Small language models (SLMs) offer 90% of the value of large models at a fraction of the cost. Devs can maximize AI ROI by training SLMs on domain-specific data.
In this article, we will explore the value of AI agents, introduce popular agentic AI platforms, and walk through a hands-on tutorial for building a simple AI agent.
Junior developers are shipping features faster with Cursor and GitHub Copilot, while senior engineers question if AI-assisted code is maintainable at scale.
Software keeps growing in complexity while losing touch with business goals. Domain-driven design brings clarity, making systems scalable, meaningful, and built to last.
In modern cloud-native systems, services often run across multiple pods or nodes for scalability and high availability, introducing challenges in data consistency.
AI-first backends let LLMs drive dynamic, personalized API logic in real time replacing static rules. Validation and guardrails keep them reliable and secure.
Choosing the right API architecture depends on your application’s specific needs like it's enterprise integration, user interaction or deep protocol-level communication.
The era of AI autonomously doing the work is here. Agentic AI systems can plan multi-step workflows, make decisions, use tools, and coordinate with other agents.
Learn how developers can use data agents for natural-language querying, Copilot Studio for AI interactions, and real-time intelligence for streaming analytics.
Optimize Spark jobs by tuning configurations, writing efficient code (Data Frames, broadcast joins), using optimized storage, and monitoring the Spark UI and logs.
Synthetic data lets quants stress-test equity strategies beyond noisy markets, preserving volatility, and building resilience before risking real capital.
In this article, I have demonstrated how Iceberg Data can be accessed through the Iceberg REST Catalog from Data Mesh with a simple Python application.