27 days, 1,700+ commits, 99,9% AI generated code
The narrative round AI growth instruments has grow to be more and more indifferent from actuality. YouTube is stuffed with claims of constructing advanced functions in hours utilizing AI assistants. The reality?
I spent 27 days constructing ObjectiveScope below a strict constraint: the AI instruments would deal with ALL coding, debugging, and implementation, whereas I acted purely because the orchestrator. This wasn’t nearly constructing a product — it was a rigorous experiment within the true capabilities of Agentic Ai growth.
The experiment design
Two parallel targets drove this challenge:
- Remodel a weekend prototype right into a full-service product
- Check the actual limits of AI-driven growth by sustaining a strict “no direct code modifications” coverage
This self-imposed constraint was essential: not like typical AI-assisted growth the place builders freely modify code, I’d solely present directions and route. The AI instruments needed to deal with every thing else — from writing preliminary options to debugging their very own generated points. This meant that even easy fixes that may take seconds to implement manually typically required cautious prompting and persistence to information the AI to the answer.
The foundations
- No direct code modifications (apart from crucial mannequin identify corrections — about 0.1% of commits)
- All bugs should be fastened by the AI instruments themselves
- All characteristic implementations should be finished totally via AI
- My position was restricted to offering directions, context, and steerage
This strategy would both validate or problem the rising hype round agentic Ai Growth instruments.
The event actuality
Let’s minimize via the advertising hype. Constructing with pure AI help is feasible however comes with important constraints that aren’t mentioned sufficient in tech circles and advertising lingo.
The self-imposed restriction of in a roundabout way modifying code turned what is perhaps minor points in conventional growth into advanced workouts in AI instruction and steerage.
Core challenges
Deteriorating context administration
- As utility complexity grew, AI instruments more and more misplaced observe of the broader system context
- Options could be recreated unnecessarily or damaged by seemingly unrelated modifications
- The AI struggled to take care of constant architectural patterns throughout the codebase
- Every new characteristic required more and more detailed prompting to forestall system degradation
- Having to information the AI to grasp and keep its personal code added important complexity
Technical limitations
- Common battles with outdated data (e.g., constant makes an attempt to make use of deprecated third social gathering library variations)
- Persistent points with mannequin names (AI consistently altering “gpt-4o” or “o3-mini” to “gpt-4” because it recognized this because the “bug” within the code throughout debugging classes). The 0.1% of my direct interventions had been solely to appropriate mannequin references to keep away from losing money and time
- Integration challenges with trendy framework options grew to become workouts in affected person instruction moderately than fast fixes
- Code and debugging high quality various between prompts. Generally I simply reverted and gave it the identical immediate once more with a lot better outcomes.
Self-debugging constraints
- What could be a 5-minute repair for a human typically become hours of fastidiously guiding the AI
- The AI often launched new points (and even new options) whereas attempting to repair present ones
- Success required extraordinarily exact prompting and fixed vigilance
- Every bug repair wanted to be validated throughout your complete system to make sure no new points had been launched
- As a rule the AI lied about what it truly applied!
Device-specific insights
Lovable
- Excelled at preliminary characteristic technology however struggled with upkeep
- Efficiency degraded considerably as challenge complexity elevated
- Needed to be deserted within the last three days as a consequence of rising response instances and bugs within the device itself
- Sturdy with UI technology however weak at sustaining system consistency
Cursor Composer
- Extra dependable for incremental modifications and bug fixes
- Higher at sustaining context inside particular person recordsdata
- Struggled with cross-component dependencies
- Required extra particular prompting however produced extra constant outcomes
- Significantly better at debugging and having management
Issue with summary ideas
My expertise with these agentic coding instruments is that whereas they might excel at concrete duties and well-defined directions, they typically battle with summary ideas, corresponding to design rules, consumer expertise, and code maintainability. This limitation hinders their capability to generate code that’s not solely purposeful but additionally elegant, environment friendly, and aligned with finest practices. This can lead to code that’s troublesome to learn, keep, or scale, probably creating extra work in the long term.
