AI/ML Resources

Your AI Coding Agent Can't Steal What It Never Had: The Docker Sandbox Isolation Story

Docker Sandbox runs AI agents in microVMs. The API key never enters the sandbox — the host proxy authenticates on the agent's behalf.

June 19, 2026

by Shamsher Khan

CORE

· 1,227 Views

When Your Documentation Manages Itself: mdship and AI-Assisted Markdown

Learn how to generate documentation using an LLM with mdship, and how to ensure that the prompts, which are now the source documentation, do not get lost.

June 18, 2026

by Peter Verhas

CORE

· 1,699 Views · 3 Likes

AI Is Finding Bugs Faster Than Enterprises Can Patch — Here's What Data Security Teams Should Do

Three structural shifts enterprise data security teams should make in 2026, based on verifiable data and a decade of experience building protection products.

June 18, 2026

by Priyanka Neelakrishnan

· 1,534 Views

Context Rot: Why Your AI Agent Gets Worse the Longer It Works

Adding more tokens to an LLM's context window quietly degrades output quality, even well before the window is full. This is context rot.

June 18, 2026

by Vineet Bhatkoti

· 1,270 Views · 1 Like

Amazon CodeWhisperer to Q Developer to Kiro: The Rise of Agentic Coding

If you’re a backend engineer working with AWS and curious about how we went from autocomplete-style AI to agentic, this one breaks down the architecture shifts.

June 18, 2026

by Jubin Abhishek Soni

CORE

· 1,407 Views

Grok AI API Tutorial: Chat, Image, Video, Tool Calling, and Web Search

Learn how to use the xAI Grok API for chat, image and video generation, tool calling, web search, and integrating external APIs in Python.

June 17, 2026

by Hilman Ramadhan

· 1,323 Views

Testing Is Not About Finding Bugs

Finding bugs is what testing produces; understanding quality is why it exists. QA's future belongs to those who understand products, customers, and risks, not just bugs.

June 17, 2026

by Abhinav Garg

· 1,235 Views · 2 Likes

Getting Started With GitHub Copilot CLI for Coding Tasks

This blog explores GitHub Copilot CLI, a terminal-based AI coding assistant that integrates with GitHub Copilot for users with a subscription.

June 16, 2026

by Gunter Rotsaert

CORE

· 1,198 Views

Your AI Is Not Failing, Your Context Is

RAG helps AI retrieve relevant data. GraphRAG connects entities and relationships. Context engineering turns both into accurate, safe, production-ready AI systems.

June 16, 2026

by Faisal Feroz

· 2,392 Views · 1 Like

Reducing RAG Hallucinations With Relationship-Aware Retrieval

An architectural idea and how it addresses the retrieval weaknesses that lead to hallucinations, with a reference implementation using RudraDB.

June 16, 2026

by Mahesh Vaijainthymala Krishnamoorthy

· 1,260 Views

Workflows vs AI Agents vs Multi-Agent Systems: A Practical Guide for Developers

Use workflows for control, agents for flexibility, and multi-agent systems only when complexity truly demands it. Add intelligence only where it makes a real difference.

June 15, 2026

by Raju Dandigam

· 11,169 Views · 3 Likes

Conversational Risk Accumulation: Stateful Guardrails Beyond Single-Turn LLM Checks

Learn how Conversational Risk Accumulation (CRA) helps detect session-level risks in long AI chats using telemetry, drift tracking, and soft guardrails.

June 15, 2026

by Sanjay Mishra

· 1,629 Views

Introducing RAI Audit Kit: Evidence-Grade Responsible AI Audits in Python

RAI Audit Kit is an open-source Python suite for repeatable, evidence-backed AI audits across ML, deep learning, LLMs, RAG, and agents.

June 15, 2026

by Sai Teja Erukude

· 1,409 Views

The Hidden Cost of AI-Generated Frontend Code

AI can create frontend code in a matter of seconds. However, subsequently, the team has to deal with reviews, accessibility, performance, and maintenance.

June 15, 2026

by Satyam Nikhra

· 1,274 Views · 1 Like

Building a Vector Index in Azure AI Search: HNSW, Profiles, and RAG Retrieval

Use Azure AI Search as your RAG vector store. Build a Python example: define an HNSW vector index, upload embeddings, and run k-NN queries.

June 15, 2026

by Jubin Abhishek Soni

CORE

· 1,073 Views

Engineering Closed-Loop Graph-RAG Systems, Part 4: Evaluating a Graph-RAG System

Graph-RAG accuracy is only the starting point; evaluate the evidence path, rule compliance, latency, and feedback loop before calling it production-ready.

June 15, 2026

by Sriharsha Makineni

· 1,048 Views

AI Assessments Are Everywhere

AI readiness assessments can spark valuable discussions, but self-reported scores have limits. Learn how to evaluate and apply them effectively.

June 12, 2026

by Rick Freedman

· 2,031 Views · 1 Like

Operationalizing Enterprise AI at Scale: Architecture, Governance, and Adoption

Enterprise AI success depends on scalable architecture, governance automation, AI operations, observability, and developer-first enablement strategies.

June 12, 2026

by Aravind Nuthalapati

CORE

· 1,944 Views · 3 Likes

Implementing the Planning Pattern With Java Enterprise and LangChain4j

Learn how to implement the Planning Pattern with Enterprise Java, Jakarta EE, CDI, and LangChain4j, enabling AI to transform business goals into executable workflows.

June 12, 2026

by Otavio Santana

CORE

· 1,611 Views · 1 Like

Engineering Closed-Loop Graph-RAG Systems, Part 3: Closing the Loop in Graph-RAG Systems

Closed-loop RAG needs feedback routing, not blind learning. Route signals carefully so the system improves without reinforcing bad answers.

June 12, 2026

by Sriharsha Makineni

· 1,266 Views

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