187 lines
12 KiB
Markdown
187 lines
12 KiB
Markdown
# linkme
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A personal job-hunting pipeline for the Swiss market. It scrapes LinkedIn (plus a handful of Swiss boards), filters out everything that doesn't fit the profile in `cv/polo.md`, appends what survives to a CSV, and pushes the good ones to Telegram throughout the day.
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One thing runs on its own once installed:
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| automation | when | what it does |
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| **daily digest** | 09:00 / 12:00 / 15:00 / 17:00 / 20:00 | fetch every search + source, push new profile-matching jobs to Telegram |
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sciCORE used to have its own separate 08:47 watcher; it's now just another source swept inside `bun fetch`/`bun daily` (see Sources below).
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## Quick start
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```bash
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bun fetch # scrape all searches, append new jobs to results/jobs.csv
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bun daily # fetch, then Telegram the new jobs that match the profile
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bun recheck # show what the filters are currently dropping + keyword suggestions
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bun run mint-datadome # refresh the basel-jobs.ch cookie (opens a Chrome window)
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```
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`bun fetch` needs no setup — reruns are safe, already-seen jobs are skipped by ID, and every search/source runs concurrently (a full run is well under 2 minutes). Telegram needs the two-line setup below.
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## Telegram setup
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```bash
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# 1. Message @BotFather → /newbot → copy the token into .env
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echo 'TELEGRAM_BOT=<token>' >> .env
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# 2. Send your bot any message ("hi"), then discover your chat ID
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bun run telegram.ts --setup # prints TELEGRAM_CHAT_ID=... — paste it into .env
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```
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Then `bun daily --dry-run` prints the digest instead of sending it, and `bun daily --resend` re-sends today's jobs from the CSV without re-fetching.
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`.env` also holds `LI_AT`, a LinkedIn session cookie used only for fetching individual *profile* pages. Never send it with a job search — with the cookie LinkedIn serves a React SPA instead of server-rendered HTML and the card parser gets nothing. `.env` is gitignored.
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## Structure
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```
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config.ts # keyword families, search areas, the sweep() generator
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fetch.ts # LinkedIn scraper, HTML parser, all title/content filters
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fetch-other.ts # the ten non-LinkedIn sources (see Sources below)
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index.ts # orchestrator: run searches → dedupe → content-check → CSV
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recheck.ts # raw-mode fetch: lists what the filters dropped
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daily.ts # digest: fetch, match against the profile, send to Telegram
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profile.ts # title-level scoring — decides what's worth a notification
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telegram.ts # Telegram delivery + --setup chat-ID discovery
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scripts/ # one-off searches (*-temp.ts); scicore-watch.sh kept for history, retired
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cv/polo.md # the profile everything is ranked against
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results/jobs.csv # output, one row per job
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results/sent.txt # links already pushed — never sent twice
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```
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## Sources
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LinkedIn is swept by `config.ts`'s search families. Everything else lives in `fetch-other.ts` and runs concurrently with it — a full `bun fetch` stays under two minutes.