Sudden learnings
The experiment yielded a number of sudden however beneficial insights about AI-driven growth:
The evolution of prompting methods
One of the crucial beneficial outcomes was creating a set of efficient debugging prompts. By trial and error, I found patterns in find out how to information AI instruments via advanced debugging eventualities. These prompts now function a reusable toolkit for different AI growth tasks, demonstrating how even strict constraints can result in transferable data.
Architectural lock-in
Maybe essentially the most important discovering was how early architectural selections grow to be practically immutable in pure AI growth. Not like conventional growth, the place refactoring is a typical follow, altering the appliance’s structure late within the growth course of proved virtually inconceivable. Two crucial points emerged:
Rising file complexity
- Information that grew bigger over time grew to become more and more dangerous to switch, as a immediate to refactor the file typically launched hours of iterations to make the issues work once more.
- The AI instruments struggled to take care of context throughout bigger quantity of recordsdata
- Makes an attempt at refactoring typically resulted in damaged performance and even new options I didn’t ask for
- The price of fixing AI-introduced bugs throughout refactoring typically outweigh potential advantages
Architectural rigidity
- Preliminary architectural selections had outsized affect on your complete growth course of, specifically when combining totally different AI instruments to work on the identical codebase
- The AI’s incapability to grasp full system implications made large-scale modifications harmful
- What could be routine refactoring in conventional growth grew to become high-risk and time consuming operations
This differs basically from typical AI-assisted growth, the place builders can freely refactor and restructure code. The constraint of pure AI growth revealed how present instruments, whereas highly effective for preliminary growth, battle with the evolutionary nature of software program structure.
Key learnings for AI-only growth
Early selections matter extra
- Preliminary architectural decisions grow to be practically everlasting in pure AI growth
- Adjustments that may be routine refactoring in conventional growth grow to be high-risk operations
- Success requires extra upfront architectural planning than typical growth
Context is every thing
- AI instruments excel at remoted duties however battle with system-wide implications
- Success requires sustaining a transparent architectural imaginative and prescient that the present AI instruments don’t appear to supply
- Documentation and context administration grow to be crucial as complexity grows
Time funding actuality
Claims of constructing advanced apps in hours are deceptive. The method requires important time funding in:
- Exact immediate engineering
- Reviewing and guiding AI-generated modifications
- Managing system-wide consistency
- Debugging AI-introduced points
Device choice issues
- Totally different instruments excel at totally different phases of growth
- Success requires understanding every device’s strengths and limitations
- Be ready to modify and even mix instruments as challenge wants evolve
Scale modifications every thing
- AI instruments excel at preliminary growth however battle with rising complexity
- System-wide modifications grow to be exponentially tougher over time
- Conventional refactoring patterns don’t translate properly to AI-only growth
The human ingredient
- The position shifts from writing code to orchestrating AI techniques
- Strategic considering and architectural oversight grow to be extra crucial
- Success is dependent upon sustaining the larger image that AI instruments typically miss
- Stress administration and deep respiratory is inspired as frustration builds up
The Artwork of AI Instruction
Maybe essentially the most sensible perception from this experiment may be summed up in a single tip: Method immediate engineering such as you’re speaking to a very dimwitted intern. This isn’t simply amusing — it’s a elementary fact about working with present AI techniques:
- Be Painfully Particular: The extra you allow ambiguous, the extra room there’s for the AI to make incorrect assumptions and “screw up”
- Assume No Context: Identical to an intern on their first day, the AI wants every thing spelled out explicitly
- By no means Depend on Assumptions: If you happen to don’t specify it, the AI will make its personal (typically incorrect) selections
- Examine The whole lot: Belief however confirm — each single output wants assessment
This mindset shift was essential for achievement. Whereas AI instruments can generate spectacular code, they lack the widespread sense and contextual understanding that even a junior builders possess. Understanding this limitation transforms frustration into an efficient technique.
The Outcome: A Full-Featured Objective Achievement Platform
Whereas the event course of revealed essential insights about AI tooling, the top end result speaks for itself: ObjectiveScope emerged as a complicated platform that transforms how solopreneurs and small groups handle their strategic planning and execution.
