『Free Form AI』のカバーアート

Free Form AI

Free Form AI

著者: Michael Berk
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概要

Free Form AI explores the landscape of machine learning and artificial intelligence with topics ranging from cutting-edge implementations to the philosophies of product development. Whether you're an engineer, researcher, or enthusiast, we cover career growth, real-world applications, and the evolving AI ecosystem in an open, free-flowing format. Join us for practical takeaways to navigate the ever-changing world of AI.2026 Michael Berk 出世 就職活動 経済学
エピソード
  • Career Strategies: Emergent vs. Deliberate (E. 31)
    2026/03/01

    The conversation delves into the concepts of hygiene vs. motivation factors in the workplace and explores the methods of creating motivation within a team. It also touches on the role of a manager in maintaining team efficiency and morale. The conversation delves into the importance of mentorship and the builder ethic, emphasizing the value of humility and team cohesion. It also explores the concept of navigating a career, discussing the emergent vs. deliberate approach and the significance of humility and bias for action.

    Takeaways

    • Hygiene vs. motivation factors
    • Creating motivation in the workplace Mentorship and the Builder Ethic
    • Team Cohesion and Humility

    Chapters

    • 00:00 Creating Motivation in the Workplace
    • 29:45 Mentorship and the Builder Ethic
    • 37:31 Navigating a Career: Emergent vs. Deliberate
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    57 分
  • Personal Finance: Advice from Dr. Adam Link (E. 30)
    2026/02/22

    The conversation delves into the scale of finance and the concept of 'enough' in relation to wealth. It explores the value of money, safe withdrawal rates, financial mistakes, and the transition to wealth management as a career path. The conversation delves into the use of AI-based coding assistants, the importance of guardrails and testing focus, AI oversight and refactoring, workflow and quality assurance, code review dynamics, human-in-the-loop review, coaching and career development, and the metaphor of 'testing in prod' for human interactions. The key takeaways include the adversarial prompting approach for AI-based coding assistants, the significance of soft power and people skills for senior engineers, and the metaphor of 'testing in prod' for human interactions and relationships.

    Takeaways

    • Finance operates on a different scale
    • The concept of 'enough' varies based on individual circumstances AI-based coding assistants benefit from an adversarial prompting approach
    • Soft power and people skills are essential for senior engineers
    • Testing in prod is a metaphor for human interactions and relationships

    Chapters

    • 00:00 The Scale of Finance
    • 11:02 Defining 'Enough'
    • 17:54 Safe Withdrawal Rate
    • 22:56 Financial Mistakes and Overconfidence
    • 29:56 AI-Based Coding Assistance
    • 37:59 Human-in-the-Loop Review
    • 51:57 Testing in Prod: Human Interactions
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    57 分
  • Agentic Systems: Architectures at Microsoft (E. 29)
    2026/02/15

    The conversation delves into Victor Dibia's career journey, global experiences, transition to a PhD, and strategic career planning. It also explores his focus on AI tooling and frameworks, as well as the evolution of Autogen and the Microsoft Agent Framework. The conversation delves into the actor-first paradigm in multi-agent systems and the concept of ensembling in machine learning. It explores the benefits of the actor-first approach and the considerations for using multiple agents in complex tasks. Additionally, it discusses the power of ensembling in complementing the biases of individual models and the potential for mixture of experts in achieving better performance.

    Topics

    • Career progression through diverse experiences
    • Actor-first paradigm in multi-agent systems
    • Autogen and Semantic Kernel

    Chapters

    • 00:00 Career Journey and Global Experiences
    • 11:03 Focus on AI Tooling and Frameworks
    • 19:11 Evolution of Autogen and Microsoft Agent Framework
    • 28:59 Actor-First Paradigm in Multi-Agent Systems
    • 36:39 Ensembling in Machine Learning
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    57 分
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