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Non-fiction writer, podcaster and occasional TV host. Author of thirteen books including The Ghost Map, Where Good Ideas Come From and Extra Life, host of the PBS series How We Got To Now, and author of the Adjacent Possible newsletter on innovation.

• Covered a lot of ground with @dblums on the Webby-winning Future Around And Find Out podcast, including: • why AI podcasts need imperfections • how Dungeons & Dragons players are embracing @NotebookLM • using AI to uncover unexpected connections • the new possibilities of AI-augmented writing • the downsides of cognitive offloading • using AI as your second brain to help you deepen your thinking, not replace it

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Machine learning engineer and consultant focused on RAG and retrieval systems. He writes about applied AI engineering at jxnl.co and is the author of the instructor library.

• Why Grep Beat Embeddings in Our SWE-Bench Agent (Lessons from Augment)

  Insights from Colin Flaherty on building autonomous coding agents and how agentic approaches reshape retrieval-augmented generation systems.

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• RAG Master Series: Complete Guide to Retrieval-Augmented Generation

  Comprehensive guide to building, improving, and scaling RAG systems. From fundamentals to advanced enterprise implementations with real-world examples and proven strategies.

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AI research engineer working on large language models. He writes the Ahead of AI newsletter and is the author of Build a Large Language Model (From Scratch).

• RAGs — RAGs: Simple implementations of Retrieval Augmented Generation (RAG) Systems

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Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.

• An Open Course on LLMs, Led by Practitioners

  Today, we are releasing Mastering LLMs, a set of workshops and talks from practitioners on topics like evals, retrieval-augmented-generation (RAG), fine-tuning and more. This course is unique because it is: Taught by 25…

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Machine-learning researcher; has written the Lil'Log survey posts on how a model technique works since 2017, and worked at OpenAI from 2018 to 2024, latterly leading its safety systems team.

• Learning with not Enough Data Part 3: Data Generation

  Here comes the Part 3 on learning with not enough data (Previous: Part 1 and Part 2). Let’s consider two approaches for generating synthetic data for training. Augmented data. Given a set of existing training samples, w…

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