asof 2026-02-11

.. TODO: push this out

tools used:¶

Windsurf Cascade: ... AI_Coding_Tools.md
Git -- version control; capture changes/ notes every step of the way
MkDocs -- documentation is very important.. note it down first; keep refining subsequently
Vercel and GitHub Pages -- shareable presentation of summary is also needed

what i learned so far from my first experiment:¶

plan your work ... initial plan
break it down into small tasks
use AI to first do a thorough analysis of the codebase... Analysis of current codebase
review that analysis yourself
sequence tasks and assign them to the AI one by one
incrementally capture successfully compiling code in version-control
at each step, compile code, run tests and ensure they are passing before committing
do execute the code and see if it runs successfully as well.
at each step, document the changes in markdown files; commit docs as well
organize documentation in a way that is easy to navigate and understand; eg mkdocs
once desired changes and enhancements are achieved, ask AI to do a thorough code review and document that code review, in terms of issues caught, suggestions for improvements, etc.
in my earlier version of code in 2017, github showed code composition as 97% java... in this code its 33% java, 57% HTML (due to markdown files)
another observation: i had been specifying in markdown; and reviewing at each step; and then committing at each step
... may be more as i learn :)

SpecKit vs Spec2Cloud — Quick Summary¶

Dimension	SpecKit	Spec2Cloud
Maintainer	GitHub (Microsoft)	Microsoft EMEA App GBB
Open Source	✅ MIT	✅ MIT
Stars	95k+ (mature)	~94 (early-stage)
AI Agents	30+ supported	Primarily GitHub Copilot
Cloud Dependency	None — cloud-agnostic	Azure-required for deploy
On-Prem Support	✅ Yes	❌ Not a primary use case
Offline / Air-gapped	✅ Explicitly supported	⚠️ Partial (spec phases only)
Deployment Target	Any / developer's choice	Azure
Extensibility	80+ community extensions	46 built-in skills
Best For	Any stack, any cloud, any AI	Azure + Copilot teams

Developer Workflow just for this mkdocs site:¶

Workflow using GitHub Pages¶

i am using an explicit repo that only has mkdocs files
sync up and bring larger markdown files from the main repo; these are the ones worked with Windsurf AI
update hand-crafted markdown files as needed
build and run the site locally to see if it looks good
if needed make changes to the markdown files and repeat steps 2-4
when satisfied, commit the changes
push the changes to the remote repository

a. i am next copying the compiled pages from "site" folder to another repo

b. using a specific repo for GitHub Pages: amitrajpurkar.github.io

c. once i copy over updated pages here, commit and push to that repo
GitHub Pages is then updated automatically

cd mkdocs-repo 
# update hand-crafted markdown files as needed
# sync up and bring larger markdown files from the main repo; these are the ones worked with Windsurf
mkdocs build --clean
mkdocs serve

diff -r site ./../../amitrajpurkar.github.io
cp -r site/* ./../../amitrajpurkar.github.io/

cd ./../../amitrajpurkar.github.io
git status
git add .
git commit -m "updated docs"
git push

# github pages.. need to get this working
# gh pages -b gh-pages

Deploying a Python application in Google Cloud can be done using various services like Google App Engine, Google Cloud Run, or Google Kubernetes Engine. Here's a step-by-step guide for deploying a Python application using Google App Engine, which is a popular choice for web applications.

Step 1: Set Up Google Cloud Account¶

Create a Google Cloud Account: Sign up for a Google Cloud account.
Create a Google Cloud Project: In the Google Cloud Console, create a new project.

Step 2: Install Google Cloud SDK¶

Install the Google Cloud SDK: Follow the instructions to install the Google Cloud SDK.
Initialize the SDK: Run gcloud init to set up your Google Cloud project.

Step 3: Prepare Your Application¶

Create Your Python Application: Organize your Python code. Ensure that you have a file named app.yaml in your project root, which contains configuration settings for your App Engine.

Example app.yaml:

runtime: python310  # Use the latest Python version available
entrypoint: gunicorn -b :$PORT main:app  # Adjust according to your app setup

handlers:
- url: /.*
  script: auto

Dependencies: Create a requirements.txt file to list your dependencies. For example:
```
flask
gunicorn
```

Create your main application file (e.g., main.py):

from flask import Flask

app = Flask(__name__)

@app.route('/')
def hello():
    return "Hello, World!"

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

Step 4: Deploy the Application¶

Deploy to App Engine: In the terminal, navigate to your project directory and run:
```
gcloud app deploy
```
Access Your Application: Once the deployment is complete, you can access your application at https://<your-project-id>.appspot.com.

Step 5: Manage and Monitor¶

Use the Google Cloud Console to manage and monitor your application. You can check logs, view traffic, and manage resources.

Additional Considerations¶

Database: If your application requires a database, consider using Google Cloud Firestore, Cloud SQL, or Datastore.
Environment Variables: You can set environment variables in the app.yaml file for sensitive information.
Scaling and Traffic Management: App Engine can automatically scale based on your traffic.

Conclusion¶

That's a high-level overview for deploying a Python application on Google Cloud using App Engine. Depending on your application needs, you might explore options like Google Cloud Run or Google Kubernetes Engine for more flexibility.

research/ links¶

* Spec-Kit: https://github.com/github/spec-kit
    * tried this... very valuable: sequence is constitution, specify, plan, task,
    * learn how to use this tool effectively.
    * another seq: specify, clarify, plan, task, analyze, 
    * use spec-kit not just for new code, but also for existing code
    * try to use spec-kit to derive the spec from the code
    * known CONSTRAINTS: this is meant for Python apps using UV (from Astral).. not for Java. -- 2026.02.11: not true, works for any language / development stack
    * ref.. https://den.dev/blog/
    * ref.. https://github.blog/ai-and-ml/generative-ai/spec-driven-development-with-ai-get-started-with-a-new-open-source-toolkit/
* Agents.md: https://agents.md
* Specification driven development:
* for Windsurf: use rules, workflows, .... https://docs.windsurf.com/windsurf/cascade/memories#rules
* one website framework... https://gohugo.io/documentation/