"Senior Full Stack Entwickler:in 80-100 %" (ZHAW, via job-room.ch)
used the colon-gender notation instead of a slash, and Entwickler
wasn't on the German-word list either, so it slipped through.
Verified against jobs.csv history: catches 31 titles using ':in'
(Berater:in, Manager:in, Analyst:in, etc.), zero false positives.
"Software Engineer auf dem Weg zum Data / AI Engineer" (ti&m, via
job-room.ch) sailed through: none of the German-word list matched
'auf dem Weg zum', and the job-room description was too short to
clear the separate description-level DE/FR word-count threshold.
auf/zum/zur/vom/für/beim don't exist as English words, so they're
safe unambiguous markers — verified against the full 2858-row
jobs.csv history: only this one title now flags.
An audit of 89 basel-jobs.ch postings against their live descriptions
found 24 that mention German and 0 of them flagged. Three separate bugs,
each verified against real postings:
Coordination broke the intervening-language rule. In "Excellent
communication skills in both English and German" (Karakun) and "Fluency
in English and German" (J. Safra Sarasin), "English" sits between the
marker and the mention, so the marker was handed to English and a hard
requirement waved through. A marker now still binds when the other
language is coordinated with German (and/und/sowie/&/as well as), but
not through "and/or" or a bare "or" — "Very good English and/or German"
is satisfiable by English alone and must stay optional.
German-language markers never matched at all: `sehr\s+gute?` cannot match
"Sehr gutes Deutsch" because the trailing \b fails on the "s". Widened to
`sehr\s+gut\w*` plus fliessend\w*, Deutschkenntnisse and erforderlich.
This is why German-written postings passed the filter untouched.
Spec-sheet language lines ("Languages: German & English") have no
adjective to bind to, so the Languages:/Sprachen: label now counts as a
required marker. It still goes through the nearest-marker comparison, so
"Language: English and German (bonus)" correctly stays optional.
28 regression cases pass, covering the 15 "a plus" phrasings that must
not be dropped. On basel-jobs.ch this moves 88 viable to 85, removing
exactly the three hard requirements and keeping every optional one.
The filter is shared by every source, so rows already in jobs.csv were
written under the broken logic and are not retroactively corrected.
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.
jobbasel.ch's search route (/job/suche-{slug}) is server-rendered with
a window.__PRELOADED_STATE__ JSON blob, no headless browser needed.
englishjobsearch.ch works the same way via its ?format=markdown param.
Both swept with the same fixed keyword list job-room.ch already uses.
basel-jobs.ch was evaluated too but left out: it's DataDome-protected,
which blocks plain curl outright, and this project's fetchers are
intentionally browser-free.
While wiring these in, found that isEnglishDescription (now
isViableDescription) only ever ran the DE/FR word-count check against
inline descriptions, never requiresGerman() - so an English-written
posting demanding fluent German (e.g. PRODYNA's "you can communicate
fluently in both German and English") sailed straight through. That
gap existed in itjobs.ch and job-room.ch too, not just the two new
sources, so the fix applies to all four.
Broaden the search beyond engineer/developer titles and stop three
classes of bad result from ever reaching the CSV.
config.ts
- PLATFORM_KEYWORDS (architect, SRE, infra, security, MLOps),
RESEARCH_KEYWORDS (research/scientific software, HPC, data platform)
and FIELD_KEYWORDS (forward-deployed, customer/sales, founding).
Separate OR-strings, not folded into KEYWORDS: LinkedIn ranks a
focused query far better than one 50-term disjunction.
- sweep() generates one search per area per family. 7 searches -> 17.
fetch.ts
- BIO_DOMAIN_RE drops life-science roles (bioinformatics, biostats,
GMP/MSAT, genomics, pathology, drug discovery). Filters on the role,
never the employer: software jobs at Roche/Novartis/sciCORE stay.
- requiresGerman() drops English postings demanding German above B1.
Two rules, both from real false results: the nearest marker wins
("Infrastructure skills are a plus" sat 95 chars before "Good
knowledge of German"), and an intervening language name breaks the
binding ("Fluent in English; German and/or French advantageous").
- GERMAN_TITLE_RE catches "(German-speaking)" without a page fetch.
- isViableContent/isViableUrl run the DE/FR and German-requirement
checks on a single fetch; isEnglishContent/isEnglishUrl kept for
recheck.ts and temp scripts.
- SOFTWARE_TITLE_RE widened so the new families survive it.
index.ts
- both content-check paths use the combined check and report which
filter dropped a job.
- move temp scripts to scripts/, cv files to cv/, jobs.csv to results/
- update index.ts and recheck.ts CSV path to results/jobs.csv
- fix imports in scripts/ to use ../config.ts and ../fetch.ts
- update CLAUDE.md and README.md to reflect new structure
- add plans/ with LinkedIn post drafts
- update .gitignore: ignore results/, jobs/, cv binaries, .DS_Store
- isEnglishContent() fetches each job page and drops descriptions with ≥6 German or French indicator words; called for every new job in index.ts before writing to CSV
- New `bun recheck` command (recheck.ts) runs searches in raw mode, lists filtered-out jobs, and prints word-frequency suggestions for SOFTWARE_TITLE_RE
- Expanded KEYWORDS and SOFTWARE_TITLE_RE to cover ML/AI, system/applications engineer, director, informatics, and more title variants
- Added `raw` flag to fetchJobs() to bypass the software-role filter
- Add CLAUDE.md with project workflow docs; add CV files and city temp scripts
- Add lead, technical, consultant, solutions, product manager, IT manager
to the software-role allowlist so relevant non-"software" titles pass
- Add JUNIOR_TITLE_RE to drop junior, intern, trainee, apprentice titles
- Paginated HTML scraper (up to 6 pages, 400ms delay between requests)
- Card-boundary-safe parsing to prevent field bleed across cards
- Language filter: drops German (m/w/d, umlauts) and French (H/F, accents) titles
- Software-role filter: requires title to contain a relevant tech term
- Global deduplication by job ID across runs
- 30-day recency filter via LinkedIn f_TPR parameter
- CSV output: date_found, posted_at, search, title, company, link
rodado