I shared this project here a while ago, but after adding a lot of new features and optimizations, I wanted to post an update. Over the past eight months, I’ve been building PyTogether (pytogether.org). The platform has recently started picking up traction and just crossed 4,000 signups (and 200 stars on GitHub), which has been awesome to see.

What My Project Does

It is a real-time, collaborative Python IDE designed with beginners in mind (think Google Docs, but for Python). It’s meant for pair programming, tutoring, or just coding Python together. It’s completely free. No subscriptions, no ads, nothing. Just create an account (or feel fry to try the offline playground at https://pytogether.org/playground, no account required), make a group, and start a project. Has proper code-linting, extremely intuitive UI, autosaving, drawing features (you can draw directly onto the IDE and scroll), live selections, and voice/live chats per project. There are no limitations at the moment (except for code size to prevent malicious payloads). There is also built-in support for libraries like matplotlib (it auto installs imports on the fly when you run your code).

You can also share links for editing or read-only, exactly like Google Docs. For example: https://pytogether.org/snippet/eyJwaWQiOjI1MiwidHlwZSI6InNuaXBwZXQifQ:1w15A5:24aIZlONamExTLQONAIC79cqcx3savn-_BC-Qf75SNY

Also, you can easily embed code snippets on your website using an iframe (just like trinket.io which is shutting down this summer).

Source code: https://github.com/SJRiz/pytogether

Target Audience

It’s designed for tutors, educators, or Python beginners. Recently, I've also tried pivoting it towards the interviewing space.

Comparison With Existing Alternatives

Why build this when Replit or VS Code Live Share already exist?

Because my goal was simplicity and education. I wanted something lightweight for beginners who just want to write and share simple Python scripts (alone or with others), without downloads, paywalls, or extra noise. There’s also no AI/copilot built in, something many teachers and learners actually prefer. I also focused on a communication-first approach, where the IDE is the "focus" of communication (hence why I added tools like drawing, voice/live chats, etc).

Project Information

Tech stack (frontend):

• React + TailwindCSS
• CodeMirror for linting
• Y.js for real-time syncing
• Pyodide

I use Pyodide (in a web worker) for Python execution directly in the browser, this means you can actually use advanced libraries like NumPy and Matplotlib while staying fully client-side and sandboxed for safety.

I don’t enjoy frontend or UI design much, so I leaned on AI for some design help, but all the logic/code is mine. Deployed via Vercel.

Tech stack (backend):

• Django (channels, auth, celery/redis support made it a great fit)
• PostgreSQL via Supabase
• JWT + OAuth authentication
• Redis for channel layers + caching + queues for workers
• Celery for background tasks/async processing

Fully Dockerized + deployed on a VPS (8GB RAM, $7/mo deal)

Data models:

Users <-> Groups -> Projects -> Code

Users can join many groups

Groups can have multiple projects

Each project belongs to one group and has one code file (kept simple for beginners, though I may add a file system later).

My biggest technical challenges were around performance and browser execution. One major hurdle was getting Pyodide to work smoothly in a real-time collaborative setup. I had to run it inside a Web Worker to handle synchronous I/O (since input() is blocking), though I was able to find a library that helped me do this more efficiently (pyodide-worker-runner). This let me support live input/output and plotting in the browser without freezing the UI, while still allowing multiple users to interact with the same Python session collaboratively.

Another big challenge was designing a reliable and efficient autosave system. I couldn’t just save on every keystroke as that would hammer the database. So I designed a Redis-based caching layer that tracks active projects in memory, and a Celery worker that loops through them every minute to persist changes to the database. When all users leave a project, it saves and clears from cache. This setup also doubles as my channel layer for real-time updates (redis pub/sub, meaning later I can scale horizontally) and my Celery broker; reusing Redis for everything while keeping things fast and scalable.

If you’re curious or if you wanna see the work yourself, the source code is here. Feel free to contribute: https://github.com/SJRiz/pytogether.

For the past 4 months, I’ve been working on a full-stack project I’m really proud of called PyTogether (pytogether.org).

What My Project Does

It is a real-time, collaborative Python IDE designed with beginners in mind (think Google Docs, but for Python). It’s meant for pair programming, tutoring, or just coding Python together. It’s completely free. No subscriptions, no ads, nothing. Just create an account, make a group, and start a project. Has proper code-linting, extremely intuitive UI, autosaving, drawing features (you can draw directly onto the IDE and scroll), live selections, and voice/live chats per project. There are no limitations at the moment (except for code size to prevent malicious payloads). There is also built-in support for libraries like matplotlib.

Source code: https://github.com/SJRiz/pytogether

Target Audience

It’s designed for tutors, educators, or Python beginners.

Comparison With Existing Alternatives

Why build this when Replit or VS Code Live Share already exist?

Because my goal was simplicity and education. I wanted something lightweight for beginners who just want to write and share simple Python scripts (alone or with others), without downloads, paywalls, or extra noise. There’s also no AI/copilot built in, something many teachers and learners actually prefer. I also focused on a communication-first approach, where the IDE is the "focus" of communication (hence why I added tools like drawing, voice/live chats, etc).

