--- title: "DecryptPrompt vs Made-With-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dsxiangli-decryptprompt-vs-gokumohandas-made-with-ml" tools: ["dsxiangli-decryptprompt", "gokumohandas-made-with-ml"] --- # DecryptPrompt vs Made-With-ML *GraphCanon updated Aug 14, 2026* ## Verdict Pick DecryptPrompt if decryptPrompt is an open-source repository that summarizes prompt and large language model research papers while offering related datasets and models for AI content generation facilitation; pick Made-With-ML if made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. [DecryptPrompt](https://github.com/DSXiangLi/DecryptPrompt) reports 3.4k GitHub stars, 320 forks, and 1 open issues, last pushed May 6, 2026. [Made-With-ML](https://madewithml.com) has 49k stars, 7.7k forks, and 26 open issues, last pushed Mar 4, 2026. Figures are from public GitHub metadata via [DecryptPrompt's repository](https://github.com/DSXiangLi/DecryptPrompt) and [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML). | | [DecryptPrompt](/tools/dsxiangli-decryptprompt.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Tagline | Summarizes Prompt&LLM Papers, Open-source Data&Models, AIGC Applications | Learn to develop, deploy and iterate on production-grade ML applications | | Stars | 3,427 | 49,074 | | Forks | 320 | 7,710 | | Open issues | 1 | 26 | | Language | - | Jupyter Notebook | | Adopt for | DecryptPrompt is an open-source repository that summarizes prompt and large language model research papers while offering related datasets and models for AI content generation facilitation. | Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Developer Tools, Model Training | Developer Tools, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DecryptPrompt](/tools/dsxiangli-decryptprompt.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 83d | 162d | | Open issues (now) | 1 | 26 | | Stars delta | Unknown | +371 (30d) | | Open issues delta | Unknown | -1 (30d) | | Full report | [trust report](/tools/dsxiangli-decryptprompt/trust.md) | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | ## Decision facts: DecryptPrompt - **Adopt for:** DecryptPrompt is an open-source repository that summarizes prompt and large language model research papers while offering related datasets and models for AI content generation facilitation. ## Decision facts: Made-With-ML - **Requirements:** A foundational understanding of Python programming is required to fully benefit from the learning resources provided. - **Adopt for:** Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. ## Choose when ### Choose DecryptPrompt if… - Tags unique to DecryptPrompt: aigc, chain-of-thought, chatgpt, demonstration. - When you need detailed summaries of prompt-engineering and LLM-related research, as DecryptPrompt is dedicated to this area. - More recently updated (last pushed May 6, 2026). ### Choose Made-With-ML if… - Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning. - Also covers Inference & Serving. - If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ## When NOT to use DecryptPrompt - Avoid using DecryptPrompt if you require a solution that supports languages other than English effectively, as the repository's descriptions are in Chinese. - If your development needs go beyond summarization and data/model provision into complex coding examples or comprehensive API documentation, DecryptPrompt might not satisfy these requirements. ## When NOT to use Made-With-ML - If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. - For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ## Common questions ### What is the difference between DecryptPrompt and Made-With-ML? DecryptPrompt: Summarizes Prompt&LLM Papers, Open-source Data&Models, AIGC Applications. Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose DecryptPrompt over Made-With-ML? Choose DecryptPrompt over Made-With-ML when Tags unique to DecryptPrompt: aigc, chain-of-thought, chatgpt, demonstration; When you need detailed summaries of prompt-engineering and LLM-related research, as DecryptPrompt is dedicated to this area; More recently updated (last pushed May 6, 2026). ### When should I choose Made-With-ML over DecryptPrompt? Choose Made-With-ML over DecryptPrompt when Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers Inference & Serving; If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### When should I avoid DecryptPrompt? Avoid using DecryptPrompt if you require a solution that supports languages other than English effectively, as the repository's descriptions are in Chinese. If your development needs go beyond summarization and data/model provision into complex coding examples or comprehensive API documentation, DecryptPrompt might not satisfy these requirements. ### When should I avoid Made-With-ML? If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ### Is DecryptPrompt or Made-With-ML more popular on GitHub? Made-With-ML has more GitHub stars (49,074 vs 3,427). Stars measure visibility, not whether either tool fits your constraints. ### Are DecryptPrompt and Made-With-ML open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DecryptPrompt or Made-With-ML? GraphCanon lists graph-backed alternatives at [DecryptPrompt alternatives](/tools/dsxiangli-decryptprompt/alternatives) and [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) ([DecryptPrompt markdown twin](/tools/dsxiangli-decryptprompt/alternatives.md), [Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dsxiangli-decryptprompt-vs-gokumohandas-made-with-ml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DecryptPrompt or Made-With-ML? DecryptPrompt: Steady. Made-With-ML: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for DecryptPrompt and Made-With-ML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DecryptPrompt trust report](/tools/dsxiangli-decryptprompt/trust); [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dsxiangli-decryptprompt`](/api/graphcanon/graph?tool=dsxiangli-decryptprompt) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_