Cursor AI and VSCode Jupyter Notebook Paths

I ran into a frustratingly common problem trying to make my Jupyter Notebooks for Pipulate work consistently across different environments like native JupyterLab, VSCode, and Cursor AI: reliably determining the notebook’s own file path. After initial attempts using os.getcwd() yielded different results depending on the environment, I enlisted ChatGPT and Gemini for deep research, comparing their speed and output quality (Gemini was faster but harder to format, ChatGPT slower but cleaner). The research confirmed there’s no single, foolproof, built-in way to get the notebook path due to Jupyter’s architecture separating the kernel from the frontend. While workarounds involving environment variables (JPY_SESSION_NAME, __vsc_ipynb_file__) or querying the Jupyter server API exist, they felt overly complex, potentially fragile, or environment-specific for my goal of creating clear workflow examples. Ultimately, I decided to reject these auto-discovery methods for my notebooks and embrace simple, explicit, hardcoded paths, accepting that users will need to adjust them, which is clearer and less brittle for the development phase before these workflows are ported into Pipulate’s controlled environment.

By Mike Levin

Saturday, March 29, 2025

Creating Cross-Platform Jupyter Notebooks in Pipulate

Alright, so yesterday I made the first Jupyter Notebook that’s officially in the Pipulate repo. I put it under a folder called notebooks which opens up its own whole can of path worms. Normally this wouldn’t be an issue, but I’m making these notebooks so that they can run just as easily from VSCode and its forks like Cursor AI and Windsurf as they do natively in JupyterLab, and I suppose even Google Colab.

Me: Show me the Python to print current path. Like when you run a script, the relative start-path changes based on the folder you’re in. And there’s tons of context, like being in a terminal, a Jupyter Notebook from JupyterLab vs a Notebook from VSCode or Cursor AI. This location inevitably changes and I need a consistent way to know what it is.

ChatGPT: To reliably get and print the current working directory consistently across different environments (scripts, terminal, JupyterLab, VSCode, Cursor AI, etc.), you can use the os module from the standard library:

import os

current_path = os.getcwd()
print(f"Current working directory: {current_path}")

Explanation:

os.getcwd() retrieves the absolute path to the current working directory, which can vary depending on how or where the script or notebook is executed.
Using this method ensures you get a consistent, absolute location regardless of your environment.

If you need the directory of the running Python file itself (useful when you run scripts directly):

import os

script_path = os.path.dirname(os.path.abspath(__file__))
print(f"Script location: {script_path}")

This method (__file__) won’t work directly in Jupyter notebooks, as notebooks don’t define __file__.

To cover both scenarios (script vs notebook context):

import os

def current_path():
    try:
        # This works in regular .py script executions
        return os.path.dirname(os.path.abspath(__file__))
    except NameError:
        # This works in notebooks and interactive shells
        return os.getcwd()

print(f"Consistent current location: {current_path()}")

This will reliably show you the path whether you’re running from:

A standalone Python script in a terminal
Jupyter Notebook (JupyterLab, VSCode, Cursor AI, Colab)
Interactive Python shell or IPython session

Unexpected Surprise With `os.getcwd()`

Uhh, the output of os.getcwd() is different in Cursor vs. JupyterLab. This is an unexpected problem, meaning we can’t even use it to automatically set a path at the beginning. Why?

Me: Is there any reason this would end up being different in a VSCode/Cursor notebook than native in JupyterLab? JupyterLab appears to use wherever the notebook you’re running resides, whereas Cursor is using wherever the running instance of the kernel was started from.

ChatGPT: Yes, there’s a specific reason for this behavior, and it comes down to how kernels are managed by different notebook environments:

Why the difference?

JupyterLab (native) typically sets the current working directory (os.getcwd()) to the location of the notebook you opened. This behavior is managed by the Jupyter server configuration. By default, when you start JupyterLab, the working directory matches where the notebook itself resides.
VSCode and Cursor AI often launch kernels differently—usually from a fixed project root or from wherever the kernel was originally started. This means os.getcwd() in notebooks opened from these environments tends to reflect the kernel’s original startup path, not the notebook’s physical location on disk.

I deleted everything it suggested after this having to do with the check. It was worthless. After going back and forth with ChatGPT 4.5 a few rounds, I came up with something that was not broken, fragile, circular or otherwise horrible:

import os
from pathlib import Path

def set_notebook_working_dir():
    try:
        # For VSCode and Cursor AI environments:
        notebook_path = Path(globals()['__vsc_ipynb_file__']).resolve()
        notebook_dir = notebook_path.parent
    except (KeyError, TypeError):
        # Fallback for JupyterLab and other standard Jupyter environments:
        notebook_dir = Path.cwd().resolve()

    os.chdir(notebook_dir)
    print(f"Working directory set to: {notebook_dir}")

set_notebook_working_dir()

But yeah, it’s still kind of horrible.

This horrible bit of code right here
Is something that I terribly fear
I must include Notebooks now
So they run, no matter how

And that’s really just the price of not relying on the ultra-reliable path of the git repo folder the kernel is always run from, trashing it up with Notebooks and other temporary work files or databases and such that should be in other subfolders. This is not an issue with .py-files, and I can see why a lot of people get frustrated with .ipynb files and the kooky kernel contexts.

But that’s the price of an always running exploratory environment (REPL - read, eval, print-loop). It has driven the massive popularity of Notebooks, and for a good reason. It’s particularly good for data-pipeline workflows, because you can sort of feel them through with Pandas DataFrames (df’s) that stay in memory from prior steps, and I don’t want to lose that magic when walking people through how to design SEO workflows and bottle them up in Pipulate.

Another Unexpected Surprise With IPython Kernel in VSCode

Ugh! Surprise, the above code is fragile depending on what kernel is selected from VSCode/Cursor. Going into Deep Research mode.

Me: Wow, so what I’m hearing is that there’s basically no reliable way to discover the path to where a current notebook is running from that works across VSCode and JuptyerLab that isn’t explicitly setting a hardwired path? Can you phrase a prompt so that I can use the Deep research to aggressively ferret out whether this is true or not and what solutions are out there?

ChatGPT 4.5: Absolutely! Here’s a precise, aggressive research prompt for Deep Research mode to conclusively determine if any robust, cross-platform solutions exist for discovering the current notebook’s path, independent of environment (JupyterLab vs. VSCode vs. Cursor AI):

Prompt for Deep Research:

Objective: Identify and conclusively evaluate if a reliable, cross-platform method exists in Python to programmatically determine the absolute filesystem path of the currently executing Jupyter notebook (.ipynb file), specifically across these popular environments:

JupyterLab (standard notebook server)
VSCode with built-in Jupyter support
Cursor AI’s Jupyter notebook integration

Constraints to clarify aggressively:

The solution must not rely on manually setting or hardcoding the notebook path.
The solution must not depend exclusively on VSCode- or Cursor-specific injected globals (such as __vsc_ipynb_file__), unless robust fallback logic exists that reliably supports other environments.
The solution should avoid additional package installations if possible, or at minimum, identify clearly what standard and non-standard dependencies would be required.
Explicitly evaluate the reliability, robustness, and environmental constraints for each suggested method.
Clearly explain if no unified solution exists, along with the definitive technical reasons why this limitation persists in Jupyter’s architecture.

