Based on real-world experience, this article explores how AI-powered Agentic DBAs are redefining database management through autonomy and intelligence.
This guide explains how N-gram language models work, how they estimate probabilities, handle unknown words, and evaluate performance using perplexity and other methods.
Domain-Specific Languages (DSLs) offer targeted solutions for rule-heavy domains. With GenAI, creating and managing DSLs becomes faster, easier, and more collaborative.
Langflow lets you visually build, test, and deploy LLM-powered apps, no backend coding required. Ideal for rapid GenAI prototyping and seamless integration.
The growing importance of Gen AI in software engineering isn’t just a buzz—it’s a shift that’s rewriting how we work and innovate in software engineering.
I created a team of specialist agents to handle different parts of a complex task. It's basically microservices for AI, making our app smarter, easier to update and more.
Revolutionizing real-time application development by combining high-performance, secure, and platform-independent computing with local AI-driven decision-making
DSS systems are designed around the logic of human decision-making as the ultimate consumer. However, in Agentic AI era, the final "consumer" is likelier to be an agent.
Most software engineers aren't fully utilizing AI tools beyond basic text writing. AI agents can significantly aid engineers in coding and career growth path.
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