▸ 5 articles
This article explains how an n8n workflow automates invoice processing by finding documents in Google Drive, extracting text with OCR, using AI to convert the data into structured fields, and appending the results to Google Sheets.
This article walks through the architecture and design decisions behind a Python-based scraping engine that unifies access to multiple open learning platforms, covering the interface abstractions, parallel fetching with proxy rotation, and practical patterns for consistent data collection across heterogeneous sources. Every open learning platform structures content differently. One exposes a JSON API, another requires navigating paginated HTML, and a third needs authentication before any document becomes reachable. Building a separate scraper per platform creates a maintenance burden and produces output that is hard to combine.
The average professional receives over a hundred emails daily, yet only a fraction demand immediate attention. Raw inboxes force a manual triage loop that costs focus and time. The Mail Prioritizer addresses this by building a two-tier classification pipeline that separates urgent messages from those that can wait, without requiring users to change their existing Gmail workflow.
This article walks through a Node.js REST API that turns a static character image into a realistic AI avatar video using HeyGen, generates custom speech with ElevenLabs, and automatically publishes the resulting video as an Instagram Reel. The key idea is to combine avatar generation, voice synthesis, media processing, and social media publishing into a single API-driven pipeline.
A practical look at building an AI assistant that turns documents stored in Google Drive into a searchable knowledge base using n8n, embeddings, Supabase, and an LLM.