AI/ML Resources

RAG Done Right: When to Use SQL, Search, and Vector Retrieval and How To Combine Them

RAG failures stem from retrieval, not models. Replace one-size-fits-all vector search with a decision framework, hybrid flow, and guardrails for reliable systems.

May 8, 2026

by Ram Ghadiyaram

CORE

· 3,720 Views · 9 Likes

How AI Is Rewriting Full-Stack Java Systems: Practical Patterns with Spring Boot, Kafka and WebSockets

Decouple heavy processing with Spring Boot, Kafka, and WebSockets: AI consumers analyze events asynchronously, while WebSockets deliver real-time insights to users.

May 8, 2026

by Ramya vani Rayala

· 3,238 Views · 1 Like

How to Implement AI Agents in Rails With RubyLLM

Learn how to implement your first AI agents with the help of RubyLLM. Define a chat interface with access to a set of SERP tools that LLM models can use in their work.

May 7, 2026

by Josef Strzibny

· 2,198 Views · 2 Likes

Why Your RAG Pipeline Will Fail Without an MCP Server

RAG was supposed to fix hallucinations. Instead, it quietly introduced a new class of production failures nobody warned you about.

May 7, 2026

by Jaswinder Kumar

· 2,714 Views · 1 Like

Identity Security in the Age of Agentic AI: What Engineers Need to Know

A practical guide to why traditional identity systems break with agentic AI, and what engineers need to architect differently.

May 7, 2026

by Ashly Joseph

· 2,395 Views · 1 Like

Designing Self-Healing AI Infrastructure: The Role of Autonomous Recovery

Distributed AI systems fail faster than humans can respond, making traditional response insufficient. Self-healing systems use telemetry and automation to recover early.

May 7, 2026

by Sayali Patil

· 5,187 Views · 5 Likes

Why AI Forces a Rethink of Everything We Know About Software Security

AI-driven development expands attack surfaces; this article shows how continuous security, zero trust, and runtime enforcement scale DevSecOps in AI pipelines.

May 7, 2026

by Apostolos Giannakidis

CORE

· 3,070 Views · 1 Like

Responsible AI Is an Engineering Problem, not a Policy Document

Responsible AI is an engineering problem and not a policy document. It must be mandatory to ensure AI systems are designed, built, and used responsibly.

May 7, 2026

by Indirakumar Rajendiran

· 1,682 Views

Production Checklist for Tool-Using AI Agents in Enterprise Apps

Tool-using AI agents are production software. The same ops discipline applies, but some traditional assumptions quietly break.

May 7, 2026

by Pier-Jean MALANDRINO

CORE

· 3,721 Views · 1 Like

Reactive Ops to Autonomous Infrastructure: How Agentic AI Is Redefining Modern DevOps

Agentic AI transforms DevOps from reacting to incidents to systems that understand, decide, and act on their own, reducing toil and enabling autonomous infrastructure.

May 7, 2026

by Venkatesan Thirumalai

· 1,924 Views

KV Cache Implementation Inside vLLM

vLLM's KV cache is a virtualized memory system built on reusable blocks and prefix-aware scheduling that enables efficient, scalable LLM inference.

May 7, 2026

by Bhala Ranganathan

CORE

· 4,922 Views

I Gave Gemini 3 My Worst Legacy Code — Here’s What Happened

Feeding legacy code to Gemini 3 reveals key refactoring lessons: prioritize modularity, decouple logic, and use AI to automate unit tests.

May 7, 2026

by Jubin Soni, FBCS

CORE

· 3,131 Views · 1 Like

Comparing Top Gen AI Frameworks for Java in 2026

A practical, in-depth comparison of the top Generative AI frameworks for Java in 2026: Genkit Java, Spring AI, LangChain4j, and Google ADK

May 7, 2026

by Xavier Portilla Edo

CORE

· 4,233 Views · 8 Likes

Context Density: How to Survive the AI Tidal Wave

As AI matures, how can humans continue to contribute value? Context density gives us the answer to the roles humans can continue to play.

May 7, 2026

by Jason Bloomberg

· 2,783 Views · 1 Like

ARC: The Architecture for Reasoning Control

AI apps fail from compounding randomness. Start small, add layered guardrails, and use AI for reasoning but code for execution to keep systems reliable.

May 6, 2026

by Ananth Iyer

· 2,281 Views

Designing Agentic Systems Like Distributed Systems

Agentic systems behave like distributed systems - unpredictable and failure-prone, requiring orchestration, contracts, and strong observability.

May 6, 2026

by Satyam Nikhra

· 2,804 Views · 1 Like

The Hidden Failure Modes of AI Systems (That Traditional Monitoring Misses)

AI systems rarely fail loudly — they degrade silently via drift, bad retrieval, and hallucinations. Detect it with semantic observability, not just infra metrics.

May 6, 2026

by Sayali Patil

· 4,366 Views · 5 Likes