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:
⏱️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
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