Glamorous Toolkit is the Moldable Development Environment, an MIT-licensed Smalltalk environment that replaces code reading with deterministic contextual tools, aimed at developers and teams who need to make large systems — and the AI-generated code flowing into them — explainable, and who are willing to program the environment itself.
What it is
Glamorous Toolkit is an environment for making sense of systems and AI, described by its maintainers as the Moldable Development Environment. It sits in the Smalltalk ecosystem: the registry tags it with both smalltalk and pharo, and it presents itself as an IDE, notebook and visualization surface rather than a library. Everything in it is assembled from a language of visual and interactive operators, and combinations of those operators form its contextual micro tools.
The concrete problem is stated plainly in the README: code reading is the bottleneck, because systems are too large and AI generates code too fast. What it replaces is the act of reading source in order to understand a system. Instead of asking a person to read through a codebase, it offers deterministic contextual tools that compress the system around each problem, so humans and AI can explore faster. Moldable Development means adapting the environment to each context rather than forcing every question through one fixed viewer.
Key capabilities
- Deterministic contextual micro tools that compress a system around one problem, presented as an alternative to reading code.
- Tools built by the user or with AI inside one integrated environment.
- A distribution containing thousands of contextual tools, the same ones used to develop and explain the environment itself.
- An underlying language of visual and interactive operators that can be combined in many ways.
- An elaborate live book shipped with the environment, covering introductory videos, hands-on tutorials such as exploring CSV data, notes for working with Git, and guidance for setting up LLM connections.
- A ready-made installation made of a Glamorous Toolkit image, a virtual machine, and other resources and libraries, with topic coverage spanning ide, notebook, visualization and knowledge-management use cases.
Who uses it and how
- Developers with a reading problem about an artifact of a system; the README's advice is to pick a problem you care about and work through it.
- Teams whose systems are too large to read, and teams whose AI assistants generate code faster than people can review by reading it.
- Practitioners exploring data and other artifacts, following shipped tutorials such as exploring CSV data.
- People wiring the environment to language models through the book's guidance on LLM connections, and contributors developing Glamorous Toolkit itself using the source build. Community activity happens on the project's Discord server and YouTube channel.
Getting started
The ready-made, self-contained distribution can be downloaded and run from gtoolkit.com/download. Installing from sources is a separate path intended for developing Glamorous Toolkit itself: running mac.sh, linux.sh or windows.ps1 installs the virtual machine, clones the sources and builds an image in about ten minutes.
How it compares
The facts provide no list of paid products that Glamorous Toolkit replaces, and they name no directly comparable tool. In this registry it stands alone in the Productivity & Utilities / Note Taking & Knowledge Management category; the smalltalk and pharo tags describe its tradition rather than a rival product.
When to use it — and when not to
The README is explicit that leveraging Glamorous Toolkit requires programming it, with learning routed through the environment itself, its introductory videos and its live book. Anyone wanting a zero-configuration note-taking application, or an environment that pays off without writing Smalltalk, should look elsewhere. The source build is a contributor path rather than an end-user one, and the project is distributed as a local image and virtual machine rather than a hosted service.
project readme (upstream, from github) — read inline
The Moldable Development environment for making sense of systems and AI
Code reading is the bottleneck. Systems are too large, and AI generates code too fast.
Glamorous Toolkit replaces reading with deterministic contextual tools that compress the system around each problem, helping humans and AI explore faster.
Build these tools yourself or with AI in one integrated environment.
Download
Ready made distribution
Simply download and run: https://gtoolkit.com/download.
The download is a self contained installation made of a Glamorous Toolkit image, a virtual machine, and a few other resources and libraries.
Install from sources
Executing the script below installs the VM, clones the sources and builds an image. It takes ~10 minutes and it is useful for developing Glamorous Toolkit itself.
- On Mac:
curl https://dl.feenk.com/scripts/mac.sh | bash
- On Linux:
curl https://dl.feenk.com/scripts/linux.sh | bash
- On Windows:
wget https://dl.feenk.com/scripts/windows.ps1 -OutFile windows.ps1; ./windows.ps1
Get started
Learn how to learn
To leverage Glamorous Toolkit, you need to program it. The best way to learn that is to do it from within the environment. The distribution contains thousands of contextual tools that we used to develop and explain the environment itself. Learning how these tools work first accelerates your overall learning. Each of these is based on an underlying language made out of visual and interactive operators that can be combined in many ways. So, first learn the underlying language:
Watch 7 minutes introductory videos.
The environment ships with an elaborate live book that documents it. The book offers concrete starting steps through selected introductory videos, hands-on tutorials such as exploring CSV data, practical setup notes for working with Git, and guidance for setting up LLM connections.
Get more starting links in the book.
Pick a problem you care about
Moldable Development is about adapting the environment to match the context. It's one thing to see it on someone else's problem, and a whole other experience when you apply it to your problem. We encourage you to pick a problem you care about and work your way through it. Ideally pick a reading problem related to understanding various artifacts.
Get inspiration from case studies in the book.
Engage with the community
License
See LICENSE.