On March 12, 2025 I hit a wall. I was knee‑deep in a side project that scrapes academic PDFs to extract text coordinates for an annotation tool (LayoutLM experiments, v0.9). After 48 hours of bouncing between Google, arXiv, and scattered PDFs I realized I was replicating work that should have been automated: I kept re-finding the same papers, losing track of citations, and re-running brittle extraction scripts that failed on diverse PDF layouts. That exact moment-staring at five slightly different parser outputs and a growing TODO list-was when I decided to stop improvising and build a proper research pipeline.
I want to tell you what I built, what broke, and why a focused set of tools is the only thing that kept the project from turning into technical debt. This is not a marketing puff piece-its a war story from someone who tried a dozen shortcuts before settling on a workflow that scales from one paper to a systematic literature review.
First, the short recipe: automate discovery; deep read with a plan; extract structured facts; and keep an auditable trail. The gap I cared about sits between quick web search and full academic review: you need answers faster than manual reading but deeper than a single-page summary. I experimented with three classes of tooling and eventually layered them together so each one plays to its strength.
