update broken minimal example links

README.md

@@ -96,7 +96,7 @@ The DSPy documentation is divided into **tutorials** (step-by-step illustration
 | **Level** |  **Tutorial** |  **Run in Colab** |  **Description** |
 | --- | -------------  |  -------------  |  -------------  | 
 | Beginner |  [**Getting Started**](intro.ipynb) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/intro.ipynb)  |  Introduces the basic building blocks in DSPy. Tackles the task of complex question answering with HotPotQA. |
-| Beginner | [**Minimal Working Example**](https://dspy-docs.vercel.app/docs/quick-start/minimal-example) | N/A | Builds and optimizes a very simple chain-of-thought program in DSPy for math question answering. Very short. |
+| Beginner | [**Minimal Working Example**](/docs/docs/quick-start/getting-started-01.md) | N/A | Builds a very simple chain-of-thought program in DSPy for question answering. Very short. |
 | Beginner | [**Compiling for Tricky Tasks**](examples/nli/scone/scone.ipynb) | N/A | Teaches LMs to reason about logical statements and negation. Uses GPT-4 to bootstrap few-shot CoT demonstrations for GPT-3.5. Establishes a state-of-the-art result on [ScoNe](https://arxiv.org/abs/2305.19426). Contributed by [Chris Potts](https://twitter.com/ChrisGPotts/status/1740033519446057077). |
 | Beginner | [**Local Models & Custom Datasets**](examples/skycamp2023.ipynb) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/skycamp2023.ipynb) | Illustrates two different things together: how to use local models (Llama-2-13B in particular) and how to use your own data examples for training and development.
 | Intermediate | [**The DSPy Paper**](https://arxiv.org/abs/2310.03714) | N/A | Sections 3, 5, 6, and 7 of the DSPy paper can be consumed as a tutorial. They include explained code snippets, results, and discussions of the abstractions and API.

docs/docs/deep-dive/optimizers/bootstrap-fewshot.md

@@ -8,7 +8,7 @@ When compiling a DSPy program, we generally invoke an optimizer that takes the p
 
 ## Setting up a Sample Pipeline
 
-We will be making a basic answer generation pipeline over GSM8K dataset that we saw in the [Minimal Example](https://dspy-docs.vercel.app/docs/quick-start/minimal-example). We won't be changing anything in it! So let's start by configuring the LM which will be OpenAI LM client with `gpt-3.5-turbo` as the LLM in use.
+We will be making a basic answer generation pipeline over the GSM8K dataset. We won't be changing anything in it! So let's start by configuring the LM which will be OpenAI LM client with `gpt-3.5-turbo` as the LLM in use.
 
 ```python
 import dspy

docs/docs/deep-dive/optimizers/miprov2.md

@@ -12,7 +12,7 @@ sidebar_position: 6
 
 ### Setting up a Sample Pipeline
 
-We'll be making a basic answer generation pipeline over GSM8K dataset that we saw in the [Minimal Example](/quick-start/minimal-example), we won't be changing anything in it! So let's start by configuring the LM which will be OpenAI LM client with `gpt-3.5-turbo` as the LLM in use.
+We'll be making a basic answer generation pipeline over the GSM8K dataset. So let's start by configuring the LM which will be OpenAI LM client with `gpt-3.5-turbo` as the LLM in use.
 
 ```python
 import dspy

docs/docs/tutorials/other_tutorial.md

@@ -9,7 +9,7 @@ sidebar_position: 99999
 | **Level** |  **Tutorial** |  **Run in Colab** |  **Description** |
 | --- | -------------  |  -------------  |  -------------  | 
 | Beginner |  [**Getting Started**](https://github.com/stanfordnlp/dspy/blob/main/intro.ipynb) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/intro.ipynb)  |  Introduces the basic building blocks in DSPy. Tackles the task of complex question answering with HotPotQA. |
-| Beginner | [**Minimal Working Example**](/quick-start/minimal-example) | N/A | Builds and optimizes a very simple chain-of-thought program in DSPy for math question answering. Very short. |
+| Beginner | [**Minimal Working Example**](/docs/docs/quick-start/getting-started-01.md) | N/A | Builds a very simple chain-of-thought program in DSPy for question answering. Very short. |
 | Beginner | [**Compiling for Tricky Tasks**](https://github.com/stanfordnlp/dspy/blob/main/examples/nli/scone/scone.ipynb) | N/A | Teaches LMs to reason about logical statements and negation. Uses GPT-4 to bootstrap few-shot CoT demonstrations for GPT-3.5. Establishes a state-of-the-art result on [ScoNe](https://arxiv.org/abs/2305.19426). Contributed by [Chris Potts](https://twitter.com/ChrisGPotts/status/1740033519446057077). |
 | Beginner | [**Local Models & Custom Datasets**](https://github.com/stanfordnlp/dspy/blob/main/skycamp2023.ipynb) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/skycamp2023.ipynb) | Illustrates two different things together: how to use local models (Llama-2-13B in particular) and how to use your own data examples for training and development.
 | Intermediate | [**The DSPy Paper**](https://arxiv.org/abs/2310.03714) | N/A | Sections 3, 5, 6, and 7 of the DSPy paper can be consumed as a tutorial. They include explained code snippets, results, and discussions of the abstractions and API.