📺 Note: This episode contains screensharing and live coding examples that work best visually. If you’d like to follow along, check out the full video version on YouTube: https://youtu.be/Qvh3VFlvJnE.

Gavin King (creator of Hibernate) joins Marco to cut through the myths of Java persistence. When should you skip layers of repositories and just write SQL, or even model database views? What actually causes lazy-loading pain, and how do stateless sessions help? We cover Hibernate, JPA, and where Jakarta Data is headed, plus pragmatic advice for legacy schemas, performance, and keeping your code DRY.

💡 Topics in this episode:

• SQL vs repositories (and why views can be your adapter layer)

• The three inheritance strategies, and the trap of table-per-class

• Stateless sessions vs stateful persistence contexts

• LazyInitializationException explained (and avoided)

• DRY first: let architecture emerge via refactoring

• Jakarta Data 1.0/1.1: annotation processing, stateful repos, dynamic queries

• Performance rule-of-thumb: minimize round-trips

⏱️ Timestamps

(00:00) Teaser

(01:00) Gavin King intro and early Hibernate motivation

(03:59) Old-school open-source culture and discovery

(08:39) SQL isn’t hard: minimize DB round-trips

(10:03) “Leaky abstractions are good” (and why)

(12:04) Architecture should emerge from code

(15:44) DRY as commandment #1

(22:05) Reviewing a typical Spring Data setup (and pitfalls)

(25:39) Inheritance strategies: joined vs single table vs table-per-class

(30:06) One screen? Just write a SQL query (pragmatism)

(33:08) When ORM shines: operations over graphs

(35:22) Use views to adapt messy legacy schemas

(43:18) Jakarta Data repos: annotation-driven, step-into-able

(44:59) Stateless Session: less magic, explicit updates

(48:51) Jakarta Data 1.1: stateful repos, dynamic queries, reactive

(01:24:22) Rapid fire questions

(01:33:15) Features Gavin would delete and lessons learned

(01:42:30) Giveaway

New episodes every other Wednesday. Subscribe for deep, developer-focused conversations.

Casey Muratori (programmer, educator, performance advocate) joins Marco to explore why modern software feels so slow, even with today’s powerful hardware. From the infamous Windows Terminal controversy to game server limits, billion-row challenges in Java, and his unfiltered take on AI. This episode dives deep into performance, programming culture, and what developers should really care about.

💡 Topics in this episode:

• Why software performance matters (and how to avoid “ridiculously slow” code)
• The “excuse parade” developers use when things lag
• Expectations for game server concurrency
• Parsing a billion rows in Java: lessons in performance
• Containers, cloud, and performance myths
• AI: helpful tool or unethical theft?
• Why Casey would get rid of Python

• 
  

  ⏱️ Timestamps:

  (00:00) Teaser

  (01:00) Meet Casey Muratori

  (04:00) The Windows Terminal story

  (15:00) The “excuse parade” of slow software

  (18:30) Game servers and performance expectations

  (24:00) The billion-row Java challenge

  (46:00) Getting 30x performance gains

  (52:00) Modern CPUs and performance myths

  (01:09:00) Should companies rewrite bloated software?

  (01:18:00) Casey’s take on AI

  (01:33:00) AI and ethics: who owns the data?

  (01:40:00) Rapid-fire questions: OS, IDEs, languages

  (01:50:00) Giveaway and Outro

  
  

• New episodes every other Wednesday. Subscribe for more deep, developer-focused conversations.

  Watch the episodes on YouTube: https://youtu.be/apREl0KmTdQ

Jodie Burchell (Data Scientist) joins Marco to cut through the hype around large language models. From GPT and Gemini to Claude and beyond — how do you really choose the right one? Benchmarks, context length, hallucinations, ethics, and AGI predictions — all on the table.

If you’ve ever asked yourself, “Which LLM should I use for my work?” — this conversation will help you see past the hype.

💡 Topics in this episode:

• How to compare LLMs for different developer tasks
• Enhanced vs adaptive thinking (is it just branding?)
• Corporate considerations: cost, privacy, and hosting
• The ethics and lawsuits shaping AI’s future
• Predictions for AGI

⏱️Timestamps:

(00:00:00) Teaser

(00:00:37) Intro

(00:01:08) From PhD and academia to data science at JetBrains

(00:02:31) Early NLP versus modern LLMs

(00:04:46) From LSTMs to Transformers (BERT and GPT)

(00:08:49) The DeepSeek surprise and model scaling limits

(00:12:25) Benchmarks, assessments, and confusion for end users

(00:17:18) Choosing models in practice

(00:21:10) “Thinking” models and reasoning limits

(00:23:48) Do you really need the newest model?

(00:25:58) Hallucinations and how to handle them

(00:28:30) Agents and RAG: real-world applications

(00:32:55) Vibe coding: hype versus reality

(00:37:46) What are AI agents? Tools, MCP, and multi-agent apps

(00:43:01) Self-hosting versus proprietary models

(00:45:20) Fine-tuning explained and Hugging Face

(00:50:17) Building reliable AI apps (tests, A/B, traces)

(00:55:33) Privacy, company data, and self-hosting concerns

(00:58:37) Ethical issues: data sourcing and labor

(01:04:23) Environmental costs and the push for smaller models

(01:06:26) Juniors, skills, and the future of coding with AI

(01:09:15) Learning fundamentals in the age of LLMs

(01:13:24) AGI: definitions, timelines, and Jodie’s twenty euro bet

(01:20:03) Rapid-fire questions: slang, food, and culture

(01:25:47) Giveaway

(01:26:26) Outro

New episodes every other Wednesday. Subscribe for more deep, developer-focused conversations.

Watch the episodes on YouTube: https://jb.gg/themarco.show-youtube

Follow Jodie Burchell:

LinkedIn: jodieburchell

Bluesky: @t-redactyl.bsky.social

Mastodon: @t_redactyl@fosstodon.org

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