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title: README |
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--- |
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## Remyx AI — ExperimentOps Infrastructure |
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📣 **Join us at [Experiment 2025](https://experiment.remyx.ai)!** |
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> A scientific interface for debugging, evaluating, and iterating on AI systems. |
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Remyx AI offers infrastructure for **ExperimentOps**, a principled layer for managing the design and evaluation of AI systems. |
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ExperimentOps is a set of practices and methods to operationalize **how we learn from a growing history of experiments** and design better systems under practical constraints. |
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### 🧪 Why ExperimentOps? |
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AI development is fundamentally empirical. But as the design space grows, it becomes computationally and operationally intractable to explore all combinations. |
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ExperimentOps provides a formal structure for reasoning under this complexity: |
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- Every system variant is an **intervention**; every evaluation is an **outcome**. |
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- By modeling experiment history causally, not just correlationally, we identify what contributes to downstream performance. |
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- Instead of trial-and-error, we build structured knowledge from cumulative evidence. |
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This causal framing enables teams to **experiment with purpose**: prioritizing what to try next, what to revisit, and what to discard. |
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### 🛠️ What You'll Find Here |
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- **Model variants** – e.g., `SpaceThinker-Qwen2.5VL-3B`, `SpaceOm`, and others trained through structured, reproducible workflows. |
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- **Open datasets** – Synthetic multimodal datasets created with tools like [VQASynth](https://github.com/remyxai/VQASynth). |
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- **Evaluation analyses** – Curated results and leaderboard comparisons published via Hugging Face model cards and evaluation tables, reflecting structured experiments conducted in Remyx and other platforms. |
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> **Mission**: Help teams reason clearly about what works and why, treating experimentation as a scientific process, not guesswork. |
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Learn more at [remyx.ai](https://remyx.ai) |
