Your Job Isn't Writing Code Anymore
It's becoming a lot more about quality control.
The conversation around AI coding assistants is changing. It's no longer about whether they can write code, but what our job becomes now that they can.
The AI Developer's New Groove Is Quality Control
A discussion on 'vibecoding' reveals our role is shifting from creator to critic.
A developer with a QA background recently noted that when using AI to code, they feel more like a tester than a builder. This perfectly captures the vibe shift happening in software development. As large language models get better at generating boilerplate and entire functions, the human's primary role moves up the stack.
This isn't about being replaced; it's a redefinition of value. The new bottleneck isn't the speed of writing code, but the speed of verifying it. Our most critical skills are becoming architectural oversight, rigorous testing, and the ability to critically analyse AI-generated output for subtle bugs, security flaws, and alignment with business goals. We're moving from bricklayers to building inspectors.
This trend demands we get serious about our QA mindset. Relying on AI's speed without robust human validation is just a faster way to build technical debt. The smart play is to double down on testing methodologies and critical thinking, treating the AI as a brilliant but sometimes unreliable junior programmer who needs constant supervision.
The New AI Toolbox
The assembly line for AI-powered copilots is officially in overdrive.
Ghost Chat: An AI that actually keeps your secrets.
This isn't just privacy-focused; it's privacy-obsessed, with no accounts, no tracking, and chats that self-destruct. It’s for conversations that can’t exist anywhere else.
NoteGPT: Your brain, but faster and without the forgetting.
It aims to be your 'second brain' by summarising meetings, videos, and PDFs into notes, flashcards, and mind maps. This is less about note-taking and more about reshaping how we process digital content.
Ahsk: The macOS assistant challenging Apple Intelligence.
This tool brings AI to every app on your Mac *now*, not later this year. By claiming to be 'better than Apple Intelligence,' it's forcing a conversation about whether the best AI will be built-in or bolted-on.
Sharper Tools for Building and Growing
Meanwhile, the tools for building and scaling are getting leaner and more focused.
Lean SaaS Engine: A 50MB RAM promise for bootstrapped founders.
This open-source Go and Next.js boilerplate is a masterclass in efficiency. It's a direct challenge to bloated starter kits, enabling founders to launch scalable apps on minimal infrastructure.
Review Frame: Finally, a reason not to dread your performance review.
It turns your GitHub history into clean, review-ready summaries. The real value is its 'no surveillance' promise, positioning it as a tool for self-reflection, not micromanagement.
Affonso: Making SaaS affiliate programs less of a nightmare.
This automates the messy parts of running an affiliate program, like tracking recurring revenue and handling payouts. It’s a sign that complex growth channels are becoming accessible to smaller teams.
Quick hits
Wave Browser: Cleans the ocean while you procrastinate.
This browser partners with 4ocean to fund plastic removal, testing if a social mission can convince users to finally switch away from Chrome.
hq0: Takes the Zoom branding out of your sales calls.
By letting you host video meetings on your own domain, this tool is a bet that owning the entire customer experience matters more than ever.
NOIZ AI: Emojis that change the sound of your voice.
This tool uses emojis to guide the emotion in AI-generated voice messages, trying to close the emotional gap in digital communication.
My takeaway
The most valuable skill in the age of AI isn't prompting, it's judgment.
AI can generate options faster than we ever could, but it can't determine which option is right, safe, or truly aligned with the goal. Our job is shifting from pure creation to high-stakes curation and validation. This requires a deeper understanding of the problem space, not just the code.
So we have to ask ourselves, are we actively training for this new role? Are we building the critical thinking and quality assurance muscles needed to oversee our new AI assistants? Or are we just getting faster at shipping the wrong thing?
What's one skill you're focusing on that an AI can't replicate?
Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().