Fieldnotes
Live
LLM pipeline that scores a job posting against your resume and returns an APPLY / CONSIDER / SKIP verdict with evidence — shipping both a deterministic DAG and an agentic planner→executor→synthesizer flow over the same steps.
The problem. Deciding whether a job is worth applying to means reading the posting, researching the company, comparing it against your resume, and being honest about the gaps. That’s a repeatable pipeline, not a gut call. Fieldnotes was previously called Job Researcher.
What it does. It fetches and parses the JD, researches the company (Gemini with Google Search grounding), scans the hiring org’s GitHub, scores resume-vs-JD similarity with embeddings, then synthesizes a verdict — match score, strengths, gaps, and an APPLY / CONSIDER / SKIP recommendation. It can also tailor the resume to a specific posting and return a PDF.
Two flows, one step library. I built POST /analyze as a deterministic DAG — five sequential steps, predictable and debuggable — and POST /analyze/agent, which hands the same steps to a planner LLM that decides tool order at runtime and returns the verdict plus its plan and a per-step trace. Same building blocks, two execution models — a concrete look at where a fixed pipeline ends and an agent earns its keep.