Comparison
awesome-automl-papers vs jax
Verdict
Pick awesome-automl-papers when tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization; pick jax when tags unique to jax: python, jax.
Markdown twin · awesome-automl-papers alternatives · jax alternatives
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Trust & integrity
| Signal | awesome-automl-papers | jax |
|---|---|---|
| Maintenance | Dormant (760d since push) As of today · github_public_v1 | Very active (0d 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 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- awesome-automl-papers
- A curated list of automated machine learning papers, articles, tutorials, slides and projects
- jax
- Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Stars
- awesome-automl-papers
- 4.2k
- jax
- 36k
Forks
- awesome-automl-papers
- 680
- jax
- 3.7k
Open issues
- awesome-automl-papers
- 2
- jax
- 2.5k
Language
- awesome-automl-papers
- -
- jax
- Python
Adopt for
- awesome-automl-papers
- -
- jax
- -
Persona
- awesome-automl-papers
- -
- jax
- -
Runtime
- awesome-automl-papers
- -
- jax
- -
License
- awesome-automl-papers
- Apache-2.0
- jax
- Apache-2.0
Last pushed
- awesome-automl-papers
- Jun 11, 2024
- jax
- Jul 11, 2026
Categories
- awesome-automl-papers
- Vector Databases, Computer Vision
- jax
- Vector Databases, Computer Vision, Evaluation & Observability
Trust and health
Maintenance
- awesome-automl-papers
- Dormant (18%)
- jax
- Very active (96%)
Days since push
- awesome-automl-papers
- 760d
- jax
- 0d
Open issues (now)
- awesome-automl-papers
- 2
- jax
- 2.5k
Owner type
- awesome-automl-papers
- User
- jax
- Organization
Full report
- awesome-automl-papers
- Trust report
- jax
- Trust report
Choose awesome-automl-papers if…
- Tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization.
- Leaner open-issue backlog (2).
When NOT to use awesome-automl-papers
- Last GitHub push was 760 days ago (dormant maintenance, Jun 11, 2024). Validate activity before betting a new project on awesome-automl-papers.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Choose jax if…
- Tags unique to jax: python, jax.
- Also covers Evaluation & Observability.
- More GitHub stars (36k vs 4.2k) - visibility, not fit.
When NOT to use jax
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hibayesian/awesome-automl-papers) · observed Jul 11, 2026
- GitHub forks (hibayesian/awesome-automl-papers) · observed Jul 11, 2026
- Last push (hibayesian/awesome-automl-papers) · observed Jun 11, 2024
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (jax-ml/jax) · observed Jul 11, 2026
- GitHub forks (jax-ml/jax) · observed Jul 11, 2026
- Last push (jax-ml/jax) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-automl-papers 4.2k · jax 36k (synced Jul 11, 2026).
Common questions
- What is the difference between awesome-automl-papers and jax?
- awesome-automl-papers: A curated list of automated machine learning papers, articles, tutorials, slides and projects. jax: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-automl-papers over jax?
- Choose awesome-automl-papers over jax when Tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization; Leaner open-issue backlog (2).
- When should I choose jax over awesome-automl-papers?
- Choose jax over awesome-automl-papers when Tags unique to jax: python, jax; Also covers Evaluation & Observability; More GitHub stars (36k vs 4.2k) - visibility, not fit.
- When should I avoid awesome-automl-papers?
- Last GitHub push was 760 days ago (dormant maintenance, Jun 11, 2024). Validate activity before betting a new project on awesome-automl-papers. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- When should I avoid jax?
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- Is awesome-automl-papers or jax more popular on GitHub?
- jax has more GitHub stars (35,999 vs 4,152). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-automl-papers and jax open source?
- Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, jax: Apache-2.0).
- Where can I find alternatives to awesome-automl-papers or jax?
- GraphCanon lists graph-backed alternatives at awesome-automl-papers alternatives and jax alternatives (awesome-automl-papers markdown twin, jax 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, awesome-automl-papers or jax?
- awesome-automl-papers: Dormant. jax: 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 awesome-automl-papers and jax?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-automl-papers trust report; jax trust report.