Comparison
DecryptPrompt vs Made-With-ML
Verdict
Pick DecryptPrompt if decryptPrompt is an open-source repository that summarizes prompt and large language model research papers while offering related datasets and models for AI content generation facilitation; 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.
Markdown twin · DecryptPrompt alternatives · Made-With-ML alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | DecryptPrompt | Made-With-ML |
|---|---|---|
| Maintenance | Steady (83d since push) As of 4w · github_public_v1 | Slowing (162d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Personal account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · 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
- DecryptPrompt
- Summarizes Prompt&LLM Papers, Open-source Data&Models, AIGC Applications
- Made-With-ML
- Learn to develop, deploy and iterate on production-grade ML applications
Stars
- DecryptPrompt
- 3.4k
- Made-With-ML
- 49k
Forks
- DecryptPrompt
- 320
- Made-With-ML
- 7.7k
Open issues
- DecryptPrompt
- 1
- Made-With-ML
- 26
Language
- DecryptPrompt
- -
- Made-With-ML
- Jupyter Notebook
Adopt for
- DecryptPrompt
- DecryptPrompt is an open-source repository that summarizes prompt and large language model research papers while offering related datasets and models for AI content generation facilitation.
- Made-With-ML
- 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
- DecryptPrompt
- -
- Made-With-ML
- -
Runtime
- DecryptPrompt
- -
- Made-With-ML
- -
License
- DecryptPrompt
- -
- Made-With-ML
- MIT
Last pushed
- DecryptPrompt
- May 6, 2026
- Made-With-ML
- Mar 4, 2026
Categories
- DecryptPrompt
- Developer Tools, Model Training
- Made-With-ML
- Developer Tools, Inference & Serving, Model Training
Trust and health
Maintenance
- DecryptPrompt
- Steady (60%)
- Made-With-ML
- Slowing (36%)
Days since push
- DecryptPrompt
- 83d
- Made-With-ML
- 162d
Open issues (now)
- DecryptPrompt
- 1
- Made-With-ML
- 26
Stars delta
- DecryptPrompt
- Unknown
- Made-With-ML
- +371 (30d)
Open issues delta
- DecryptPrompt
- Unknown
- Made-With-ML
- -1 (30d)
OSV dependency advisories
- DecryptPrompt
- No lockfile (source not queried)
- Made-With-ML
- Published findings
Full report
- DecryptPrompt
- Trust report
- Made-With-ML
- Trust report
Choose DecryptPrompt if…
- Tags unique to DecryptPrompt: aigc, chain-of-thought, chatgpt, demonstration.
- When you need detailed summaries of prompt-engineering and LLM-related research, as DecryptPrompt is dedicated to this area.
- More recently updated (last pushed May 6, 2026).
When NOT to use DecryptPrompt
- Avoid using DecryptPrompt if you require a solution that supports languages other than English effectively, as the repository's descriptions are in Chinese.
- If your development needs go beyond summarization and data/model provision into complex coding examples or comprehensive API documentation, DecryptPrompt might not satisfy these requirements.
Choose Made-With-ML if…
- 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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (DSXiangLi/DecryptPrompt) · observed Jul 28, 2026
- GitHub forks (DSXiangLi/DecryptPrompt) · observed Jul 28, 2026
- Last push (DSXiangLi/DecryptPrompt) · observed May 6, 2026
- License file (unknown) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (GokuMohandas/Made-With-ML) · observed Aug 14, 2026
- GitHub forks (GokuMohandas/Made-With-ML) · observed Aug 14, 2026
- Last push (GokuMohandas/Made-With-ML) · observed Mar 4, 2026
- License file (MIT) · observed Aug 14, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: DecryptPrompt 3.4k · Made-With-ML 49k (synced Jul 28, 2026).
Common questions
- What is the difference between DecryptPrompt and Made-With-ML?
- DecryptPrompt: Summarizes Prompt&LLM Papers, Open-source Data&Models, AIGC Applications. 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 DecryptPrompt over Made-With-ML?
- Choose DecryptPrompt over Made-With-ML when Tags unique to DecryptPrompt: aigc, chain-of-thought, chatgpt, demonstration; When you need detailed summaries of prompt-engineering and LLM-related research, as DecryptPrompt is dedicated to this area; More recently updated (last pushed May 6, 2026).
- When should I choose Made-With-ML over DecryptPrompt?
- Choose Made-With-ML over DecryptPrompt when 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 DecryptPrompt?
- Avoid using DecryptPrompt if you require a solution that supports languages other than English effectively, as the repository's descriptions are in Chinese. If your development needs go beyond summarization and data/model provision into complex coding examples or comprehensive API documentation, DecryptPrompt might not satisfy these requirements.
- 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 DecryptPrompt or Made-With-ML more popular on GitHub?
- Made-With-ML has more GitHub stars (49,074 vs 3,427). Stars measure visibility, not whether either tool fits your constraints.
- Are DecryptPrompt and Made-With-ML open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to DecryptPrompt or Made-With-ML?
- GraphCanon lists graph-backed alternatives at DecryptPrompt alternatives and Made-With-ML alternatives (DecryptPrompt 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, DecryptPrompt or Made-With-ML?
- DecryptPrompt: Steady. 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 DecryptPrompt and Made-With-ML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DecryptPrompt trust report; Made-With-ML trust report.