Deliverables clearly requested:

A summary stating clearly and unambiguously if a single reliable cross-environment solution exists.
A list of known practical solutions (including well-known community workarounds, Python snippets, IPython magic commands, or specialized Jupyter extensions), their explicit constraints, and trade-offs.
Final recommendations for practical, minimal-effort paths forward for typical users working across JupyterLab and VSCode/Cursor AI environments.

This will trigger a thorough investigation using Deep Research mode to clearly answer this surprisingly tricky question once and for all.

Interesting note, it put the above recommended prompt in a markdown blockquote for the first time I’ve seen. I un-blockquoted it so that it didn’t look horrible here, and so that it copy/pastes better.

Okay, so I’ve got a good query to ferret out the best answer based on Deep Research, so as per my usual process these days, I put ChatGPT, Gemini and (attempt to) Grok3 in a 3-way research horse race. But Grok3 comes back with:

Grok was unable to finish replying. Please try again later or use a different model.

And at 8:00 AM EST on a Saturday morning! And I tried several times. I guess their servers are melting.

And while I watch the research from the 2 horses still in the race, wow there is no doubt Google Gemini Deep Research is the lightning bruiser of research. It considers a vast number of sites and it walks through the steps quickly and it gets to the end report fast.

ChatGPT Deep research on the other hand is a kind of take-your-time, look at one thing at a time, meandering sort of researcher. I mean it takes something like 10 times longer than Gemini. If in game tropes Gemini is the lightning bruiser, ChatGPT appears to be a level-1 Dungeons & Dragons character.

Google Gemini Deep Research

Me: [Prompt sent]

A surprisingly entertaining and few moments later, the Google Gemini Deep Research is done! I now go through the familiar Export to Docs and subsequent Download / Markdown (.md) routine, selecting it and forgetting for awhile, because it’s going to take awhile to get that .md file. You’d think it was doing the research again!

OMG, all the time that I gained in the speed of Gemini research, I lost in formatting it’s output. There’s new stuff here, and it’s really the export from Google Docs that’s doing this. But there seems to be no other choice at the moment. I’m going to document underneath the research results.

Gemini:

Programmatic Determination of Jupyter Notebook Path Across Environments: An Evaluation

I. Introduction

The objective of this report is to conclusively evaluate the existence of a reliable, cross-platform method in Python to programmatically determine the absolute filesystem path of the currently executing Jupyter notebook (.ipynb file). This evaluation specifically considers three popular environments: JupyterLab (standard notebook server), VSCode with built-in Jupyter support, and Cursor AI’s Jupyter notebook integration. The analysis will address several key constraints, including the prohibition of hardcoded paths or exclusive reliance on environment-specific variables without robust fallbacks. Furthermore, the report will explore potential solutions utilizing standard Python libraries, IPython magic commands, and Jupyter extensions, while also identifying any necessary dependencies. Finally, if a unified solution proves non-existent, the underlying technical reasons within Jupyter’s architecture will be elucidated.

II. Inherent Challenges in Obtaining the Notebook Path

Determining the absolute path of a running Jupyter notebook programmatically presents a unique challenge due to the architectural separation between the Jupyter frontend and the kernel where the Python code executes 1. The kernel, which is responsible for running the Python code within the notebook, operates independently of the specific environment hosting the notebook interface. This design allows for flexibility, enabling a single kernel to serve multiple frontends or even operate remotely. Consequently, the kernel does not inherently possess direct knowledge of the notebook file’s location on the filesystem as perceived by the frontend application 2. This fundamental separation is a primary reason why a universally straightforward method to retrieve the notebook’s path across all environments is difficult to establish. The Jupyter Protocol, which facilitates communication between the frontend and the kernel, was not designed with the explicit goal of exposing the notebook’s file path to the kernel 2.

III. Limitations of the __file__ Variable

In standard Python scripts (.py files), the file variable conventionally holds the path to the currently executing file 4. However, this attribute is not consistently available or reliable within the context of Jupyter notebooks 4. Research indicates that file is generally not defined in the Jupyter Notebook environment because the code is not executed as a traditional script file 5. Some sources explicitly state that file does not function within a Jupyter notebook’s context 4. While there might be instances where certain environments, such as VSCode with Jupyter support, might temporarily make file available or provide an analogous variable, relying on this across all environments is problematic 9. For example, even within VSCode, the availability of file might be inconsistent, potentially disappearing after a kernel restart 9. Therefore, a cross-platform solution cannot depend on the presence or consistent behavior of the file variable in Jupyter notebooks.

IV. Exploring the os Module and Current Working Directory

The os module in Python offers functionalities to interact with the operating system, including retrieving the current working directory (CWD) using os.getcwd() 4. While this method is readily available and does not require additional package installations, its reliability in providing the notebook’s absolute path across the target environments is questionable 2. The CWD reflects the directory from which the Jupyter server or the kernel was launched, which may or may not be the same directory containing the notebook file 11. Users can also programmatically change the CWD within a notebook using os.chdir(), further decoupling it from the notebook’s location 5. While in many standard JupyterLab sessions, the CWD might initially correspond to the notebook’s directory, this is not guaranteed across all execution scenarios or within IDE integrations like VSCode or Cursor AI 2. Therefore, relying solely on os.getcwd() is not a robust cross-platform solution for obtaining the absolute path of the notebook file itself.

V. Investigation of IPython Magic Commands

IPython magic commands, prefixed with %, offer convenient ways to interact with the IPython kernel and the environment 16. While commands like %pwd can display the current working directory 17, they suffer from the same limitations as os.getcwd() discussed in the previous section. Research did not reveal any standard IPython magic command that directly returns the absolute filesystem path of the currently executing notebook file in a manner that is guaranteed to be consistent across JupyterLab, VSCode, and Cursor AI. Some attempts involve using JavaScript within the notebook to interact with the Jupyter frontend and pass information back to the kernel 16. However, the reliability and availability of the IPython.notebook object and its attributes like notebook_name and notebook_path can vary depending on the Jupyter environment and version 16. This approach might also require multiple executions to retrieve the correct path due to the asynchronous nature of JavaScript execution within the kernel 16.

VI. Examining Environment Variables

Environment variables are dynamic named values that can affect the way running processes will behave on a computer 19. While Jupyter and its various implementations might set certain environment variables, there is no standard environment variable that consistently and reliably exposes the absolute path of the currently running notebook across JupyterLab, VSCode, and Cursor AI 22. Some environments might have internal or specific environment variables, but relying on these would violate the constraint of not depending exclusively on environment-specific injected globals without fallbacks 22. Furthermore, the availability and naming conventions for such variables could change without notice, making them unsuitable for a robust cross-platform solution.

