--- title: "ai-getting-started vs comet-examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/a16z-infra-ai-getting-started-vs-comet-ml-comet-examples" tools: ["a16z-infra-ai-getting-started", "comet-ml-comet-examples"] --- # ai-getting-started vs comet-examples *GraphCanon updated Aug 15, 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 comet-examples if comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format. [ai-getting-started](https://ai-getting-started.com/) reports 4.1k GitHub stars, 660 forks, and 16 open issues, last pushed Aug 21, 2024. [comet-examples](https://github.com/comet-ml/comet-examples) has 176 stars, 67 forks, and 26 open issues, last pushed Jul 28, 2026. Figures are from public GitHub metadata via [ai-getting-started's repository](https://github.com/a16z-infra/ai-getting-started) and [comet-examples's repository](https://github.com/comet-ml/comet-examples). | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [comet-examples](/tools/comet-ml-comet-examples.md) | | --- | --- | --- | | Tagline | A Javascript AI getting started stack for weekend projects | Examples of Machine Learning code using Comet.ml | | Stars | 4,141 | 176 | | Forks | 660 | 67 | | Open issues | 16 | 26 | | 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. | Comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Developer Tools, Model Training, Vector Databases | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [comet-examples](/tools/comet-ml-comet-examples.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 723d | 5d | | Open issues (now) | 16 | 26 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/a16z-infra-ai-getting-started/trust.md) | [trust report](/tools/comet-ml-comet-examples/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: comet-examples - **Adopt for:** Comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format. ## Choose when ### Choose ai-getting-started if… - ai-getting-started is primarily TypeScript; comet-examples is Jupyter Notebook. - Tags unique to ai-getting-started: deployment, image models, javascript, text models. - Also covers Developer Tools, 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 comet-examples if… - comet-examples is primarily Jupyter Notebook; ai-getting-started is TypeScript. - Tags unique to comet-examples: comet-ml, deep-learning-algorithms, machine-learning-platform, python. - Also covers Evaluation & Observability. - When you are working with deep learning frameworks such as PyTorch or TensorFlow and want to integrate Comet.ml for experiment tracking and model management. ## 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 comet-examples - If you are looking for a tool without third-party dependencies, as Comet-examples necessitates the use of Comet.ml which requires registration. - Avoid using this repository if your project does not need advanced experiment management features and simple code examples would suffice. ## Common questions ### What is the difference between ai-getting-started and comet-examples? ai-getting-started: A Javascript AI getting started stack for weekend projects. comet-examples: Examples of Machine Learning code using Comet.ml. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-getting-started over comet-examples? Choose ai-getting-started over comet-examples when ai-getting-started is primarily TypeScript; comet-examples is Jupyter Notebook; Tags unique to ai-getting-started: deployment, image models, javascript, text models; Also covers Developer Tools, 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 comet-examples over ai-getting-started? Choose comet-examples over ai-getting-started when comet-examples is primarily Jupyter Notebook; ai-getting-started is TypeScript; Tags unique to comet-examples: comet-ml, deep-learning-algorithms, machine-learning-platform, python; Also covers Evaluation & Observability; When you are working with deep learning frameworks such as PyTorch or TensorFlow and want to integrate Comet.ml for experiment tracking and model management. ### 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 comet-examples? If you are looking for a tool without third-party dependencies, as Comet-examples necessitates the use of Comet.ml which requires registration. Avoid using this repository if your project does not need advanced experiment management features and simple code examples would suffice. ### Is ai-getting-started or comet-examples more popular on GitHub? ai-getting-started has more GitHub stars (4,141 vs 176). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-getting-started and comet-examples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to ai-getting-started or comet-examples? GraphCanon lists graph-backed alternatives at [ai-getting-started alternatives](/tools/a16z-infra-ai-getting-started/alternatives) and [comet-examples alternatives](/tools/comet-ml-comet-examples/alternatives) ([ai-getting-started markdown twin](/tools/a16z-infra-ai-getting-started/alternatives.md), [comet-examples markdown twin](/tools/comet-ml-comet-examples/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-comet-ml-comet-examples.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 comet-examples? ai-getting-started: Dormant. comet-examples: Very active. 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 comet-examples? 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); [comet-examples trust report](/tools/comet-ml-comet-examples/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/_