AI/ML Resources

Cutting AI Token Costs With MgntUtils Stack Trace Filtering

Learn how AI can reduce log volume and token costs by compressing stack traces while preserving the details needed for effective debugging and analysis.

August 24, 2026

by Michael Gantman

· 1,963 Views · 2 Likes

Building Meeting Audio RAG on Microsoft Foundry With Fast Transcription and Foundry IQ

Build a production-ready meeting audio RAG pipeline with Microsoft Foundry, and connect to a Foundry agent that answers questions with meeting and time citations.

August 21, 2026

by Jubin Soni, FBCS

CORE

· 4,885 Views · 3 Likes

Stop Paying Your AI Agent to Do the Same Job Twice

Modern AI agent pipelines benefit significantly from a strategically positioned knowledge-base cache layer that sits upstream of the orchestration flow.

August 21, 2026

by Shivi Kashyap

· 3,123 Views · 3 Likes

Agents and Tools in Agentic AI: A Simple Explanation

This article provides a simple introduction to agents and tools in agentic AI. It explains why we need tools and the role of the agent and the model in this process.

August 21, 2026

by Ruchi Saini

· 2,064 Views · 4 Likes

How to Build and Scale Generative AI Infrastructure

Managing generative AI at scale requires strategies to reduce costs and latency, improve observability, and build reliable infrastructure.

August 21, 2026

by Chidiebere Njoku

· 1,997 Views · 6 Likes

Stop Hand-Rolling Chat UIs: Streaming LLM Tokens Into React Native Without the Jank

In a React Native chat, the keyboard and a streaming, resizing list fight over the scroll position and cause jank. KeyboardChatScrollView fixes it.

August 20, 2026

by Tammo Ronke

· 3,457 Views · 1 Like

AWS Bedrock vs Vertex AI vs Azure Foundry: Stop Comparing Benchmarks, Start Asking This Instead

Compare AWS Bedrock, Google Vertex AI, and Azure AI Foundry to choose the right cloud for your AI workloads based on data, models, and governance.

August 20, 2026

by Balaji Venkatasubramaniyar

CORE

· 3,398 Views · 3 Likes

Prompt, Fine-Tune, or Compile: The Three Ways to Build Anything in AI

Learn when to use model APIs, fine-tuning, or declarative code for AI products, and how to manage these three tiers as your product evolves.

August 20, 2026

by Dhyey Mavani

· 1,817 Views · 2 Likes

How AI Is Actually Changing SRE Tools, Part 2: ITOps, Chaos Engineering, and the Rest of the Job

Across every category, AI is good at surfacing options and drafts; the SRE still owns the judgment call with real consequences.

August 20, 2026

by Vidyasagar (Sarath Chandra) Machupalli FBCS

CORE

· 2,440 Views · 4 Likes

Securing AI Retrieval Pipelines and Adding Identity-Aware Access Controls to RAG Systems

A step-by-step tutorial for adding per-chunk permission enforcement, prompt injection defenses, and audit logging to RAG pipelines — without rebuilding your data.

August 19, 2026

by Shekar Munirathnam

· 3,236 Views · 3 Likes

Deploying an Enterprise LLM Chatbot on Databricks With RAG, MLflow, Vector Search, and Model Serving

Build and ship a production chatbot: author the RAG chain, log it with MLflow, register it to Unity Catalog, trace it, then deploy to Model Serving.

August 19, 2026

by Seshendranath Balla Venkata

· 1,929 Views · 2 Likes

How Docker Is Becoming an AI Development Platform

Local AI dev chaos fixed by moving LLM, vector DB, and app into one Compose file, reproducible, but it's not a Kubernetes replacement.

August 19, 2026

by Pruthvi Raj Seknametla

· 32,376 Views · 6 Likes

Why Is the Agent Card Important?

Build AI agents with A2A and Agent Cards to enable seamless agent discovery, communication, and task collaboration across specialized agents.

August 19, 2026

by Ajay Singh

· 2,019 Views · 2 Likes

Containerizing LLMs: Best Practices for Docker-Based AI Workloads

Bloated LLM Docker images and silent OOM kills taught me: separate weights from images, use runtime, not devel bases, and budget GPU/host memory separately.

August 19, 2026

by Pruthvi Raj Seknametla

· 29,804 Views · 5 Likes

Multi-Agent Software Engineering: Can AI Teams Build Production Systems?

Multi-agent AI reaches production readiness through orchestration, durable workflows, observability, and resilience rather than simply adding more models.

August 19, 2026

by Uthej Mopathi

CORE

· 3,053 Views · 4 Likes

Java Enterprise Is Already Ready for the AI Era

Java Enterprise is ready for AI today. Jakarta EE integrates with AI providers and frameworks, while Jakarta Agentic AI and Jakarta EE 12 strengthen it.

August 18, 2026

by Otavio Santana

CORE

· 3,616 Views · 7 Likes

MCP vs A2A vs ACP: How AI Agents Talk to Each Other

Compare MCP, A2A, and ACP to understand how AI agents access tools, delegate work, manage tasks, and communicate securely in production systems.

August 18, 2026

by Yakaiah Bommishetti

· 3,067 Views · 3 Likes

Member Spotlight: Pavan Belagatti

In the first article of this series, we caught up with Pavan to talk about his tech journey, what keeps him learning, and his passion for agentic AI and AI/ML.

August 18, 2026

by Dominique Pugh

· 1,278 Views · 2 Likes

The Embedding Model You Choose Matters More Than Your LLM

Everyone debates GPT-4 vs Claude vs Gemini. Meanwhile, the biggest RAG performance bottleneck is your embedding model. Here's the data, and the fix.

August 17, 2026

by David Taiwo Balogun

· 2,390 Views · 5 Likes

Engineering as a Service Is What Happens When You Let Vibe Coding Win

Vibe coding makes non-engineers capable of prototyping software. What survives is production intuition: knowing how systems fail in ways that were never in the spec.

August 17, 2026

by Dinesh Elumalai

CORE

· 1,365 Views · 4 Likes

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