Stay Ahead in AI with Product Engineering and AI Native Tools
summary
Framework that turns a monthly market‑research workflow into a modular AI pipeline: scrape headlines (News API or crawlers), clean text, produce embeddings via Hugging Face embeddings, reduce to 2D, cluster, and surface cluster summaries (LLM or centroid). Proof‑of‑concept web app is generated in under an hour with Lovable using fake data, then AI code (Colab) plugs into the backend. Measured gains include 60% less time for the bottleneck stages, plus stakeholder thumbs‑up and iterative MVP improvements.
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chapters (10)
- 0:00Welcome
- 7:13AI native tools and product engineering
- 12:11Best Travels case study
- 21:51Decision flow modeling
- 30:27Bottlenecks and impact measurement
- 41:41Proof of concept with Lovable
- 55:34Minimum viable product
- 1:09:22Modular AI architecture
- 1:14:18MVP evaluation and iteration
- 1:21:07Q&A
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