The AI Gave Me 300 Lines of Nothing

Overcomplication. AI development is getting weird, and tools are getting weirder.

Your AI Co-pilot Is a Prolific Idiot

It just gave you 300 lines of code to fix a bug, and the bug is still there.

We’ve all been there. You feed an AI a simple error message, and it returns with a mountain of irrelevant code, somehow leaving the original bug completely untouched. A developer on Reddit captured this perfectly: the AI added 300 lines of nonsense, and the error was exactly the same. This isn't a rare glitch; it's the new normal of AI-assisted coding.

This is about more than just bad suggestions. It’s a new kind of technical debt. AI models, lacking true contextual understanding, default to verbosity over precision, creating bloat that is difficult to audit and maintain. The real work is shifting from writing code to aggressively reviewing it. We're spending less time architecting solutions and more time undoing the work of a very fast, very confident, and very wrong junior developer.

The solution isn't just better prompting. It's better oversight. One developer built a real-time codebase diagram tracker just to watch their AI's logic unfold and catch it going rogue. This is the future. It’s less about asking the AI to write the perfect code and more about building the tools to visualise, verify, and untangle the mess it makes along the way.

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Building a Better Co-pilot

While we wrestle with AI's weirdness, a whole ecosystem of tools is emerging to make it more manageable.

Stagewise: The frontend agent for your legacy nightmare

This is a frontend agent that works on your existing, messy codebase. It's for developers who need to iterate on a UI without getting lost in someone else's code.

Genstack: A universal remote for AI models

This SDK lets you easily swap out AI models like GPT-4 or Claude without rewriting everything. It's a universal remote for the AI model zoo, which is crucial when one model gives you junk.

Claude Utils: Finally, you can paste images into Claude

A tiny utility that solves a massive frustration by finally letting you paste images directly into Claude. It’s proof that the most valuable tools often fix the smallest, most annoying problems.

The AI Assistant Gets a Real Job

Beyond the code, AI is being packaged up to handle entire real-world workflows.

Guse: The love child of a spreadsheet and an AI agent

Imagine AI automation that works like a spreadsheet, turning chaotic inputs into structured data. Guse is making complex AI workflows accessible to people who don't write code.

Move AI: Your AI butler for that soul-crushing move

This service uses AI to coordinate your entire move, from finding movers to setting up utilities. It’s a perfect example of AI tackling complex, real-world logistics that we all hate.

Relyable: A flight simulator for your AI voice agent

It's a flight simulator for your AI voice agents, letting you test them before they talk to real customers. You can't just ship an AI and hope for the best, especially when it has a voice.

Quick hits

HTC VIVE Eagle AI Glasses: Your face is the new user interface
HTC's new AI glasses want to be your real-life 'Her' companion, whispering translations and taking notes for you.

PersonaRoll: Because what the world needs is more AI-generated 'authenticity'
A new tool promises to make you go viral by creating an AI doppelganger that mimics your personal brand.

GPT-5 SEO Brand Visibility: Managing your brand's AI-generated gossip
This service checks what AI models like GPT-5 know about your brand, so you can fix your reputation before the AI makes it reality.

My takeaway

The gap between what AI can generate and what it should generate is where all the interesting work is happening now.

We're moving past the initial 'wow' of code generation and into the messy reality of maintenance, debugging, and quality control. The most valuable tools will be better editors, analysers, and visualisers. This is the shift from AI as a creator to AI as a collaborator that needs serious adult supervision.

The next wave of billion-dollar tools might not be foundational models, but the infrastructure that makes them safe and productive to use at scale. We need to stop obsessing over prompt-craft and start building the guardrails. The real question is how we manage, not just generate, this new world of code.

What happens when our entire codebase is a conversation with a prolific, slightly unhinged intern?

Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().