Hacker News to Google Sheets, pain points logged
You find a perfect Hacker News thread. It’s full of real frustration, real wording, real “please somebody fix this” energy. Then you lose it. A week later, you’re back to guessing what customers care about, because the receipts are buried in tabs and bookmarks.
This HN Sheets automation hits marketers hardest when they’re writing landing pages, but product folks and agency owners feel it too. You want a clean list of pain points, in customer language, without spending your morning copy-pasting comments into a spreadsheet.
This workflow watches the Hacker News front page, filters for high-signal posts, uses an AI agent to extract business pain points, then appends the results to Google Sheets so you can search, sort, and reuse them.
How This Automation Works
The full n8n workflow, from trigger to final output:
n8n Workflow Template: Hacker News to Google Sheets, pain points logged
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The Problem: High-signal research gets lost in tabs
Hacker News is amazing for market research, and also a little chaotic. A single good thread can contain ten different angles: what people tried, what failed, what they’re paying for, what they refuse to pay for, and the exact words they use to describe the pain. But when you “save it for later,” later never comes. Or it comes during a copy sprint, when you don’t have time to re-read 200 comments, hunt down the linked article, and translate it into something your team can act on.
It adds up fast. Here’s where it breaks down.
- You end up with a messy mix of bookmarks, screenshots, and half-finished notes that can’t be searched or shared cleanly.
- Manually skimming a long thread can easily eat about 30 minutes, and you still miss the best nuggets buried mid-way down.
- Even when you capture insights, they’re rarely structured, so pattern-finding becomes a separate project.
- The language drift is real: you rewrite pain points in “marketing speak” and lose the customer’s original phrasing.
The Solution: Turn Hacker News into a searchable pain-point database
This workflow pulls fresh posts from the Hacker News front page, then filters them down to the ones most likely to contain real business pain. It uses engagement signals as a proxy for “worth reading” (lots of comments, lots of points, and recent), extracts the essential metadata you actually care about, and then fetches the linked content for additional context. From there, an AI reasoning agent (running on OpenRouter with a GPT-4.1-class model) reads what’s been collected and identifies pain points that are useful for positioning, product, or content. Finally, it parses the results into a clean structure and appends them to a Google Sheet, which becomes your running research log.
The workflow starts with a manual run in n8n, which means you can use it on demand (daily, weekly, whenever you feel like it). It collects and filters Hacker News stories, processes them in batches, and uses AI to summarize pain points into consistent fields. Then it writes one row per item to Google Sheets, ready to sort by theme, industry, or urgency.
What You Get: Automation vs. Results
| What This Workflow Automates | Results You’ll Get |
|---|---|
|
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Example: What This Looks Like
Say you review the front page three times a week and normally open 10 threads each time. If you spend about 15 minutes per thread skimming comments, grabbing quotes, and pasting notes into a doc, that’s roughly 7 hours a week. With this workflow, you kick off one run, let it filter for high-engagement posts, and have structured rows land in Google Sheets in about 10 minutes of hands-on time. You still choose what to act on. You’re just not doing the busywork.
What You’ll Need
- n8n instance (try n8n Cloud free)
- Self-hosting option if you prefer (Hostinger works well)
- Google Sheets to store and search results
- OpenRouter to run the AI summaries
- OpenRouter API key (get it from your OpenRouter account settings)
Skill level: Intermediate. You’ll mostly connect credentials and confirm a few node settings, plus basic comfort with Google Cloud OAuth setup.
Don’t want to set this up yourself? Talk to an automation expert (free 15-minute consultation).
How It Works
You trigger the run. This workflow starts from a manual run inside n8n, so you can run it when you want fresh research (Monday morning is a classic).
Hacker News posts are collected and screened. It retrieves front-page stories, then filters them by engagement and recency so the AI isn’t wasting time on low-signal links.
The workflow enriches each item and extracts pain points. It picks the essential fields, processes stories in batches, and uses an OpenRouter-powered agent to identify business-related pain points from the content and context.
Clean rows are appended to Google Sheets. A structured parser formats the output, headers are set once, and the final combined result is written to your spreadsheet for easy filtering and reuse.
You can easily modify the filtering rules (points, comments, freshness) to match your niche. See the full implementation guide below for customization options.
Step-by-Step Implementation Guide
Step 1: Configure the Manual Trigger
Set up the workflow entry point so you can run it on demand while you finalize data mappings.
- Add and open Manual Run Trigger.
- Leave default settings and connect Manual Run Trigger to Retrieve Hacker News.
Step 2: Connect Google Sheets
Prepare the sheet output destination for the analyzed stories.
- Open Write Results to Sheet and select the Google Sheets document and target worksheet you want to write to.
- Credential Required: Connect your Google Sheets credentials.
⚠️ Common Pitfall: If the sheet is empty, make sure the worksheet exists and you have edit access with the connected account.
Step 3: Retrieve and Filter Stories
Pull stories from Hacker News, apply filtering, and shape the core fields used downstream.
