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docs: make getting started assistant-first #646

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16 changes: 3 additions & 13 deletions README.md
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Expand Up @@ -23,27 +23,17 @@ When two or more galaxies are aligned perfectly down our line-of-sight, the back

This is called strong gravitational lensing and **PyAutoLens** makes it **simple** to model strong gravitational lenses, using JAX to **accelerate lens modeling on GPUs**.

> 🤖 **AI-assisted use:** you can also learn and drive **PyAutoLens** with AI — either a browser chat assistant (ChatGPT, Claude) pointed at [**autolens_assistant**](https://github.com/PyAutoLabs/autolens_assistant), or a fully agentic coding tool (Claude Code, Codex) that runs lens modeling end-to-end on your machine. See the **Three Ways to Learn PyAutoLens** section below.

## Getting Started

The following links are useful for new starters:
The [**PyAutoLens AI Assistant**](https://github.com/PyAutoLabs/autolens_assistant) supports conversation agents such as ChatGPT and coding agents such as Claude Code and Codex. You can get started simply by asking it a question about gravitational lensing or describing the task you would like to perform with **PyAutoLens**. See the assistant for its full scope and instructions.

The following human-readable documentation and examples are also useful for new starters:

- [The PyAutoLens readthedocs](https://pyautolens.readthedocs.io/en/latest): which includes [an overview of PyAutoLens's core features](https://pyautolens.readthedocs.io/en/latest/overview/overview_1_start_here.html), [a new user starting guide](https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) and [an installation guide](https://pyautolens.readthedocs.io/en/latest/installation/overview.html).
- [The introduction Jupyter Notebook on Google Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.7.23.1/notebooks/imaging/start_here.ipynb): try **PyAutoLens** in a web browser (without installation).
- [The autolens_workspace GitHub repository](https://github.com/PyAutoLabs/autolens_workspace): example scripts covering every **PyAutoLens** use case.
- [The HowToLens GitHub repository](https://github.com/PyAutoLabs/HowToLens): a Jupyter notebook lecture series teaching strong lensing and lens modeling from the ground up.

## Three Ways to Learn PyAutoLens

There are three ways to learn how to use **PyAutoLens**, which you can freely mix and match:

1. **Manual navigation** — read the workspace guides yourself, starting from the [new user guide](https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) and the `start_here` notebooks, which are organised by lens scale and dataset type.
2. **AI chat assistant** — ask questions to a conversational AI assistant such as ChatGPT or Claude in the browser. Go to the [autolens_assistant](https://github.com/PyAutoLabs/autolens_assistant) repository and copy the ready-to-use example prompt from its README into ChatGPT or Claude to get started. This is ideal for learning the API, working out how to perform a calculation, and creating end-to-end example Python scripts.
3. **Fully agentic AI** — drive **PyAutoLens** end-to-end with an agentic coding tool such as [Claude Code](https://claude.com/claude-code) or [Codex](https://developers.openai.com/codex) together with [autolens_assistant](https://github.com/PyAutoLabs/autolens_assistant). These can inspect your data, write and run scripts, and manage a lens-modeling project directly on your machine.

See [autolens_assistant](https://github.com/PyAutoLabs/autolens_assistant) for more on the AI-assisted options (2 and 3).

## Community & Support

Support for **PyAutoLens** is available via our Slack workspace, where the community shares updates, discusses
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Expand Up @@ -8,13 +8,11 @@ When two or more galaxies are aligned perfectly down our line-of-sight, the back

This is called strong gravitational lensing and **PyAutoLens** makes it simple to model strong gravitational lenses, using JAX to **accelerate lens modeling on GPUs**.

```{note}
🤖 **AI-assisted use:** alongside reading the guides, you can learn and drive **PyAutoLens** with AI — either a browser chat assistant (ChatGPT, Claude) pointed at [autolens_assistant](https://github.com/PyAutoLabs/autolens_assistant), or a fully agentic coding tool (Claude Code, Codex) that runs lens modeling end-to-end on your machine. The [new user guide](https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) describes all three ways to learn.
```

# Getting Started

The following links are useful for new starters:
The [**PyAutoLens AI Assistant**](https://github.com/PyAutoLabs/autolens_assistant) supports conversation agents such as ChatGPT and coding agents such as Claude Code and Codex. You can get started simply by asking it a question about gravitational lensing or describing the task you would like to perform with **PyAutoLens**. See the assistant for its full scope and instructions.

The following human-readable documentation and examples are also useful for new starters:

- [The PyAutoLens readthedocs](https://pyautolens.readthedocs.io/en/latest): which includes [an overview of PyAutoLens's core features](https://pyautolens.readthedocs.io/en/latest/overview/overview_1_start_here.html), [a new user starting guide](https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) and [an installation guide](https://pyautolens.readthedocs.io/en/latest/installation/overview.html).
- [The introduction Jupyter Notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.7.23.1/notebooks/imaging/start_here.ipynb), where you can try **PyAutoLens** in a web browser (without installation).
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22 changes: 3 additions & 19 deletions docs/overview/overview_2_new_user_guide.md
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Expand Up @@ -7,27 +7,11 @@ different types of data (e.g. imaging, interferometer, and point-source observat

The autolens_workspace contains a suite of example Jupyter Notebooks, organised by lens scale and dataset type.

## Three Ways To Learn PyAutoLens
## AI Assistant

There are three ways to learn how to use **PyAutoLens**, which you are free to mix and match:
The [**PyAutoLens AI Assistant**](https://github.com/PyAutoLabs/autolens_assistant) supports conversation agents such as ChatGPT and coding agents such as Claude Code and Codex. You can get started simply by asking it a question about gravitational lensing or describing the task you would like to perform with **PyAutoLens**. See the assistant for its full scope and instructions.

1. **Manual Navigation**: Read the workspace guides yourself. To find the example notebook best suited to your
science case, work through the two questions below ("What Scale Lens?" and "What Dataset Type?"), which point you
to the right starting point. This is the traditional route, and the rest of this guide supports it.

2. **AI Chat Assistant**: Ask questions to a conversational AI assistant such as ChatGPT or Claude in the browser.
Go to the [autolens_assistant](https://github.com/PyAutoLabs/autolens_assistant) repository and copy the ready-to-use
example prompt from its README into ChatGPT or Claude — it gets you asking useful questions from the very first
message. This is ideal for learning the API, working out how to perform a calculation, and creating end-to-end
example Python scripts.

3. **Fully Agentic AI**: Use an agentic coding tool such as [Claude Code](https://claude.com/claude-code) or
[Codex](https://developers.openai.com/codex) together with [autolens_assistant](https://github.com/PyAutoLabs/autolens_assistant).
These can inspect your data, write and run scripts, and manage an end-to-end lens modeling project directly on your
machine. See the [autolens_assistant](https://github.com/PyAutoLabs/autolens_assistant) repository for more information.

The rest of this guide supports **manual navigation**: we begin by answering two simple questions to find your most
appropriate starting point.
The rest of this human-readable guide begins with two simple questions to help you find the most appropriate example notebook for your science case.

## What Scale Lens?

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