VII. Exploring Jupyter Extensions

Jupyter extensions can extend the functionality of the Jupyter Notebook and JupyterLab interfaces 29. While it is conceivable that a JupyterLab extension could be developed to expose the notebook’s path to the kernel, this would not inherently solve the problem for VSCode or Cursor AI unless those environments also support the same extension 31. Furthermore, relying on extensions introduces an additional dependency and might require installation by the user, which the user query aims to minimize 30. While extensions might offer environment-specific solutions, they do not address the need for a single, reliable, cross-environment method within standard Python code.

VIII. Practical Solutions and Their Limitations

Despite the lack of a unified solution, several practical approaches and workarounds have been proposed and utilized by the community. These methods often come with specific constraints and trade-offs regarding their reliability and compatibility across different environments.

VSCode-Specific Global Variable (__vsc_ipynb_file__): VSCode’s Jupyter support injects a global variable named vsc_ipynb_file which, when available, directly provides the absolute path of the currently running notebook 2. This is a highly effective solution for users working primarily within VSCode. However, it is explicitly tied to VSCode and will not be available in standard JupyterLab or Cursor AI environments, thus violating the cross-platform requirement unless a robust fallback mechanism is implemented.
Accessing Local Variables in VSCode (IPython.extract_module_locals): Another VSCode-specific method involves using IPython.extract_module_locals() to access the vsc_ipynb_file variable from the IPython environment 2. This approach also suffers from the same limitation of being exclusive to VSCode.
Using ipykernel.get_connection_file(): The ipykernel module, which is a standard dependency for running Jupyter notebooks, provides a function called get_connection_file() 12. This function returns the path to the JSON connection file used by the kernel. The directory containing this connection file is often the same as the directory where the notebook is located 12. By using os.path.dirname() on the result of get_connection_file(), one can potentially obtain the notebook’s directory. While this method leverages a standard Jupyter component, its reliability in consistently pointing to the notebook’s directory across all three target environments (especially within the integrated environments of VSCode and Cursor AI) requires further investigation and might not always provide the absolute path to the notebook file itself.
Leveraging a Separate .py File: A more portable approach involves creating a separate Python file (e.g., notebook_utils.py) in the same directory as the notebook. This file can contain code that uses the standard file variable to determine its own path, and consequently, the directory of the notebook 2. The notebook can then import a function from this .py file to get the directory.
```
# In notebook_utils.py  
import os  
def get_notebook_directory():  
    return os.path.dirname(os.path.abspath(__file__))

# In the Jupyter notebook  
from notebook_utils import get_notebook_directory  
notebook_directory \= get_notebook_directory()  
print(f"Notebook directory: {notebook_directory}")
```
This method relies on the standard behavior of file in Python modules and provides a relatively reliable way to get the directory of the notebook, assuming the notebook and the .py file are in the same location. However, it does not directly provide the full path to the .ipynb file and necessitates an extra file.
Accessing locals()[‘__session__’]: Some users have reported that accessing locals()[‘session’] within a Jupyter notebook running in JupyterLab or VSCode can reveal information about the session, including the notebook’s path 2. This method relies on internal implementation details of the Jupyter environments and is not a documented or guaranteed feature. Therefore, its reliability across different versions of JupyterLab and VSCode, as well as its availability in Cursor AI, is uncertain, making it unsuitable for a robust long-term solution.
JavaScript Interaction: As mentioned earlier, using JavaScript within a notebook to access the frontend’s information about the notebook file and then passing it back to the Python kernel is a possibility 16. However, this approach depends on the stability and availability of the Jupyter frontend’s JavaScript API (IPython.notebook) and might require asynchronous handling, potentially leading to complexities and inconsistencies across different environments.

IX. Summary of Practical Solutions and Their Compatibility

The following table summarizes the practical solutions discussed and their potential compatibility across the target environments:

Method	JupyterLab	VSCode	Cursor AI	Requires Extra Package	Provides Full Path	Notes
vsc_ipynb_file global variable	No	Yes	No	No	Yes	VSCode-specific
IPython.extract_module_locals()	No	Yes	No	No	Yes	VSCode-specific
ipykernel.get_connection_file() + os.path.dirname	Likely	Likely	Likely	No	No	Provides directory, reliability in integrated environments needs testing
Separate .py file with file	Yes	Yes	Yes	No	No	Provides directory, requires an extra file
locals()[‘session’]	Potentially	Potentially	Unknown	No	Yes	Relies on internal implementation details, not guaranteed
JavaScript interaction (IPython.notebook)	Potentially	Potentially	Potentially	No	Potentially	Depends on frontend API stability and asynchronous handling
os.getcwd() / %pwd	Likely	Likely	Likely	No	No	Reflects current working directory, which may not be the notebook’s location

X. Final Recommendations

For users seeking a programmatic way to determine the Jupyter notebook’s path across JupyterLab, VSCode, and Cursor AI, the analysis reveals that no single, perfectly reliable, and dependency-free solution currently exists. Given the constraints and trade-offs of the explored methods, the following recommendations are provided:

Prioritize VSCode-Specific Solution with Fallback: If the primary working environment is VSCode, leveraging the vsc_ipynb_file global variable or IPython.extract_module_locals() offers the most direct way to obtain the full notebook path. However, for cross-environment compatibility, this should be combined with a robust fallback mechanism for JupyterLab and Cursor AI.

  import os  
  try:  
      notebook_path \= globals()['__vsc_ipynb_file__']  
      print(f"Notebook path (VSCode): {notebook_path}")  
  except KeyError:  
      # Fallback for other environments  
      try:  
          from ipykernel import get_connection_file  
          connection_file \= get_connection_file()  
          notebook_directory \= os.path.dirname(connection_file)  
          print(f"Notebook directory (fallback): {notebook_directory}")  
          # Consider further steps to construct the full path if needed  
      except Exception as e:  
          print(f"Error in fallback: {e}")

Utilize a Separate .py File for Directory: For a more consistently cross-platform approach to obtain the notebook’s directory (though not the full path), using a separate .py file in the same directory is a viable option. This method relies on standard Python behavior and avoids environment-specific dependencies.
Carefully Evaluate ipykernel.get_connection_file(): The ipykernel.get_connection_file() method shows promise for retrieving the notebook’s directory across different environments. However, its behavior and the exact path it provides should be thoroughly tested in JupyterLab, VSCode, and Cursor AI to ensure its reliability for the intended use case.
Avoid Sole Reliance on CWD: Methods based on the current working directory (os.getcwd() or %pwd) should be used with caution, as they do not reliably point to the notebook’s location in all scenarios.

