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In-depth articles, tutorials and insights on system design, architecture, coding and everything in between.
Low-Level Design for an AI Agent Orchestration Engine: Designing a Stateful Execution Framework
TLDR: In this guide, we design a stateful, graph-based AI agent execution engine in Java using clean object-oriented principles. By structuring execution as nodes and edges over a shared state, we pre
System Design: Designing an Autonomous AI Coding Agent (Devin at Scale)
TLDR: Designing an autonomous AI coding agent at scale is not a prompt engineering task; it is a complex systems problem. The system requires secure multitenancy via Firecracker microVMs, a low-latenc
Algorithms for AI: Trie, Graph Sorting, and K-Way Merge for LLM Systems
TLDR: Generative AI relies heavily on classic computer science algorithms under the hood. In this guide, we explore how to implement Trie prefix filtering for constrained model decoding, Topological S
Single Trunk Model Explained: Golden Branches, Feature Toggles, and Continuous Feature Delivery
TLDR: The single trunk model exists to reduce integration pain, shorten feedback loops, and keep one golden branch always releasable. Teams make it work with strong CI, short-lived feature branches, f
System Design for Agentic AI Systems: From Distributed Systems Principles to Production
TLDR: Agentic AI systems are distributed systems with non-deterministic workers. If you design them with queue-first execution, explicit state machines, idempotency keys, bounded retries, and strong o
ANN Index Types Explained: When to Choose Flat, HNSW, IVF, or IVF-PQ
TLDR: If your dataset is small and correctness is critical, use Flat. If you need high recall with low latency and enough RAM, use HNSW. If your corpus is huge and memory is your bottleneck, use IVF-P
Data Lineage Explained: Tracking Data Flow Across Your Organization
TLDR: 📊 Data lineage is the complete genealogy of your data — where it comes from, how it's transformed, and where it ends up. It's critical for debugging pipelines, proving compliance, and understan
Data Governance Essentials: Framework and Best Practices
TLDR: 📋 Data governance is the framework that answers "who owns this data, who can access it, and what quality standards must it meet?" Without governance, data pipelines become chaotic. Implement it
OWASP Credential Stuffing Key Terms Explained with Practical Examples
TLDR: Credential-stuffing defense works only when you treat login as a layered, risk-adaptive system: detect attack shape, add step-up authentication, combine bot and fingerprint signals, prevent user