~65% of active job search time is consumed by repetitive, rule-based tasks. This piece breaks down the five structural problems with LinkedIn-based sourcing and proposes a semi-automated system to solve them.

13 potential failure modes in the job search automation system – URL rot, title standardization, list maintenance, cold starts, feedback loops, and more. Most dissolve on closer inspection; the rest have clear mitigations.

Most career pages look JS-rendered, but the data behind them is public JSON fetched via API. Handling the top four ATS platforms covers ~80% of the target company universe – no browser automation needed.

Five lists from Part 1 mapped to eight SQLite tables – companies, job postings, CV versions, cover letters, applications, and a complete audit trail. The schema design decisions behind each choice, and why SQLite beats a spreadsheet for a serious job search pipeline.