XI. Conclusion

In conclusion, the quest for a single, universally reliable, and dependency-free Python method to programmatically determine the absolute filesystem path of a Jupyter notebook across JupyterLab, VSCode, and Cursor AI faces significant hurdles due to Jupyter’s fundamental architecture. The separation between the kernel and the frontend, while offering flexibility, limits the kernel’s inherent access to the notebook file’s location. While environment-specific solutions like the vsc_ipynb_file variable in VSCode exist, they do not fulfill the cross-platform requirement without additional fallback logic. Methods leveraging standard Python libraries or the ipykernel module offer more portability but might only provide the notebook’s directory or require careful testing across different environments. Ultimately, users need to be aware of the limitations of each approach and choose the solution that best aligns with their primary working environment, potentially implementing fallback strategies to enhance cross-environment compatibility. The absence of a unified solution underscores the inherent architectural considerations within Jupyter, and a truly seamless method might necessitate future enhancements to the Jupyter protocol or closer integrations within specific development environments.

Works cited

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Notes on Google Docs Markdown Formatting

As noted earlier, from Gemini Deep Research you have to Export to Docs before you can get markdown – and there were a bunch of new surprises this time. It backslashed the hell out of everything. I mean sheesh! You’d think they were Microsoft.

Me: I need the regular expression that I can use in vim/nvim where I can replace all the following:

\-
\+
\[
\]
\_
\#

…with the non backslashed versions!

So in other words, I’ll do a visual selection like:

:'<,'>

And then do a /s to start the replace process. I need the rest of that. Here is the article: [Pastes Google Docs markdown output of Gemini research report]

ChatGPT: In Vim/Neovim, you can perform the replacement using this substitution command for your visual selection (:'<,'>):

:'<,'>s/\\\([\-+\[\]_#]\)/\1/g

Explanation:

\\: Matches the literal backslash.
\([\-+\[\]_#]\): Captures exactly one of these special characters -, +, [, ], _, #.
\1: Replaces the match with the captured character itself (without the backslash).
g: Global replacement for each line in the selected region.

This command efficiently removes the backslashes before each of these characters, as desired.

Exhausting! And then I still had to put the backticks ```python around the Python code, and then I had to replace all the vertical pipes | in the citations. It’s exhausting even to describe it. All the speed advantages of Gemini research feel cancelled out by how difficult it is to publish the report in markdown.

ChatGPT 4.5 Deep research

Me: [Pastes prompt]

ChatGPT: Just to confirm before I kick this off: Are you open to solutions that involve small helper functions, browser-based hacks (e.g., using JavaScript in the notebook), or light third-party packages (like notebook, IPython, or ipywidgets), as long as they’re widely used and easy to add? Or do you want to exclude all non-standard packages entirely?

Me: Small helper functions are my preference, but I guess I am open to packages like notebook given this kind of thing is likely what they’re for. IPython should be there because it’s a Jupyter environment in either case. But I don’t like the sound of ipywidgets given the fragility I’ve encountered with them in the past.

ChatGPT: Great. I’ll dig into whether a reliable, cross-platform Python method exists to determine the absolute path of the currently running Jupyter notebook—covering JupyterLab, VSCode, and Cursor AI. I’ll evaluate all known solutions, including those using built-in Python, IPython, and commonly accepted packages like notebook, while avoiding fragile or overly complex tools.

I’ll get back to you with a clear summary, working examples, and pros/cons of each method.

Determining the Current Jupyter Notebook’s Path Across Environments

Summary: Is There One Cross-Environment Solution?

In short, no single built-in method universally works across all Jupyter environments. This is by design – the Jupyter architecture separates the execution kernel from the user interface, so the kernel isn’t guaranteed to know what notebook file (if any) is driving it (python - How to obtain Jupyter Notebook’s path? - Stack Overflow). However, recent improvements and environment-specific tricks can be combined to get the notebook’s absolute path in most common scenarios (JupyterLab, VS Code, Cursor AI). In practice, you will likely need a small helper that checks for different clues depending on the environment. Below we outline the known solutions, their constraints, and a recommended approach.

Why It’s Hard to Get the Notebook Path

Jupyter’s creators deliberately did not make the notebook filename available to the kernel. Consider that a kernel may not correspond to a file at all, or could be running on a remote server where the file path has no meaning to the kernel (python - How to obtain Jupyter Notebook’s path? - Stack Overflow). As one core developer explained, there are many scenarios (remote execution, no single file, etc.) where a “notebook path” concept breaks down, and “the Jupyter protocol has not been designed to do so” (python - How to obtain Jupyter Notebook’s path? - Stack Overflow). In other words, there was historically no official API to ask “what notebook am I in?” and “no plan to change this… in the short or long term” (python - How to obtain Jupyter Notebook’s path? - Stack Overflow).

That said, for typical local usage (one notebook = one file on disk), users have long desired this functionality. Over time, several approaches – both official and ad-hoc – have emerged to fill the gap. Each comes with caveats, and none works absolutely everywhere without conditions. Below we evaluate each approach and its cross-environment reliability.

Approaches to Obtaining the Notebook Path

1. Official Jupyter Environment Variable `JPY_SESSION_NAME` (`session` variable)

Overview: Newer versions of Jupyter (using Jupyter Server in JupyterLab or Notebook v7+) set an environment variable JPY_SESSION_NAME when launching a kernel, which contains the notebook’s path or name. The IPython kernel reads this and injects a __session__ variable into the Python namespace (ipykernel/ipykernel/ipkernel.py at main · ipython/ipykernel · GitHub). This effectively provides the notebook path from inside the notebook process.

How to use: In a cell, you can access __session__ directly, or via os.environ['JPY_SESSION_NAME']. For example:

import os
nb_path = os.environ.get("JPY_SESSION_NAME")
print("Notebook path:", nb_path)

If your Jupyter server is recent, this should return the relative or absolute path of the notebook. You can then convert to an absolute path with os.path.abspath(nb_path) if needed.

Supported environments: This method works in JupyterLab and recent Jupyter Notebook servers. It was introduced in Jupyter’s backend and IPython fairly recently (mid-2022 onward) and requires that you are running a sufficiently up-to-date Jupyter server and ipykernel. When properly configured, JPY_SESSION_NAME will contain the notebook filename or path, which IPython exposes as __session__ (python - How do I get the current IPython / Jupyter Notebook name - Stack Overflow) (ipykernel/ipykernel/ipkernel.py at main · ipython/ipykernel · GitHub). Jupyter developers consider this the intended solution for notebook path going forward (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub) (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub).

Constraints:

This is only set when the kernel is started by a Jupyter server with an associated notebook file. It won’t exist if you run an IPython kernel outside of Jupyter (e.g. a standalone ipython or a kernel started without a notebook file).
In older versions (or certain configurations), JPY_SESSION_NAME might have been missing or even set to a dummy value (like a UUID) instead of the real filename (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub). For example, early JupyterLab versions showed an ID instead of the file name, which was later fixed to use the actual notebook name (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub). Ensure your Jupyter Server is updated to get the correct behavior.
The __session__ variable is specific to IPython-based kernels (Python). Other kernel languages may not set an analogous variable due to naming or implementation differences (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub). (Our focus here is Python.)

