I built an AI that finds and scores my next job

说明与用途

一个自动收集、过滤和评分职位发布的管道，将噪声减少到每周12个相关机会，提高了职业转换者的求职效率。

问题

Career changers lack tailored job discovery tools that filter for specific constraints and validate postings before application.

结果

The pipeline processes ~1,500 postings yearly, parking 1,539 and presenting 12 high‑confidence, vetted job opportunities weekly, enabling focused application efforts.

工作流

• Collect job postings from 16 APIs, RSS feeds, and Playwright scrapes
• Deduplicate results in a SQLite store
• Apply deterministic hard filters (title, location, recruiter spam, skill experience)
• Score survivors with an LLM against personal constraints
• Verify posting bodies to eliminate false positives
• Manually review and submit applications

证据

Every week my tooling sweeps around twenty job sources and pulls in a few hundred postings.

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Stage 2, hard elimination. Before any model sees anything, cheap deterministic filters kill the obviously wrong postings.

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Stage 3, LLM scoring. The survivors get read by a language model that scores fit against my honest profile.

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Stage 4, verification. Nothing enters the apply queue until the real posting body has been fetched and read.

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I kept waiting for a project impressive enough to prove I could engineer software. At some point I noticed the most honest proof was already running: tested Python, real scrapers, a dedupe store, CI on the ETL framework, and a system in daily use solving a real problem with real stakes.

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