The BIG AI Use Cases We Use Right Now! (Ep. 450)

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Today’s "Be About It" show focuses entirely on demos from the hosts. Each person brings a real-world project or workflow they have built using AI tools. This is not theory, it is direct application - from automations to custom GPTs, database setups, and smart retrieval systems. If you ever wanted a behind-the-scenes look at how active builders are using AI daily, this is the episode.

Key Points Discussed

Brian showed a new method for building advanced custom GPTs using a “router file” architecture. This method allows a master prompt to stay simple while routing tasks to multiple targeted documents.

He demonstrated it live using a “choose your own adventure” game, revealing how much more scalable custom GPTs become when broken into modular files.

Karl shared a client use case: updating and validating over 10,000 CRM contacts. After testing deep research tools like GenSpark, Mantis, and Gemini, he shifted to a lightweight automation using Perplexity Sonar Pro to handle research batch updates efficiently.

Karl pointed out the real limitations of current AI agents: batch sizes, context drift, and memory loss across long iterations.

Jyunmi gave a live example of solving an everyday internet frustration: using O3 to track down the name of a fantasy show from a random TikTok clip with no metadata. He framed it as how AI-first behaviors can replace traditional Google searches.

Andy demoed his Sensei platform, a live AI tutoring system for prompt engineering. Built in Lovable.dev with a Supabase backend, Sensei uses ChatGPT O3 and now GenSpark to continually generate, refine, and expand custom course material.

Beth walked through how she used Gemini, Claude, and ChatGPT to design and build a Python app for automatic transcript correction. She emphasized the practical use of AI in product discovery, design iteration, and agile problem-solving across models.

Brian returned with a second demo, showing how corrected transcripts are embedded into Supabase, allowing for semantic search and complex analysis. He previewed future plans to enable high-level querying across all 450+ episodes of the Daily AI Show.

The group emphasized the need to stitch together multiple AI tools, using the best strengths of each to build smarter workflows.

Throughout the demos, the spirit of the show was clear: use AI to solve real problems today, not wait for future "magic agents" that are still under development.

#BeAboutIt #AIworkflows #CustomGPT #Automation #GenSpark #DeepResearch #SemanticSearch #DailyAIShow #VectorDatabases #PromptEngineering #Supabase #AgenticWorkflows

Timestamps & Topics

00:00:00 🚀 Intro: What is the “Be About It” show?

00:01:15 📜 Brian explains two demos: GPT router method and Supabase ingestion

00:05:43 🧩 Brian shows how the router file system improves custom GPTs

00:11:17 🔎 Karl demos CRM contact cleanup with deep research and automation

00:18:52 🤔 Challenges with batching, memory, and agent tasking

00:25:54 🧠 Jyunmi uses O3 to solve a real-world “what show was that” mystery

00:32:50 📺 ChatGPT vs Google for daily search behaviors

00:37:52 🧑‍🏫 Andy demos Sensei, a dynamic AI tutor platform for prompting

00:43:47 ⚡ GenSpark used to expand Sensei into new domains

00:47:08 🛠️ Beth shows how she used Gemini, Claude, and ChatGPT to create a transcript correction app

00:52:55 🔥 Beth walks through PRD generation, code builds, and rapid iteration

01:02:44 🧠 Brian returns: Transcript ingestion into Supabase and why embeddings matter

01:07:11 🗃️ How vector databases allow complex semantic search across shows

01:13:22 🎯 Future use cases: clip search, quote extraction, performance tracking

01:14:38 🌴 Wrap-up and reflections on building real-world AI systems

The Daily AI Show Co-Hosts: Jyunmi Hatcher, Andy Halliday, Beth Lyons, Brian Maucere, and Karl Yeh