It reads the morning papers before I wake up.
Prospect is an autonomous job-prospecting engine: a multi-agent system that discovers job postings, matches them against persona-scoped resumes, and delivers ranked alerts — end to end, without me touching it.
Why it exists
Job hunting is a pipeline problem. Postings are scattered across newspapers, boards, and feeds; matching them against a resume is retrieval; tailoring an application is generation. So I built it as a pipeline: OCR at the front, RAG in the middle, Telegram at the end.
Architecture
Five components. Each is honest about its state.
smart-job-scanner-v2
11-stage OCR + LLM extraction pipeline; chews through 15–20 GB of newspaper PDFs daily; semantic matching; Telegram delivery
RUNNING LOCALGitHub →merlin-cli / bridge
Tool-calling runtime executing sandboxed local commands from LLM decisions; generation engine
RUNNING LOCALPRIVATEpersona-context-engine
FAISS embeddings strict-mapping projects to three base resumes — no context bleeding between personas
RESEARCHPRIVATEjob-discovery-engine
LangGraph state machine orchestrating discovery; NVIDIA NIM integration
IN DEVELOPMENTPRIVATESHIPPED — running and done.
RUNNING LOCAL — battle-tested on my hardware, not published.
IN DEVELOPMENT — being built now.
RESEARCH — proven in experiments, not integrated.
Private components are described here and available on request.
Proof
SCREENSHOT — Sid to capture: Telegram alert arriving with ranked matches
SCREENSHOT — Sid to capture: pipeline run — pages OCR'd, blocks extracted
What I’d tell another engineer
Parts of this system are boring on purpose — the Telegram bot and the multi-key Gemini client are legacy code transplanted from v1 because they never failed. Parts are hard — stealth crawling and LaTeX-safe generation are still in development, and I say so above. A system that reports its own state honestly is the point.
Want this kind of pipeline thinking on your team?