Tue, Jul 7, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
Building Multi-Agent Forecasting Systems
Stefan Jansen
Author, ML for Trading (Ch. 24: agents) · Founder, Applied AI
Tue, Jul 7, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
Building Multi-Agent Forecasting Systems
Stefan Jansen
Author, ML for Trading (Ch. 24: agents) · Founder, Applied AI
What you'll learn
Turn vague asks into verifiable specs
Walk away with the align pattern: one question at a time, a checkable spec.md before any work begins.
Swap Claude and Codex without losing state
Pick up the same project from either host because state lives in files: no orchestrator, no re-explaining.
Catch session drift before it bites
Use the handoff and continue pattern: verification snapshots flag when the repo has moved under you between sessions.
Why this topic matters
Coding agents do their best work once they know what to ship. The hard part is the first half: removing ambiguity, planning milestones, tracking progress, building memory that survives a session ending. And the artifact doesn't have to be code. The same workflow holds for research notes, decks, or analyses. This lesson walks the six-step chain that holds long work together.
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.