--- title: "ai-getting-started vs Made-With-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/a16z-infra-ai-getting-started-vs-gokumohandas-made-with-ml" tools: ["a16z-infra-ai-getting-started", "gokumohandas-made-with-ml"] --- # ai-getting-started vs Made-With-ML *GraphCanon updated Sep 20, 2026* ## Verdict Pick ai-getting-started if ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations; 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. [ai-getting-started](https://ai-getting-started.com/) reports 4.1k GitHub stars, 659 forks, and 16 open issues, last pushed Aug 21, 2024. [Made-With-ML](https://madewithml.com) has 50k stars, 7.8k forks, and 25 open issues, last pushed Mar 4, 2026. Figures are from public GitHub metadata via [ai-getting-started's repository](https://github.com/a16z-infra/ai-getting-started) and [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML). | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Tagline | A Javascript AI getting started stack for weekend projects | Learn to develop, deploy and iterate on production-grade ML applications | | Stars | 4,142 | 49,547 | | Forks | 659 | 7,778 | | Open issues | 16 | 25 | | Language | TypeScript | Jupyter Notebook | | Adopt for | ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations. | 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 | MIT | | Categories | Developer Tools, Model Training, Vector Databases | Developer Tools, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 759d | 199d | | Open issues (now) | 16 | 25 | | Stars delta | +1 (30d) | +473 (30d) | | Open issues delta | 0 (30d) | -1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/a16z-infra-ai-getting-started/trust.md) | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | ## Decision facts: ai-getting-started - **Adopt for:** ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations. ## 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 ai-getting-started if… - ai-getting-started is primarily TypeScript; Made-With-ML is Jupyter Notebook. - Tags unique to ai-getting-started: deployment, image models, javascript, text models. - Also covers Vector Databases. - ai-getting-started ships Docker support for self-hosted deployment. - * Use this tool if you are already familiar with or prefer working in TypeScript and want an easy entry point into AI project development. ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; ai-getting-started is TypeScript. - 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 ai-getting-started - * If your focus is on developing large-scale, production-level applications, this tool may not offer the necessary scalability features. - * Not suitable if you require highly customized vector stores or specific AI model training environments beyond what the package provides as it focuses more on a general setup. ## 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 ai-getting-started and Made-With-ML? ai-getting-started: A Javascript AI getting started stack for weekend projects. 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 ai-getting-started over Made-With-ML? Choose ai-getting-started over Made-With-ML when ai-getting-started is primarily TypeScript; Made-With-ML is Jupyter Notebook; Tags unique to ai-getting-started: deployment, image models, javascript, text models; Also covers Vector Databases; ai-getting-started ships Docker support for self-hosted deployment; * Use this tool if you are already familiar with or prefer working in TypeScript and want an easy entry point into AI project development. ### When should I choose Made-With-ML over ai-getting-started? Choose Made-With-ML over ai-getting-started when Made-With-ML is primarily Jupyter Notebook; ai-getting-started is TypeScript; 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 ai-getting-started? * If your focus is on developing large-scale, production-level applications, this tool may not offer the necessary scalability features. * Not suitable if you require highly customized vector stores or specific AI model training environments beyond what the package provides as it focuses more on a general setup. ### 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 ai-getting-started or Made-With-ML more popular on GitHub? Made-With-ML has more GitHub stars (49,547 vs 4,142). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-getting-started and Made-With-ML open source? Yes - both are open-source projects on GitHub (ai-getting-started: MIT, Made-With-ML: MIT). ### Where can I find alternatives to ai-getting-started or Made-With-ML? GraphCanon lists graph-backed alternatives at [ai-getting-started alternatives](/tools/a16z-infra-ai-getting-started/alternatives) and [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) ([ai-getting-started markdown twin](/tools/a16z-infra-ai-getting-started/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/a16z-infra-ai-getting-started-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, ai-getting-started or Made-With-ML? ai-getting-started: Dormant. 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 ai-getting-started and Made-With-ML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-getting-started trust report](/tools/a16z-infra-ai-getting-started/trust); [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=a16z-infra-ai-getting-started`](/api/graphcanon/graph?tool=a16z-infra-ai-getting-started) - 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/_