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| source | scope | notes |
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| **LinkedIn** | 23 searches | keyword families × areas, generated by `sweep()` |
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| **job-room.ch** | Switzerland | the federal employment service board (RAV); by far the largest feed |
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| **basel-jobs.ch** | Basel region | behind DataDome — needs a cookie, see below |
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| **jobbasel.ch** | Basel region | React SPA, but `/job/suche-{slug}` is server-rendered with a `window.__PRELOADED_STATE__` JSON blob |
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| **englishjobsearch.ch** | BS + BL cantons | any page accepts `?format=markdown` and returns clean server-rendered markdown |
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| **itjobs.ch** | Switzerland | small (~210 jobs); locking it to Basel returned only ~9, so it's swept nationally |
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| **datacareer.ch** | Basel | 8 jobs on one page, no pagination |
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| **stellen-basel.ch** | Basel region | WordPress REST API |
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| **Basel intl orgs** | Basel | BIS (via the UNjobs mirror), Swiss TPH, Basel Institute on Governance |
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| **scicore.unibas.ch** | Basel | sciCORE's open-positions announcement list; used to be its own separate 08:47 watcher, now just another source |
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| **swisscom.com** | Switzerland | Swisscom's own Workday career site, pulled via its public CXS JSON API; small board and mostly German-language, so few survive the filters |
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| **more-jobs.ch** | Switzerland | Server-rendered aggregator; keyword sweep of `/recherche?q=`, then one detail fetch per title-filtered candidate (real date + DE/FR check). Reposts of jobs other sources already carry are dropped by normalized title+company |
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| **startup.jobs** | Switzerland | Behind Cloudflare: a headless Chrome reads the page's short-lived Algolia search key, then the Algolia index is swept over a Swiss bounding box (split recursively past Algolia's 500-hit cap). Detail pages are fetched in-browser for the DE/FR check; reposts other sources carry are dropped by normalized title+company |
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Two of these were dead until recently and are worth knowing about:
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**datacareer.ch** spent months returning 403 on every request. The cause was our own User-Agent — its nginx inverts the usual bot rule and blocks browser-like UAs while serving honest ones (no UA, `curl`, Googlebot). It gets `PLAIN_HEADERS` instead of the shared spoofed-Chrome ones.
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**basel-jobs.ch** sits behind DataDome. Plain `fetch` gets 403 on any UA, headless Chrome over CDP gets 403, and Playwright's own `launch()` gets 403 even headful — but a real Chrome launched normally with `--remote-debugging-port` and attached to afterwards gets through. Clearance then lives entirely in a `datadome` cookie that replays fine over plain `fetch`, so a browser is only ever needed to *mint*, never to scrape:
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```bash
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bun run mint-datadome # opens a real Chrome window for ~10s, caches the cookie
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```
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The cache is `results/.datadome.json` (gitignored). Because minting needs a visible window, a launchd run can't do it — if the cookie goes stale the source logs `unavailable` and yields nothing while every other source continues, so the digest never fails over it. Set `LINKME_NO_BROWSER=1` to suppress minting entirely.
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It's scraped in three passes, all of which cover different sets: the `informatik-jobs` category is a curated taxonomy rather than the whole board, and the two search modes query different indexes — `?terms=X&websearch=1` finds role words and postings filed outside IT, while the bare `?terms=X` is the only mode that matches stack terms (`react`, `frontend` and `backend` return literally zero under `websearch=1`).
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## How a job gets through
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Two stages. Cheap title filters first, then one page fetch per survivor.
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**Title filters** (inside `fetchJobs`, no network cost):
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1. **Switzerland guard** — the card's location must say Switzerland. LinkedIn ignores `geoId` on unauthenticated requests and will happily return global results.
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2. `NON_ENGLISH_RE` — German gender markers (`m/w/d`, `/in`), umlauts, French `H/F` and accents.
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3. `JUNIOR_TITLE_RE` — junior, intern, trainee, werkstudent, entry-level.
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4. `JAVA_TITLE_RE` — Java-titled roles.
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5. `BIO_DOMAIN_RE` — bioinformatics, biostatistics, genomics, GMP/MSAT, bench-scientist titles. Filters on the **role, not the employer**: software and data jobs at Roche, Novartis or Swiss TPH stay in.
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6. `SOFTWARE_TITLE_RE` — requires a real tech/role term. Skipped in `raw` mode, which is what `bun recheck` uses.
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7. `GERMAN_TITLE_RE` — "(German-speaking)", "deutschsprachig".
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**Content check**: `isViableContent`/`isViableUrl` (LinkedIn, one page fetch + 350 ms per job in `index.ts`) and `isViableDescription` (`fetch-other.ts` sources that already carry the description inline, no extra fetch) all run the same two checks — DE/FR indicator words dropped at ≥ 6, then `requiresGerman()` drops English postings demanding German above B1.
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That last one is deliberately more than a keyword match, because the optional/required boundary is where every bug in it has been. Rafael is German B1, so *"a plus"* / *"advantageous"* / *"B1"* must survive and *"fluent"* / *"C1"* / *"sehr gutes Deutsch"* must not.