ObjectiveScope transforms how founders and groups handle technique and execution. At its core, AI-powered evaluation eliminates the battle of turning advanced technique paperwork into actionable plans — what sometimes takes hours turns into a 5-minute automated course of. The platform doesn’t simply observe OKRs; it actively helps you create and handle them, guaranteeing your targets and key outcomes truly align along with your strategic imaginative and prescient whereas mechanically holding every thing updated.
For the each day chaos each founder faces, the clever precedence administration system turns overwhelming job lists into clear, strategically-aligned motion plans. No extra Sunday night time planning classes or fixed doubt about engaged on the correct issues. The platform validates that your each day work really strikes the needle in your strategic objectives.
Crew collaboration options remedy the widespread problem of holding everybody aligned with out limitless conferences. Actual-time updates and role-based workspaces imply everybody is aware of their priorities and the way they connect with the larger image.
Actual-World Influence
ObjectiveScope addresses crucial challenges I’ve repeatedly encountered whereas advising startups, managing my very own ventures or simply speaking to different founders.
I’m spending 80% much less time on planning, eliminating the fixed context switching that kills productiveness, and sustaining strategic readability even through the busiest operational intervals. It’s about reworking strategic administration from a burdensome overhead into a simple each day rhythm that retains you and your group targeted on what issues most.
I’ll be increasing ObjectiveScope to handle different key challenges confronted by founders and groups. Some concepts within the pipeline are:
- An agentic chat assistant will present real-time strategic steerage, eliminating the uncertainty of decision-making in isolation.
- Sensible personalization will be taught out of your patterns and preferences, guaranteeing suggestions truly suit your working fashion and enterprise context.
- Deep integrations with Notion, Slack, and calendar instruments will finish the fixed context-switching between apps that fragments strategic focus.
- Predictive analytics will analyze your efficiency patterns to flag potential points earlier than they affect objectives and recommend useful resource changes when wanted.
- And eventually, versatile planning approaches — each on-demand and scheduled — will guarantee you’ll be able to keep strategic readability whether or not you’re following a steady plan or responding to fast market modifications.
Every enhancement goals to rework a standard ache level into an automatic, clever resolution.
Trying Ahead: Evolution Past the Experiment
The preliminary AI-driven growth part was only the start. Transferring ahead, I’ll be taking a extra hands-on strategy to constructing new capabilities, knowledgeable by the insights gained from this experiment. I definitely can’t take the danger of letting AI fully free within the code after we are in manufacturing.
This evolution displays a key studying from the primary part of the experiment: whereas AI can construct advanced functions by itself, the trail to product excellence requires combining AI capabilities with human perception and direct growth experience. Not less than for now.
The Emergence of “Lengthy Pondering” in Coding
The shift towards “lengthy considering” via reasoning fashions in AI growth marks a crucial evolution in how we would construct software program sooner or later. This rising strategy emphasizes deliberate reasoning and planning — primarily buying and selling fast responses for better-engineered options. For advanced software program growth, this isn’t simply an incremental enchancment; it’s a elementary requirement for producing production-grade code.
This functionality shift is redefining the developer’s position as properly, however not in the best way many predicted. Moderately than changing builders, AI is elevating their place from code implementers to system architects and strategic downside solvers. The actual worth emerges when builders concentrate on the duties AI can’t deal with properly but: battle examined system design, architectural selections, and inventive problem-solving. It’s not about automation changing human work — it’s about automation enhancing human functionality.
Subsequent Steps: Can AI run your complete enterprise operation?
I’m validating whether or not ObjectiveScope — a device constructed by AI — may be operated totally by AI. The following part strikes past AI growth to check the boundaries of AI operations.
Utilizing ObjectiveScope’s personal strategic planning capabilities, mixed with varied AI brokers and instruments, I’ll try to run all enterprise operations — advertising, technique, buyer help, and prioritization — with out human intervention.
It’s a meta-experiment the place AI makes use of AI-built instruments to run an AI-developed service…
Keep tuned for extra!