I am excited to share the results of my 3-month journey here. Transitioning from a career as a PhD in Archaeology to building AI systems has been a profound experience. Today, I am presenting my fully functional 'International Content Scout'. What you see on the screen: This is a 'Man-in-the-Loop' architecture built in n8n. - The Creative Core: I send a raw thought via Telegram. The AI Agent (GPT-4o) instantly generates expert content, video scripts, and engagement posts in English, Polish, and Spanish. Simultaneously, DALL-E 3 creates a unique visual for the topic. - The Control Center: The system doesn't post anything without my word. I receive everything in Telegram and use Interactive Buttons to Approve or Decline. - The Execution: Once approved, the content is queued for direct publication via Facebook Graph API and LinkedIn API. About the Development: This project was co-architected with Gemini 1.5 Pro, an advanced AI model from Google. During weeks of collaboration, Gemini acted as a mentor, technical consultant, and debug partner, bridging the gap between scientific methodology and modern AI automation.

did Ryan make a video about it or not, will you make one ? @Ryan Nolan

I'm always very skeptical of tools that come out that promise "ai employees". Most of them are just wrappers around OpenClaw. Its a nice cash grab, but probably isn't a long term play. I have done a deep dive on PaperClip. I really like the framework. it being open source makes it very easy to customize to specific use cases or spend limits. They are already thinking ahead of OpenClaw with the goal state definition of the agents. I recommend taking a look: https://github.com/paperclipai/paperclip

Need someone US based who is an expert in ManyChat. If you have additional coding skills that will also be beneficial. I will hire for our first project immediately. If the client likes the work it will be much larger.

If you are working with large namespaces and have lots of upserts I highly recommend checking out: https://turbopuffer.com/ Much faster and cheaper than standard Vector DBs. Turbopuffer is a serverless vector and full-text search engine built on top of object storage (like S3). It's designed to be fast, roughly 10x cheaper than traditional vector databases, and highly scalable. It's used in production by companies like Cursor, Anthropic, Notion, Linear, Atlassian, Ramp, and Grammarly — handling over 2.5 trillion documents, 10M+ writes/s, and 10k+ queries/s. Turbopuffer supports three search modes: vector search, full-text search (BM25), and hybrid search combining both. For vector search, it uses a centroid-based approximate nearest neighbor (ANN) index based on a system called SPFresh. On a cold query, the centroid index is downloaded from object storage first, then the closest centroids identify which clusters of vectors to fetch — only the relevant clusters are pulled, not the entire dataset. This keeps cold queries feasible even on very large datasets. For full-text search, it uses an inverted index with BM25 scoring. Both index types also support metadata filtering. The system is focused on first-stage retrieval — efficiently narrowing millions of documents down to a manageable set of candidates, which can then be re-ranked or processed further downstream.