- Open Retrieve Hacker News and confirm the desired Hacker News feed or query settings.
- Open Filter Stories by Traits and configure your inclusion/exclusion rules for titles, scores, or other fields.
- Open Pick Essential Fields and choose only the fields required for analysis and output (e.g., title, URL, score, author).
Step 4: Route and Batch Records
Split records for AI processing while also preparing the dataset for sheet output.
- Confirm Pick Essential Fields outputs to both Combine Inputs and Iterate Records in parallel.
- Open Iterate Records and set the batch size appropriate for your AI processing limits.
Tip: Smaller batches reduce AI rate-limit errors; start with 1–5 items per batch.
Step 5: Set Up the AI Analysis Chain
Configure AI reasoning, external calls, and structured parsing for consistent outputs.
- Open AI Reasoning Agent and define the prompt or system instructions used to analyze each story.
- Open OpenRouter Chat Engine and select your preferred model for the AI agent.
- Open External API Call and configure the API endpoint used by the agent if needed.
- Open Structured Result Parser and define the expected schema for AI outputs.
- Open Topic Check A and specify the logic that determines whether analyzed items proceed to output.
Credential Required: Connect your OpenRouter credentials.
Credential Required: Connect your HTTP Request credentials if the external API requires authentication.
Note: External API Call and Structured Result Parser are AI tool/sub-nodes used by AI Reasoning Agent—add credentials on AI Reasoning Agent, not on the sub-nodes.
Step 6: Combine Results and Prepare Sheet Headers
Merge the structured analysis with prepared headers before writing to the spreadsheet.
- Open Set Sheet Headers and define the header fields that match your output schema.
- Confirm Set Sheet Headers connects to Combine Inputs.
- Open Combine Inputs and ensure it merges inputs from Pick Essential Fields and Set Sheet Headers.
Step 7: Configure Output to Google Sheets
Finalize the data destination and confirm rows are added correctly.
- Open Write Results to Sheet and set the write mode (append vs. overwrite) according to your reporting needs.
- Verify Combine Inputs connects to Write Results to Sheet so the merged data is written to the sheet.
Step 8: Test and Activate Your Workflow
Run a manual test to validate data flow, then activate for ongoing use.
- Click Execute Workflow to run Manual Run Trigger and follow the data through each node.
- Confirm that Write Results to Sheet adds rows with the expected headers and AI analysis fields.
- When successful, switch the workflow to Active for production use.
Common Gotchas
- Google Sheets OAuth credentials can expire or be missing scopes. If it stops writing rows, check the credential in n8n first, then confirm the Google Cloud project still has the Sheets API enabled.
- If you’re using Wait nodes or external rendering, processing times vary. Bump up the wait duration if downstream nodes fail on empty responses.
- OpenRouter keys can be valid but blocked by billing limits or model access. Check your OpenRouter dashboard for rate limits, then confirm the Chat Model node is set to a model your account can use.
Frequently Asked Questions
About an hour if you already have Google and OpenRouter accounts.
No. You’ll connect accounts and paste in an API key, then tweak a couple of filters if you want.
Yes. n8n has a free self-hosted option and a free trial on n8n Cloud. Cloud plans start at $20/month for higher volume. You’ll also need to factor in OpenRouter usage costs (often a few cents per run, depending on model and how many posts you process).
Two options: n8n Cloud (managed, easiest setup) or self-hosting on a VPS. For self-hosting, Hostinger VPS is affordable and handles n8n well. Self-hosting gives you unlimited executions but requires basic server management.
Yes, and you probably should. You can change the thresholds in the “Filter Stories by Traits” check, and you can also adjust the “Topic Check” logic to focus on your niche (for example, only dev tools or only B2B SaaS). Common tweaks include lowering the comment threshold when news is slow, adding keyword rules to exclude topics you never sell into, and changing the sheet columns in the “Set Sheet Headers” and structured parser output.
Usually it’s an OAuth issue. Reconnect the Google Sheets credential in n8n, then verify the Google Cloud project has the Sheets API enabled and the OAuth consent screen is still valid. If it used to work and suddenly doesn’t, permissions or scopes are the first thing I’d check, honestly.
Practically, it handles “front page volume” comfortably, and batch processing keeps it stable. On n8n Cloud, your limit is mostly your monthly executions on the plan you choose, while self-hosting depends on your server. If you try to process a lot more than the filtered high-engagement posts, your bottleneck will usually be AI cost and rate limits, not n8n itself.
For this use case, usually yes, because the workflow relies on filtering, batching, and structured AI parsing that’s awkward (or expensive) in simpler tools. n8n is also easier to extend: you can add a second sheet tab per topic, route certain pain points to email, or run a second AI pass for “best direct quotes.” Zapier or Make can still work if you only want to save links and a basic summary. If you’re unsure, Talk to an automation expert and you’ll get a straight recommendation.
Once this is running, your best market research stops living in browser tabs. It shows up where you can actually use it: a sheet your whole team can search, filter, and build on.
Need Help Setting This Up?
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