News API.
- Configuration:
- URL template: `https://hacker-news.firebaseio.com/v0/item/{{ $json.children }}.json?print=pretty`
- Inputs: Comment IDs from SplitOutChildrenIDs
- Outputs: Comment JSON objects including text
- Edge Cases:
- Missing or deleted comments (null or error response)
- API timeouts or failures
#### 2.3 Data Aggregation and AI Analysis
- **Overview:**
Aggregates all comment texts into a single string and uses Google Gemini LLM to analyze and extract the best learning resources, formatted in Markdown.
- **Nodes Involved:**
- CombineIntoSingleText
- Google Gemini Chat Model
- Basic LLM Chain
- **Node Details:**
- **CombineIntoSingleText**
- Type: Aggregate
- Role: Concatenates all comment texts into one aggregated text field for AI processing.
- Configuration: Aggregate field: `text`
- Inputs: Comment JSONs from FindHNComments
- Outputs: Single JSON with combined text
- Edge Cases:
- No comments to aggregate (empty output)
- **Google Gemini Chat Model**
- Type: LangChain Google Gemini Chat Model
- Role: Provides the underlying AI language model for the LLM chain.
- Configuration:
- Model: `models/gemini-1.5-flash`
- Credentials: Google PaLM API credentials (must be configured)
- Inputs: Prompt from Basic LLM Chain
- Outputs: AI-generated text response
- Edge Cases:
- API authentication errors
- Rate limits or model unavailability
- **Basic LLM Chain**
- Type: LangChain LLM Chain
- Role: Defines the prompt and calls the Google Gemini Chat Model to analyze comments and extract top resources.
- Configuration:
- Prompt template dynamically inserts user topic and aggregated comments.
- Instructions to categorize resources by type and difficulty, perform sentiment analysis, and output Markdown formatted recommendations.
- Inputs: Aggregated comment text from CombineIntoSingleText, user topic from form
- Outputs: Markdown text with resource recommendations
- Edge Cases:
- Prompt formatting errors
- AI output missing expected structure
#### 2.4 Formatting and Delivery
- **Overview:**
Converts the AI-generated Markdown into HTML and sends a personalized email with the top learning resources to the user.
- **Nodes Involved:**
- Convert2HTML
- SendEmailWithTopResources
- **Node Details:**
- **Convert2HTML**
- Type: Markdown
- Role: Converts Markdown text from AI output into HTML for email formatting.
- Configuration: Mode: markdownToHtml
- Inputs: Markdown text from Basic LLM Chain
- Outputs: HTML content
- Edge Cases:
- Malformed Markdown causing conversion issues
- **SendEmailWithTopResources**
- Type: Email Send
- Role: Sends an email to the user with the HTML formatted top resource recommendations.
- Configuration:
- Subject: "Here are Top HN Recommendations for Learning <topic>" (dynamic)
- To: User email from form input
- From: Fixed email address (configured SMTP account)
- HTML body: Includes count of comments read and the HTML content from Convert2HTML
- Credentials: SMTP credentials (configured separately)
- Inputs: HTML content from Convert2HTML, user email and topic from form
- Outputs: Confirmation of email sent
- Edge Cases:
- SMTP authentication failure
- Invalid recipient email
- Email delivery issues
#### 2.5 Workflow Completion
- **Overview:**
Marks the end of the workflow execution.
- **Nodes Involved:**
- Finished
- **Node Details:**
- **Finished**
- Type: No Operation (NoOp)
- Role: Acts as a terminal node to indicate workflow completion.
- Inputs: Email sent confirmation
- Outputs: None
- Edge Cases: None
---
### 3. Summary Table
| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
|--------------------------|-----------------------------------------|----------------------------------------|------------------------|----------------------------|--------------------------------------------------------------------------------------------------------|
| GetTopicFromToLearn | Form Trigger | Captures user topic and email | (Trigger) | SearchAskHN | "We'll find the best resources from HackerNews and send you an email" |
| SearchAskHN | Hacker News Search | Searches "Ask HN" posts by topic | GetTopicFromToLearn | SplitOutChildrenIDs | |
| SplitOutChildrenIDs | Split Out | Extracts comment IDs from posts | SearchAskHN | FindHNComments | |
| FindHNComments | HTTP Request | Fetches comment data from Hacker News | SplitOutChildrenIDs | CombineIntoSingleText | |
| CombineIntoSingleText | Aggregate | Aggregates all comment texts | FindHNComments | Basic LLM Chain | |
| Google Gemini Chat Model | LangChain Google Gemini Chat Model | Provides AI model for analysis | Basic LLM Chain (ai_languageModel) | Basic LLM Chain | Requires Google Gemini API credentials (Google PaLM API) |
| Basic LLM Chain | LangChain LLM Chain | Analyzes comments and extracts resources | CombineIntoSingleText | Convert2HTML | Prompt dynamically inserts user topic and comments; outputs Markdown formatted recommendations |
| Convert2HTML | Markdown | Converts Markdown to HTML | Basic LLM Chain | SendEmailWithTopResources | |
| SendEmailWithTopResources| Email Send | Sends personalized email to user | Convert2HTML | Finished | Requires SMTP credentials; email subject and body dynamically include user topic and comment count |
| Finished | No Operation (NoOp) | Marks workflow completion | SendEmailWithTopResources | (None) | |
---
### 4. Reproducing the Workflow from Scratch
1. **Create a Form Trigger node**
- Name: `GetTopicFromToLearn`
- Path: `/learn`
- Button Label: "Submit"
- Form Title: "What do You want to learn ?"
