AI/ML Resources

6 Types of AI Orchestration Every Tech Leader Needs to Know

Learn the six types of AI orchestration and how they help teams build reliable, scalable, and production-ready AI systems.

July 9, 2026

by Balaji Venkatasubramaniyar

· 2,584 Views · 2 Likes

The AI Reliability Gap: Why Enterprise AI Is Failing Long Before It Reaches Production

Enterprise AI isn't failing because models aren't smart enough. Learn why reliability, governance, and engineering are the real challenges in production.

July 9, 2026

by Igboanugo David Ugochukwu

CORE

· 2,078 Views · 1 Like

If You Can Write Acceptance Criteria, You Can Write an AI Routing Policy

Your AI Routing Policy isn't about picking the cheapest model. It's a repeatable team decision assigning each task to the cheapest sufficient path.

July 9, 2026

by Stefan Wolpers

CORE

· 1,877 Views · 1 Like

Harness Engineering for AI: Why the Model Is Only Half the System

Learn harness engineering for AI and build production-ready systems with memory, guardrails, tools, verification, feedback, and observability.

July 8, 2026

by Manas Dash

CORE

· 2,171 Views · 1 Like

AI Is Making PHP Cool Again

The stack everyone called dead has an edge in the AI era, and it comes down to one boring thing: every Laravel project on earth puts the same file in the same place.

July 8, 2026

by Matt Watson

· 1,771 Views

AI Won't Keep You from Hitting the Scalability Wall

AI coding tools can dramatically speed up integration builds — but speed isn't the bottleneck. The real issue is the long-term ownership cost.

July 8, 2026

by Bru Woodring

· 1,815 Views

Building an AI Incident Copilot: How I Automated the First 15 Minutes of Every Production Incident

incopilot is an open-source Python CLI that automates the first 15 minutes of production incident triage — collecting logs, detecting failure patterns, and incidents.

July 7, 2026

by Sudhakararao Sajja

· 3,041 Views · 1 Like

AI Can't Defend What It Can't See

AI can only defend what it can see. Give it incomplete data, and it won't warn you. It quietly reports everything as healthy while real attacks slip through unseen.

July 7, 2026

by Jithu Paulose

· 2,380 Views · 2 Likes

Designing Tool-Calling AI Agents That Survive Production: A LangGraph Approach

AI agents work in demos and break into production. This LangGraph tutorial builds tool-calling agents that are fail-safe: validated, bounded, and observable.

July 7, 2026

by Shubham Gupta

· 1,978 Views

Building a Mortgage Agent With FRED Data, FastAPI, and LLM Tool Calling

A walkthrough of an open-source intelligent FastAPI app that pairs Freddie Mac benchmarks from FRED with a first-time buyer chat assistant.

July 7, 2026

by Sushma Kukkadapu

· 2,380 Views

How to Build a Production-Ready RAG Pipeline With Vector DBs

RAG prototypes are easy. Production RAG is not. This covers vector DB tradeoffs, chunking patterns, re-ranking, and evaluation setups that hold up at scale.

July 6, 2026

by Mark Saxon

· 3,683 Views · 1 Like

Developer’s Checklist: How to Build an FHE Application

This step-by-step checklist breaks down FHE app development from plaintext design to encrypted implementation and offers open-source tools to get you started.

July 6, 2026

by David Archer

· 1,111 Views

LangChain With SQL Databases: Natural Language to SQL Queries

Building an SQL interface that is easy to use for NON-SQL users and provides immediate and accurate answers to business queries.

July 6, 2026

by Varun Joshi

· 1,200 Views · 1 Like

Prompt Injection Attacks and Hidden Security Risks in LLM Applications

Prompt injection hijacks an LLM by exploiting its inability to separate data from commands. Direct and indirect attacks require layered defenses, not one fix.

July 3, 2026

by Karini Kapoor

· 2,981 Views · 2 Likes

Building an AI Agent That Responds to Real-Time Events With AWS Bedrock, Kinesis, DynamoDB, and S3

Build an AI agent that processes real-time events with Amazon Bedrock and a serverless AWS architecture powered by Kinesis, DynamoDB, and S3.

July 3, 2026

by Jubin Soni, FBCS

CORE

· 2,251 Views · 1 Like

Real-Time AI Feature Engineering With Spark Structured Streaming and Databricks Feature Store

How Spark Structured Streaming and the Databricks Feature Store work together to build point-in-time-correct features from Kafka events to streaming transformations.

July 2, 2026

by Jubin Soni, FBCS

CORE

· 2,326 Views

From Pilot to Production: The Six Agent Patterns That Determine Whether Your AI Program Scales or Stalls

Most AI agent programs don't fail because of technology. They fail because nobody owns the agent and nobody monitors it.

July 2, 2026

by BALAJI BARMAVAT

· 1,623 Views · 1 Like

Multi-Agent Software Engineering: One Coding Agent Isn't Enough

Multiple AI agents with clear roles and checks deliver real software better than one agent, but cost more and only suit large tasks.

July 2, 2026

by Jithu Paulose

· 2,123 Views

Why AI-Generated Code Is Making Regression Testing More Important, Not Less

AI-generated code introduces integration failures that spec-based tests cannot catch. Regression testing grounded in real production behavior is the fix.

July 1, 2026

by Sancharini Panda

· 1,721 Views

AI-Augmented React Development: How I Rebuilt My Workflow Without Losing Control of the Code

AI accelerates React 18 workflows but breaks down in large enterprise codebases. Here’s where it helps, where it fails, and the guardrails your team needs.

July 1, 2026

by Sathwik Nagulapati

· 2,385 Views · 1 Like

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