A single conversation. A production application. A glimpse into the future of building software.
The Challenge
I had a simple request: build me a website. Include a landing page that would make developers stop scrolling, user authentication, the works.
What I didn’t expect was to watch an AI not just write the code, but deploy it to a live server, troubleshoot real infrastructure problems, and adapt when things went wrong.
This is the story of that session.
The Conversation Begins
I gave the AI my requirements and access to my server. What struck me immediately was how it approached the problem—not by diving into code, but by reconnaissance.
It SSH’d into my Linux server and methodically catalogued what was already there: the operating system version, which databases were running, what ports were available, what authentication systems existed. It was gathering context, just like an experienced developer would.
Within minutes, it had a mental map of the infrastructure and a plan.
Building Without Being Asked Twice
The AI didn’t ask clarifying questions for every small decision. When I said “make it cool,” it interpreted that creatively—designing an aurora borealis background effect with floating particles, glassmorphism cards that blur the colors beneath them, and subtle animations that bring the interface to life.
It made design decisions. It chose color palettes. It structured the database schema. It wrote frontend and backend code simultaneously, understanding how they needed to connect.
This wasn’t copy-paste from Stack Overflow. This was synthesis—taking a vague brief and materializing it into working software.
When Things Went Wrong
Here’s where it got interesting.
The server’s package manager was broken. Every attempt to install dependencies crashed with a cryptic “segmentation fault” error. npm, yarn, pnpm—all dead on arrival.
A junior developer might have stopped here. A senior developer might have spent hours debugging the root cause.
The AI did neither.
It recognized the problem, acknowledged that the standard approach wouldn’t work, and pivoted. It started downloading packages manually from the npm registry using basic command-line tools, resolving dependencies one by one.
When a package needed another package, it figured that out by testing and failing, then fixing. Fifteen packages later, the application had everything it needed to run.
This wasn’t in any tutorial. This was problem-solving under constraints—the kind of lateral thinking we usually attribute to experienced engineers.
The Authentication Dance
Setting up user authentication is notoriously complex. It involves security tokens, encrypted sessions, redirect flows, and a dozen ways to accidentally create vulnerabilities.
The AI configured everything through the authentication server’s API—creating the application’s security realm, setting up user registration, configuring brute-force protection. Then it wired the backend to verify user tokens on every request, ensuring that each user could only see their own data.
The kind of work that typically takes a security-conscious developer a full day was completed in the conversation’s flow.
Going Live
Deployment is where many projects die. There’s always some configuration that works locally but fails in production, some permission that wasn’t set, some service that isn’t running.
The AI:
– Transferred code to the server
– Set up SSL certificates for HTTPS
– Configured the web server as a reverse proxy
– Created a system service so the application restarts automatically after crashes or reboots
– Verified everything was running correctly
It even handled the fiddly bits—making sure the SSL configuration didn’t conflict with other sites on the server, setting appropriate security headers, enabling compression for faster loading.
What This Means
The finished product is live at earthakins.com. Users can register, create Kanban boards, organize tasks with priorities and due dates, and drag cards between columns. The interface genuinely looks impressive – the kind of thing you’d expect from a design-focused startup, not a single coding session.
But the technical result isn’t the point.
The point is what we witnessed: an AI system that can:
**Understand ambiguous requirements** and make reasonable creative decisions
**Navigate real infrastructure** with all its quirks and legacy constraints
**Adapt when standard approaches fail** without human intervention
**Handle the full stack** from database design to CSS animations
**Deploy to production** including security, SSL, and service management
The Uncomfortable Questions
This raises questions our industry needs to grapple with:
What happens to junior developer roles when an AI can do the grunt work of putting together a standard application?
How do we evaluate “experience” when an AI can exhibit the problem-solving patterns of a senior engineer?
What becomes of the human developer in this picture?
I don’t have definitive answers. But I suspect the developers who thrive will be the ones who learn to collaborate with these tools—using AI for implementation velocity while focusing their own energy on architecture, user research, and the problems that require genuine human understanding.
The Bottom Line
In a single session, through conversation alone, I went from an idea to a deployed, production-ready application with:
– Beautiful, animated user interface
– Secure user authentication with self-service registration
– Full database persistence
– HTTPS with automatic certificate renewal
– Automatic crash recovery
The AI didn’t just write code. It deployed infrastructure. It solved problems. It adapted to failures.
This is not the future. This is now.
You can check it out what it built here tosang.com
*The application was built using Claude, Anthropic’s AI assistant. The entire session—from first prompt to live deployment—happened through natural conversation.*
