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raulgupta 89ca9ad647 Initial commit: matchmaking-v2 on-demand multi-board job-search agent

On-demand Scout service (replaces the nightly aggregator):
- FastAPI agent-mesh service; card name "matchmaking-service" (HTTP /a2a/tasks
  + Redis-stream worker matching the sibling-service pattern)
- run_search: parallel multi-board sweep (Naukri/blackfalcondata + Foundit +
  LinkedIn), city-filtered at the board, cheapest-per-result first
- per-board adapters + normalizers -> ScoutJob with rich read-only details and
  offsite apply links; recall mode + MVQ guard
- result cache for dev replay ($0); per-board cost knobs; retry-on-5xx
- research/: engine design, board cost economics, India-actor shortlist, POC

2026-06-18 19:22:03 +05:30

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browser-apply — browser-use (open source) POC

The pivot away from heuristics. Instead of mocking an apply flow, an AI agent drives a real Chrome browser — it looks at the page and clicks/types like a human. No cross-origin wall, because it is the browser.

The moving parts (what's actually running)

  your task (plain English)
        │
        ▼
   ┌─────────┐     "what do I click next?"      ┌──────────────┐
   │  Agent  │ ───────────────────────────────► │  OpenAI LLM  │
   │(browser-│ ◄─────────────────────────────── │ (the brain)  │
   │  use)   │     "click element #3, type X"   └──────────────┘
        │
        │ Chrome DevTools Protocol (cdp-use)
        ▼
   ┌──────────────┐
   │ real Chrome  │  ← navigates, reads the page, clicks, types, uploads
   └──────────────┘

browser-use (v0.13.1) — the library. Runs the loop: screenshot/read page → ask the LLM what to do → do it → repeat until the task is done.
The LLM (OpenAI here) — the decision-maker. Sees the page, decides the next action. It's swappable: OpenAI, Anthropic, Google, or local (Ollama).
Chrome — your installed Google Chrome, driven over the DevTools Protocol. No separate browser download needed. browser-use auto-adds ad/cookie-blocker extensions.

Setup (already done once)

python3 -m venv venv               # Python >= 3.11 (you have 3.13.2)
source venv/bin/activate
pip install browser-use            # pulls openai/anthropic/google SDKs + cdp-use
# Chrome is reused from /Applications — nothing else to install

Secrets live in .env (git-ignored):

OPENAI_API_KEY=sk-...

Run the smoke test

source venv/bin/activate
python verify.py          # headless (no window)
HEADLESS=0 python verify.py   # SEE Chrome open and the agent drive it

Expected: it visits example.com and prints The main heading on the page is 'Example Domain'.

What's proven so far

✅ The full stack works: browser launches, LLM reasons, agent reads a live page (verify.py).
✅ Vision résumé parsing works on the real Resume.pdf (parse_resume.py).

Real job-apply flow (built)

Applies to one real posting: LinkedIn job → hand-off to OpenAI's official job board → fill & submit.

File	Role
`parse_resume.py`	Vision model (gpt-4o-mini + PyMuPDF) → structured `profile` from `Resume.pdf`. Mirrors resume-builder's `parse_service.py`.
`browser_config.py`	Shared `BrowserProfile` (dedicated `chrome-profile/`, real Chrome, visible) so login persists into apply.
`login_once.py`	One-time: open LinkedIn, you log in yourself, session saved.
`apply.py`	Parses résumé → drives the LinkedIn→OpenAI-board apply → auto-submits → structured result.

source venv/bin/activate
python login_once.py                 # one time: log into LinkedIn in the window
python apply.py                      # parses résumé, applies, auto-submits
AUTO_SUBMIT=0 python apply.py        # fill everything but STOP before submit (human review)

Honesty rules kept: a liveness gate (dead posting → report, never fake a submit), résumé is the source of truth (no invented data), and auto-submit is one flag away from review-first.

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