From Maps to AI Agents – Part 4: Beyond the Code | A Tour of the AI Landscape & Tools

This is Part 4 of the tutorial series on building cutting-edge agentic geospatial applications!

In this video, we take a slight detour from direct coding to explore the broader Artificial Intelligence landscape that powers modern applications. Understanding these components is crucial for effectively building and integrating AI into our geospatial workflows.

We cover three key areas (based on the mind map in the video):

1) AI Models: Discussing the current ecosystem of Large Language Models (LLMs), including popular open-source options (like Meta Llama, Google Gemma, DeepSeek, Qwen) and leading commercial models (from OpenAI, Google Gemini, Anthropic, xAI).

2) AI & Humans: Exploring the relationship between humans and AI, the current state, the importance of staying competitive, and diving into how generative AI impacts our critical thinking, workflow (shifting from "doing" to "overseeing"), and the skills needed in the age of AI, referencing insights on confidence and evaluation.

3) AI Tools for Developers: Highlighting various tools that leverage AI to assist in the development process, from VSCode extensions and custom agents (like Cursor, Windsurf) to platforms like Google Firebase Studio, Vercel VO, and upcoming agents like Lovable and Devin.

While we don't add new features to our app in this video, understanding these models, tools, and human factors is essential context for the next stages of building sophisticated agentic systems.

You can find the main project starter kit here (though no code was added in this specific video):
https://github.com/GeoRetina/geo_agen...

📌 Coming Up Next:
In Part 5, we'll return to our application development and start integrating an AI agent using the Vercel AI SDK, putting the concepts from the series into practice! Make sure to subscribe for more updates!

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