In my last post about building the PDF to Markdown converter , I listed some “what’s next” ideas at the end. One of them was:
FastAPI wrapper: Create an HTTP API for web apps to use
Well, I did it. And I went a step further — I built a full drag-and-drop web UI on top of it.
The CLI still works exactly as before. This is an addition, not a replacement. But now when I want to convert a batch of PDFs without thinking about terminal commands, I just open a browser tab.
There’s a word that shows up in both Claude Code’s documentation and in PAI’s architecture: skills. And because they share the same word — and even the same file conventions — it’s easy to assume they’re roughly equivalent. One is just a slightly fancier version of the other.
They’re not. The relationship is closer to HTTP and a web framework. Claude Code’s skill mechanism is the protocol. PAI is the framework built on top of it.
If you’ve ever tried to find a comprehensive list of upcoming AI conferences, you know the pain. There’s no single source. AAAI has their page. NeurIPS has theirs. ICML posts deadlines on OpenReview. Half the emerging summits only exist on LinkedIn event pages or buried in Reddit threads.
I wanted a simple, searchable directory of AI conferences — one site where I could see what’s coming up, filter by topic, and get the key details. But I didn’t want to manually curate it. I’ve seen too many “awesome lists” on GitHub that are lovingly maintained for three months and then abandoned.
This site — Augmented Resilience — didn’t get built the way most blogs do. There was no staring at blank Hugo config files, no manually hunting down Namecheap SSH docs, no scrambling to remember whether the deploy script needed the public/ folder cleaned before each build.
Instead, I described what I wanted. The AI knew my hosting setup (Namecheap shared hosting), my stack (Hugo with the re-terminal theme), my repo (GitHub, SSH-keyed), and my editor (Obsidian). When a build error surfaced — a theme name mismatch between hugo.toml and the actual directory — it was diagnosed and fixed before I had time to Google it. When the deploy script needed writing, it was scaffolded against my specific environment. When I accidentally left sensitive data in an early draft, it caught it before the commit.
You know that feeling when you download a brilliant research paper, only to realize you can’t easily feed it into your AI workflow? Or when you want to add documentation to your knowledge base, but it’s locked in a format that doesn’t play well with version control or LLM tools?
Yeah, I was there last week.
I had just downloaded a fascinating 1.3MB research paper on Generative Engine Optimization and wanted to process it with my AI tools. But PDFs are terrible for this. They’re designed for printing, not for processing. What I needed was Markdown—clean, portable, AI-friendly Markdown.