- Form Fields:
- Text input labeled "I want to learn" with placeholder "Python, DevOps, Ai, or just about anything"
- Email input labeled "What's your email ?" (required) with placeholder "john.doe@example.com"
- Response Text: "We'll shortly send you an email with top recommendations."
2. **Add a Hacker News node**
- Name: `SearchAskHN`
- Operation: Search posts
- Limit: 150
- Tags: ["ask_hn"]
- Keyword: Set expression to `{{$json["I want to learn"]}}` from `GetTopicFromToLearn` node output
- Connect `GetTopicFromToLearn` main output to this node.
3. **Add a Split Out node**
- Name: `SplitOutChildrenIDs`
- Field to split out: `children` (comment IDs)
- Connect `SearchAskHN` output to this node.
4. **Add an HTTP Request node**
- Name: `FindHNComments`
- HTTP Method: GET
- URL: Use expression `https://hacker-news.firebaseio.com/v0/item/{{$json["children"]}}.json?print=pretty`
- Connect `SplitOutChildrenIDs` output to this node.
5. **Add an Aggregate node**
- Name: `CombineIntoSingleText`
- Aggregate field: `text` (concatenate all comment texts)
- Connect `FindHNComments` output to this node.
6. **Add a LangChain Google Gemini Chat Model node**
- Name: `Google Gemini Chat Model`
- Model Name: `models/gemini-1.5-flash`
- Credentials: Configure Google PaLM API credentials (Google Gemini API)
- No direct input connection; will be linked as AI model in next node.
7. **Add a LangChain LLM Chain node**
- Name: `Basic LLM Chain`
- Prompt Type: Define
- Text Prompt: Use a multi-line expression that:
- Inserts user topic from `GetTopicFromToLearn` (`I want to learn`)
- Inserts aggregated comments text from `CombineIntoSingleText` (`$json.text`)
- Instructions to find top learning resources, categorize by type and difficulty, perform sentiment analysis, and output Markdown formatted list (see prompt in workflow overview)
- Connect `CombineIntoSingleText` output to this node.
- Set AI language model to `Google Gemini Chat Model` node.
8. **Add a Markdown node**
- Name: `Convert2HTML`
- Mode: markdownToHtml
- Markdown input: Use expression `{{$json.text}}` from `Basic LLM Chain` output
- Connect `Basic LLM Chain` output to this node.
9. **Add an Email Send node**
- Name: `SendEmailWithTopResources`
- To Email: Expression from `GetTopicFromToLearn` (`What's your email ?`)
- From Email: Your configured sender email (e.g., `allsmallnocaps@gmail.com`)
- Subject: Expression `"Here are Top HN Recommendations for Learning " + {{$json["I want to learn"]}}`
- HTML Body: Expression combining comment count from `SplitOutChildrenIDs` (`{{$node["SplitOutChildrenIDs"].all().length}}`) and HTML content from `Convert2HTML` (`{{$json.data}}`)
- Credentials: Configure SMTP credentials for sending email
- Connect `Convert2HTML` output to this node.
10. **Add a No Operation node**
- Name: `Finished`
- Connect `SendEmailWithTopResources` output to this node.
11. **Activate the workflow**
- Ensure all credentials are set (Google Gemini API and SMTP)
- Test by submitting the form at `/learn` path.
---
### 5. General Notes & Resources
| Note Content | Context or Link |
|----------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| Add your [Google Gemini API credentials](https://n8n.io/integrations/n8n-nodes-base.googlepalm/) | Required for AI language model node to function |
| Add your [SMTP credentials](https://n8n.io/integrations/n8n-nodes-base.emailsend/#operations) | Required for sending emails |
| Workflow built as part of [#100DaysOfAgenticAi](https://github.com/ibrhdotme/100DaysOfAgenticAi/) | Project credit and source |
| Form and email subject can be customized as needed | Modify `GetTopicFromToLearn` form fields and `SendEmailWithTopResources` subject parameter |
| Screenshot available in workflow metadata | Visual reference for node arrangement and form UI |
---
This documentation fully describes the workflow’s structure, logic, and configuration, enabling reproduction, modification, and troubleshooting by advanced users or automation agents.