Cohort 1

Amir Vahid

Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Using AI coding assistants and tools to write, review, and ship code faster.

Automating professional workflows with AI tools — productivity, operations, and task automation.

Reliable work from coding agents starts with the loop, not the prompt.

CEO, Applied AI; Author, ML for Trading

Stefan Jansen

Stefan Jansen is the author of Machine Learning for Trading (3rd ed.), maintainer of six open-source Python libraries and 450+ teaching notebooks, and founder of Applied AI. For the last year-plus he has built production systems with coding agents across Claude Code and Codex — the exact loop-engineering discipline this workshop teaches.

Loop Engineering: Reliable Work From Coding Agents

ML for Trading · Applied AI · Since 2013

Author, ML for Trading · Founder, Applied AI · Investing since 2013

<h3>Why I enrolled</h3>
<p class="p2-tall">Learn to work with agents</p>
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<h3>What I’ll apply</h3>
<p class="p2-tall"><strong>I’m excited to replace ad hoc prompting with structured, reliable AI engineering workflows. I plan to apply these methods to build production-grade coding agents for two very different domains: quantitative trading and next-generation water desalination. The biggest takeaway for me was treating AI agents as engineered systems—with planning, verification, and iterative refinement—rather than just chatbots.</strong></p><p class="p2-tall"><strong>I’m looking forward to seeing how others in the cohort apply these ideas to their own projects.</strong></p>
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Certificate

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