How does the weight node interact with the webhook and the Upwork scraper template? Are there best practices for configuring both to avoid delays or missed updates, and is there a better or easier way?
The weight (actually wait) node in Make simply pauses the scenario for a set amount of time; it isn’t a webhook. The webhook URL is generated at runtime and the scenario resumes when the webhook is called. You can define a callback URL in Appify so that when the Upwork actor finishes it calls that webhook and the flow continues. In the example Santi uses n8.pulsense.com/webhook as the callback. There’s also a newer Appify API endpoint that runs the actor synchronously and returns the data set items directly, eliminating the need for the wait‑node‑plus‑webhook pattern. Using that endpoint is the simpler, more reliable way to avoid delays or missed updates.
There is no webhook on the Upwork scraper in Maker School. I'm not really sure what you're referring to. You're referring to the HTTP request. You have to call the automation page, though there have been some issues. I don't recommend using my own scraper; I use Upwork. The Upwork extractor is good, and the Upwork scraper without stale job posts works well. The most popular scraper is now rated poorly, so try the newer ones—they're more reliable. Cloud Genius is the next option.
The execution time limit of 40 minutes versus your estimated 20‑hour run clearly doesn’t match. The simplest solution is to split the long‑running part into a sub‑workflow. Use the Execute Workflow node to call another workflow that handles the looping over items. Set up the sub‑workflow with a weight or delay to smooth out API errors and rate limits, and make sure you turn off the option to wait for the sub‑workflow to finish so the main flow continues instantly. This pattern is essentially the same as sending an HTTP request to your own N8N workflow and handling the response with webhooks. It works on both the Pro and even the Starter plan because the heavy lifting happens in the background sub‑workflow.
Sure. Let me walk you through the solution. Your current scenario loops over an input—say a thousand records—using a built‑in iterator and then feeds each record into an AI module that takes several seconds to run. At 5 seconds per execution, you’d hit the 45‑minute timeout after roughly 600 runs, which makes the whole process take a very long time. The fix is to split the scenario: instead of processing everything in one long loop, have the iterator immediately send each record to an external service via an HTTP webhook. The webhook executes almost instantly (around 0.001 seconds), so you can process millions of records within the same timeout window. In practice, you can create a small JSON array, iterate over it, and for each item make a webhook call that triggers the AI module on a separate scenario. This way the sender scenario finishes quickly, and the receiver scenario handles the AI work without ever hitting the timeout. You can also use Make’s sub‑scenario feature or a dedicated Make.com module, but webhooks are the simplest and most reliable method. Just ensure your Perplexity search and ChatGPT modules are placed in the receiving scenario, and you’ll avoid the timeout entirely.
He said he’d build the frontend with Lovable and the backend with NADN, connecting them via webhook and a respond‑to‑webhook node, noting most of the work can be done in natural language with drag‑and‑drop for the final steps. To validate demand, he’d package the SaaS, create 10 different pitches and 10 audience segments (100 combos), send about 1,000 cold emails a day, testing 10 per combo daily, and after a month look at the 300 emails per group to see which pitches keep, iterate, and repeat the process for another month or two until the highest‑ROI pitch emerges; he added that the same process yields validated wording for inbound ads (meta/Google) if he ever wants to run them.