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AI-assisted job-hunt platform. Human-approved pipeline: discover -> score -> approve -> draft -> send. LLM at decision points only, deterministic state machine owns the flow.
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Python 77%
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hermes b77c8b0044 Bootstrap: README, ADR-0001 (LLM at decision points), data model, API contract, worker task cards 
Design foundation for POC. Monorepo: FastAPI+Postgres backend, Vue 3 frontend,
Python packages (llm-gateway, artifacts, connectors). Approval gate and
token budgets are architectural constraints per measured prototype findings.
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apps Bootstrap: README, ADR-0001 (LLM at decision points), data model, API contract, worker task cards 2026-07-30 17:56:07 +00:00
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README.md Bootstrap: README, ADR-0001 (LLM at decision points), data model, API contract, worker task cards 2026-07-30 17:56:07 +00:00

README.md

Jobhunt Platform

An AI-assisted job-hunt platform where you stay in control. The system discovers jobs, scores them against your profile, drafts application material and prepares sends — but nothing external ever happens without your explicit approval.

Open source. Self-hosted. Single-user first, multi-user later.

Core ideas

  1. Approval gate, architecturally enforced. Every external action (send email, submit application) requires a server-side confirmed Approval referencing the exact artifact hash. No approval row, no send. This is a hard constraint, not a style guide.
  2. State machine owns the flow, LLM answers questions inside it. Pipeline transitions live in a table. The LLM scores, extracts, critiques and suggests — it never picks the next step. Deterministic orchestration, probabilistic judgment.
  3. Per-task model routing with token budgets. Cheap model for extraction/scoring, strong model for prose review, budgets enforced per task so the bill stays boring.
  4. The user drafts, the system reviews. Measured reality: human-drafted prose outperforms full AI drafts. Default cover-letter flow is user-writes, AI-reviews with tracked suggestions.

Architecture

apps/web          Vue 3 + Vite + Tailwind — tabs: CV editor, Research, Applications (kanban), Application detail
apps/api          FastAPI + PostgreSQL — REST API, state machine, scheduler, approval enforcement
packages/
  llm-gateway/    Model routing, per-task budgets, structured JSON IO, retry/fallback policy
  connectors/     Job source adapters -> normalized JobPosting (LinkedIn read-focused, jobindex, paste-a-URL)
  artifacts/      CV + cover-letter generation: Jinja templates -> PDF (fpdf2), versioning, hashing
docs/             ADRs, data model, API contract, worker task cards

Pipeline

discovered -> scored -> approved -> drafting -> sent -> interviewing -> offer -> closed
                    \-> rejected (by user)      \-> expired

External comms are only possible from approved/drafting states, and only with a matching confirmed Approval.

Quick start (POC)

cp .env.example .env   # add LLM provider keys
docker compose up -d postgres
cd apps/api && uv venv .venv && . .venv/bin/activate && uv pip install -e .
pytest                 # backend tests
uvicorn app.main:app --reload
cd apps/web && npm install && npm run dev

Status

POC scaffolding in progress. See docs/ for the design.