AI as My Technical Co-Founder: Building Thios with LLMs
I'm a solo founder. No co-founder, no technical team, no advisory board. Just me.
Except that's not quite true.
I have a technical co-founder. It doesn't have equity, doesn't attend meetings, and doesn't argue about product direction. But it writes code, debugs issues, and ships features faster than any human could.
It's AI. Specifically, large language models (LLMs).
The Solo Founder Problem
Building a hardware + software company alone is brutal:
- CAD design - Mechanical engineering
- Electronics - Circuit design and PCB layout
- Firmware - Embedded C/C++
- Backend - APIs, databases, authentication
- Frontend - Web apps, mobile apps
- DevOps - Deployment, monitoring, scaling
- Content - Documentation, marketing, support
No one person can be expert in all of this. You either:
- Hire a team (expensive, slow, dilutive)
- Outsource (risky, coordination overhead)
- Learn everything (impossible, burnout)
- Ship slowly (lose momentum, miss market)
Or... you use AI.
How I Use AI
Let me be specific. This isn't hand-waving about "AI helps me." Here's exactly what I do:
1. Code Generation (60% of dev time)
Before AI:
- Google for examples
- Read documentation
- Copy-paste from Stack Overflow
- Debug for hours
- Repeat
With AI:
- Describe what I need
- Get working code in seconds
- Iterate with natural language
- Ship in minutes
Example:
"Create a Next.js API route that verifies a user session cookie, queries MySQL for user data, and returns JSON with error handling."
30 seconds later, I have working code. Not perfect, but 80% there.
2. Debugging (30% of dev time)
Before AI:
- console.log everything
- Read error messages
- Google cryptic errors
- Try random fixes
- Cry
With AI:
- Paste error message
- Paste relevant code
- Get explanation + fix
- Apply and test
- Move on
Example:
Error: Cannot read property 'map' of undefined
AI: "Your items array is undefined. Add a null check or default to empty array."
Fixed in 10 seconds.
3. Architecture Decisions (10% of dev time)
Before AI:
- Read blog posts
- Watch conference talks
- Ask on Reddit
- Overthink
- Choose wrong anyway
With AI:
- Describe my constraints
- Get 3-5 options with tradeoffs
- Ask follow-up questions
- Make informed decision
- Implement
Example:
"Should I use server-side rendering or static generation for my blog? I have 10 posts now, will have 100 eventually, posts have comments, and I'm on a VPS."
AI gives me a thoughtful analysis in 30 seconds. I make the call.
Real Examples from Thios
Let me show you actual AI contributions to this project:
Example 1: Shopping Cart System
Task: Implement a shopping cart that syncs across www, blog, and store subdomains.
My prompt:
"I need a React Context for a shopping cart that stores items in localStorage, syncs across subdomains using the same domain, and provides add/remove/clear functions. Items have id, name, price, quantity, type."
AI output:
- Complete CartContext.tsx
- localStorage sync logic
- TypeScript types
- Hook for consuming context
- Error handling
Time saved: 2-3 hours → 15 minutes
Example 2: Stripe Checkout Integration
Task: Create API route for Stripe checkout with multiple items.
My prompt:
"Create a Next.js API route that accepts an array of cart items, creates a Stripe checkout session with line items, handles both digital and physical products (collect shipping for physical), and returns the checkout URL."
AI output:
- Complete API route
- Stripe SDK integration
- Line item mapping
- Conditional shipping collection
- Error handling
- TypeScript types
Time saved: 4-5 hours → 20 minutes
Example 3: Blog Comment System
Task: Build a nested comment system with MySQL backend.
My prompt:
"Create a comment system with: MySQL table schema for nested comments (parent_id), API routes for GET/POST/DELETE, React component for displaying nested comments, and reply functionality."
AI output:
- SQL schema
- Three API routes
- React component with recursion
- Reply UI
- User authentication checks
Time saved: 8-10 hours → 45 minutes
Example 4: Deployment Scripts
Task: Automate SFTP deployment to DreamHost VPS.
My prompt:
"Write an expect script that: connects to SFTP, uploads .next/, public/, src/ folders, uploads package files, then SSH to restart the Node.js server on port 8001."
AI output:
- Complete expect script
- Error handling
- Progress indicators
- Server restart logic
Time saved: 3-4 hours → 10 minutes
The Numbers
Let me quantify the impact:
Development Speed
- Before AI: ~10 hours/week of productive coding
- With AI: ~30 hours/week of productive coding
- Multiplier: 3x
Feature Velocity
- Before AI: 1-2 features/week
- With AI: 5-10 features/week
- Multiplier: 5x
Bug Resolution
- Before AI: 2-3 hours per bug
- With AI: 15-30 minutes per bug
- Multiplier: 6x
Learning Curve
- Before AI: Days to learn new tech
- With AI: Hours to learn new tech
- Multiplier: 10x
What AI Can't Do (Yet)
Let's be honest about limitations:
1. Product Vision
AI doesn't know what to build. It can help execute, but you decide what matters.
2. User Empathy
AI can't talk to customers, feel their pain, or understand context. You do discovery.
3. Creative Design
AI can generate variations, but breakthrough ideas? That's still you.
