Generative AI Agents: How they Work and How to Build One
summary
Argues that purpose-built agents can autonomously do real tasks and demonstrates a LinkedIn ghost‑writing agent that scrapes news via Brave News API, embeds past tweets into a vector store using PGVector and OpenAI embeddings, runs on a Go web server, and composes posts reflecting prior opinions. Notes practical limits: GPT‑4 Turbo 128k/4k context vs output, token caps, hallucination risks, and data‑leak concerns. Prototype generates plausible posts in demo but is not auto‑posting to live accounts.
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chapters (17)
- 0:00Introduction and demo preview
- 1:51ChatGPT vs AI agents
- 3:39What are AI agents
- 4:25Autonomy and task focus
- 6:14Limitations of language models
- 10:52Agent architecture overview
- 11:34External resource adapters
- 13:21Long-term memory systems
- 14:45Vector databases explained
- 20:50Dynamic prompt creation
- 21:34User experience design
- 22:36Choosing the right model
- 24:43Data safety considerations
- 28:53Building a simple agent
- 31:21LinkedIn ghost writer demo
- 34:13Agent effectiveness and SEO
- 36:41Summary and key takeaways
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