I Replaced a $200/Month AI Training Data Pipeline with 50 Lines of Python

A data science team I worked with was paying $200/month for a research monitoring service. It sent them new papers in their field every morning. I looked at what it actually did: query arXiv, filter by keywords, format as email. That's it. I replaced it with 50 lines of Python. Here's how. The Problem ML teams need to track new research. Options: Semantic Scholar API — great but rate-limited Google Scholar — no official API, blocks scrapers Paid services ($100-500/mo) — Iris.ai, Connected Papers Pro, etc. But two APIs give you everything for free: arXiv (2.4M+ papers) and Crossref (140M+ papers). The 50-Line Solution import requests import xml.etree.ElementTree as ET from datetime import datetime , timedelta def search_arxiv ( query , max_results = 20 ): """ Search arXiv for recent papers. """ url = f ' http://export.arxiv.org/api/query?search_query=all: { query } &sortBy=submittedDate&sortOrder=descending&max_results= { max_results } ' response = requests . get ( url ) root = ET . fro