VS Code and Cursor: As of early 2025, the VS Code Jupyter extension has begun to support JPY_SESSION_NAME as well. New versions of VS Code (and by extension Cursor, which builds on VS Code’s Jupyter support) set this environment variable when starting the kernel (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub) (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub). If you are using the latest VS Code Jupyter extension, you may find that os.environ["JPY_SESSION_NAME"] and __session__ work there too. (Earlier versions did not set it properly, so don’t rely on this in older VS Code releases.) Always verify by printing __session__ in your environment.

Bottom line: Using __session__/JPY_SESSION_NAME is the preferred and most portable solution for modern Jupyter environments – it’s simple and doesn’t require external packages. Just remember to fall back if it’s not present.

2. VS Code’s Built-in `__vsc_ipynb_file__` Global

Overview: The Visual Studio Code Jupyter integration injects a special global variable containing the full path of the notebook file when running in VS Code. This variable is named __vsc_ipynb_file__. You can retrieve it from Python’s globals().

How to use: In a notebook cell running under VS Code, do:

path = globals().get("__vsc_ipynb_file__")

This will return the absolute path to the .ipynb file (or None if not defined). In fact, you can access it directly as __vsc_ipynb_file__ (a defined global) (python - How to obtain Jupyter Notebook’s path? - Stack Overflow). This is essentially VS Code’s analog of a __file__ for notebooks.

Supported environments: Only VS Code’s built-in Jupyter support (and derivatives like Cursor AI, which uses VS Code under the hood) provide this variable. In a standard JupyterLab or classic notebook server, __vsc_ipynb_file__ will not exist. This is exclusively an VS Code feature (python - How to obtain Jupyter Notebook’s path? - Stack Overflow).

Cursor’s notebook integration is a fork of VS Code, so it uses the same mechanism. In practice, if you run a notebook cell in Cursor and check globals(), you should find __vsc_ipynb_file__ populated with the file path (unless the Cursor team renamed it). There’s no official __cursor_ipynb_file__ documented – they rely on the same VS Code Jupyter extension functionality (Jupyter Notebooks in VSCode and Cursor AI | Mike Levin on Linux, Python, vim & git (LPvg)) (Jupyter Notebooks in VSCode and Cursor AI | Mike Levin on Linux, Python, vim & git (LPvg)). So, treat Cursor as behaving like VS Code in this regard.

Constraints:

Obviously, this requires the VS Code environment. It will not work in JupyterLab or other interfaces. Code that blindly uses __vsc_ipynb_file__ will fail on JupyterLab (the variable won’t exist).
It’s an implementation detail of VS Code, not an official Jupyter feature. While it’s unlikely to change without notice (since many users now depend on it), it is not part of the Jupyter project itself.
Using this alone isn’t “cross-platform,” but it’s useful as a fallback in a combined strategy.

Reliability: Within VS Code, this is very reliable – it directly provides the full path that VS Code has open. It does not depend on any server API or network calls. For VS Code users, this is the simplest solution (and required prior to JPY_SESSION_NAME support). Just remember to include another method for when you run the notebook elsewhere.

3. Querying the Jupyter Server via its API

Overview: This is a general workaround that works by asking the notebook server which notebooks are running. The approach is: find your kernel’s ID, then ask the Jupyter server which notebook is associated with that kernel. This method can work in both classic Notebook and JupyterLab servers (and even some VS Code scenarios), because it uses the server’s REST API. It has been packaged by the community in tools like ipynbname.

How it works (under the hood): When a Jupyter server is running, it exposes an HTTP API at http://localhost:<port>/api/sessions (among others). This returns JSON data for each running notebook, including the notebook’s file path and the kernel ID (Map kernel id/session id to notebook path · Issue #4325 · jupyter/notebook · GitHub) (Map kernel id/session id to notebook path · Issue #4325 · jupyter/notebook · GitHub). The steps are typically:

Get the kernel ID: The IPython kernel writes connection info to a file like kernel-<ID>.json. Using ipykernel or IPython, you can get the path of this file. For example:
```
import ipykernel, re
connection_file = ipykernel.get_connection_file()
kernel_id = re.search('kernel-(.*).json', connection_file).group(1)
```
Now kernel_id is the unique ID of your kernel.
Find the running server and query it: If you have one notebook server running, you can get its information (including URL and access token) via Jupyter’s internal APIs. For instance, the notebook package provides notebookapp.list_running_servers() to list all servers on the machine. Each entry has the server’s URL and token (if any) (Jupyter server sets incorrect session path, type and notebook name · Issue #886 · microsoft/vscode-jupyter · GitHub). You can then call the sessions API:
```
import urllib.request, json
for server in notebookapp.list_running_servers():
    url = server['url'] + 'api/sessions?token=' + server.get('token','')
    data = json.load(urllib.request.urlopen(url))
    # data is a list of sessions (notebooks) on this server
    for session in data:
        if session['kernel']['id'] == kernel_id:
            nb_path = session['notebook']['path']  # relative path on server
            notebook_dir = server.get('notebook_dir', server.get('root_dir', ''))
            full_path = os.path.join(notebook_dir, nb_path)
            print("This notebook's path:", full_path)
```
This will locate the session with a matching kernel ID and retrieve its notebook['path'] (plus the server’s root directory to form an absolute path) (Jupyter server sets incorrect session path, type and notebook name · Issue #886 · microsoft/vscode-jupyter · GitHub). In classic Jupyter, notebook_dir is the server’s base directory; in JupyterLab (Jupyter Server), it might use root_dir instead (python - How do I get the current IPython / Jupyter Notebook name - Stack Overflow).
Use the result: full_path is the path to your notebook file on disk.

Using ipynbname: The above logic can be complex to implement correctly. The third-party package ipynbname does exactly this for you. After pip install ipynbname, you can simply call:

import ipynbname
print(ipynbname.path())  # returns a pathlib.Path to the current notebook

This will either return the path or raise an error if it cannot determine it. Under the hood, it uses the kernel ID and the REST API method. (Another package, jupyter_notebook from IPython, had similar functionality historically.)

Supported environments: This technique works in local Jupyter Notebook or JupyterLab servers where the server is running on the same machine as the kernel. It can also work if you connect to a remote server with the proper token. Essentially, as long as list_running_servers() can find the server (which it can if the server writes a runtime file on disk) and the API is reachable, it will work. This includes most normal usage of JupyterLab/Notebook.

