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# OpalaTex: The Unified Academic & R&D Computing Suite
OpalaTex is a free, open-source research and development platform designed to bridge technical writing, scientific computing, and artificial intelligence. Built specifically for researchers, data scientists, and engineers, OpalaTex eliminates workflow fragmentation by combining manuscript authoring, experimental code generation, data visualization, and private AI into a single, cohesive ecosystem.
## Key Features
### 1. Academic Authoring & Document Preparation
### 2. Scientific Computing, Experiments & Data Visualization
### 3. Flexible AI Assistance (Local Privacy & Cloud Scale)
## The OpalaTex Workflow
```text [Raw Data & Scientific Hypotheses] ↓ [OpalaTex Assistant] → Generates analysis & simulation scripts ↓ [Data Visualization] → Renders publication-grade TikZ / Matplotlib plots ↓ [Manuscript Drafting] → Compiles LaTeX papers & Beamer slide decks ↓ [Google Drive Sync] → Automatic cloud backup & cross-device sync
```
## Why OpalaTex?
Traditional scientific writing treats coding, visualization, and typesetting as disconnected tasks spread across terminals, notebooks, and text editors. OpalaTex closes this gap by unifying computation with document production, providing a modern, private, and reproducible environment for the future of academic research.
## Tested Models
Not all models work the same way. Small models, such as LFM 2.6B (quantized or not), do extraordinary things for their size, like searching the web and generating coherent text and LaTeX. However, they are terrible at correcting LaTeX. Large models, like GLM 5.3, GLM 5.3 Flash, Kimi K3, Gemini 3.8 Flash, GPT 5.6, and others at the same tier, are very good at almost every task in the research workflow, from generating computational experiments to writing and correcting LaTeX. Quantized intermediate models, like Ollama's Gemma4 26B running locally, achieve good results in text and LaTeX generation, and even in code generation. However, the VRAM requirements needed to expand the local context window can severely limit their tasks without increasing hardware investment costs.
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The build and commit information is derived from build infrastructure records.
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