Zero Human Code: What I Learned from Forcing AI to Build (and Fix) Its Own Code for 27 Straight Days

27 days, 1,700+ commits, 99,9% AI generated code

The narrative around AI development tools has become increasingly detached from reality. YouTube is filled with claims of building complex applications in hours using AI assistants. The truth?

I spent 27 days building ObjectiveScope under a strict constraint: the AI tools would handle ALL coding, debugging, and implementation, while I acted purely as the orchestrator. This wasn’t just about building a product — it was a rigorous experiment in the true capabilities of Agentic Ai development.

The experiment design

Two parallel objectives drove this project:

1. Transform a weekend prototype into a full-service product
2. Test the real limits of AI-driven development by maintaining a strict “no direct code changes” policy

This self-imposed constraint was crucial: unlike typical AI-assisted development where developers freely modify code, I would only provide instructions and direction. The AI tools had to handle everything else — from writing initial features to debugging their own generated issues. This meant that even simple fixes that would take seconds to implement manually often required careful prompting and patience to guide the AI to the solution.

The rules

• No direct code modifications (except for critical model name corrections — about 0.1% of commits)
• All bugs must be fixed by the AI tools themselves
• All feature implementations must be done entirely through AI
• My role was limited to providing instructions, context, and guidance

This approach would either validate or challenge the growing hype around agentic Ai Development tools.

The development reality

Let’s cut through the marketing hype. Building with pure AI assistance is possible but comes with significant constraints that aren’t discussed enough in tech circles and marketing lingo.

The self-imposed restriction of not directly modifying code turned what might be minor issues in traditional development into complex exercises in AI instruction and guidance.

Core challenges

Deteriorating context management

• As application complexity grew, AI tools increasingly lost track of the broader system context
• Features would be recreated unnecessarily or broken by seemingly unrelated changes
• The AI struggled to maintain consistent architectural patterns across the codebase
• Each new feature required increasingly detailed prompting to prevent system degradation
• Having to guide the AI to understand and maintain its own code added significant complexity

Technical limitations

• Regular battles with outdated knowledge (e.g., consistent attempts to use deprecated third party library versions)
• Persistent issues with model names (AI constantly changing “gpt-4o” or “o3-mini” to “gpt-4” as it identified this as the “bug” in the code during debugging sessions). The 0.1% of my direct interventions were solely to correct model references to avoid wasting time and money
• Integration challenges with modern framework features became exercises in patient instruction rather than quick fixes
• Code and debugging quality varied between prompts. Sometimes I just reverted and gave it the same prompt again with much better results.

Self-debugging constraints

• What would be a 5-minute fix for a human often turned into hours of carefully guiding the AI
• The AI frequently introduced new issues (and even new features) while trying to fix existing ones
• Success required extremely precise prompting and constant vigilance
• Each bug fix needed to be validated across the entire system to ensure no new issues were introduced
• More often than not the AI lied about what it actually implemented!

Tool-specific insights

Lovable

• Excelled at initial feature generation but struggled with maintenance
• Performance degraded significantly as project complexity increased
• Had to be abandoned in the final three days due to increasing response times and bugs in the tool itself
• Strong with UI generation but weak at maintaining system consistency

Cursor Composer

• More reliable for incremental changes and bug fixes
• Better at maintaining context within individual files
• Struggled with cross-component dependencies
• Required more specific prompting but produced more consistent results
• Much better at debugging and having control

Difficulty with abstract concepts

My experience with these agentic coding tools is that while they may excel at concrete tasks and well-defined instructions, they often struggle with abstract concepts, such as design principles, user experience, and code maintainability. This limitation hinders their ability to generate code that is not only functional but also elegant, efficient, and aligned with best practices. This can result in code that is difficult to read, maintain, or scale, potentially creating more work in the long run.

Unexpected learnings

The experiment yielded several unexpected but valuable insights about AI-driven development:

The evolution of prompting strategies

One of the most valuable outcomes was developing a collection of effective debugging prompts. Through trial and error, I discovered patterns in how to guide AI tools through complex debugging scenarios. These prompts now serve as a reusable toolkit for other AI development projects, demonstrating how even strict constraints can lead to transferable knowledge.

Architectural lock-in

Perhaps the most significant finding was how early architectural decisions become nearly immutable in pure AI development. Unlike traditional development, where refactoring is a standard practice, changing the application’s architecture late in the development process proved almost impossible. Two critical issues emerged:

Growing file complexity

• Files that grew larger over time became increasingly risky to modify, as a prompt to refactor the file often introduced hours of iterations to make the things work again.
• The AI tools struggled to maintain context across larger amount of files
• Attempts at refactoring often resulted in broken functionality and even new features I didn’t ask for
• The cost of fixing AI-introduced bugs during refactoring often outweigh potential benefits

Architectural rigidity

• Initial architectural decisions had outsized impact on the entire development process, specially when combining different AI tools to work on the same codebase
• The AI’s inability to comprehend full system implications made large-scale changes dangerous
• What would be routine refactoring in traditional development became high-risk and time consuming operations

This differs fundamentally from typical AI-assisted development, where developers can freely refactor and restructure code. The constraint of pure AI development revealed how current tools, while powerful for initial development, struggle with the evolutionary nature of software architecture.

