Gemini

Most real-time financial systems rely on deep stacks of infrastructure, from custom APIs to cloud VMs and high-frequency data ingestion pipelines. But what if a single developer could deploy a daily-updating, AI-powered stock analysis engine without a single server? That’s exactly what Taniv Ashraf set out to do — and accomplished — in his recent case study on a fully serverless architecture using Google Gemini, GitHub Actions, and static web hosting. The result is an elegantly simple yet conceptually powerful demonstration of how qualitative LLM analysis and automation tools can replace entire categories of financial tooling — if wielded strategically. ...

Gemini 2.5 and the Rise of the 2 Million Token Era In March 2025, Google introduced Gemini 2.5 Pro with a 2 million token context window, marking a major milestone in the capabilities of language models. While this remains an experimental and high-cost frontier, it opens the door to new possibilities. To put this in perspective (approximate values, depending on tokenizer): 📖 The entire King James Bible: ~785,000 tokens 🎭 All of Shakespeare’s plays: ~900,000 tokens 📚 A full college textbook: ~500,000–800,000 tokens This means Gemini 2.5 could, in theory, process multiple entire books or large document repositories in one go—though with substantial compute and memory costs that make practical deployment currently limited. ...