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Agenda: 29th September 2025 Time: 2:00pm to 3:00pm
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Description
This project will review all
quanteconlectures to make improvements, check for typos and review mathematical notation, modernise code (style and function) such as migrating much of ournumpyandnumbacode to make use of the more computationally efficient libraryjax[cpu,gpu].This project is the core focus for QuantEcon over the coming months and consists of three areas of work:
quanteconworkflowsEnglishtoChineseMore details for each of these can be found below, in addition to useful resources.
Some metrics for success:
Timeline: August 2025 to October 2025
Important
anaconda=2025.06andpython=3.13Modernize and Review Lectures + Code
This project includes:
sphinx-proofandsphinx-exercisedirectives to modernise the text layout (see style guide)SyntaxWarnings,FutureWarnings,DeprecationWarningsnumpy, numbato make use ofjax[cpu]and/orjax[gpu](Notes: this will require reading and thinking through the context of each lecture to adjust comments around timing comparisons, and discussion around how the code is written, undertaking timing comparisons and documenting them (so we can write a blog post about the improvements)).Important
For changes to https://github.com/QuantEcon/lecture-python-intro and https://github.com/QuantEcon/lecture-python.myst please
coordinate changes with the Translation Team (see Translation Project below) via Project Tagging.
Software Baseline:
anaconda=2025.06, python=3.13A good example of a well put together Issue and PR:
Here are some good examples of code updates for reference:
Important
We are currently writing a style guide update to include specific style changes around the use of
NamedTuplesetc. to assistwith conversion to JAX.
AI Workflows (Issue closure, code reviews)
Note
If you have a particular interest in AI then please get in touch with @mmcky to discuss joining this team.
This team will experiment with a range of AI tooling and will write up a guide on how to use AI for the broader team.
This project is to document how best we can use AI to help with productivity improvements, in addition to getting suggestions for making sensible code improvements. A good example is when @oyamad used
ChatGPTto review some of the code in our intermediate python lecture series and opened the following issue. What is excellent about this example is it is not a copy-paste from ChatGPT but rather a synthesis of a core suggestion that was discovered while using the tool. This is a great example of the types of discovery AI tools can enable.The AI project includes:
It is important to remember, AI is a companion tool, not a final decision maker. There is some discussion of how to use AI here
Translation Project
We have used
Claude AIto translate our English lectures into Mandarin. Reviewers will take the translated lectures and review them to improve the quality of the translation. This includes improvements to language, applying the quantecon glossary to harmonise terms used across lectures, thinking through QuantEcon processes to make suggestions on how to improve management of multilingual lecture sites.Communications and PR Reviews
For PR comments and reviews please tag @mmcky and @HumphreyYang.
Please DO NOT tag @jstac or @thomassargent30.
We are implementing a review system, that we hope will make it more rewarding and time efficient for everyone. Humphrey and I can review the PRs and provide feedback to the team. Keep an eye on each section above as we may add additional reviewers for different parts of the project as we go along.
The reviewer team will process and collect PRs and escalate them to @jstac and @thomassargent30 on a fortnightly basis.
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