As the complexity of LLM-powered applications increases, understanding what’s happening under the hood becomes crucial—not just for debugging but for continuous optimization and ensuring system reliability. This is where LangSmith shines, providing developers with powerful tools to trace, visualize, and debug their AI workflows.

As large language models (LLMs) like OpenAI’s GPT-4 continue to evolve, so do the frameworks and techniques that make them easier to use and integrate into real-world applications. Whether you're building a chatbot, automating document analysis, or creating intelligent agents that can reason and use tools, understanding how to interact with LLMs is key. This post walks through a practical journey of using both the OpenAI API and LangChain — exploring everything from basic prompt engineering to building modular, structured, and even parallelized chains of functionality.

Vietnam’s banking sector is undergoing rapid digital transformation, creating new opportunities for core banking solution providers. Both established “legacy” core banking vendors and newer neo-core fintech players are competing to modernize the technology foundations of Vietnam’s traditional banks and emergent digital-only banks. This report provides a comprehensive market overview, analyzes marketing channels and customer acquisition strategies in this B2B fintech space, compares key local and foreign core banking vendors (with market share and positioning), and examines the decision drivers of Vietnamese banks. Insights are tailored for a neo-core platform planning its first entry into Southeast Asia, highlighting how to effectively market core banking solutions in Vietnam’s context.

Thailand’s banking sector is undergoing rapid digital transformation, driving growth in core banking software investments. Core banking software refers to the back-end systems that process daily banking transactions and manage accounts. Globally, this market is expanding at roughly 9–10% annually, reaching an expected $21.6 billion by 2030

In the wake of Donald Trump’s presidency, the landscape of globalization has undergone a profound transformation. While the machinery of international trade and cooperation still grinds on, its underlying values and leadership dynamics have fundamentally shifted. Regardless of one's view on Trump’s domestic policies, there is little doubt that his tenure has left a deep and lasting imprint on the global order.

When organizations talk about change, they often start with strategy decks, new structures, revised cultures, and updated systems. Leaders gather data, analyze trends, and craft well-reasoned transformation plans. But despite all that effort, real change rarely sticks. Why? Because it misses the mark on what matters most—people.

In April 2025, the global financial markets were rocked by a sharp and sudden downturn. The S&P 500 plunged nearly 5%—its worst single-day drop in years—while the Dow Jones tumbled over 1,600 points. The cause? A wave of new tariffs abruptly announced by Donald Trump’s administration, including a blanket 10% tax on all imports and steeper penalties on goods from key trading partners like China, Japan, and the European Union. Within hours, China retaliated with a 34% tariff on U.S. goods, igniting fears of a global trade war and deepening economic uncertainty.

There was a time in my life when everything felt like it was falling apart. I was heartbroken, overwhelmed, and lost. When my girlfriend broke up with me, it wasn’t just the end of a relationship—it was the collapse of everything I had built emotionally around that connection. Friends tried to help, but none truly understood the storm I was facing inside. I felt like I was drowning in silence.

The rapid evolution of large language models (LLMs) has brought unprecedented capabilities to artificial intelligence, but it has also introduced significant challenges in computational cost, scalability, and efficiency. The Mixture of Experts (MoE) architecture has emerged as a groundbreaking solution to these challenges, enabling LLMs to scale efficiently while maintaining high performance. This blog post explores the concept, workings, benefits, and challenges of MoE in LLMs.

Self-attention is a cornerstone of modern machine learning, particularly in the architecture of large language models (LLMs) like GPT, BERT, and other Transformer-based systems. Its ability to dynamically weigh the importance of different elements in an input sequence has revolutionized natural language processing (NLP) and other domains like computer vision and recommender systems. However, as LLMs scale to handle increasingly long sequences, newer innovations like sparse attention and ring attention have emerged to address computational challenges. This blog post explores the mechanics of self-attention, its benefits, and how sparse and ring attention are pushing the boundaries of efficiency and scalability.