·Tech
Real-World Agentic AI: Case Studies from the Field
Hype vs Reality. We analyze three real-world deployments of Agentic AI in Logistics, Healthcare, and Retail, breaking down the architecture and the business results.
Latest Insights
91 posts
·AI Agents and LLMs
Responsible AI for Builders, Not Policymakers
Stop talking about ethics and start building with safety. Learn the practical engineering guardrails, audit trails, and logging strategies for responsible AI.
·AI Agents and LLMs
From APIs to Platforms: The New AI Stack
The AI tech stack is moving beyond the OpenAI API. Explore the layers of the modern AI platform: vector stores, orchestration, and specialized deployment.
·AI Agents and LLMs
Why Most AI Proofs of Concept Fail in Production
Moving from a prototype to production is the hardest part of AI. Explore the 4 major killers of AI PoCs: data quality, cost, latency, and governance.
·AI Agents and LLMs
AI Security Risks Every Engineer Should Know
AI is the new attack surface. Learn about prompt injection, data leakage, and model misuse, and how to build production-grade security for your AI systems.
·AI Agents and LLMs
LLMs Beyond Chat: Real Business Use Cases
Chatbots are just the entry point. Discover how enterprises are using Large Language Models for automated search, summarization, and complex decision support.
·AI Agents and LLMs
The Agentic Shift: How AI Agents Are Redefining Software Architecture
Move beyond simple chat interfaces. Explore how autonomous AI agents are transforming software design from static code to dynamic, self-optimizing systems.
·Artificial Intelligence
Computer Vision Foundations: From Pixel Data to Semantic Understanding
A deep dive into the engineering of Computer Vision, exploring core tasks, system architectures, and the levels of processing required to turn raw imagery into actionable intelligence.
·Artificial Intelligence
What is NLP (Natural Language Processing) and how does it work?
A deep dive into the mechanics of Natural Language Processing, exploring how machines understand human language, from tokenization to transformers.
·Artificial Intelligence
Vectors in AI and Machine Learning: How to create a vector in Python using NumPy
A comprehensive guide for software engineers on understanding vectors, why they are the bedrock of AI, and how to manipulate them efficiently using Python and NumPy.
·Artificial Intelligence
Architecting Resilient Autonomous Agents for Production
A deep dive into building reliable, production-ready autonomous agent systems, focusing on error handling, state management, and observability.
·Artificial Intelligence
Autonomous Agents: The New Senior Engineers?
Why autonomous AI agents are moving from toy demos to production infrastructure, and what it means for your engineering team.
·Artificial Intelligence
K-Nearest Neighbor(KNN) Algorithm: A Simple, Supervised Machine Learning Method
An engineer's guide to the KNN algorithm, exploring its utility in classification and regression, its simplicity, and its performance trade-offs in production.
·Artificial Intelligence
Linear regression and logistic regression: It's role in AI and Machine Learning
A deep dive into the foundational logic of AI: understanding the difference between predicting values (Linear) and predicting probabilities (Logistic).
·Artificial Intelligence
MCP Server for AI Agents: Why and How to Use It
A deep dive into the Model Context Protocol (MCP), explaining why it's the missing link for production AI agents and how to implement it.
·Artificial Intelligence
Supervised vs. Unsupervised Learning, Overfitting/Underfitting: What are these?
A developer's guide to the core concepts of machine learning: from data labeling to the delicate balance of model complexity.
·Artificial Intelligence
Neural Networks: How they work and why they are so powerful (with code examples)
A deep dive into the architecture of neural networks, exploring layers, activation functions, and why they dominate modern AI.
·Artificial Intelligence
Prompt Engineering for Beginners: A Practical Guide to Writing Effective Prompts for AI Agents
Stop guessing and starting engineering. A technical guide to the principles of reliable prompt design for AI agents.
·Artificial Intelligence
Self-Healing AI Agents: How to Build Resilient AI Systems
Why most AI agents fail in production and how to build systems that detect, correct, and learn from their own errors.