Comparison
best_AI_papers_2022 vs awesome
Verdict
Pick best_AI_papers_2022 when license: best_AI_papers_2022 is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, best_AI_papers_2022 is MIT.
Markdown twin · best_AI_papers_2022 alternatives · awesome alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | best_AI_papers_2022 | awesome |
|---|---|---|
| Maintenance | Dormant (997d since push) As of today · github_public_v1 | Active (11d 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 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- best_AI_papers_2022
- A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- best_AI_papers_2022
- 3.2k
- awesome
- 484k
Forks
- best_AI_papers_2022
- 198
- awesome
- 36k
Open issues
- best_AI_papers_2022
- 0
- awesome
- 92
Language
- best_AI_papers_2022
- -
- awesome
- -
Adopt for
- best_AI_papers_2022
- -
- awesome
- -
Persona
- best_AI_papers_2022
- -
- awesome
- -
Runtime
- best_AI_papers_2022
- -
- awesome
- -
License
- best_AI_papers_2022
- MIT
- awesome
- CC0-1.0
Last pushed
- best_AI_papers_2022
- Oct 18, 2023
- awesome
- Jun 30, 2026
Categories
- best_AI_papers_2022
- Vector Databases, AI Agents, LLM Frameworks
- awesome
- LLM Frameworks
Trust and health
Maintenance
- best_AI_papers_2022
- Dormant (18%)
- awesome
- Active (82%)
Days since push
- best_AI_papers_2022
- 997d
- awesome
- 11d
Open issues (now)
- best_AI_papers_2022
- 0
- awesome
- 92
Full report
- best_AI_papers_2022
- Trust report
- awesome
- Trust report
Choose best_AI_papers_2022 if…
- License: best_AI_papers_2022 is MIT, awesome is CC0-1.0.
- Tags unique to best_AI_papers_2022: computer-science, deep-learning, ai, artificial-intelligence.
- Also covers Vector Databases, AI Agents.
When NOT to use best_AI_papers_2022
- Last GitHub push was 997 days ago (dormant maintenance, Oct 18, 2023). Validate activity before betting a new project on best_AI_papers_2022.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- 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.
Choose awesome if…
- License: awesome is CC0-1.0, best_AI_papers_2022 is MIT.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 3.2k) - visibility, not fit.
When NOT to use awesome
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (louisfb01/best_AI_papers_2022) · observed Jul 11, 2026
- GitHub forks (louisfb01/best_AI_papers_2022) · observed Jul 11, 2026
- Last push (louisfb01/best_AI_papers_2022) · observed Oct 18, 2023
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: best_AI_papers_2022 3.2k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between best_AI_papers_2022 and awesome?
- best_AI_papers_2022: A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose best_AI_papers_2022 over awesome?
- Choose best_AI_papers_2022 over awesome when License: best_AI_papers_2022 is MIT, awesome is CC0-1.0; Tags unique to best_AI_papers_2022: computer-science, deep-learning, ai, artificial-intelligence; Also covers Vector Databases, AI Agents.
- When should I choose awesome over best_AI_papers_2022?
- Choose awesome over best_AI_papers_2022 when License: awesome is CC0-1.0, best_AI_papers_2022 is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 3.2k) - visibility, not fit.
- When should I avoid best_AI_papers_2022?
- Last GitHub push was 997 days ago (dormant maintenance, Oct 18, 2023). Validate activity before betting a new project on best_AI_papers_2022. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. 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.
- When should I avoid awesome?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is best_AI_papers_2022 or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 3,188). Stars measure visibility, not whether either tool fits your constraints.
- Are best_AI_papers_2022 and awesome open source?
- Yes - both are open-source projects on GitHub (best_AI_papers_2022: MIT, awesome: CC0-1.0).
- Where can I find alternatives to best_AI_papers_2022 or awesome?
- GraphCanon lists graph-backed alternatives at best_AI_papers_2022 alternatives and awesome alternatives (best_AI_papers_2022 markdown twin, awesome 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, best_AI_papers_2022 or awesome?
- best_AI_papers_2022: Dormant. awesome: 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 best_AI_papers_2022 and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: best_AI_papers_2022 trust report; awesome trust report.