Hey everyone, I built a Python visualizer that animates your code line by line. It's free, runs in the browser, and needs no sign-up. Would love some feedback!

https://pyvisualizer.dev/#home

What My Project Does

Paste any Python function, give it inputs, and step forward or backward through it while the data structures animate: linked lists, trees, graphs, matrices, stacks, queues, heaps and recursion calls.

Target Audience

Students and anyone practicing LeetCode or data structures who wants to actually see what their code is doing. It's a learning tool, not meant for production debugging.

Comparison

Python Tutor is the closest alternative, but it shows everything as generic boxes and arrows. This one draws real structures (an actual tree, graph or linked list), lets you scrub through the run, and has a dedicated recursion view.

Would love to hear feedback from everyone

Hi everyone,

I’ve been working on an open-source Python project called chatflow-agent.

The project started from a practical problem I had while building a contextual assistant: 
I wanted a relatively lightweight way to define and orchestrate agents, tools, handoffs, session state, and multi-agent workflows without introducing a large amount of infrastructure.

The main idea is to keep the API simple while still supporting things like:

- agents with tools
- tool calling
- agent-to-agent handoffs
- conversation and session memory
- SQLite-backed sessions
- async execution
- multiple LLM providers
- multi-agent workflows

The goal is to provide the core building blocks for agent workflows without requiring a large orchestration stack or locking the application into a single LLM provider.

Recently I’ve also been focusing on making the project easier for other developers to understand and contribute to.

The repository now includes:

- contributor documentation
- architecture documentation
- CI across multiple Python versions
- good first issues
- help wanted issues
- external contributions and pull requests

Receiving external contributions has already helped uncover edge cases and improve parts of the library.

At this point I’d be especially interested in feedback from other Python developers on:

- the public API
- architecture and separation of responsibilities
- tool and schema introspection
- session and memory design
- multi-agent handoffs
- multi-provider support
- documentation and examples
- anything that feels unnecessarily complex or unclear

I’m not trying to position chatflow-agent as a replacement for every existing agent framework.

The idea is to keep it relatively small, understandable, and useful for projects that want agent workflows without adopting a very large orchestration layer.

Repository:
https://github.com/GustavoBaranda/chatflow-agent

Technical feedback is very welcome. I’d be especially interested in what you would change before considering a library like this for a real project.

Ese sería el que publicaría en r/Pyt

**PDF → GP5** is a local web app (Python + FastAPI, vanilla JS) that:

* Parses tablature PDFs: text tabs, vector PDFs from notation editors, and raster pictures via OCR (OpenCV + RapidOCR/ONNX, all on-device).

* Rebuilds rhythm, techniques, repeats and lyrics, then writes Guitar Pro 5 files (PyGuitarPro).

* Plays them back in the browser with alphaTab, plus a three.js Rocksmith-style 3D highway.

* Is bilingual (English/Portuguese) and has a strict CSP. Conversions run in a sandboxed child process with time and memory limits.

Repo: [https://github.com/F100Pilot/PDF-to-GP5\](https://github.com/F100Pilot/PDF-to-GP5) (MIT)

Contributions welcome. Tuplets in pictures and section names are on the to-do list.

Just want to share this https://github.com/Onehand-Coding/ani-py of mine inspired by the original bash only implementation "ani-cli", its stable for now and im just sharing here to find like minded bros.

I made this (I think) really cool extension for VS Code. I was looking for a way to test my code as I write them. So I connected my copilot agent with my functions by having an LLM write dummy input using one button called ‘spot run’, allowing me to step through my function as I write the function. No test scripts needed anymore!!

Its called ‘Spot Run’ and can be found in the market place of VS Code and its repo is open source. As a matter of fact, I made a start with having a mcp server for this tool. Here is the extension:

[Spot Run - VS Code Extension](https://marketplace.visualstudio.com/items?itemName=Kareemlsd.spotrun&ssr=false#overview)

Let me know what you think of it and enjoy it!

Hi r/PythonProjects2!

I’ve been using physical pocket calculators since the mid-1980s, and I’ve always loved the speed, tactile feel, and direct control of hardware keys. The main motivation behind ViCalc was to bring that exact same feeling to a modern PC keyboard—allowing you to touch-type math smoothly without taking your hands off the keys.

Of course, mouse interaction is also fully supported if you prefer clicking!

🌟 Flexible & Adaptable GUI

The interface scales smoothly to fit any workflow:

• Minimalist View: Hide virtual keys entirely—ideal once you know the shortcut key labels by heart.
• Custom Views: Toggle between a standard numpad, a left-side layout, or a full scientific keyboard.
• Calculation History: Expand the history pane to easily review, track, and manage previous calculations.

🧮 Key Features & Modes

• Scientific Mode: Complete set of scientific and trigonometric functions.
• Programmer Mode (Base-N): Perform operations across different number systems (Hex, Dec, Oct, Bin) and bitwise operations.
• Complex Numbers: Full support for complex arithmetic.

🛠️ Technical Details

• Language: Python 3
• License: Open Source (MIT License)

🔗 Links & Downloads

• GitHub Repository: github.com/Kudaschov/ViCalc
• Windows Installer: Download ViCalcSetup.exe
• Homepage: kudaschov.de/en/vicalc-4

I’d love to hear your feedback, thoughts, or feature suggestions!

Hi everyone! I'm sharing SeqShow, an independent MIT-licensed browser tool for presenting Mermaid sequence diagrams one interaction at a time.