Key learnings for AI-only development

Early decisions matter more

• Initial architectural choices become nearly permanent in pure AI development
• Changes that would be routine refactoring in traditional development become high-risk operations
• Success requires more upfront architectural planning than typical development

Context is everything

• AI tools excel at isolated tasks but struggle with system-wide implications
• Success requires maintaining a clear architectural vision that the current AI tools don’t seem to provide
• Documentation and context management become critical as complexity grows

Time investment reality

Claims of building complex apps in hours are misleading. The process requires significant time investment in:

• Precise prompt engineering
• Reviewing and guiding AI-generated changes
• Managing system-wide consistency
• Debugging AI-introduced issues

Tool selection matters

• Different tools excel at different stages of development
• Success requires understanding each tool’s strengths and limitations
• Be prepared to switch or even combine tools as project needs evolve

Scale changes everything

• AI tools excel at initial development but struggle with growing complexity
• System-wide changes become exponentially more difficult over time
• Traditional refactoring patterns don’t translate well to AI-only development

The human element

• The role shifts from writing code to orchestrating AI systems
• Strategic thinking and architectural oversight become more critical
• Success depends on maintaining the bigger picture that AI tools often miss
• Stress management and deep breathing is encouraged as frustration builds up

The Art of AI Instruction

Perhaps the most practical insight from this experiment can be summed up in one tip: Approach prompt engineering like you’re talking to a really dimwitted intern. This isn’t just amusing — it’s a fundamental truth about working with current AI systems:

• Be Painfully Specific: The more you leave ambiguous, the more room there is for the AI to make incorrect assumptions and “screw up”
• Assume No Context: Just like an intern on their first day, the AI needs everything spelled out explicitly
• Never Rely on Assumptions: If you don’t specify it, the AI will make its own (often wrong) decisions
• Check Everything: Trust but verify — every single output needs review

This mindset shift was crucial for success. While AI tools can generate impressive code, they lack the common sense and contextual understanding that even a junior developers possess. Understanding this limitation transforms frustration into an effective strategy.

The Result: A Full-Featured Goal Achievement Platform

While the development process revealed crucial insights about AI tooling, the end result speaks for itself: ObjectiveScope emerged as a sophisticated platform that transforms how solopreneurs and small teams manage their strategic planning and execution.

ObjectiveScope transforms how founders and teams manage strategy and execution. At its core, AI-powered analysis eliminates the struggle of turning complex strategy documents into actionable plans — what typically takes hours becomes a 5-minute automated process. The platform doesn’t just track OKRs; it actively helps you create and manage them, ensuring your objectives and key results actually align with your strategic vision while automatically keeping everything up to date.

For the daily chaos every founder faces, the intelligent priority management system turns overwhelming task lists into clear, strategically-aligned action plans. No more Sunday night planning sessions or constant doubt about working on the right things. The platform validates that your daily work truly moves the needle on your strategic goals.

Team collaboration features solve the common challenge of keeping everyone aligned without endless meetings. Real-time updates and role-based workspaces mean everyone knows their priorities and how they connect to the bigger picture.

Real-World Impact

ObjectiveScope addresses critical challenges I’ve repeatedly encountered while advising startups, managing my own ventures or just talking to other founders.

I’m spending 80% less time on planning, eliminating the constant context switching that kills productivity, and maintaining strategic clarity even during the busiest operational periods. It’s about transforming strategic management from a burdensome overhead into an effortless daily rhythm that keeps you and your team focused on what matters most.

I’ll be expanding ObjectiveScope to address other key challenges faced by founders and teams. Some ideas in the pipeline are:

• An agentic chat assistant will provide real-time strategic guidance, eliminating the uncertainty of decision-making in isolation.
• Smart personalization will learn from your patterns and preferences, ensuring recommendations actually fit your working style and business context.
• Deep integrations with Notion, Slack, and calendar tools will end the constant context-switching between apps that fragments strategic focus.
• Predictive analytics will analyze your performance patterns to flag potential issues before they impact goals and suggest resource adjustments when needed.
• And finally, flexible planning approaches — both on-demand and scheduled — will ensure you can maintain strategic clarity whether you’re following a stable plan or responding to rapid market changes.

Each enhancement aims to transform a common pain point into an automated, intelligent solution.

Looking Forward: Evolution Beyond the Experiment

The initial AI-driven development phase was just the beginning. Moving forward, I’ll be taking a more hands-on approach to building new capabilities, informed by the insights gained from this experiment. I certainly can’t take the risk of letting AI completely loose in the code when we are in production.

This evolution reflects a key learning from the first phase of the experiment: while AI can build complex applications on its own, the path to product excellence requires combining AI capabilities with human insight and direct development expertise. At least for now.

The Emergence of “Long Thinking” in Coding

The shift toward “long thinking” through reasoning models in AI development marks a critical evolution in how we might build software in the future. This emerging approach emphasizes deliberate reasoning and planning — essentially trading rapid responses for better-engineered solutions. For complex software development, this isn’t just an incremental improvement; it’s a fundamental requirement for producing production-grade code.

This capability shift is redefining the developer’s role as well, but not in the way many predicted. Rather than replacing developers, AI is elevating their position from code implementers to system architects and strategic problem solvers. The real value emerges when developers focus on the tasks AI can’t handle well yet: battle tested system design, architectural decisions, and creative problem-solving. It’s not about automation replacing human work — it’s about automation enhancing human capability.

Next Steps: Can AI run the entire business operation?

I’m validating whether ObjectiveScope — a tool built by AI — can be operated entirely by AI. The next phase moves beyond AI development to test the boundaries of AI operations.

Using ObjectiveScope’s own strategic planning capabilities, combined with various AI agents and tools, I’ll attempt to run all business operations — marketing, strategy, customer support, and prioritization — without human intervention.

It’s a meta-experiment where AI uses AI-built tools to run an AI-developed service…

Stay tuned for more!