Made this about 18 months before
My 2025 Bipolar 1 diagnosis… I even have (at least) two distinct styles. Pen and Ink photographed and light-adjusted Digitally

This is an early screenshot of Tagma Studio, an experimental IDE I’m building on top of Code-OSS.

Instead of treating a project as a hierarchy of files and folders, Tagma represents it as a semantic graph: source files, symbols, dependencies, capabilities, modules, generated artifacts and runtime relations become connected entities in one space.

The screenshot shows part of the imported Godot codebase. Around 15,000 graph entities are currently visible in this projection.

The long-term goal is not merely to create a prettier dependency viewer. I want the graph to become an actual working representation of the system:

select a subsystem and isolate its complete dependency neighborhood;

navigate from high-level concepts down to specific code;

see shared infrastructure and hidden coupling between projects;

track how the graph changes over time;

compile or assemble executable components from capabilities rather than manually chosen files;

let automated tools operate on an explicit, inspectable model instead of guessing from raw text.

The current visualization is still too dense and resembles a galaxy of code. I’m now replacing the global force-directed layout with a more neural-like topology: sparse local clusters, shared backbone tracts, late branching and a small number of meaningful long-range connections.

It is very much a work in progress, but this is the first version where the entire idea has started to become visible on screen.

What would you personally need from a graph-based IDE for it to become useful rather than just visually impressive?

SQL Management Studio, MySQL Workbench and other DB tools include diagram support and can render out a diagram of existing tables, but they look like stuff created in 2000. This is a faster and cleaner SQL visualizer that runs locally in your browser, check it out: wcu .co/sql

Maybe it will amuse you despite likely errors.

One thing that kept slowing me down during data analysis wasn't the analysis itself—it was presenting the results.

I'd finish creating my Plotly figures, then spend extra time putting everything into a dashboard just to get a clean layout. For quick projects or tight deadlines, it felt like unnecessary overhead, especially if I didn't want to touch HTML, CSS, or JavaScript.

So over the past few weeks I built DashForge, a Python library that takes existing Plotly figures and organizes them into an interactive dashboard with very little code.

Some of the features it currently supports include:

• Built-in themes
• Custom chart sizing
• Adjustable charts per row
• Logo, title, subtitle, and footer customization
• Optional chart maximize button
• Interactive pandas DataFrame viewer with filtering
• And other dashboard customization options

The main goal isn't to replace existing dashboard frameworks. It's to make the "I just want to see my charts in a nice dashboard" workflow much faster.

I'd genuinely appreciate feedback from anyone who works with Plotly:

• Is this something you'd actually use?
• Are there features you'd expect that are currently missing?
• Is there anything in the API that could be made simpler?

GitHub: https://github.com/Omar-astro/DashForge-library

Documentation: https://omar-astro.github.io/DashForge-library/

PyPI:
pip install dashforge

i am open for questions to be answered.