AI/ML Resources

VL-JEPA: End of LLMs? Or the End of How We Think About Them?

Learn how Meta's VL-JEPA challenges the assumptions behind LLMs by shifting AI from next-word prediction to semantic understanding and world modeling.

August 6, 2026

by Ram Ghadiyaram

CORE

· 1,724 Views · 1 Like

Building an AI Visibility Checker With Cloudflare Workers (Without a Backend)

I built six AI visibility tools without a traditional backend, using one Cloudflare Worker to solve CORS while all scoring logic runs client-side in the browser.

August 6, 2026

by Nena Jasar

· 2,666 Views · 1 Like

Build Your First Knowledge Graph From Unstructured Documents Using Python

Learn how to convert a small set of unstructured engineering documents into a searchable knowledge graph using Python, spaCy, and NetworkX.

August 6, 2026

by Sriharsha Makineni

· 1,792 Views · 1 Like

AI-Assisted Development Without Chaos

Learn how to govern AI-assisted software development with risk-based reviews, automated validation, security controls, and end-to-end traceability.

August 6, 2026

by Miguel Garcia

CORE

· 2,804 Views · 2 Likes

Orchestrating Trusted Environments: Securing Untrusted Code Execution With Docker and GKE Agent Sandbox

A technical blueprint for building multi-tenant AI platforms by securely executing untrusted code with Docker and GKE Agent Sandbox.

August 6, 2026

by Anuj Ashok Potdar

· 2,059 Views · 1 Like

Practical QA Workflow Showing How Teams Integrate LLM Testing into Real CI/CD Pipelines

Learn how QA teams integrate semantic evaluation, mocked unit tests, toxicity checks, and regression tracking into CI/CD pipelines.

August 5, 2026

by Minkle Kalra

· 1,409 Views · 1 Like

TensorFlow vs PyTorch: The Real Difference Isn’t Accuracy

A direct CNN benchmark on CIFAR-10 shows TensorFlow and PyTorch achieve identical accuracy (~68%). Choose TensorFlow for production and PyTorch for flexibility.

August 5, 2026

by Rakshath Naik

· 1,325 Views

Why LLM Pipelines Fail in Production and How Temporal and Kafka Fix Them

LLM pipelines fail from retries, failures, and long-running workflows; Kafka provides reliable event streaming, while Temporal ensures durable, fault-tolerant execution.

August 5, 2026

by Akhil Madineni

CORE

· 2,615 Views · 2 Likes

Securing AI Agents at the API Layer: 5 Controls That Actually Matter

AI agents don't break your API rules; they expose the ones you never enforced. This article covers five gateway-level controls that bring autonomous agents under control.

August 5, 2026

by Priyanka Jayavel

· 5,007 Views · 1 Like

Agentic RAG: Basic RAG Plus MCP Tool Calls

Classic RAG is great at answering "what does the policy say?" It's terrible at answering "how many leave days do I have left?"

August 4, 2026

by Balaji Venkatasubramaniyar

· 2,141 Views

GraphRAG Retrieval Is Three Decisions: Granularity, Mechanism, and Paradigm

This article walks you through the major retrieval mechanisms with runnable Cypher examples and explains when to use each.

August 4, 2026

by Lokesh Prakash Manohar

· 1,265 Views · 1 Like

Understanding Agentic SDLC: The Future of Software Engineering

This article walks you through the fundamentals of agentic SDLC, its core components, and how it works in modern software engineering.

August 4, 2026

by Pavan Belagatti

CORE

· 2,701 Views · 2 Likes

Why AI Testing Needs Confidence Scores, Not Just Pass/Fail Results

AI testing needs more than pass/fail results. Confidence scores reveal uncertainty, improve reliability, and enable trustworthy enterprise AI.

August 3, 2026

by Rajeshkumar Rajaseakaran Nair

· 1,603 Views · 3 Likes

The Tectonic AI Platform: A Framework for Taming App Sprawl and Data Fragmentation

Vibe coding and AI-driven development often lead to application sprawl and data fragmentation. Using a Tectonic AI Platform framework can help.

August 3, 2026

by Saravanan Muniraj

· 1,269 Views · 2 Likes

Five Layers Between Your AI Agent and a Production Outage

Single-layer AI guardrails hit a 40% false-positive rate. My five-layer architecture on live AWS achieved 94% accuracy and 0% false negatives on destructive operations.

August 3, 2026

by Manvitha Potluri

· 1,188 Views · 1 Like

Why Enterprise AI Agents Fail: A Runtime Data Governance Pattern for Reliable Answers

Why enterprise AI agents fail on production data, and a runtime governance pattern using data contracts, lineage signals, and guardrails to prevent it.

August 3, 2026

by Avinash Maddineni

· 2,070 Views · 3 Likes

Your AI-Generated Reports Have No Paper Trail

Most data lineage tools stop tracking when an LLM generates output. This article explains the inference boundary gap and a framework to fix it.

July 31, 2026

by Sashank siwakoti

· 2,985 Views · 1 Like

Retrieval Augmented Generation With Spring AI 2.0, Claude, and PGvector

Build a RAG service with Spring AI 2.0, Claude, and PGvector that answers questions from your own documents with a single API key.

July 31, 2026

by Murat Balkan

CORE

· 2,369 Views · 2 Likes

Your AI Agent Shipped an Answer. But Did It Earn the Right To?

Bad Agentic AI output is rarely a generation problem. It's usually bad retrieval, bad planning, or bad execution upstream. Measure every stage, not just the result.

July 30, 2026

by Sibanjan Das

· 1,456 Views · 1 Like

I Built a RAG Agent on Azure AI Foundry in an Afternoon. Here's What Nobody Tells You.

Azure AI Foundry turns RAG setup from a week of manual plumbing into an afternoon of configuration — but access control, security, and cost planning are still on you.

July 30, 2026

by Balaji Venkatasubramaniyar

· 1,936 Views · 1 Like

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