---
title: "Made-With-ML vs m-courtyard"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-mcourtyard-m-courtyard"
tools: ["gokumohandas-made-with-ml", "mcourtyard-m-courtyard"]
---

# Made-With-ML vs m-courtyard

*GraphCanon updated Jul 15, 2026*

## Verdict

Pick Made-With-ML when made-With-ML is primarily Jupyter Notebook; m-courtyard is TypeScript; pick m-courtyard when m-courtyard is primarily TypeScript; Made-With-ML is Jupyter Notebook.

[Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. [m-courtyard](https://github.com/Mcourtyard/m-courtyard) has 156 stars, 14 forks, and 1 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [m-courtyard's repository](https://github.com/Mcourtyard/m-courtyard).

| | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [m-courtyard](/tools/mcourtyard-m-courtyard.md) |
| --- | --- | --- |
| Tagline | Learn how to develop, deploy and iterate on production-grade ML applications. | M-Courtyard: Local AI Model Fine-tuning Assistant for Apple Silicon. Zero-code, zero-cloud, privacy-first desktop app powered by Tauri + React + mlx-lm. |
| Stars | 48,703 | 156 |
| Forks | 7,661 | 14 |
| Open issues | 27 | 1 |
| Language | Jupyter Notebook | TypeScript |
| Adopt for | - | - |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Other |
| Categories | AI Agents, LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [m-courtyard](/tools/mcourtyard-m-courtyard.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Very active (96%) |
| Days since push | 132d | 4d |
| Open issues (now) | 27 | 1 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/mcourtyard-m-courtyard/trust.md) |

## Choose when

### Choose Made-With-ML if…

- Made-With-ML is primarily Jupyter Notebook; m-courtyard is TypeScript.
- License: Made-With-ML is MIT, m-courtyard is Other.
- Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning.
- Also covers AI Agents.

### Choose m-courtyard if…

- m-courtyard is primarily TypeScript; Made-With-ML is Jupyter Notebook.
- License: m-courtyard is Other, Made-With-ML is MIT.
- Tags unique to m-courtyard: ai-assistant, apple-silicon, desktop-app, fine-tuning.
- Also covers Inference & Serving.

## When NOT to use Made-With-ML

- Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

## When NOT to use m-courtyard

- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

## Common questions

### What is the difference between Made-With-ML and m-courtyard?

Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. m-courtyard: M-Courtyard: Local AI Model Fine-tuning Assistant for Apple Silicon. Zero-code, zero-cloud, privacy-first desktop app powered by Tauri + React + mlx-lm.. See the comparison table for live GitHub stats and shared categories.

### When should I choose Made-With-ML over m-courtyard?

Choose Made-With-ML over m-courtyard when Made-With-ML is primarily Jupyter Notebook; m-courtyard is TypeScript; License: Made-With-ML is MIT, m-courtyard is Other; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers AI Agents.

### When should I choose m-courtyard over Made-With-ML?

Choose m-courtyard over Made-With-ML when m-courtyard is primarily TypeScript; Made-With-ML is Jupyter Notebook; License: m-courtyard is Other, Made-With-ML is MIT; Tags unique to m-courtyard: ai-assistant, apple-silicon, desktop-app, fine-tuning; Also covers Inference & Serving.

### When should I avoid Made-With-ML?

Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

### When should I avoid m-courtyard?

Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

### Is Made-With-ML or m-courtyard more popular on GitHub?

Made-With-ML has more GitHub stars (48,703 vs 156). Stars measure visibility, not whether either tool fits your constraints.

### Are Made-With-ML and m-courtyard open source?

Yes - both are open-source projects on GitHub (Made-With-ML: MIT, m-courtyard: Other).

### Where can I find alternatives to Made-With-ML or m-courtyard?

GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [m-courtyard alternatives](/tools/mcourtyard-m-courtyard/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [m-courtyard markdown twin](/tools/mcourtyard-m-courtyard/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/gokumohandas-made-with-ml-vs-mcourtyard-m-courtyard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Made-With-ML or m-courtyard?

Made-With-ML: Slowing. m-courtyard: 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 Made-With-ML and m-courtyard?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [m-courtyard trust report](/tools/mcourtyard-m-courtyard/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml)
- 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/_