Issue #102 Agents Edwin Dong, Anurag Shinde
Self-Improving Agents, Simply Explained
Free editionPRO edition
This week’s PRO issue includes a Colab notebook, going over a simple, ~50 line core idea implementation of the Darwin Gödel machine. Please do read the full article before attempting the code tutorial.
As always, the code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
The monthly interview question bank can be found here. These are industry standard questions that are tested across major tech companies, AI research firms, and more. The questions are sorted by difficulty and topic. Check back frequently for more questions!
Every major leap in AI has required human intervention, where researchers design the architecture, write the training code, and engineer the tools.
The Darwin Gödel Machine (DGM), a new system published at ICLR 2026, asks a different question: What if the AI could do all of that itself?
The DGM is a self-improving coding agent that reads its own source code, proposes changes to make itself better, implements those changes, and tests whether they have worked.
Over 80 iterations, it improved its score on the SWE-bench coding benchmark from 20% to 50% and on the Polyglot multilanguage benchmark from 14.2% to 30.7%, all without any human intervention.
These are some significant gains, and the final agent performs on par with the best open-source, human-engineered solutions, systems built by teams of expert developers over months.
The DGM was able to match that level autonomously, starting from a lightweight base agent with two basic tools.
This edition is for PRO members.
