AI/ML Resources

MERGE and Liquid Clustering: Common Performance Issues

A practical look at common pitfalls and performance challenges when using MERGE operations on liquid-clustered Delta tables, and how to avoid them.

January 21, 2026

by Avi Yehuda

· 1,591 Views

Multimodal AI Architecture: Unifying Vision, Text, and Sensor Intelligence

Most Android AI features stay single-modal; this architecture fuses vision, text, and sensor inputs to deliver smarter, context-aware, privacy-conscious experiences.

January 21, 2026

by Mohan Sankaran

· 1,511 Views · 5 Likes

AI-Driven Autonomous CI/CD for Risk-Aware DevOps

AI-driven autonomous CI/CD pipelines enable risk-aware DevOps by predicting failures, reducing downtime, improving reliability, and lowering cognitive load for SREs.

January 21, 2026

by Oreoluwa Omoike

· 1,186 Views

Automating Traceability with Generative AI

Here is an architectural pattern to automate traceability checks by combining batch configuration extraction with Generative AI validation.

January 20, 2026

by Dippu Kumar Singh

· 1,821 Views

Enterprise LLM Architecture Patterns, From RAG to Agentic Systems

A practical guide to 11 proven LLM architecture patterns, with real-world examples for building scalable, trustworthy, production-ready AI systems.

January 20, 2026

by Ram Ghadiyaram

CORE

· 5,426 Views · 6 Likes

Why High Performance Storage is Important for AI Cloud Build

Learn the technology and architecture behind building AI Cloud and why high performance storage is important. Explore the latest benchmarks and understand the market.

January 20, 2026

by Anjul Sahu

· 1,771 Views

“Just Don’t Put PII in the Prompt” Is a Trap: A Two-Plane Architecture for Safe LLM Apps

Separate Data Plane (sensitive data/actions) from Prompt Plane (sanitized context). Use IDs + summaries so secrets never enter prompts.

January 20, 2026

by Mohan Sankaran

· 1,081 Views · 5 Likes

DeepSeek’s mHC: Manifold-Constrained Hyper-Connections, Explained for Practical Use

An overview of how DeepSeek’s manifold-constrained hyper-connections (mHC) stabilizes multi-stream residual networks while improving performance at scale.

January 19, 2026

by Praveen Kumar Myakala

· 1,414 Views · 2 Likes

Copilot, Code, and CI/CD: Securing AI-Generated Code in DevOps Pipelines

AI coding tools boost speed but weakens security and developer judgment. Here’s how hidden vulnerabilities escape review and what must change before a breach hits.

January 19, 2026

by Igboanugo David Ugochukwu

CORE

· 1,548 Views

RAG at Scale: The Data Engineering Challenges

The data engineering challenges that go beyond the basic concept of RAG when running RAG systems at scale in production.

January 16, 2026

by Guru Hegde

· 2,405 Views · 1 Like

From RAG to RAG + RAV: A Practical Pipeline for Factual LLM Responses

RAG reduces hallucinations, RAV verifies each claim, and together they yield far more trustworthy LLM answers with optional corrections for accuracy.

January 16, 2026

by Sai Teja Erukude

· 2,594 Views · 3 Likes

From Chatbot to Agent: Implementing the ReAct Pattern in Python

This article provides a raw Python implementation, moving beyond high-level frameworks to show exactly how the agentic loop works under the hood.

January 16, 2026

by Nikita Kothari

· 2,500 Views · 2 Likes

Integrating CUDA-Q with Amazon Bedrock AgentCore: A Technical Deep Dive

This integration lets AI agents use GPU-accelerated quantum simulations as tools in their workflows. Learn more in this deep dive.

January 16, 2026

by Rakesh Kumar Pal

· 1,501 Views · 1 Like

RAG on Android Done Right: Local Vector Cache Plus Cloud Retrieval Architecture

Most mobile RAG fails on latency and flaky networks. A local vector cache + cloud retrieval architecture keeps responses fast, fresh, and grounded.

January 16, 2026

by Mohan Sankaran

· 1,430 Views · 5 Likes

Securing AI-Generated Code: Preventing Phantom APIs and Invisible Vulnerabilities

AI coding tools accelerate delivery but create new security blind spots. Learn how phantom APIs emerge — and what developers can do to catch them early.

January 15, 2026

by Igboanugo David Ugochukwu

CORE

· 2,015 Views · 1 Like

DevSecOps for MLOps: Securing the Full Machine Learning Lifecycle

Why ML systems are uniquely vulnerable to security attacks — and how MLSecOps closes the gaps in data, models, and pipelines.

January 15, 2026

by Igboanugo David Ugochukwu

CORE

· 1,972 Views · 2 Likes

From Aspects to Advisors: Design Modular Cross-Cutting Features with Spring AI

Understand how Spring AI Advisors work and see how Aspect Oriented Programming concepts can be applied when interacting with LLMs.

January 15, 2026

by Horatiu Dan

CORE

· 2,013 Views · 5 Likes

Taming Reinforcement Learning Chaos: An MLOps Architecture for Experiment Management

Reinforcement learning is powerful, but managing thousands of iterations is a nightmare. Here is a practical architecture for building a lightweight experiment system.

January 15, 2026

by Dippu Kumar Singh

· 1,256 Views · 1 Like

Real-Time Recommendation AI Architecture: Streaming Events and On-Device Ranking

This Android recommendation architecture streams events to the backend and uses on-device ranking to deliver fast, resilient, privacy-aware recommendations.

January 15, 2026

by Mohan Sankaran

· 1,102 Views · 6 Likes

9 Tips for Building Apps to Withstand AI-Driven Bot Attacks

Here are nine tips for app dev teams to strengthen app protections and frustrate AI-driven bot attacks, without harming user experience.

January 15, 2026

by Philip Piletic

CORE

· 1,513 Views · 1 Like

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