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AI coding tools have come a long way. I use them daily and they've made me more productive.
But a lot of us don't work on new repos. We work on codebases that have been around for years. Functions 3000+ lines long. Code from 2015 still running in production.
When you point AI at that kind of code, it struggles. The context fills up. It loses the bigger picture. Suggests a fix that works in isolation but breaks something three files away.
The tools work great on fresh projects. On older codebases, the experience is very different.
The context memory problem? Nobody has solved it yet. Once the conversation gets long, the AI loses track. Basically type and hope.
If you're on legacy code and AI isn't the magic wand it seems in demos you're not doing it wrong.
What's your experience using AI on older codebases?
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AI is slowly removing the “interface” from software.
Think about how we use software today.Open the app.
Find the right screen.
Fill in the fields.
Click a button.
Repeat.
Now imagine telling the product what you want and letting it figure out the steps. That’s what makes agentic AI interesting. The product doesn't just show information anymore. It understands the goal, works through the task, and takes action.
But this creates a completely different UX problem: If AI is doing the work, what does the user actually need to see?
Do we still need dashboards with 20 filters?
Do we still need 5-step workflows?
Do we even need traditional navigation for every task?
I think Product Designers will spend less time asking:
“Where should this button go?”
And more time asking:
“What should the product do for the user?”
This is much bigger than adding an AI chatbot.
Are we designing the next generation of interfaces, or slowly designing them away?
Giving the code editor a much-needed AI upgrade. 🔥
This is such an amazing design! Wow!
🚀 Turning AI & ML Projects into Real, Usable Applications 🤖💜
From building a Streamlit AI-powered application to developing a Customer Churn Prediction model, every project teaches me something new — not just about technology, but about solving real-world problems with AI & Machine Learning. ✨
🔹 Streamlit App — turning ML ideas into interactive and user-friendly web applications
🔹 Customer Churn Predictor — using Machine Learning to predict whether a customer is likely to churn or stay, along with churn probability and customer risk level 📊🚦
🔹 Business Insights — providing actionable recommendations that can help businesses focus on customer retention 💡
🔹 End-to-End Project — from data preprocessing and model training to testing, building and deploying a working ML application 🌐
This project is designed to show how a Machine Learning model can move beyond a notebook and become a real, usable application that can help businesses understand customer churn risk and make better decisions. 🤝
This is more than just a project showcase — it’s a reminder that every line of code brings me one step closer to becoming the AI/ML engineer I aspire to be. ✨
Still learning.
Still building.
Still improving. 🚀
Alhamdulillah for every small step. 🤍
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