A GPT-6.1 Sol review of a well-used personal fitness tracker produced 13 prioritized findings and five fix batches. The experience sharpened a hybrid local/cloud workflow and a practical approach to auditing vibe code.
Lovable just doubled its valuation to $13.3 billion in eight months, racing every other vibe coding tool toward the cheapest possible token. A Slack thread pointed me at the airline price wars as the closest historical parallel, and the fifty-year history of low-cost carriers, deregulation, and serial bankruptcies says this ends in consolidation, not universal survival.
Knowledge workers have been vibe coding for decades — it was just called VBA. That history has a second lesson buried in it: the technically better spreadsheet lost the 1980s, and the one with distribution won. Claude Code is winning on quality the way Lotus 1-2-3 did. Copilot is betting that doesn’t matter, and this time Microsoft isn’t just waiting around for the product to catch up.
I kept typing the same paragraph into Coder Agents chat every time a session ran long or I needed to SSH into the homelab. A Slack conversation with a colleague turned that annoyance into five reusable slash commands — and along the way I discovered Coder Agents actually has two entirely separate skill systems, only one of which the `/` menu will ever show you.
A weekly homelab housekeeping pass (Ollama out, Home Assistant current, RustDesk re-pulled), a quote-marks bug that turned out to be a content pipeline gap rather than a font problem, and a full audit of this blog’s own “By the Numbers” habit that found it wasn’t as consistent as assumed.
A senior architect at a financial services firm told me chat context is now more valuable than code. It turns out a whole ecosystem — an arXiv paper with seven interoperable language implementations, two “Git for memory” projects, and zero agreement across model providers — is already racing to answer his question.
Six months of running a side project with AI agents convinced Rob Whiteley that the real dividing line in the AI era isn’t technical background or access to tools. It’s curiosity, and that reframes who actually gets left behind.
Building an app across dozens of disconnected agent sessions accumulates bugs. That’s the tax on staying hyper-vigilant across two axes at once: does it work the way a person experiences it, and does it hold up against someone trying to break it. This week split cleanly into both. Here are the four small bugs, each hiding in a corner some check didn’t cover. Monday’s post covers the security axis.
A personal gaming-settings tracker built end-to-end with an agent: 28 curated profiles, a JSON API, an MCP server, and a dropdown that quietly pointed at data that didn’t exist. Five lessons from building it solo.
A finance intern is spending her summer observing business processes and vibe coding automation tools. Not a CS major. Not shadowing someone. Building something real. It is a small example that says something big about how AI is reshaping internships, careers, and what the word “developer” actually means.
Vibe coding has moved from hobbyist curiosity to enterprise rollout across knowledge workers, and the next wave of AI adoption will be defined by governance and token economics.
I built a trip planning site for my F1 Montreal group. Then I ripped out every hardcoded value, added a setup wizard, ran a security audit, and open-sourced it. Here’s what it takes to turn a single-purpose vibe coded app into something anyone can fork and deploy.
Someone vibe coded an app with Google AI Studio. The Gemini API key shipped in the client-side JavaScript bundle. Google suspended the project. Here’s why every AI coding tool gets this wrong, why regular audits are the only real defense, and what you can do before it happens to you.
The Round 5 bakeoff produced four implementations. None of them shipped. What shipped was a merge of the best pieces from all four, then a polish pass against real data. Bakeoff → Merge → Polish is a generalizable pattern for any feature where the design space is genuinely unclear.
Four LLM models built the same admin feature in isolated Coder Agents sessions. I judged them blind. The headline result: Sonnet 4.6 beat Opus 4.6 on a coding task. The deeper story is what each model did with the same prompt — and what it took to make the bakeoff fair in the first place.
I pointed a current-gen AI agent at a fitness tracker I vibe coded a year ago. Five PRs later: security holes patched, a 1,300-line component broken apart, sync queries batched, and accessibility fixed. Here’s what the audit found, how it was structured, and why every vibe coded app deserves a spring cleaning.