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I ran an experiment this week that's a good example of what I actually build: put two AI models, Claude and Codex, in a structured debate against each other, no script, ten rounds, until they either converged or one gave up.
The question I gave them was about startup ideas, but that's not really the point. What's interesting is watching two models argue, concede points, catch each other rationalizing weak answers, and converge on a shared framework neither started with. That's a different kind of AI output than a single prompt-response, and it's the kind of multi-model orchestration work I build for clients.
Full writeup on Medium:
If you're trying to get more out of running multiple AI models together instead of just picking one, that's exactly the kind of problem I work on.
medium.com
5 Apps You Could Build Today That Could Pay You in 30 Days
Claude and Codex tore each other’s startup ideas apart for ten rounds. This is the list that survived.
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I recently had the chance to pitch an AI project, and after a grueling month of continuously following up with the client, it still failed.
This failure taught me a lot of lessons, especially the importance of adapting the business problem to fit the client's needs from the very first pitch, in order to help them raise funding.
No matter how beautiful your design is, if it goes beyond what the client actually needs but doesn't help them secure the money, it's a failed design.
Next time, I'll ask the hard questions upfront: What are you actually trying to raise money for? What does success look like to your investors?
before touching the design at all.
Many lessons learned, and many more chances still ahead..
I'm sorry to hear that. Despite of this high quality of the work, but your lesson is the most precious. Thank you for sharing this. I hope you get better opportunity ahead soon!
A founder recently asked me to build an AI chatbot.
After understanding the business, I recommended something completely different.
The real bottleneck wasn't answering user questions.
It was a manual review process.
Every new shop submitted to the platform had to be verified, checked for duplicates, and rewarded by an admin.
Instead of another chatbot, I designed an AI-first workflow:
• MCP servers exposing business tools securely
• AI agents to verify submissions and detect duplicates
• Automated reward assignment
• Human review only for uncertain cases
The objective wasn't to replace people.
It was to eliminate repetitive work and let humans focus on decisions that actually require judgment.
This is why I enjoy building AI products.
The best AI solution isn't always the most sophisticated model, it's the one that solves the right business problem.
That's the difference between adding AI to a product and engineering a business around AI.
Let's build your AI product together.
#AgenticAI #Automation #AIEngineering #forwarddeployedengineering
The best prompt I wrote wasn't a prompt. It was a sketch.
For the Contra × Envato Human by Design Challenge, I wanted to explore what happens when human creativity leads and AI follows.
I started with a Moodboard, explored countless AI generations, and realized something:
They had the substance but they didn't have the heart.
AI helped refine the artwork, but the vision, personality, and decisions behind Nocti were always human.
Meet Nocti. My submission for the Contra × Envato Human by Design Challenge. 🌙✨
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