Global Indexes, A Reversed Commit, And A Dead Extension Model: How Postgres Features Actually Reach Users

Side-Stepping The Data Movement Tax: How Fragmented Data Stacks Are Converging Back Onto Postgres

Agent Workloads Are Just Beginning To Expose What Row-Level Security Really Costs

Repack Took a Decade To Graduate Into Postgres Core. The Features Still Waiting Could Take Longer.

OWASP Checked Its AI Risk Rankings Against 6,600 Real Incidents And The Surprises Are All Below The Model

Postgres' Logical Replication Stream Gets A Second Life As A Cache Invalidation Engine

'There Is No Boss': How Postgres Decides Its Future In Public

Everyone Is Handing AI Agents The Keys To Production Postgres. POSETTE’s Sharpest Talks Were About The Brakes.

The Best Lesson From POSETTE’s Vendor Day: When Your Postgres Feels Slow, It's Rarely Postgres

What A Morning With Postgres’ Maintainers Reveals About How The Database Really Gets Made

POSETTE '26: Postgres Has Stopped Trying To Prove It Belongs And Started Absorbing The Stack Around It

How Postgres' Rise To Enterprise Default Is Outpacing The Operational Model Behind It

What Happens To Agent Memory When You Swap The Model? Two Practitioners Are Building The Answer In The Database Layer.

The Line Between The Database And AI Memory Layer Is Getting Blurry. The Trick Is Making Sure Your Schemas Are Not Paying For It.

To Stop AI Amplifying Security Issues, Some Experts Are Grounding Security In Hardware

How a Laid-Off Atlassian Engineer's Sovereign Breakdown Started a Timer On Every Vibe-Coded Codebase

Authorization Has Always Been Hard, and a New Generation of Builders Is Discovering Why

What Does a Compiler Look Like When Its Audience Isn't Human? Vercel's Zero Just Soft-Launched the Answer

After pgBackRest's Close Call, Postgres Shops Are Drafting New Runbooks For Continuity

pgBackRest Is Back, But Open Source Has a Stewardship Problem It Can't Keep Ignoring

AI-Generated Pull Requests Are Crashing Postgres Instances Daily, And the Only Way Through Is Architectural

Decoding 'Architecture As An Algorithm' and Production AI Explainability In Regulated Industries

The Case Of 'A Billion APIs': Overcoming AI-Induced Monolithic Tech Debt No One Can Maintain

Tell One AI Model Another Will Review Its Work, and Output Quality Jumps 1.5x

Old-School Speccing Makes a Comeback As Devs Find The Limits of Natural Language LLM Interactions

How Mississippi Built The Nation's First Statewide AI Initiative For Education And Workforce Readiness

From Banking to Blackboard: An Engineer's Case For CI/CD-Level Governance Of AI Code

Why The Success Of AI In Regulated Industries Depends On Compliance-As-Architecture

AI Parallelized Biomedical Research, But The Verification Layer Needs To Match The Throughput

Grainger's MarTech Lead On How Disciplined AI Governance Prevents Expensive Shelfware

You Can't 'Write Off' AI QA Tax at the Read/Write Layer, You Can Only Move When The Bill Comes Due

What Happens When Read-Only Access is the Only AI Guardrail That Actually Holds?

When AI Agents Go 'God Mode,' the Security Perimeter Must Move to the Database

One Layer Away, Always: A Framework for AI in Production Data Systems

You Can't Vibe Code A Payments Platform, But Even Regulated Industries Are Rethinking Writing By Hand

AI Readiness Starts With the Queries Nobody Bothered to Optimize

Agents Collapsed The Wall Between Analytics And Operations, And Postgres Is What's On The Other Side

A Financial Service Engineer's Search For Provability Amid The MCPification Of Everything

You Can't Outrun Governance, but Reducing Hurdles to Production-Readiness Starts With Condensing AI Pipelines

Let The Agents Cook. But First, Secure The Infrastructure.

The Traditional OLTP Playbook Breaks RAG. Here's What Replaces It.

Same IDE, Two Developers: One Shipped A Month Of Front-End In An Afternoon. The Other Wiped The Drive.

Six Agents, One Pull Request, and the Emerging Art of 'Skills-as-Infrastructure'

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Debasish Bhattacharjee

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April 9, 2026

The Traditional OLTP Playbook Breaks RAG. Here's What Replaces It.

AI/ML engineering leader, Debasish Bhattacharjee, sat down with The Read Replica to discuss why production RAG succeeds or fails at the data layer, and why the hardest calls around lineage and observability are pulling directors back into the codebase.

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