Three bugs that looked fixed from the wrong vantage point. An unquoted YAML date that crashed the public homepage one month after we wrote a blog post about the same bug. A model string that worked until its deprecation date passed. A security commit that stacked three invisible failures on top of each other. And a lesson about what accumulates when you build fast with agents.
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.
A CEO panel at an AI event sparked a simple but powerful question every startup founder should ask themselves: does your business get better as AI models improve, or does it get worse?
Coder 2.34 shipped User Secrets — per-user credential storage that injects into every workspace automatically. We upgraded, audited 29 secrets across four projects, and found exactly two that belonged there. Here's how we decided, how we migrated, and what we cleaned up along the way.
A model refresh on the homelab (Qwen 3.6, new embeddings, 469 llama.cpp builds), a feature sprint on the vacation planning site (calendar sync, expense tracking, and three bugs that taught us more than the features did), and automating Substack syndication after discovering two more undocumented quirks. Three unrelated workstreams, one theme: maintenance is where the real learning happens.
At a C-suite roundtable in Palo Alto last week, ten-plus executives from a mix of gaming platforms, enterprise systems providers, job sites, and other Bay Area titans landed on the same analogy without being prompted: we've seen this before. The lift-and-shift era of AI is already here. The native era — where you redesign workflows from scratch for agents, not humans — is what comes next.
I've been running OpenClaw on the homelab for a month. A recommendation sent me down the Hermes Agent rabbit hole — and the research before the first real test revealed my daily driver model was broken for tool calling all along.
One missing pair of quotes in one frontmatter field took down the admin drafts page. YAML 1.1 auto-parsed the date to a JS Date object, formatDate called .includes on it, and the route 500'd. Here's the bisect from a mobile screenshot to a one-line fix, why only the drafts page broke, and the lesson about trusting types at the YAML boundary. Part two of a two-part Friday Fixes — see #1 for the scheduled-publish workflow bugs that landed the same day.
The scheduled-publish GitHub Action broke twice in nine days. Bug one: a grep that matched body text instead of frontmatter, triggered by a post about the feature itself. Bug two: a dead-code line introduced by the fix for bug one — racy under set -euo pipefail, probabilistically silent for eight days, then 42 consecutive failures with zero notifications.
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.
Three AI agents audited the blog and produced three different reports. Closing them out was its own job — triage, phasing, verification, and ten commits across two repos with zero build failures. Here's the remediation arc, what shipped, what got deferred, and what the process revealed about working through someone else's audit.
Our AEO audit gave vibescoder.dev a clean bill of health. Cloudflare's isitagentready.com gave it a 25 out of 100. Both audits were right — they were measuring two different competencies. Here's the side-by-side, what each one caught, and the two genuine gaps we shipped fixes for — taking the score from 25 to 33 (and on track for 39 after the next scan).
DeepSeek V4-Pro, V4-Flash, and Zyphra ZAYA1 are three of the most exciting new models in local AI. None of them run on our RTX 5090 homelab — for completely different reasons. Here's the research, the math, and what it means for anyone building a local inference rig.
Three rounds of iPhone screenshots to fix spacing that should have been right the first time. The fix wasn't smaller padding — it was teaching the agent the pixel math once so it never forgets. Plus: admin pillbox for drafts, hamburger menu shortcut, Invalid Date bugs, and scheduled publishing for every draft.
As AI agents make code generation trivial, the real value shifts from storing source code to preserving the chat conversations that created it.
I asked an AI agent to turn off my RGB lights on Linux. 85 terminal commands, 35 failures, 4 hangs, 2 dead download links, one wrong build system, and the GPU is still glowing. This is the post.
A CRLF bug silently broke every workspace for weeks. Then we fixed it, taught the agent to remember, moved templates to Git, squashed a nested heredoc, cut boot time from 91 seconds to 5, automated the screenshot pipeline, and built scheduled publishing — which this post used to publish itself. Ten fixes, one week.
How AI agents are transforming software development the same way Google Maps revolutionized travel - making the impossible feel effortless and opening up new worlds of exploration.