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Move Disco entry to end of list
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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| Link | Description |
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| --- | --- |
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| [Disco](https://github.com/leap-laboratories/discovery-engine) | Superhuman exploratory data analysis. Finds the feature interactions and subgroup effects in tabular data that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Free for public data. |
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| [The Data Science Lifecycle Process](https://github.com/dslp/dslp) | The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo |
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| [Data Science Lifecycle Template Repo](https://github.com/dslp/dslp-repo-template) | Template repository for data science lifecycle project |
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| [TabGAN](https://github.com/Diyago/Tabular-data-generation) | Synthetic tabular data generation using GANs, Diffusion Models, and LLMs with adversarial filtering and privacy metrics. |
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| [WFGY ProblemMap](https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md) | Open source “failure atlas” of 16 recurring issues in LLM and RAG pipelines, with observable symptoms and suggested fixes for data science teams. |
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| [Deploybase](https://deploybase.ai/) | Track real-time GPU and LLM pricing across all cloud and inference providers. |
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| [DeepAnalyze](https://github.com/ruc-datalab/DeepAnalyze) | An agentic LLM for autonomous data science, which can autonomously complete a wide range of data science tasks without human intervention. |
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| [Disco](https://github.com/leap-laboratories/discovery-engine) | Superhuman exploratory data analysis. Finds the feature interactions and subgroup effects in tabular data that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Free for public data. |
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