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Spent today building a fully autonomous content ops pipeline. No manual triggers. No babysitting.
Dynamic resource discovery across a cloud storage tree → GraphQL mutations against a third-party publishing API → idempotent state checks so it never double-posts → cron-orchestrated execution on serverless infra, running unattended, forever.
Zero hardcoded config for future data. Drop new assets in and the system just... finds them. No redeploys. No code touched.
Verified end-to-end in production. Watched it write real, live posts with my own eyes before I called it done.
Mine's already queued through December 2027. Sit with that for a second. 😌
This is the boring-but-hard part of "AI-powered automation" that nobody talks about not the model, the plumbing around it. Auth flows, rate limits, idempotency, graceful degradation when you hit a hard API ceiling mid-run.
Built it in an afternoon.
Happy to nerd out. DMs open.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Just shipped my own portfolio site — designed and built end to end.
No templates, no page builder. Figma for the design, then coded in HTML/CSS/JS + Next.js — working alongside AI tools to move faster without cutting corners. Custom animations, full mobile optimization, built in from day one.
Same process I bring to client work: design with intent, build it myself, ship something that actually performs.
love that custom cursor, so much cleverness in every detail!
I built an AI-powered n8n automation that generates personalized ice breakers for cold emails.
It takes a company’s website URL, analyzes the business, extracts key information, and uses AI to create a unique opening line for outreach.
Instead of manually researching every prospect, this workflow helps personalize cold emails at scale while saving hours of repetitive work.
Built with n8n + AI + web data extraction.
Strong use case. The quality control matters as much as the generation: I’d keep the source snippet and URL beside every ice breaker, then use a fallback when the evidence is thin instead of letting the model infer. Have you added a similarity check so repeat prospects don’t receive nearly identical openings?
I recently built this workflow in n8n to automate the first stage of handling customer support requests.
The idea was to take an incoming ticket and let the system handle the repetitive work:
→ Receive the customer request through a webhook
→ Process and structure the incoming data
→ Use an AI Agent to understand the ticket
→ Classify it as Genuine, Spam, or Unclear
→ Route it based on the classification
→ Trigger the appropriate email action
→ Log the interaction in Google Sheets
I also added an Unclear path so that tickets the AI isn’t confident about don’t get blindly processed.
The goal isn’t to remove humans from customer support.
It’s to reduce the repetitive sorting and routing work, so the team can spend more time on the requests that actually need their attention.
Would you automate customer support differently? I’d love to hear how you’d approach it.
The Unclear lane is exactly the right guardrail. I’d add two production checks: redact PII before sending ticket text to the model, and measure false-positive/false-negative rates by category rather than a single accuracy score. Logging resolutions and human overrides back into...