Sometimes, the smallest fixes make the biggest difference. That was the case when a simple "dummy node" turned our struggling Reddit scraper into a stable, reliable automation engine. If you've ever built a complex n8n workflow involving APIs or AI, you might have hit a wall with execution limits or bloated outputs. We did too. Here's how one insignificant-looking node fixed our problem and why it's worth considering in your own automations.

The Problem With Complex AI-Driven Workflows

When too much data becomes too much

The primary keyword here is "dummy node", but the real issue started with our Reddit scraper pumping out massive payloads. Each run retrieved thousands of posts and comments, often funnelled through AI tools like GPT for classification and summarisation.

The result? Bloated data outputs and overloaded downstream nodes.

In platforms like n8n, every node passes data along the chain. If the payloads aren't trimmed or controlled, each subsequent node processes more than it needs, causing timeouts, memory issues, or workflow crashes.

Real-world symptoms of system overload

Here's what we noticed:

• Workflows began timing out unpredictably
  
  

• Logs were flooded with unnecessary JSON
  
  

• Debugging became a nightmare due to massive execution histories
  
  

• Cloud-hosted workflows hit memory caps and stopped mid-loop
  
  
  

This wasn’t just inconvenient. It made our automation brittle and unscalable.

Why Reddit scrapers are especially prone to bloated outputs

Reddit's API returns rich metadata: post titles, bodies, comments, scores, flairs, and more. While this is great for comprehensive analysis, most of it is useless for immediate processing.

Add AI tools into the mix, and the problem compounds. Each AI call might return lengthy completions or nested objects, multiplying the size of each item travelling through the flow.

Meet the Dummy Node

What is a dummy node in workflow automation?

A dummy node is a no-op (no operation) node. It doesn’t transform, filter, or manipulate data. It simply returns a static or minimal response.

In n8n, this can be accomplished using the "Set" node with an empty field configuration or a Function node that returns a hard-coded value. Its purpose is to interrupt or isolate data flow intentionally.

Why we added it to our Reddit scraper

After weeks of patching and optimising, we realised our loops and conditional paths were carrying forward bulky outputs from AI nodes. We didn’t always need those results, especially after decision points.

So, we introduced a dummy node right after key branching logic.

This node served one purpose: to strip away prior output and return a clean slate.

How it works: static return, dynamic impact

The dummy node outputs a single object like:

[

  {

    "clean": true

  }

]

This minimal payload dramatically lightens the load for the next step. The result?

• Lighter memory use
  
  

• No carry-over of unnecessary GPT or Reddit API data
  
  

• Clearer logs
  
  

• More predictable workflow performance
  
  
  

The Results: Small Fix, Big Stability Gains

Reduced memory footprint

After implementing dummy nodes, we saw a 40 percent decrease in memory usage per execution.

According to Cloudflare, managing execution payload size is critical in serverless environments. Our flows instantly became more efficient with no code changes elsewhere.

Faster execution and fewer timeouts

Execution time dropped from 2+ minutes to under 45 seconds in some cases. By stripping payloads after key nodes, we avoided pushing unneeded data into loops or webhook responses.

Timeout errors, especially in cloud environments, reduced by 90 percent.

Easier debugging and maintenance

Smaller logs meant we could actually read them. Finding failed paths became faster. We also reduced retry attempts because fewer errors occurred from bloated data.

It also made our testing cleaner. Dummy nodes helped isolate logic flows for validation without artificial dependencies.

Why Every Workflow Should Consider a Dummy Node

Situations where a dummy node helps

• After AI tools like GPT that return large outputs
  
  

• Post-conditional branches, where one path doesn’t require full upstream data
  
  

• Before webhooks, to avoid unnecessary response size
  
  

• Inside loops, where only the loop state matters
  
  
  

If your workflow is slow, flaky, or hard to debug, inserting a dummy node might be the fix.

Controlling logic vs. adding complexity

Some might argue that introducing "do nothing" nodes adds clutter. But in reality, they offer clarity. By intentionally cutting off output, you define clear boundaries in your logic. This makes workflows easier to reason about.

It’s like using comments in code — not required, but incredibly helpful.

The minimalist mindset for better automation

Great automations are lean. Dummy nodes help enforce that by stripping away what doesn’t need to continue. Instead of building around bloated outputs, they let you reset the context mid-flow.

In the same way that a licence tracking system limits unnecessary compliance inputs, dummy nodes reduce workflow noise and focus your data flow.

Applying This Fix in Your Own Workflows

Using dummy nodes in n8n and other platforms

In n8n, try this:

• Insert a Set node
  
  

• Uncheck all input fields
  
  

• Add a single output field like { "pass": true }


  

Or use a Function node:

return [

  { json: { reset: true } }

];

In Zapier, you can mimic this by using a Code by Zapier step that returns a basic object.

In Make (Integromat), a Data Store or simple Tools module can be used similarly.

Example scenarios: webhooks, loops, and external APIs

• Webhook sanitisation: before sending a final response, clear previous outputs
  
  

• Loop resets: use dummy nodes to refresh data inside iterations
  
  

• Post-AI filtering: break chains after language models to avoid output floods
  
  
  

Common pitfalls to avoid

• Don’t overuse dummy nodes; place them only where output trimming is beneficial
  
  

• Ensure essential data isn’t accidentally dropped
  
  

• Test downstream nodes to verify they still function with the minimal payload
  
  
  

Final Thoughts: Sometimes Doing Nothing Does Everything

Our Reddit scraper was failing for a reason we never expected: too much data, too far downstream. The fix wasn’t a shiny new module or an external integration. It was a dummy node.

Sometimes, the best optimisation is subtraction. In the world of automation, especially when pairing scraping and AI, less really is more.

If you’re scaling your automations and want to avoid common pitfalls, we’d love to help. Reach out here to explore your options.

FAQs Dummy Node in Reddit Scraper

What is a dummy node in workflow automation?

A dummy node is a placeholder step that returns minimal or static data. It helps control output in workflows by breaking chains of unnecessary data flow.

How does a dummy node prevent data overload?

By returning a clean payload, dummy nodes stop large outputs (like from GPT or APIs) from travelling through the rest of your workflow, reducing memory use and improving performance.

Can I use dummy nodes in platforms other than n8n?

Yes. Dummy nodes are a pattern, not a feature. You can implement them in Zapier, Make, or even hand-coded scripts. The goal is to intentionally limit what data moves forward.

When should I not use a dummy node?

Avoid dummy nodes where essential data is needed later in the flow. Overuse can lead to confusion or broken logic if not properly managed.

What are other lightweight fixes for bloated workflows?

• Use conditional filters to block unnecessary paths

• Limit API response fields with query parameters

• Split large flows into smaller sub-workflows