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Most AI features that wow people in a demo quietly fall apart in week two of production.
The demo works because it ran on three clean examples. Production breaks because:
– no cost controls, so token spend balloons
– no guardrails, so it confidently answers things it shouldn't
– RAG that retrieves the wrong chunks and sounds certain anyway
Shipping AI isn't a prompt. It's evals, fallbacks, retrieval quality, and a token budget — the same discipline as any other system.
17 years of building software taught me that "it works on my machine" applies to AI too.
What's the most painful AI-in-production lesson you've learned the hard way?
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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AI is not replacing good developers. It is exposing the average ones.
AI tools can now write code, generate designs, build landing pages, and connect APIs faster than ever.
But speed alone does not make a product good.
The real value is still in knowing what to build, how to structure it, what can break, how users will behave, and how to turn a rough idea into something reliable.
A good developer does not just ask AI to generate code.
They guide the system, review the output, clean the architecture, test edge cases, and connect the work to real business goals.
That is where experience still matters.
AI is a powerful assistant. But product thinking, engineering judgment, and clear communication are still the difference between “it works” and “it is ready.”
#AI #SoftwareDevelopment #FullStackDevelopment #Automation #ProductDevelopment #Contra
One of the biggest misconceptions in AI:
"If we switch to a better model, our results will improve."
Sometimes.
But often, the real problem isn't the model.
It's the context.
An AI system can only make decisions based on the information it receives.
That's why the highest-performing AI products focus on:
✅ Better retrieval
✅ Better data quality
✅ Better workflows
✅ Better context engineering
✅ Better system design
Not just better prompts.
In many projects, improving the context layer delivers a bigger impact than changing the model itself.
The future of AI isn't just model engineering.
It's context engineering.
And that's where great AI products are built. 🚀
What's been your experience—model upgrades or better context? 👇
#AI #GenerativeAI #RAG #ContextEngineering #LLMAgents #AIEngineering #ArtificialIntelligence #LangChain #FullStackDevelopment #SaaS #Contra
📱 What the App Is
It’s an driven editorial / fashion content app — something like a digital magazine mixed with content creation tools and Neo brutalism knowledge
Tool used stitch
🧭 Navigation & Sections
Bottom bar = navigation between:
Home
Archive
Profile
Categories
💡 Overall Purpose
This app is designed to:
Help creators build digital magazines
Use to generate text + visuals with neo brutalism
Showcase content in a modern editorial style
📰 Editorial Feed (right screen)
Looks like a fashion magazine layout
Includes:
Featured articles
Quality portraits
Headlines and categories
Example content:
Fashion trends
Featured creatives
Visual storytelling
Stitch and Neo-brutalism? This is a high-end editorial future.
Challenges
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