4. Strategic Decisions
Should you pivot? Raise money? Partner with X? AI gives data, you decide.
5. Domain Expertise
AI knows general patterns, but deep hardware knowledge? You bring that.
6. Quality Judgment
AI generates code, but is it good code? You review and refine.
My AI Workflow
Here's my actual daily process:
Morning (Planning)
- Review yesterday's progress
- Define today's goals
- Break goals into tasks
- Prioritize with AI's help
Development (Execution)
- Start with hardest task
- Describe to AI what I need
- Review generated code
- Test and iterate
- Commit when working
- Repeat
Evening (Reflection)
- Document what shipped
- Note what didn't work
- Plan tomorrow
- Update roadmap
AI Touchpoints
- Every task: Initial code generation
- Every bug: Debugging assistance
- Every decision: Sanity check
- Every commit: Code review
The Tools I Use
Specific LLMs and how I use them:
Claude (Anthropic)
- Use: Complex reasoning, architecture decisions
- Strength: Long context, nuanced understanding
- Weakness: Slower, sometimes verbose
GPT-4 (OpenAI)
- Use: Quick code generation, debugging
- Strength: Fast, broad knowledge
- Weakness: Can be overconfident
Cursor (IDE)
- Use: In-editor AI assistance
- Strength: Context-aware, seamless
- Weakness: Requires subscription
GitHub Copilot
- Use: Autocomplete, boilerplate
- Strength: Fast, integrated
- Weakness: Sometimes too eager
Controversial Take: AI > Junior Dev
Here's where I'll lose some of you:
For a solo founder, AI is better than hiring a junior developer.
Why?
Speed
- Junior dev: Needs onboarding, context, review
- AI: Instant, no ramp-up
Cost
- Junior dev: $60-80K/year + benefits + equity
- AI: $20-40/month
Availability
- Junior dev: 40 hours/week, needs sleep
- AI: 24/7, never tired
Breadth
- Junior dev: Specialized in 1-2 areas
- AI: Competent in everything
Iteration Speed
- Junior dev: Hours to days for feedback
- AI: Seconds
Ego
- Junior dev: Feelings, opinions, conflicts
- AI: No ego, just output
But...
A senior dev is still better than AI. Experience, judgment, and creativity matter. But most solo founders can't afford senior devs.
AI fills that gap.
Ethical Considerations
I think about this a lot:
1. Job Displacement
Am I contributing to developer unemployment? Maybe. But:
- I couldn't afford to hire anyway
- AI creates new roles (prompt engineering, AI training)
- Net effect on jobs is unclear
2. Code Quality
Is AI-generated code "real" engineering? I think yes, if:
- You understand what it does
- You can debug it
- You can modify it
- You take responsibility for it
3. Attribution
Should I credit AI in commits? Currently I don't, but maybe I should?
4. Dependency
Am I too reliant on AI? What if it goes away? Valid concern. I'm learning as I go, so I'm not helpless without it.
5. Open Source
If AI trained on open source code, should AI-generated code be open source? Interesting question. I'm open-sourcing Thios anyway.
The Future
Where is this going?
Short Term (2024-2025)
- AI gets better at understanding context
- More specialized models for hardware, CAD, etc.
- Better integration with dev tools
- Lower costs
Medium Term (2025-2027)
- AI can manage entire features end-to-end
- Autonomous debugging and testing
- AI pair programming becomes standard
- Solo founders compete with teams
Long Term (2027+)
- AI generates entire applications from specs
- Human role shifts to product and strategy
- "Developer" means something different
- Solo founders build unicorns
Advice for Solo Founders
If you're building alone, here's what I've learned:
1. Embrace AI Early
Don't wait. Start using it today. The learning curve is worth it.
2. Learn to Prompt
Good prompts get good results. Treat it like a skill to develop.
3. Stay in the Loop
Review everything AI generates. You're still the engineer.
4. Iterate Fast
AI enables rapid iteration. Use that advantage.
5. Focus on Product
Let AI handle implementation. You focus on what to build.
6. Document Everything
AI can't remember context. You need to.
7. Build in Public
Share your AI workflow. Help others learn.
Conclusion
I'm not a "10x developer." I'm a 1x developer with a 10x tool.
AI hasn't replaced me. It's amplified me.
I still make the decisions. I still write the hard code. I still debug the gnarly bugs. But AI handles the grunt work, the boilerplate, the "I've done this 100 times before" stuff.
That frees me to focus on what matters: building something people want.
For Thios, AI has been transformative. Without it, I'd still be on feature #3. With it, I've shipped:
- A full e-commerce store
- A blog platform
- A shopping cart system
- Stripe integration
- Printify integration
- Authentication
- Comment system
- And more
All in a few months. As a solo founder.
AI is my technical co-founder. And I couldn't have built Thios without it.
What about you?
Are you using AI in your development workflow? What's working? What's not?
Drop a comment below. Let's learn from each other.
And if you're skeptical about AI, I get it. I was too. But try it for a week. You might be surprised.
Here's to building faster, together. 🚀
Tools Mentioned:
P.S. - Yes, AI helped me write this post. I outlined it, AI drafted sections, I edited and refined. That's the workflow.