In VS Code, it depends on how VS Code started the kernel:

If VS Code is using an actual Jupyter server under the hood, this method might work. VS Code often launches a hidden local Jupyter server for handling notebooks, which would register in list_running_servers(). In some cases, however, VS Code used to start kernels directly and not expose them via the standard Jupyter server mechanism (especially on Windows) (Jupyter server sets incorrect session path, type and notebook name · Issue #886 · microsoft/vscode-jupyter · GitHub). This could make list_running_servers() return nothing or the session info incomplete (as earlier VS Code versions didn’t set notebook names properly in sessions) (Jupyter server sets incorrect session path, type and notebook name · Issue #886 · microsoft/vscode-jupyter · GitHub) (Jupyter server sets incorrect session path, type and notebook name · Issue #886 · microsoft/vscode-jupyter · GitHub). For example, a known VS Code bug caused all notebooks to appear as “Untitled.ipynb” via the API (Jupyter server sets incorrect session path, type and notebook name · Issue #886 · microsoft/vscode-jupyter · GitHub), which would foil this method. If using a recent VS Code and Jupyter extension, this is likely improved, but it’s not as foolproof as the dedicated __vsc_ipynb_file__.
In Cursor AI, since it leverages the same VS Code Jupyter extension, the same caveats apply. If Cursor is running through a Jupyter server in the background, ipynbname might work, but it’s not guaranteed.

Constraints and trade-offs:

This method requires the notebook or jupyter_server Python package to be installed in the environment (to call list_running_servers()). Classic Jupyter installations have it, but if you only installed ipykernel (say for VS Code), you might need to install notebook or jupyter_server manually.
It makes an HTTP request to the local server. This means it can be slow (a slight delay) and can fail if the server is not reachable or requires a token you don’t have. We included the token from the server info to handle the common case. Behind certain proxies or if the server’s address is not straightforward (e.g., container setups), this could have issues (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub).
If multiple Jupyter servers are running, you need to ensure you query the right one (we matched by kernel ID above). If the same kernel ID somehow existed on two servers (unlikely, since IDs are UUIDs), there could be confusion.
This approach will not work at all if there is no Jupyter server (for instance, if the kernel was started manually or via some IDE that doesn’t register a server). In those cases, there’s nowhere to query.

Reliability: When applicable, this technique is quite robust and works for classic Jupyter environments. It was the go-to workaround before __session__ existed. The downside is the complexity and potential need for an external library (requests or ipynbname). If you’re comfortable installing ipynbname, it encapsulates these details and handles some edge cases (like working with JupyterHub, which may have different URL prefixes (JupyterHub support · Issue #8 · msm1089/ipynbname - GitHub)). For a truly cross-environment script, you might use this as a last resort fallback if simpler options fail.

4. Other Notable Solutions and Why to Avoid Them

Hardcoding or passing the path in manually: Some recommend manually setting a variable with the notebook name (or using an argv argument). This violates the constraint of not relying on manual intervention and is error-prone. It’s not dynamic or robust, so we exclude it except as a last resort in highly controlled scenarios.
Using the working directory (os.getcwd() or Path.cwd()): Often, the notebook’s directory is the working directory, but not always. The user might have changed os.chdir, or the launch directory may be different from the notebook’s actual location (especially in IDEs). This gives you a directory path, not the filename, and can be incorrect depending on context (python - How to obtain Jupyter Notebook’s path? - Stack Overflow). It’s not a reliable indicator of the notebook file location.
Using IPython/Jupyter **JavaScript in the frontend:** Older hacks involve executing JavaScript in a cell to grab the notebook name from the browser (e.g. using IPython.notebook.notebook_name in classic Notebook, or DOM queries) and then communicating it back to Python (python - How do I get the current IPython / Jupyter Notebook name - Stack Overflow) (python - How do I get the current IPython / Jupyter Notebook name - Stack Overflow). While these can work in classic Jupyter Notebook, they break in JupyterLab (which doesn’t expose IPython.notebook JS object in the same way) and won’t work at all in headless or non-browser interfaces (VS Code doesn’t even use the same frontend). They are also fragile if the frontend UI changes. Tools like ipyparams set the notebook name using JavaScript under the hood and create a Python variable. These approaches tend to be brittle – for example, users reported ipyparams.notebook_name sometimes returning None or failing after a server restart (ipyparams.notebook_name does not every time · Issue #2 - GitHub). We recommend avoiding JavaScript-based hacks for general, cross-platform use.
Using ipywidgets or hidden HTML to send the filename: Another variant is to have a widget or HTML display that reads window.location (for Jupyter Notebook) and sends that string to the kernel. This again relies on the browser context and specific frontend, making it unreliable in other environments (and adds unnecessary overhead of widget communications). It’s an ingenious trick, but not “minimal-effort” – it requires maintaining a custom JavaScript snippet alongside your Python, which can break with frontend updates.
Custom extensions or modifications: Some specialized Jupyter extensions or forked kernels might offer their own way to get the notebook path (for instance, a fork of IPython that sets a variable). These are not mainstream and reduce portability. One example is JetBrains IDEs which might run notebooks in their environment differently (not in scope here), or cloud services like Google Colab (which simply do not allow direct file access for notebooks – you must mount Drive or use their API to know filenames).

In summary, methods outside the three main ones above tend to be environment-specific, outdated, or require installing additional front-end components. They are generally not recommended when cleaner solutions exist.

Practical Cross-Environment Strategy

Given the above options, here’s a practical approach to reliably get the notebook path across JupyterLab, VS Code, and Cursor AI with minimal fuss:

First, try the official __session__ variable (which covers JupyterLab and should also cover modern VS Code if available). This is a one-liner check in Python:
```
import os
nb_path = os.environ.get("JPY_SESSION_NAME")
# or equivalently: nb_path = globals().get("__session__")
```
If nb_path is not None, you’ve got the notebook path (it may be relative to the server root; you can os.path.abspath + perhaps join with working directory if needed). This is a safe, zero-dependency call. In a new JupyterLab or Jupyter Notebook server, this will be set to the notebook’s path (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub).
If that fails, check for VS Code’s global:
```
if nb_path is None:
    nb_path = globals().get("__vsc_ipynb_file__")
```
In VS Code (or Cursor) this will return the absolute path to the file (python - How to obtain Jupyter Notebook’s path? - Stack Overflow). In other environments it remains None. This covers the case where __session__ isn’t set (e.g., older VS Code or if using an older IPython kernel that doesn’t support it).
If still not found, fall back to the server query method: This is the more involved approach using the Jupyter REST API. You can either implement it as shown above or use ipynbname for simplicity. For example:
```
if nb_path is None:
    try:
        import ipynbname
        nb_path = str(ipynbname.path())
    except Exception as e:
        nb_path = None
```
If the notebook is running on a Jupyter server that ipynbname can detect, this will give you the full path. (Ensure you pip install ipynbname in the environment first.) If you prefer not to install anything, you could inline a similar logic with notebookapp or jupyter_server as described. Note that this step will likely not work in cases where no server is present or the server doesn’t expose the info (in which case no method can work).
Handle the “no result” case: Even after these attempts, there are edge cases where you simply cannot get the path:
- If you connected to a kernel that wasn’t launched by opening a .ipynb file (e.g., an interactive kernel not attached to any notebook file).
- If you are running in an environment like Google Colab (which intentionally doesn’t divulge file paths for notebooks).
- If something is misconfigured (no permissions to query the API, etc.).
In such cases, the function should return None or raise an informative error. It’s important to clearly document this possibility: not every kernel has an associated notebook file.

