Introduction: “Why Is This AI Project So Much Harder Than I Expected?”
If you’ve ever launched an AI side project with confidence, only to face unexpected challenges weeks later, you’re not alone. I experienced this when I began working with Janitor AI.
At first, it felt straightforward: connect the components, adjust the prompts, launch, and iterate. But reality hit hard. Things broke down. Costs increased. Users acted in ways I didn’t foresee. And my technical choices? Some of them came back to bite me.
This post isn’t a polished success story. It’s an honest account of the mistakes I made, what I learned from them, and how I would approach Janitor AI differently today. If you’re building AI products, exploring automation, or involved in open-source software development, I hope this helps you avoid some painful lessons.
The Idea Behind Janitor AI (And Why I Thought It Was Easy)
Janitor AI started as an enjoyable experiment. It was designed to tackle repetitive tasks, automate interactions, and provide smart responses in specific workflows. Initially, it wasn’t intended to be a large platform.
Like many developers, I assumed:
- The models would “just work”
- Prompt tweaks would solve most issues
- Infrastructure could be figured out later
Spoiler: that mindset caused most of my problems.
What Janitor AI Was Built to Do
Janitor AI was created to automate repetitive digital tasks and conversations. It essentially "cleaned up" workflows that wasted time and mental energy. You can think of it as a smart assistant that takes care of predictable interactions so people can focus on other things.
At least, that was the plan.
What I didn’t realize was how complicated it could become when actual users engaged with it.
Core Features of Janitor AI
Before discussing mistakes, it’s important to recognize what did work. Janitor AI wasn’t a failure; it just adapted through challenges.
Key Features
- AI-powered automation for repetitive interactions
- Custom prompt configurations for different workflows
- Context-aware responses that adapted to ongoing conversations
- Scalable API-based architecture
- Flexible integration with existing systems
These features made Janitor AI attractive to early users and showed that there was real value in the idea.
Benefits Users Actually Got
Despite the rough edges, Janitor AI delivered meaningful benefits.
Real Benefits Observed
- Reduced manual workload
- Faster response times
- Consistent handling of repetitive tasks
- Improved productivity in niche workflows
- Lower cognitive load for users
These benefits confirmed the concept, but they did not eliminate the technical debt I was quietly building up.
Common Mistakes I Made While Building Janitor AI
Even with careful planning, AI projects rarely go smoothly. While developing Janitor AI, I faced several common challenges that taught me important lessons about design, user behavior, and scaling.
Mistake #1: Treating Prompts as the Product
In the beginning, I focused almost entirely on prompt engineering. I thought that if I could just create the perfect prompt, everything else would fall into place.
What went wrong:
- Prompts became bloated and fragile
- Small changes caused unexpected behavior
- Debugging felt like guessing, not engineering
What I’d do differently: I’d treat prompts as configurations, not core logic. Business rules, validation, and safeguards should be in the code, not hidden in text. This is where lessons from open-source development really apply: transparency and structure are important.
Mistake #2: Ignoring Real User Behavior
I tested Janitor AI. That turned out to be a huge blind spot.
Real users:
- Asked weird, unpredictable questions
- Tried to break the system (intentionally or not)
- Used features in ways I never imagined
I assumed “happy paths.” Users live on edge cases.
What I’d do differently: I’d bring in beta users sooner and document everything inputs, failures, retries. Open-source communities thrive because feedback loops are short and honest. I tried to refine things before listening, and that slowed down progress.
Mistake #3: Underestimating Cost and Scaling Issues
This one was tough. At a small scale, Janitor AI was cost-effective. But as it grew, it became uncomfortable. At peak usage, it was downright stressful.
Where I messed up:
- No cost controls per user
- No intelligent throttling
- Overly verbose model responses
I quickly realized that AI tokens are not just another API call. What I would do differently is plan for scalability from the very beginning.
- Set hard usage limits
- Cache responses aggressively
- Design with efficiency in mind
Many teams in open-source software development recognize that someone will push your system harder than it’s meant to go.
Mistake #4: Overbuilding Before Validating
I added features that nobody needed: dashboards, settings panels, complex configurations, things that seemed important but didn’t solve real user problems.
Why I did it:
- It felt productive
- It looked impressive
- It avoided uncomfortable feedback
What I’d do differently: I’d aim to deliver the smallest useful version possible. Then, I’d improve based on actual usage. Janitor AI didn’t need complexity; it needed clarity.
Mistake #5: Treating Moderation as an Afterthought
This was one of my most naïve mistakes. AI systems don’t work well just because I wish they would. Without proper moderation, outputs can drift, conversations can escalate, and liability turns into a real concern.
- Outputs can drift
- Conversations can escalate
- Liability becomes real
I thought I could “handle it later.” That turned out to be a mistake.
What I’d do differently: I’d build moderation into the system from day one, including filters, role separation, and content boundaries. Open-source communities are good examples of how shared rules support healthy projects.
Mistake #6: Building Alone for Too Long
I tried to do everything by myself: design, backend, AI logic, moderation, and documentation. Burnout came fast.
Burnout came on fast. What I learned:
- Solo speed doesn’t scale
- Fresh eyes catch obvious issues
- Collaboration improves decision-making
What I’d do differently: I’d involve contributors earlier, even on an informal basis. Many successful projects thrive through shared ownership, especially in open-source software development.
Mistake #7: Poor Observability and Debugging
When problems arose, I often didn’t know the cause. My logs were minimal, and the metrics were vague. Debugging AI behavior felt like staring into fog.
What I’d do differently: I’d invest early in:
- Structured logging
- Input/output tracing
- Clear error reporting
AI systems are probabilistic; you need visibility to trust them.
What Janitor AI Ultimately Taught Me
Despite the mistakes, Janitor AI was one of the most educational projects I’ve ever worked on.
It taught me:
- AI products are systems, not demos
- Users will surprise you always
- Cost, ethics, and UX matter as much as accuracy
- Transparency beats cleverness Most importantly, it taught me that failure isn’t a waste if you document and share your lessons.
What I’d Do If I Started Janitor AI Again Today
If I rewound time, my approach would look very different:
- Start with a narrow, validated use case
- Separate AI logic from application logic
- Build cost-awareness into every feature
- Invite feedback early and often
- Treat moderation as core infrastructure Lean on community thinking inspired by open source development services Not perfect, but far more sustainable.
Conclusion: Mistakes Are Part of Building Real AI Products
Janitor AI didn’t fail; it helped me grow as a developer.
If you’re working on AI tools, automation, or open-source software projects, don’t shy away from building imperfectly. Just be honest about what goes wrong, why it went wrong, and what you’ll do differently next time.
That honesty leads to better software and better developers.
Frequently Asked Questions (FAQs)
1. What is Janitor AI used for?
Janitor AI is designed to automate repetitive tasks and interactions using AI-driven workflows and conversational logic.
2. What was the biggest mistake in building Janitor AI?
Relying too heavily on prompts instead of structured application logic caused instability and maintenance issues.
3. Is Janitor AI an open-source project?
While not fully open-source, many lessons from open-source software development apply to its design and growth.
4. How can developers avoid high AI usage costs?
By setting usage limits, caching responses, optimizing prompts, and monitoring consumption early.
5. Should beginners build AI projects solo?
Starting solo is acceptable, but involving others early helps reduce blind spots and improves long-term sustainability.
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