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For each mention of German it takes the *nearest* qualifying marker, and ignores a marker when another language name sits between it and the mention — *"Fluent in English; German and/or French advantageous"* qualifies English, not German. But that rule has an exception: when the other language is **coordinated** with German the marker governs both, so *"Excellent communication skills in both English and German"* is correctly a hard requirement. Coordination means `and`/`und`/`sowie`/`&` — explicitly not `and/or`, since *"Very good English and/or German"* is satisfiable by English alone.
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Two more cases it handles: German-language phrasing (*"Sehr gutes Deutsch in Wort und Schrift"*, which for a long time matched nothing at all), and spec-sheet lines like *"Languages: German & English"* that have no adjective to bind to — there the `Languages:` label itself counts, though still only if no optional marker sits closer.
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**Profile match** (`profile.ts`, digest only) scores the title against the CV's stack and seniority signals, needing 4+ points to notify. Hard rejects encode standing decisions: Microsoft stack, Java/C++/SAP, life-science domains, non-software functions, and the blocked companies Jobgether (an aggregator, not an employer) and ALLPS (postings require German despite passing the English filter).
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Jobs are deduped globally by ID against `results/jobs.csv` on startup, so nothing is ever fetched or sent twice.
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## Output
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`results/jobs.csv` — `date_found`, `posted_at`, `search`, `title`, `company`, `link`, `source`, `location`. `search` is the query or source that found the job; `location` is the posting's own location, which is what the digest groups on (a "Bern" sweep can surface a job actually based in Lausanne). Open it in any spreadsheet and delete rows as you apply or dismiss.
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## Configuring searches
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`config.ts` holds four keyword families as separate OR-strings — `KEYWORDS`, `PLATFORM_KEYWORDS`, `RESEARCH_KEYWORDS`, `FIELD_KEYWORDS`. They're kept apart on purpose: LinkedIn ranks a focused query far better than one 50-term disjunction. `sweep()` generates one search per area per family (Research skips Bern; Platform and Field add a Switzerland-wide remote pass).
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```ts
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export const searches: SearchConfig[] = [
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{
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label: "Basel (city)",
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keywords: KEYWORDS,
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geoId: "103383283",
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workType: ["onsite", "hybrid"],
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timePosted: "month",
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},
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];
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```
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`SearchConfig` also takes `jobType`, `seniority`, and `location` (use `"City, Switzerland"` when the geoId is unknown — LinkedIn resolves by name). A `url:` field overrides all filter params, so you can paste a LinkedIn search URL directly.
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Known Swiss geoIds:
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| area | geoId |
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| Basel (city) | `103383283` |
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| Basel Metropolitan Area | `90009885` |
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| Basel-Country | `107117530` |
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| Zürich (city) | `102436504` |
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| Zürich Metropolitan Area | `107814425` |
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| Zürich Area | `90009888` |
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## One-off searches
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Don't edit `config.ts` for a single search — write a throwaway in `scripts/`:
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```ts
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import { KEYWORDS } from "../config.ts";
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import { fetchJobs } from "../fetch.ts";
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const jobs = await fetchJobs({
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label: "Luzern", keywords: KEYWORDS,
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location: "Luzern, Switzerland",
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workType: ["onsite", "hybrid"], timePosted: "month",
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});
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for (const j of jobs) console.log(j.title, j.company, j.link);
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```
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```bash
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bun run scripts/luzern-temp.ts
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```
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`*-temp.ts` scripts are disposable — delete them when the search is done.
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## Scheduling
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The digest runs as a launchd agent, not cron — this machine has no cron at all.
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```bash
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launchctl list | grep linkme # is it loaded?
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launchctl unload ~/Library/LaunchAgents/com.rafapolo.linkme-daily.plist # stop it
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```
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If the Mac is asleep at the scheduled time, launchd runs the job once it wakes rather than skipping the day.
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Logs: `results/daily.log`.
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## recheck
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`bun recheck` re-runs every search with `SOFTWARE_TITLE_RE` bypassed and lists each job the filters would drop, plus a word-frequency table of those titles. Use it to spot false negatives; ignore the generic suggestions (`engineer`, `development`, `senior`) and look for real role terms worth adding to the regex.
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