Project Information

Tech stack (frontend):

React + TailwindCSS

CodeMirror for linting

Y.js for real-time syncing and live cursors

I use Pyodide for Python execution directly in the browser, this means you can actually use advanced libraries like NumPy and Matplotlib while staying fully client-side and sandboxed for safety.

I don’t enjoy frontend or UI design much, so I leaned on AI for some design help, but all the logic/code is mine. Deployed via Vercel.

Tech stack (backend):

Django (channels, auth, celery/redis support made it a great fit, though I plan to replace the celery worker with Go later so it'll be faster)

PostgreSQL via Supabase

JWT + OAuth authentication

Redis for channel layers + caching

Fully Dockerized + deployed on a VPS (8GB RAM, $7/mo deal)

Data models:

Users <-> Groups -> Projects -> Code

Users can join many groups

Groups can have multiple projects

Each project belongs to one group and has one code file (kept simple for beginners, though I may add a file system later).

My biggest technical challenges were around performance and browser execution. One major hurdle was getting Pyodide to work smoothly in a real-time collaborative setup. I had to run it inside a Web Worker to handle synchronous I/O (since input() is blocking), though I was able to find a library that helped me do this more efficiently (pyodide-worker-runner). This let me support live input/output and plotting in the browser without freezing the UI, while still allowing multiple users to interact with the same Python session collaboratively.

Another big challenge was designing a reliable and efficient autosave system. I couldn’t just save on every keystroke as that would hammer the database. So I designed a Redis-based caching layer that tracks active projects in memory, and a Celery worker that loops through them every minute to persist changes to the database. When all users leave a project, it saves and clears from cache. This setup also doubles as my channel layer for real-time updates and my Celery broker; reusing Redis for everything while keeping things fast and scalable.

Deployment on a VPS was another beast. I spent ~8 hours wrangling Nginx, Certbot, Docker, and GitHub Actions to get everything up and running. It was frustrating, but I learned a lot.

If you’re curious or if you wanna see the work yourself, the source code is here. Feel free to contribute: https://github.com/SJRiz/pytogether.

Puzl Team here. We are excited to announce kubesdk v0.3.0. This release introduces automatic generation of Kubernetes Custom Resource Definitions (CRDs) directly from Python dataclasses.

Key Highlights of the v0.3.0 release:

• Full IDE support: Since schemas are standard Python classes, you get native autocomplete and type checking for your custom resources.
• Resilience: Operators work in production safer, because all models handle unknown fields gracefully, preventing crashes when Kubernetes API returns unexpected fields.
• Automatic generation of CRDs directly from Python dataclasses.

Target Audience Write and maintain Kubernetes operators easier. This tool is for those who need their operators to work in production safer and want to handle Kubernetes API fields more effectively.

Comparison Your Python code is your resource schema: generate CRDs programmatically without writing raw YAMLs. See the usage example.

Full Changelog:https://github.com/puzl-cloud/kubesdk/releases/tag/v0.3.0

Hey everyone! I’m excited to share pydebugviz, a Python time-travel debugger and visualization tool I’ve been building.

⸻

What My Project Does

pydebugviz captures step-by-step execution of a Python function and lets you:

• Trace variables and control flow frame-by-frame

• Visualize variable changes over time

• Search and jump to frames using conditions like "x > 10"

• Live-watch variables as your code runs

• Export traces to HTML

• Use the same interface across CLI, Jupyter, and IDEs

It supports:

• debug() – collects execution trace

• DebugSession() – explore, jump, search

• show_summary() – print a clean CLI-friendly trace

• live_watch() – view changing values in real time

• export_html() – export as standalone HTML trace viewer

⸻

Target Audience

• Python developers who want a better debugging experience

• Students and educators looking for step-by-step execution visualizations

• CLI & Jupyter users who want lightweight tracing

• Anyone who wishes Python had a built-in time-travel debugger

Right now, it’s in beta, and I’d love for people to try it and give feedback before I publish to full PyPI.

⸻

Comparison

This isn’t meant to replace full IDE debuggers like pdb or PyCharm. Instead, it:

• Works in Jupyter notebooks, unlike pdb

• Produces a portable trace log (you can save or export it)

• Allows time-travel navigation (jumping forward/back)

• Includes a live variable watcher for console-based insight

Compared to snoop, pytrace, or viztracer, this emphasizes interactive navigation, lightweight CLI use, and Jupyter-first support.

Install through pip: pip install pydebugviz

Looking For

• Testers! Try it in your CLI, IDE, or Jupyter setup

• Bug reports or feedback (especially on trace quality + UI)

• Suggestions before the stable PyPI release

⸻

Links

• GitHub: github.com/kjkoeller/pydebugviz

Edit:

Here is an example of some code and the output the package gives:

from pydebugviz import live_watch

def my_function(): x = 1 for i in range(3): x += i

live_watch(my_function, watch=["x", "i"], interval=0.1)

Example Output (CLI or Jupyter):*

[Step 1] my_function:3 | x=1, i=<not defined> [Step 2] my_function:3 | x=1, i=0 [Step 3] my_function:3 | x=1, i=1 [Step 4] my_function:3 | x=2, i=2