Wed, Sep 30, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
Loop Engineering: Reliable Work From Coding Agents
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.
Wed, Sep 30, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
Loop Engineering: Reliable Work From Coding Agents
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.
What you'll learn
See a coding agent do work that isn't coding
Watch one agent run an analysis, drive a browser, and produce a document, from a single well-framed task.
Frame a task an agent can actually finish
Learn how to scope and hand off work so the agent knows what done means and stays on track without you.
Drive the loop instead of babysitting it
See when to let the agent run, when to step in, and how to tell a finished task from a stuck one.
Why this topic matters
Most people point a coding agent at a bug and stop there. The same tool that edits a file can read a codebase, run an analysis, drive a browser, draft a document, and check its own work, if you drive the loop well. This Lightning Lesson shows how to get real work out of a coding agent beyond writing code: how to frame a task, keep the agent on track, and know when the loop is done.
You'll learn from
Stefan Jansen
Author, ML for Trading · Founder, Applied AI · Investing since 2013
Stefan is the author of ML for Trading — the book and open-source companion code (19,000+ GitHub stars) that have become a practitioner reference for applying ML to financial markets. The 2026 third edition expands to nine cross-asset case studies, with a foreword by Antonio Gulli, Senior Director, Google.
He maintains the Zipline fork the quant community relies on, and built the six-library stack — data to live — behind the third edition's case studies. Investment partner since 2013, he has built trading platforms and live strategies across asset classes. In 2016 he founded Applied AI, which brings production ML to investment teams and other data-rich verticals. He has taught ML to 110,000+ professionals through DataCamp and General Assembly, incl. at Bloomberg and BlackRock.