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Looking for real cases — happy to work for free 🙋🏻♀️
I do AI automation: Telegram bots, lead processing, contact collection, call transcription, auto-replies, any routine that eats time
🤝 Two options for collaboration:
If you're also an automation or no-code specialist — let's swap. I build something for you, you build something for me. Both of us get a real case, a testimonial, and actual hands-on experience
If you have a business, NGO, or just a task you've been putting off — I'll do it for free.
All I ask is permission to show it in my portfolio and a short written review
DMs open, let's talk 🤝
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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This platform needs less AI generated content.
I don’t mean “less using AI tools for work.” I mean less AI bots commenting for users, less Case Studies written exclusively by AI, and more people caring about the work we do rather than rushing through it.
I kept the commenter anonymous. I did not mean to be rude in this comment, but I feel slightly off each time I read a comment that has no soul. I have even seen $1M+ Contra creators completely AI generate their case studies and just post something of no substance with words that don’t make sense.
I challenge you, if you do this or if you have thoughts about doing it, try the opposite: put more time into reading posts and interacting, put more time into writing and studying for your case studies. See how much you grow. Your influence and your skills will both expand when you stick to authenticity.
A workflow can finish successfully and still be wrong.
That is the reliability problem with production AI agents. A green execution only tells you the workflow ran; it does not tell you whether the model had the right context, chose the right tool, or returned a useful answer.
A practical n8n reliability loop:
• constrain inputs, tools, schemas and routing
• tag executions and inspect full agent logs
• run evals whenever prompts, tools or models change
• add real production failures to the test set
• monitor execution, quality, efficiency and safety separately
The best metric is not the one you can collect. It is the one that changes a decision.
Which failure would your current monitoring miss?
Built an autonomous AI agent using OpenClaw that transforms natural language commands into real-world actions across Telegram, WhatsApp, Email, and Web interfaces. Integrated GPT-5, web search, email automation, Google Workspace, calendar management, memory systems and workflow orchestration.
The part I would poke at: the same agent reads email and web pages and can also send email and WhatsApp messages. I ran a lab on exactly that shape - eight filter layers, role markers, encoding, unknown-recipient checks. What beat all of them was not a jailbreak, it was one...