PostgreSQL
Open-source relational database
Episodes
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#5909: Connecting Postgres to Google WorkspaceHow a Postgres-backed app on Cloud Run talks to Gmail, Drive, Calendar, Docs, Sheets and Slides — without duplicating data. -
#5818: 46,434 Mailing Lists That Still WorkDaniel wants a public town hall. Turns out the oldest tool on the internet — the mailing list — might be the right one. -
#5017: Reverse-Engineering a Backend from the FrontendBuilding a backend when only the frontend exists—database first, then ORM, API, and the hidden traps. -
#4902: Backward Batching: Fixing Slow Uploads Without Blocking UsersHow one developer used background threads and deferred AI processing to fix a cataloguing workflow. -
#4661: Database Migrations: The Full LifecycleFrom schema change to production — the complete guide to safe, zero-downtime database migrations. -
#4546: Redis: What It Actually Is and Why It's EverywhereRedis powers half the internet's caching, but almost nobody knows what it actually is. We break it down from the ground up. -
#4442: When Prompt Enhancers Hallucinate Your IntentHow to build a prompt enhancer that doesn't rewrite your constraints into oblivion. -
#4289: Spreadsheets vs. SQL: Is the Grid Dying?Airtable's AI agent just collapsed the usability gap. Does the spreadsheet still have a purpose? -
#4069: How AI Is Rewriting Your Tech Stack ChoicesWhat happens when AI agents change how we pick frameworks, ORMs, and databases? -
#3874: How to Tag 4000 Episodes Without Losing Your MindWhy tagging breaks at scale, and how a two-stage AI pipeline fixes it for good. -
#3241: How to Write a Product Spec That Makes AI Find You the OneStop typing three words into Google. The SPEC framework helps AI find exactly what you need. -
#2684: When Agent Skills Collide: Context Windows & Plugin DesignHow to handle overlapping agent skills and whether context windows will ever make the problem go away. -
#2677: Memory Layers for AI Agents: SaaS vs Self-HostedZep, mem0, Letta, Graphiti, Cognee — which memory layer should you commit to for your AI agent? -
#2674: Why Your Agent's Context Window Is Getting Eaten Before You StartStop shipping the whole toolbox to every session. A bridge plugin pattern that fetches skills on demand instead. -
#2634: The Two-Stage Pipeline for Persistent User MemoryHow to extract durable personal context from raw prompts and build a self-healing memory layer for AI systems. -
#2550: Idempotent Pipelines: Checkpoints, Manifests & Safe Re-RunsHow to design scripts and pipelines so re-running them is safe, even after a crash mid-execution. -
#2501: Describing a Neighborhood: Databases Without ScreensCan you design a relational database using only your voice? We coach a beginner through PostgreSQL from scratch. -
#2492: When AI Agents Collapse Stack Evaluation from Weeks to SecondsHow Claude Code and agentic AI are turning GitHub into a discovery layer and collapsing library evaluation from weeks to seconds. -
#2475: Docker Volumes: Why They Can't Move and What To DoDocker made apps portable but left your data stuck. Here's how to actually move volumes between hosts. -
#2469: Embedding Model Deprecation: RAG's Silent KillerWhen OpenAI retires an embedding model, your RAG pipeline breaks silently. Here’s how to fix it. -
#2458: Can Graph Databases Go Mainstream?Graph databases are powerful but niche. Will they ever power mainstream CRMs and ERPs? -
#2359: When the Sandbox Doesn't Fit: Sysadmins Using a Dev ToolDiscover why Claude Code excels as a sysadmin tool despite being designed for developers — and the challenges that come with it. -
#2346: Your Schema Is a ContractHow to design relational schemas that don’t haunt you later—entity modeling, normalization tradeoffs, and when (not) to use JSON columns. -
#2336: How ADRs Solve AI's Institutional Memory ProblemArchitectural Decision Records (ADRs) aren’t just documentation—they’re a way to give AI coding assistants the context they lack. -
#2203: Knowledge Without Tools: Why MCPs Aren't Just for ExecutionMCPs can be pure knowledge providers with zero tools. Here's why that matters for agents querying government data and authoritative sources. -
#2192: How We Built a Podcast PipelineHilbert reveals the complete technical architecture behind 2,000+ episodes—from voice memos to GPU-powered TTS, with Claude models, LangGraph workf... -
#2113: Goldfish vs Elephant: The Stateful Agent DilemmaStateless agents are cheap and fast, but stateful ones remember your window seat. Which architecture wins? -
#1944: PostgreSQL: The Thirty-Year MiracleHow does a volunteer-run database power the New York Stock Exchange and survive every tech trend without burning out? -
#1938: JSON-to-SQL Type Mapping: A Practical GuideMapping JSON to SQL isn't as simple as it looks. Discover the hidden traps in data types that can cause performance hits and data corruption. -
#1931: Where Your AI Pipeline Actually DiesWhy do AI pipelines crash? It’s not the models—it’s the plumbing. We break down how to manage data between stages. -
#1920: InfluxDB vs. Postgres: The Time-Series ShowdownWe compare specialized time-series databases like InfluxDB against traditional SQL options like Postgres with Timescale extensions. -
#1907: Why We Still Fine-Tune in 2026Despite million-token context windows, fine-tuning remains essential. Here’s why behavior, not just facts, matters. -
#1774: The Internal Heat Shield: Telling Hard Truths in DevRelDevRel isn't just swag and conferences—it's the critical feedback loop keeping developers loyal in an AI-driven world. -
#1763: Backend Grunt Work Is Dead. What Now?AI agents now write 80% of boilerplate code, but the real backend engineering challenges remain. -
#1718: The Ralph Wiggum Technique: AI That Codes ItselfStop babysitting AI agents. Learn the Ralph Wiggum technique to automate iterative coding loops and let AI finish the job itself. -
#1277: When Military Data Pipelines Mirror Tech StartupsExplore how modern military command structures are evolving into high-stakes data pipelines using Kafka, ETL, and edge computing. -
#1233: Why "Just Use Postgres" Isn't Always EnoughCan one database do it all? Explore why hardware constraints and data geometry keep specialized databases like Snowflake and ClickHouse alive. -
#1221: Beyond Migrations: Breaking the SQL Straitjacket with AIStop writing manual SQL migrations. Explore how AI agents are transforming the database from a rigid "straitjacket" into a flexible, evolving state. -
#1213: Is Your JSON Store Just a Postgres Feature Now?Explore why the NoSQL revolution didn't kill SQL and how document databases are finding a new second life in the era of AI and RAG pipelines. -
#1211: Escaping JOIN Hell: The SQL Developer’s Guide to Neo4jStop struggling with 15-deep JOINs. Learn how Neo4j turns relationships into first-class citizens for faster, more intuitive data modeling. -
#1170: The Frozen Backend Paradox: Modern Static ArchitectureExplore the "frozen backend" paradox and how modern static sites use build-time logic and sharded search to mimic complex dynamic applications. -
#1123: When One Database Isn't EnoughCan Postgres 18 finally replace the data warehouse? We dive into data gravity, columnar storage, and the physics of scaling in the AI age. -
#621: From a Dead Motherboard to Five NinesDiscover how the world’s biggest platforms stay online when hardware fails. Herman and Corn break down the invisible systems of high availability. -
#263: Why AI Needs a New Kind of MemoryHerman and Corn explore why SQL is losing ground to graph and vector databases. Discover why the "edge" is the new gold standard for AI accuracy. -
#200: Why Graph RAG Beats Flat Vectors for AI MemoryExplore how the AI stack is evolving from simple vector search to complex Graph RAG and why the "boring" Postgres database is winning the race.