Comparison
PHUDGE vs Made-With-ML
Verdict
Pick PHUDGE when tags unique to PHUDGE: ai, custom-dataset, evaluation, feedback-collection; pick Made-With-ML when tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning.
Markdown twin · PHUDGE alternatives · Made-With-ML alternatives
GraphCanon updated today
Trust & integrity
| Signal | PHUDGE | Made-With-ML |
|---|---|---|
| Maintenance | Dormant (734d since push) As of today · github_public_v1 | Slowing (132d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of today · osv@v1 | Published findings 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
- PHUDGE
- Official repo for the paper PHUDGE: Phi-3 as Scalable Judge. Evaluate your LLMs with or without custom rubric, reference answer, absolute, relative and much more. It contains a list of all the availab
- Made-With-ML
- Learn how to develop, deploy and iterate on production-grade ML applications.
Stars
- PHUDGE
- 53
- Made-With-ML
- 49k
Forks
- PHUDGE
- 7
- Made-With-ML
- 7.7k
Open issues
- PHUDGE
- 1
- Made-With-ML
- 27
Language
- PHUDGE
- Jupyter Notebook
- Made-With-ML
- Jupyter Notebook
Adopt for
- PHUDGE
- -
- Made-With-ML
- -
Persona
- PHUDGE
- -
- Made-With-ML
- -
Runtime
- PHUDGE
- -
- Made-With-ML
- -
License
- PHUDGE
- -
- Made-With-ML
- MIT
Last pushed
- PHUDGE
- Jul 10, 2024
- Made-With-ML
- Mar 4, 2026
Categories
- PHUDGE
- Inference & Serving, LLM Frameworks, Model Training
- Made-With-ML
- AI Agents, LLM Frameworks, Model Training
Trust and health
Maintenance
- PHUDGE
- Dormant (18%)
- Made-With-ML
- Slowing (36%)
Days since push
- PHUDGE
- 734d
- Made-With-ML
- 132d
Open issues (now)
- PHUDGE
- 1
- Made-With-ML
- 27
OSV dependency advisories
- PHUDGE
- No lockfile (source not queried)
- Made-With-ML
- Published findings
Full report
- PHUDGE
- Trust report
- Made-With-ML
- Trust report
Shared compatibility
- Python · PHUDGE: Python runtime · Made-With-ML: Python runtime
Choose PHUDGE if…
- Tags unique to PHUDGE: ai, custom-dataset, evaluation, feedback-collection.
- Also covers Inference & Serving.
- Leaner open-issue backlog (1).
When NOT to use PHUDGE
- Last GitHub push was 734 days ago (dormant maintenance, Jul 10, 2024). Validate activity before betting a new project on PHUDGE.
- 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.
Choose Made-With-ML if…
- Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning.
- Also covers AI Agents.
- More GitHub stars (49k vs 53) - visibility, not fit.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (deshwalmahesh/PHUDGE) · observed Jul 15, 2026
- GitHub forks (deshwalmahesh/PHUDGE) · observed Jul 15, 2026
- Last push (deshwalmahesh/PHUDGE) · observed Jul 10, 2024
- License file (unknown) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- 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 on cards: PHUDGE 53 · Made-With-ML 49k (synced Jul 15, 2026).
Common questions
- What is the difference between PHUDGE and Made-With-ML?
- PHUDGE: Official repo for the paper PHUDGE: Phi-3 as Scalable Judge. Evaluate your LLMs with or without custom rubric, reference answer, absolute, relative and much more. It contains a list of all the availab. Made-With-ML: Learn how 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 PHUDGE over Made-With-ML?
- Choose PHUDGE over Made-With-ML when Tags unique to PHUDGE: ai, custom-dataset, evaluation, feedback-collection; Also covers Inference & Serving; Leaner open-issue backlog (1).
- When should I choose Made-With-ML over PHUDGE?
- Choose Made-With-ML over PHUDGE when Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers AI Agents; More GitHub stars (49k vs 53) - visibility, not fit.
- When should I avoid PHUDGE?
- Last GitHub push was 734 days ago (dormant maintenance, Jul 10, 2024). Validate activity before betting a new project on PHUDGE. 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.
- 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.
- Is PHUDGE or Made-With-ML more popular on GitHub?
- Made-With-ML has more GitHub stars (48,703 vs 53). Stars measure visibility, not whether either tool fits your constraints.
- Are PHUDGE and Made-With-ML open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to PHUDGE or Made-With-ML?
- GraphCanon lists graph-backed alternatives at PHUDGE alternatives and Made-With-ML alternatives (PHUDGE markdown twin, Made-With-ML 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, PHUDGE or Made-With-ML?
- PHUDGE: 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 PHUDGE and Made-With-ML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: PHUDGE trust report; Made-With-ML trust report.