Loop Engineering, Without the BS

Hosted by Hugo Bowne-Anderson

Tue, Jul 28, 2026

11:00 PM UTC (30 minutes)

Virtual (Zoom)

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Build AI Agents from First Principles
Hugo Bowne-Anderson
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What you'll learn

See the old pattern underneath the new name

Reduce loops to generate, score, revise, repeat, then compare them with search, gradient descent, and ordinary testing.

Know when a loop earns its token bill

Decide whether repeated model calls improve the result, save human time, or merely produce a larger token bill.

Stop agents from grading their own homework

Use held-out evals, separate access, human labels, and validation boundaries the agent cannot quietly rewrite.

Why this topic matters

Every agent workflow now wants a loop. Generate, judge, revise, repeat, and keep spending tokens until the chart goes up. Sometimes this is useful optimization. Sometimes it is an LLM grading its own homework with extra steps. We’ll separate the engineering from the hype, decide when a loop is worth running, and build eval boundaries that stop agents from rewriting the answer key.

You'll learn from

Hugo Bowne-Anderson

AI & data engineer, consultant, educator of 6+ million students (ex-Yale)

Hugo Bowne-Anderson is an independent data & AI consultant with extensive experience in the tech industry. He has advised and taught teams building AI-powered systems, including engineers from Netflix, Meta, and Amazon. He is the host of Vanishing Gradients, where he explores developments in data science and artificial intelligence, and has written for publications including Harvard Business Review and VentureBeat. Previously, Hugo served as Head of Developer Relations at Outerbounds and held roles at Coiled and DataCamp, where his work in data science education reached over 6 million learners. He has taught at Yale University, Cold Spring Harbor Laboratory, and conferences including SciPy and PyCon, and is a passionate advocate for democratizing AI skills and open-source tools.

Coached teams at

Google
Instagram
OpenAI
Netflix
Yale
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