Wikipedia to LinkedIn posts with OpenAI and Ideogram
Turn any Wikipedia topic into a polished LinkedIn post using OpenAI and Ideogram. Stay consistent,…
Automate academic and technical research from search to summaries. Collect sources, track updates, extract key findings, and deliver briefings to Slack, email, or Sheets—fast.
A workflow watches your sources (Google Search, RSS feeds, databases, or saved queries) and pulls in new links on a schedule. It then cleans the list—removes duplicates, tags topics, and stores everything in a research log like Google Sheets or Notion. If you upload PDFs to Google Drive, the workflow can extract text and ask an AI model to summarize methods, findings, and limitations. Finally, it sends a brief to your inbox or Slack, so you act on it quickly.
Usually, no. Most Flowpast workflows are plug-and-play: connect accounts, set keywords, pick where outputs go, and you’re done.
Honestly, it often cuts the busywork in half. The biggest savings come from automating repetitive steps: searching, copying links, logging citations, and rewriting notes into a shareable summary. If you’re doing weekly scans, you can get back about 2 hours per week quickly, and more once you standardize your templates. You still decide what matters, but you spend far less time gathering and formatting information.
You’ll need an n8n workspace (cloud or self-hosted), plus access to the tools you want to connect—commonly Google Drive/Sheets and an AI provider like OpenAI. Have a short list of keywords, target sites or feeds, and a place to store outputs (Notion or a shared drive). If you want PDF handling, make sure your documents are readable (not scanned images) or add an OCR step. Start with one pipeline, then expand to alerts and weekly digests.
Turn any Wikipedia topic into a polished LinkedIn post using OpenAI and Ideogram. Stay consistent,…
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