perf: run fetch sources concurrently instead of sequentially

Every LinkedIn search and every fetch-other.ts source hits a different
host, so there was no real reason for the old for-loop to wait on each
one in turn - that's what made a full run take 30-40+ minutes. Runs
them concurrently now (bounded: 3 at a time within each group, both
groups started together), which brought a real run down to under 90
seconds.

existingLinks.add() now happens synchronously right after the .has()
check and before any await, which is what makes concurrent workers
safe against double-claiming the same link.

Concurrency surfaced a real regression: LinkedIn started returning
occasional 429s under parallel load, and fetchJobs treated that the
same as end-of-results, silently truncating the rest of that search.
Added retry-with-backoff (3 attempts, 1s/2s/4s) so a rate limit no
longer costs real listings.

CLAUDE.md

@@ -54,6 +54,7 @@ plans/             # LinkedIn post drafts
     - These boards use UN-flavoured titles ("Information Management Officer", "ICT Specialist") that `SOFTWARE_TITLE_RE` alone drops, so `INTL_TECH_RE` widens the net **for these sources only**. Boards this small can afford the noise; missing a rare opening can't.
   - **itjobs.ch** is swept Switzerland-wide (no `location` param) — the board is small, ~210 jobs over 7 pages, and locking it to Basel returned only ~9. It 404s one page past the end instead of serving an empty list. Each item embeds its full `description`, so the DE/FR check runs in-process via `isEnglishDescription` rather than costing a page fetch per job. Use `job_location.name` for location — it's the English form; the bare `location` field is German.
 - **`index.ts`** — orchestrator: loads existing IDs from `results/jobs.csv`, calls `fetchJobs`, calls `isEnglishContent` for each new job, appends survivors to CSV. Writes both `search` (the query/source that found the job) and `location` (the posting's own location, as parsed off its card by `fetch.ts`/`fetch-other.ts` — no extra page fetch) to the CSV. Migrates old CSVs on startup: adds `source` if missing, then `location` (backfilled `""` for rows written before the column existed).
+  - Every LinkedIn search and every `fetch-other.ts` source hits a different host, so they run **concurrently** (bounded: 3 at a time within each group, both groups started together) instead of one after another — cut a full `bun fetch` from ~16-20 min to well under 2 min. `existingLinks.add()` happens synchronously right after the `.has()` check and before any `await`, which is what makes concurrent workers safe against double-claiming the same link. Concurrency surfaced a real gap: LinkedIn started returning occasional 429s under parallel load, which `fetchJobs` used to treat as end-of-results (silently truncating that search) — `fetchJobs` now retries a 429 up to 3× with backoff (1s/2s/4s) before giving up.
 - **`recheck.ts`** — calls `fetchJobs(..., { raw: true })` to bypass `SOFTWARE_TITLE_RE`, then lists every new job that *would* be filtered out and prints word-frequency suggestions.
 - **`daily.ts`** — the notification layer on top of `index.ts`. Snapshots the CSV's links, shells out to `bun fetch`, then diffs to find what's new. Survivors are filtered through `matchesProfile` and grouped by area (Basel / Remote / Zürich / Bern) so the message scans on a phone. `results/sent.txt` is written **only after a successful send**, so a Telegram failure retries next run instead of silently losing jobs. A non-zero exit from `bun fetch` sends a failure notice rather than failing quietly.
   - The area grouping matches against the CSV's `location` column (the posting's real location) first, falling back to `search` (the query/source label) only when `location` is empty — i.e. for rows written before the `location` column existed. This fixed two real mislabels: a LinkedIn "Bern" sweep can surface a job actually based in Lausanne (search label ≠ real location), and `fetch-other.ts` sources (job-room.ch, itjobs.ch, stellen-basel.ch, datacareer.ch) write the source name into `search` instead of a city, which used to dump their jobs into "⚪️ Other" regardless of where they actually were.