There’s a lot of hype (and hate!) right now around "Vibe Coding". And rightly so. The idea that you can build out software entirely by kicking back and commanding AI agents to do the typing for you is an incredibly exciting concept, but most people trying this are hitting a brutal reality after just a few days: AI Code Rot. First it works. Then it fails. And you have no idea how to fix it. I decided to build an app: a complex dinner scheduling engine. It imports recipe URLs and outputs schedules, shopping lists, and a host of tips for feeding big groups that I’ve developed after 15 years of running a weekend supper club. Central to the project was importing URLs and breaking down (often badly written) steps into machine-understandable units which could be timed and rebuilt into slick schedules. I was told more than once that this would be ‘impossible’. Every recipe is an exception! And they might be right... if you want 100% compliance. But that never happens anyway (unless you’re NASA). The issue is always: how good is good enough? When is an app useful? Or, more accurately, how many times am I willing to let a machine tell me that a leg of lamb is technically a hot beverage before I reach for the hammer? Obviously I had no idea just how hard reading human recipes and extracting multiple metrics would be. I failed badly early on; my scaffolding collapsing under the weight of a thousand if/then statements. But I tried again after discovering a module called ‘compromise’ that uses Neuro Linguistics to understand parts of speech. It can be taught when ‘roast, salt, steam, dust or zest’ is a verb or a noun. It know what an instruction sounds like as opposed to a suggestion. It was fantastically useful. I would like to thank Spencer Kelly (spencermountain on GitHub) for his work. I couldn’t have done mine without it. Just a note on how funny AI agents can be; ‘funny’ as in I properly laughed. After a long session with an intractable linguistic problem, an agent said to me “looks like we have a Hotel California situation! Your sentences can check in but your phrases can never leave.” Where the hell did that come from? When a project hits a certain scale, the AI begins heavily hallucinating, tangling math into your UI components, and generating spaghetti code. An LLM will naturally take the path of least resistance to fix an immediate bug, gradually turning your codebase into a completely undebuggable "God Object." Aside from some self-taught and very basic C+ stuff thirty years ago, I have zero background in coding, yet I commanded autonomous agents to build a 22,000+ line, mathematically complex Directed Acyclic Graph (DAG) scheduling engine. I didn't do this by learning how to code; I did this by learning how to manage the AI. Why manage? isn’t AI brilliant? Yes. And no. The knowledge you can extract is literally endless. It is a font of facts and facilities but… it can be dumb as doodoo. Let’s do a metaphor. You build a house. Perfect. You have some delightful potted laurel plants framing your front door. “Hmm,” you say “that plant needs to be an inch to the left of the porch.” So the builder pushes the pot with his boot. The AI on the other hand... it leaves the plant where it is and starts demolishing the house! They only see what’s in front of them. You have to work hard to curate their ‘context’. Or as an agent just reported: “Your critique is architecturally correct. We are treating a symptom downstream instead of amputating the root cause upstream. Why I proposed the downstream fix: Under my strict "Minimal Change / Conservative Architect" constraints, I opted for the path of least mathematical resistance—patching the calculator directly to handle the messy data." I wish we could adjust the agent's priors - their hard coded attitudes. Counter-intuitively they will happily help you constrain their chaotic behaviour. Ask an agent how to make it behave and it will gleefully write you a rule-book.

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