Below is an example helper function combining these strategies (for illustration):

import os, json, urllib.request, re
from pathlib import Path
def get_notebook_path():
    # Try official env variable
    if "__session__" in globals() or "JPY_SESSION_NAME" in os.environ:
        path = globals().get("__session__", os.environ.get("JPY_SESSION_NAME"))
        if path:
            return os.path.abspath(path)
    # Try VS Code variable
    path = globals().get("__vsc_ipynb_file__")
    if path:
        return os.path.abspath(path)
    # Try querying the notebook server
    try:
        import ipykernel
        # Find kernel ID from connection file
        connection_file = ipykernel.get_connection_file()
        kernel_id = re.search("kernel-(.*).json", connection_file).group(1)
        # Use jupyter_server if available, else notebook
        try:
            from jupyter_server import serverapp as app
        except ImportError:
            from notebook import notebookapp as app
        for server in app.list_running_servers():
            url = server["url"]
            token = server.get("token", "")
            try:
                sessions_url = url + "api/sessions"
                if token:
                    sessions_url += f"?token={token}"
                data = json.load(urllib.request.urlopen(sessions_url))
            except Exception:
                continue  # move to next server
            for sess in data:
                if sess.get("kernel", {}).get("id") == kernel_id:
                    nb_path = sess.get("notebook", {}).get("path") or sess.get("path")
                    # Compose full path
                    root_dir = server.get("root_dir") or server.get("notebook_dir") or ""
                    return os.path.abspath(os.path.join(root_dir, nb_path))
    except Exception:
        pass
    return None

print(get_notebook_path())

The function above tries __session__, then __vsc_ipynb_file__, then uses either jupyter_server.serverapp or notebook.notebookapp to find the server and query its sessions. This approach is fairly comprehensive. In JupyterLab or Notebook, it will typically find the path via __session__ or the API. In VS Code, it will grab __vsc_ipynb_file__ (or possibly __session__ if available). Only if all else fails will it return None.

Important: The combined approach is only as “reliable” as the environments allow. In nearly all normal use cases (local notebooks in Jupyter or VSCode/Cursor), this will succeed. It addresses the user’s constraints by not relying on only one environment’s quirk without fallback, and it avoids extra installations except in the optional ipynbname usage (we attempted a manual HTTP call to avoid needing requests).

Final Recommendations

Use the official __session__/JPY_SESSION_NAME mechanism whenever possible. It’s simple and works in standard Jupyter environments (and likely future-proofs VS Code as it adopts the standard). This should be the first choice in any solution.
Augment with environment-specific hints for broader coverage. If you need a truly portable notebook (e.g., you share a notebook that might be run in VS Code or Jupyter by different users), implement a small check for __vsc_ipynb_file__ so that VS Code users get a result. This adds virtually no overhead and respects the no-manual-setting rule.
Consider shipping a fallback like ipynbname for edge cases. If your use-case must get the notebook path and you’re worried about odd setups, bundling ipynbname (or using a try/except to import it) is a viable strategy. It does require an extra package, but it’s lightweight. Clearly document that the package may need to be installed. This covers scenarios where the official variable isn’t present but a Jupyter server is running.
Acknowledge when it’s not possible. Despite best efforts, there are scenarios where no programmatic solution exists (for example, a remote kernel launched without a file). In these cases, explicitly handle the None case. For instance, prompt the user to provide the path or skip features that depend on it. Being transparent about this limitation is better than a fragile “guess” that could be wrong.
Keep Jupyter and its packages up to date. Many of the newer solutions (like __session__) require relatively recent versions of Jupyter Server and ipykernel. Upgrading JupyterLab/Notebook and the VS Code Jupyter extension will improve the reliability of get_notebook_path logic. For example, an update in Jupyter Server ensured JPY_SESSION_NAME truly carries the file name rather than a random ID (Changelog — Jupyter Server documentation) (Document JPY_SESSION_NAME environment variable, which contains current notebook’s file path · Issue #16282 · jupyterlab/jupyterlab · GitHub) – such fixes are only available if you update.
Avoid complex front-end hacks in production code. They might seem to work in one interface but will cause confusion elsewhere. The goal is a unified solution; mixing in JavaScript or requiring a specific front-end defeats that purpose.

Conclusion: A single fully built-in cross-platform call doesn’t exist because of underlying design reasons (python - How to obtain Jupyter Notebook’s path? - Stack Overflow). However, by leveraging recent developments and a few conditional checks, we can achieve a robust solution that covers JupyterLab, VS Code, and Cursor AI’s notebook integration. In practice, a small helper function (as shown above) can abstract these details. This gives users a reliable notebook path most of the time, while clearly handling the exceptional cases where the information isn’t obtainable.

Disappointment in Path Discovery Solutions

First and foremost, booooo! The Waldorf and Statler peanut gallery heckle the cosmos! This is not what I wanted to find. Where’s my magic bullet hand-waving solved problem? There is none! And the “industry way” this is being solved gives me this:

import os
nb_path = os.environ.get("JPY_SESSION_NAME")
print("Notebook path:", nb_path)

Output:

Notebook path: /home/mike/repos/pipulate/path_finder-3d3263eb-4395-49bf-be91-c4bd54760f9d.ipynb

Booooooo!

Rejecting Auto-Discovery in Favor of Hardcoded Paths

Bob is not your uncle. I am going to hardwire paths.

Philosophical Considerations About Path Management in Pipulate

A bit of philosophical thinking here about Pipulate.

In the past, I would have stuck with relative paths from where the script is run, effectively doing away with the path issue entirely. It’s the least brittle thing to do if you want softwired paths because most everything can agree on the concept of the folder from which your script was run.

The Complexity of Paths in Jupyter Notebooks

However, when it’s Notebooks, there’s an always-running server which sort of keeps your script in a state of frozen suspended animation. Your notebook is always running until you restart the kernel. And so now there’s both your script in the picture and a Jupyter server. Which gets the current directory concept tied to it?

The directory you were in when you started the Jupyter server?
Or the directory your .ipynb file is in relative from there?
And what’s worse is how running IPython kernels get used by VSCode.

Project Jupyter’s Design Philosophy and Path Discovery

There is both complexity here and Project Jupyter considerations. They don’t want absolute paths to notebook locations easily discovered, because it might be entirely in-memory or on remote servers through network protocols and such. So there is no clean auto-discovery mechanism, and when you try with hacks, your helper-functions are huge and look like garbage littering up your pristine examples you’re trying to make for newbs.

