Here is an assessment of your legacy projects, how out-of-date they are, and what would be required to bring them into the modern AI era.

Most of these projects rely on libraries and APIs from 2020-2021. In the world of AI and cloud development, this is a significant gap.

• Frontend/Backend Frameworks: React, Flutter, and Node.js versions are mostly End-of-Life (EOL) or several major versions behind, requiring significant refactoring (e.g., Flutter Null Safety, Firebase v9+ modular SDK).
• AI Models: Many projects use specialized, task-specific models (AutoML, Universal Sentence Encoder, COCO-SSD). Today, Multimodal LLMs (like Gemini 1.5 or GPT-4o) can often replace these entire pipelines with a single API call.

Remember when we used to marvel at LLMs that could handle a few thousand tokens of context? Well, hold onto your hats, folks, because Google just dropped Gemini 1.5, a multimodal LLM with a 1 million token context window. That's not just a bigger window, it's a whole new way of looking at how we interact with LLMs.

Sure, the ability to dump massive documents, movies, and audio into a single prompt is cool. Imagine asking an LLM to compare two movies after consuming both in their entirety, or to summarize a dense legal document. But the real magic of Gemini 1.5 isn't just the size of the window, it's the near-perfect recall and improved reasoning that comes with it.

Jeff Dean himself pointed out the significance of this breakthrough on Twitter, referencing DeepMind's "needle in a haystack" tests. These tests showed that Gemini 1.5 can not only access millions of tokens, b