If you follow the business world, you’ve heard the drumbeat. Scarcely a week goes by without a major tech company—be it Google, Amazon, Meta, or Microsoft—announcing a new round of “restructuring.” Thousands of jobs are cut, and amidst the corporate-speak about “streamlining” and “finding efficiencies,” a new two-letter buzzword has taken center stage: AI. The official narrative is compelling. We’re told that new generative AI tools are so powerful, so efficient, that the company can suddenly achieve more with fewer people. Roles in customer service, HR, recruiting, and even entry-level coding are being automated. The layoffs, therefore, are not a failure of management, but an inevitable, futuristic step in technological progress. ...

Geoffrey Hinton is the Nobel Prize-winning academic known as the “Godfather of AI” for his foundational work on neural networks. He spent decades at Google, building the very technology that now powers our world. Then, he quit. He left his high-paying role so he could speak freely about the dangers of the technology he helped create. His warnings have ranged from existential risk to the “end of humanity.” But in a recent, stunningly blunt interview, Hinton swapped his philosopher’s hat for a CFO’s visor. He didn’t talk about paperclip-maximizing terminators; he talked about simple, cold, hard capitalism. ...

For the past few years, Retrieval-Augmented Generation (RAG) has been the cornerstone of scaling Large Language Models (LLMs) to massive knowledge bases. Since early LLMs suffered from limited input length—with models like GPT-4 handling only about 8,192 tokens (roughly 12 pages)—RAG provided an elegant, if complex, workaround: retrieve the most relevant fragments and feed those to the LLM. However, the rapid evolution of LLMs and their specialized ranking capabilities, combined with exploding context windows, suggests that the traditional RAG architecture we built and optimized is fundamentally on the decline. ...

In September 2024, Sam Altman published a blog post discussing “The Intelligence Age” and stated, “It is possible that we will have superintelligence in a few thousand days (!); it may take longer, but I’m confident we’ll get there”. In the whirlwind of AI advancements, it’s easy to believe that Artificial General Intelligence (AGI) is just around the corner. Every week, a new model is released that shatters previous benchmarks, writes better code, or creates more realistic images. The hype cycle suggests we are on the precipice of a new dawn. Yet, one of the most respected minds in the field, Andrej Karpathy, recently poured a dose of realism on the fire, stating that AGI is still “a decade away.” ...

The world of AI is buzzing with a new paradigm: agentic AI. Instead of simply responding to a single prompt, these AI systems can reason, plan, use tools, and even critique their own work to accomplish complex, multi-step tasks. They represent a significant leap from the non-agentic models we’ve grown accustomed to. As this technology matures, building effective and reliable agents has become a critical skill. AI luminary Andrew Ng, through his Coursera courses published one week ago and YouTube talks, has laid out a clear, practical framework for developing these intelligent systems. This playbook distills his key principles into actionable best practices for anyone looking to build the next generation of AI. ...