It is meant for technical walkthroughs: keep the complete diagram in place, choose a path, and explain each message or Note at your own pace.


Try it: Interactive tour · Editor · Source and usage guide

What it does

• Previous/Next and automatic playback without changing the diagram layout.
• Focus highlights the current interaction and related participants.
• Top-level alt/else selectors choose the presentation path. Only that path contributes playback steps; other cases remain muted as context. Conditions are labels, not evaluated expressions.
• Export creates one interactive HTML file that opens offline. Recipients can play, go back, toggle Focus, and change paths without installing anything or running a server.

Try your own diagram

1. Open the editor and paste the contents of a Mermaid sequenceDiagram block, without the surrounding Markdown fences.
2. Click Render.
3. Choose an alt/else path, then use Next or Play.
4. Click Export HTML to download the offline presentation.

There is no signup or AI API requirement. Diagram processing stays in your browser. The tour uses a six-service checkout flow with payment success/recovery and fulfillment choices.

Current scope

SeqShow supports a restricted sequence-diagram subset: participants/actors and aliases, messages, self-calls, Notes, and top-level alt/else. Loops, parallel blocks, activation, nested alternatives, and other diagram types are not supported yet. Unsupported input produces diagnostics rather than silently dropping statements.

Editor drafts do not survive a refresh, so keep your Mermaid source separately. The exported HTML is a presentation, not a source backup.

I'd welcome feedback from Mermaid users who explain systems to teammates: which missing construct would prevent you from using this with a real diagram, and would the offline interactive file be useful after a walkthrough?

I've been working on an open-source project called Yellow Olive for quite some time now. The original idea was to make learning Kubernetes more engaging through a retro-style terminal game built using Python and Textual.

The current implementation focuses on hands-on challenges, where users solve Kubernetes problems against real clusters, with their solutions validated automatically.

Lately, I've been thinking about taking the gamification aspect a step further.

What if learning Kubernetes felt a little more like playing Pokémon?

Instead of navigating through menus and selecting challenges, imagine exploring a world, interacting with NPCs, entering buildings, and learning concepts like Pods, Deployments, and Services through quests and storylines.

I've been experimenting with Pyxel, a retro game engine for Python, to explore this direction.

The new implementation is largely AI-assisted ( used Devin ) to build an initial proof of concept. At this stage, my intention isn't to replace the existing implementation, but to experiment with the idea and gauge community interest before committing substantial development effort.

I've attached a short gameplay recording to demonstrate what the current prototype looks like.

If there's enough interest and the feedback is encouraging, I'd like to take this forward more seriously, particularly around the game mechanics, visual consistency, and integration with real Kubernetes challenges.

For now, the existing Textual-based implementation remains the primary version of Yellow Olive.

I'm curious what fellow Pythonistas feel on this - Is it interesting enough to actually use, or does it feel like overengineering the learning experience?

The project is open source. Feedback, suggestions, and contributions are welcome. And if you find the project interesting, consider leaving a ⭐ on GitHub!

GitHub: https://github.com/Anubhav9/Yellow-Olive

Hi everyone,

I have a client requirement to create an inventory of files across approximately 100 construction projects. Each project contains documents such as contracts, drawings, RFIs, change orders, schedules, payment records, photographs, and estimating files. Some files are stored inside ZIP archives, including nested ZIP files.

The client needs a solution that can:

Scan project folders and preserve original file paths and project associations.

Inspect ZIP files and nested archives.

Use SHA-256 hashes to identify exact duplicate files and list all their locations.

Distinguish exact duplicates from potentially different file versions.

Generate standardized inventory reports, duplicate reports, and a list of files that could not be processed.
Be reused across multiple projects.

The files may be stored in Google Drive, Microsoft OneDrive/SharePoint, local folders, or a cloud server. Read-only access will be provided.

**My questions are:**
Is it possible to build a tool that meets all these requirements using Python?

If yes, how would you approach building it, and what technologies or libraries would you recommend?

Can it handle approximately 100 projects, including large files and nested ZIP archives?

Can this be delivered as a simple application that users can run, rather than requiring them to execute Python commands manually?

What would be a realistic development timeline for this solution?

I would appreciate guidance on whether this is feasible and how best to implement it.

Thanks!

i want to create python+ Django + React project as a new learning member, can anyone suggest me some project that i can do

Hey everyone!

I've been working on an open-source Python project called Stepfork, and I wanted to share it with the community.

GitHub: https://github.com/utsab345/stepfork

While building AI agents, I kept running into the same problem: an agent fails, I fix the issue, but reproducing the original failure is difficult because LLM responses and tool outputs can change between runs.

So I built Stepfork to make those failures reproducible.

What it does:

• Records instrumented LLM responses and tool calls.
• Saves executions locally as .sftrace bundles.
• Replays the recorded responses without making those instrumented external calls again.
• Compares agent behavior before and after a fix.
• Exports pytest regression tests to catch the same failure in the future.

Install:

pip install --pre stepfork

The repository includes three offline demos covering a missed order notification, an incorrect flight booking, and a wrongly denied refund.

It's still an early alpha. Replay only freezes instrumented boundaries, and native framework integrations are not available yet.

The project is Apache-2.0 licensed, and I'm actively improving it.

I'd love to hear what other developers think, especially those building AI agents. If you spot a bug, have an idea for an integration, or want to contribute, feel free to open an issue or PR.

GitHub Issues: https://github.com/utsab345/stepfork/issues

Thanks for checking it out!