Comparison
Made-With-ML vs m-courtyard
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.
Markdown twin · Made-With-ML alternatives · m-courtyard alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | Made-With-ML | m-courtyard |
|---|---|---|
| Maintenance | Slowing (132d since push) As of today · github_public_v1 | Very active (4d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | Published findings As of today · osv@v1 | No lockfile (source not queried) As of today · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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.
Stars
- Made-With-ML
- 49k
- m-courtyard
- 156
Forks
- Made-With-ML
- 7.7k
- m-courtyard
- 14
Open issues
- Made-With-ML
- 27
- m-courtyard
- 1
Language
- Made-With-ML
- Jupyter Notebook
- m-courtyard
- TypeScript
Adopt for
- Made-With-ML
- -
- m-courtyard
- -
Persona
- Made-With-ML
- -
- m-courtyard
- -
Runtime
- Made-With-ML
- -
- m-courtyard
- -
License
- Made-With-ML
- MIT
- m-courtyard
- Other
Last pushed
- Made-With-ML
- Mar 4, 2026
- m-courtyard
- Jul 11, 2026
Categories
- Made-With-ML
- AI Agents, LLM Frameworks, Model Training
- m-courtyard
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- Made-With-ML
- Slowing (36%)
- m-courtyard
- Very active (96%)
Days since push
- Made-With-ML
- 132d
- m-courtyard
- 4d
Open issues (now)
- Made-With-ML
- 27
- m-courtyard
- 1
Owner type
- Made-With-ML
- User
- m-courtyard
- Organization
OSV dependency advisories
- Made-With-ML
- Published findings
- m-courtyard
- No lockfile (source not queried)
Full report
- Made-With-ML
- Trust report
- m-courtyard
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (GokuMohandas/Made-With-ML) · observed Jul 15, 2026
- GitHub forks (GokuMohandas/Made-With-ML) · observed Jul 15, 2026
- Last push (GokuMohandas/Made-With-ML) · observed Mar 4, 2026
- License file (MIT) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (Mcourtyard/m-courtyard) · observed Jul 15, 2026
- GitHub forks (Mcourtyard/m-courtyard) · observed Jul 15, 2026
- Last push (Mcourtyard/m-courtyard) · observed Jul 11, 2026
- License file (Other) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: Made-With-ML 49k · m-courtyard 156 (synced Jul 15, 2026).
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 and m-courtyard alternatives (Made-With-ML markdown twin, m-courtyard markdown twin), 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 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; m-courtyard trust report.