Embracing Hardwired Paths as a Practical Solution

And so… and so… with a great sense of defeat and disappointment, I’m going with hardwired paths declared at the beginning of Jupyter Notebook mock-ups of SEO workflows intended for porting to Pipulate.

They will have to change for different users with different usernames and paths, per their OSes. I’ll try to keep it as OS-independent as reasonable, maybe with pathlib Path(). However, I will go with explicit “has to be changed” paths. Everyone doing this kind of work will immediately understand what they’re looking at. It will be least brittle. And it will be least code.

The Distinction Between Notebook Development and Pipulate Production

And that’s all just a true Notebook issue, not a Pipulate one.

Pipulate is not really Jupyter Notebooks and there will be no IPython kernel. Users using Pipulate workflows will not even see the Python code! So ultimately, this is only an important point for the developer-type portion of my audience developing the workflows in Notebook in the first place – BEFORE the Pipulate port where all path issues disappear, because it’s a 100% Nix Flakes deterministic controlled environment.

Being Cited by ChatGPT: A Strange Academic Loop

I’m cited again by ChatGPT! Oh, the strange loop! This must be common for academics, doing research and finding themselves cited in the course of doing new research! Hey, isn’t that what the original Google PageRank algorithm was based on? Yes, indeedy! The rats are scratching each others backs again?

Fixing Markdown Rendering Issues with Title Tags

My citation was screwed up in the markdown rendering because of the vertical pipes in the title tags! That’s going to be big. I’m going to go get those vertical pipes out of my title tags right now, because I want to be cited without causing markdown rendering problems… okay, done! No more vertical bars in my title tags. Pshwew! I replaced them with slashes, which are semantically more sound, given the way they’re used in breadcrumb trails and directory structure trees. That’s huge. Deserving of its own separate article.

The Ease of Publishing with ChatGPT-Generated Markdown

Other than that one replacing of vertical bar pipes in my own cited title tag, there were no other formatting issues! OMG, as slow as ChatGPT is compared to Google, publishing the findings is like 1000x easier! You just export the nearly perfect markdown, paste it into whatever markdown-supporting publishing system you’re using, and Voila! Bob’s your uncle.

An AI’s Opinion On This Article (Gemini 2.5 Experimental)

Okay, here are the requested components based on the article:

Summary from the Author’s First-Person Perspective:

[Moved to top]

Title and Headline Ideas:

Problem/Solution Focused:
- The Unsolvable Jupyter Notebook Path Problem: Why I Chose Hardcoding
- Finding Your Jupyter Notebook Path: A Cross-Platform Quest (and Why It Fails)
- Jupyter Paths in VSCode vs. JupyterLab: Giving Up on Auto-Discovery
- Reliable Notebook Paths Across Environments? Forget It.
AI Research Angle:
- Gemini vs. ChatGPT: Researching the Jupyter Path Problem
- Asking AI to Solve Jupyter’s Path Puzzle: A Deep Dive
- AI Confirms: No Magic Bullet for Jupyter Notebook Paths
Narrative/Intrigue:
- Down the Jupyter Rabbit Hole: My Battle with Notebook Paths
- Defeated by Paths: An Unexpected Turn in Pipulate Development
- The Elusive Notebook Path: Why Jupyter Makes It So Hard
Technical Keyword Focused:
- Decoding Jupyter Paths: __session__, __vsc_ipynb_file__, and API Workarounds
- os.getcwd() is Not Enough: The Challenge of Jupyter Notebook Paths

AI’s Opinion of the Article:

This article provides a valuable, real-world account of tackling a common and often underestimated pain point in the Jupyter ecosystem: reliably determining a notebook’s location programmatically across different execution environments. The author effectively documents the problem, the troubleshooting process involving interaction with AI assistants, and the comparative experience using different AI research tools (Gemini and ChatGPT), which adds an interesting meta-layer.

The deep dive into the technical reasons why this is difficult – the kernel/frontend separation in Jupyter’s architecture – and the evaluation of various proposed solutions (os.getcwd, __file__ limitations, JPY_SESSION_NAME, __vsc_ipynb_file__, API querying via tools like ipynbname) are thorough and well-supported by the research findings presented (presumably from the AI interactions).

The author’s conclusion to abandon complex auto-discovery in favor of hardcoded paths for their specific use case (creating clear example notebooks for later porting) is a pragmatic and well-reasoned decision. It highlights a practical trade-off developers often face between striving for fully automated solutions and opting for simplicity, clarity, and robustness, especially in contexts like documentation or examples.

The commentary on the AI research experience itself, including the speed difference and output formatting challenges (especially with Gemini’s export), and the amusing “strange loop” of being cited by the AI, adds personality and relatability to the technical narrative. Overall, it’s an informative and engaging piece detailing a technical investigation that leads to a practical, if perhaps philosophically unsatisfying, conclusion.

Cursor AI and VSCode Jupyter Notebook Paths

By Mike Levin

Saturday, March 29, 2025

Creating Cross-Platform Jupyter Notebooks in Pipulate

Explanation:

If you need the directory of the running Python file itself (useful when you run scripts directly):

To cover both scenarios (script vs notebook context):

Unexpected Surprise With os.getcwd()

Why the difference?

Another Unexpected Surprise With IPython Kernel in VSCode

Google Gemini Deep Research

Programmatic Determination of Jupyter Notebook Path Across Environments: An Evaluation

Works cited

Notes on Google Docs Markdown Formatting

Explanation:

ChatGPT 4.5 Deep research

Determining the Current Jupyter Notebook’s Path Across Environments

Summary: Is There One Cross-Environment Solution?

Why It’s Hard to Get the Notebook Path

Approaches to Obtaining the Notebook Path

1. Official Jupyter Environment Variable JPY_SESSION_NAME (__session__ variable)

2. VS Code’s Built-in __vsc_ipynb_file__ Global

3. Querying the Jupyter Server via its API

4. Other Notable Solutions and Why to Avoid Them

Practical Cross-Environment Strategy

Final Recommendations

Disappointment in Path Discovery Solutions

Rejecting Auto-Discovery in Favor of Hardcoded Paths

Philosophical Considerations About Path Management in Pipulate

The Complexity of Paths in Jupyter Notebooks

Project Jupyter’s Design Philosophy and Path Discovery

Embracing Hardwired Paths as a Practical Solution

The Distinction Between Notebook Development and Pipulate Production

Being Cited by ChatGPT: A Strange Academic Loop

Fixing Markdown Rendering Issues with Title Tags

The Ease of Publishing with ChatGPT-Generated Markdown

An AI’s Opinion On This Article (Gemini 2.5 Experimental)

Unexpected Surprise With `os.getcwd()`

1. Official Jupyter Environment Variable `JPY_SESSION_NAME` (`session` variable)

2. VS Code’s Built-in `__vsc_ipynb_file__` Global