Comparison
ai-getting-started vs Rapid-MLX
Verdict
Pick ai-getting-started when ai-getting-started is primarily TypeScript; Rapid-MLX is Python; pick Rapid-MLX when rapid-MLX is primarily Python; ai-getting-started is TypeScript.
Markdown twin · ai-getting-started alternatives · Rapid-MLX alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | ai-getting-started | Rapid-MLX |
|---|---|---|
| Maintenance | Dormant (688d since push) As of 1d · github_public_v1 | Very active (0d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | 31 low (31 low) As of 1d · osv@v1 | No lockfile As of today · none |
Tagline
- ai-getting-started
- A Javascript AI getting started stack for weekend projects, including image/text models, vector stores, auth, and deployment configs
- Rapid-MLX
- The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replace
Stars
- ai-getting-started
- 4.1k
- Rapid-MLX
- 3.3k
Forks
- ai-getting-started
- 663
- Rapid-MLX
- 382
Open issues
- ai-getting-started
- 16
- Rapid-MLX
- 23
Language
- ai-getting-started
- TypeScript
- Rapid-MLX
- Python
Adopt for
- ai-getting-started
- -
- Rapid-MLX
- -
Persona
- ai-getting-started
- -
- Rapid-MLX
- -
Runtime
- ai-getting-started
- -
- Rapid-MLX
- -
License
- ai-getting-started
- MIT
- Rapid-MLX
- Apache-2.0
Last pushed
- ai-getting-started
- Aug 21, 2024
- Rapid-MLX
- Jul 11, 2026
Categories
- ai-getting-started
- Computer Vision, Inference & Serving, Vector Databases
- Rapid-MLX
- Inference & Serving, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- ai-getting-started
- Dormant (18%)
- Rapid-MLX
- Very active (96%)
Days since push
- ai-getting-started
- 688d
- Rapid-MLX
- 0d
Open issues (now)
- ai-getting-started
- 16
- Rapid-MLX
- 23
Owner type
- ai-getting-started
- Organization
- Rapid-MLX
- User
Security scan
- ai-getting-started
- 31 low (31 low)
- Rapid-MLX
- No lockfile
Full report
- ai-getting-started
- Trust report
- Rapid-MLX
- Trust report
Choose ai-getting-started if…
- ai-getting-started is primarily TypeScript; Rapid-MLX is Python.
- License: ai-getting-started is MIT, Rapid-MLX is Apache-2.0.
- Tags unique to ai-getting-started: typescript.
- Also covers Computer Vision.
- ai-getting-started ships Docker support for self-hosted deployment.
When NOT to use ai-getting-started
- Last GitHub push was 689 days ago (dormant maintenance, Aug 21, 2024). Validate activity before betting a new project on ai-getting-started.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Choose Rapid-MLX if…
- Rapid-MLX is primarily Python; ai-getting-started is TypeScript.
- License: Rapid-MLX is Apache-2.0, ai-getting-started is MIT.
- Tags unique to Rapid-MLX: apple-silicon, claude-code, cursor, deepseek.
- Also covers LLM Frameworks.
When NOT to use Rapid-MLX
- 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.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (a16z-infra/ai-getting-started) · observed Jul 11, 2026
- GitHub forks (a16z-infra/ai-getting-started) · observed Jul 11, 2026
- Last push (a16z-infra/ai-getting-started) · observed Aug 21, 2024
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (raullenchai/Rapid-MLX) · observed Jul 11, 2026
- GitHub forks (raullenchai/Rapid-MLX) · observed Jul 11, 2026
- Last push (raullenchai/Rapid-MLX) · 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: ai-getting-started 4.1k · Rapid-MLX 3.3k (synced Jul 11, 2026).
Common questions
- What is the difference between ai-getting-started and Rapid-MLX?
- ai-getting-started: A Javascript AI getting started stack for weekend projects, including image/text models, vector stores, auth, and deployment configs. Rapid-MLX: The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replace. See the comparison table for live GitHub stats and shared categories.
- When should I choose ai-getting-started over Rapid-MLX?
- Choose ai-getting-started over Rapid-MLX when ai-getting-started is primarily TypeScript; Rapid-MLX is Python; License: ai-getting-started is MIT, Rapid-MLX is Apache-2.0; Tags unique to ai-getting-started: typescript; Also covers Computer Vision; ai-getting-started ships Docker support for self-hosted deployment.
- When should I choose Rapid-MLX over ai-getting-started?
- Choose Rapid-MLX over ai-getting-started when Rapid-MLX is primarily Python; ai-getting-started is TypeScript; License: Rapid-MLX is Apache-2.0, ai-getting-started is MIT; Tags unique to Rapid-MLX: apple-silicon, claude-code, cursor, deepseek; Also covers LLM Frameworks.
- When should I avoid ai-getting-started?
- Last GitHub push was 689 days ago (dormant maintenance, Aug 21, 2024). Validate activity before betting a new project on ai-getting-started. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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 Rapid-MLX?
- 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Is ai-getting-started or Rapid-MLX more popular on GitHub?
- ai-getting-started has more GitHub stars (4,141 vs 3,250). Stars measure visibility, not whether either tool fits your constraints.
- Are ai-getting-started and Rapid-MLX open source?
- Yes - both are open-source projects on GitHub (ai-getting-started: MIT, Rapid-MLX: Apache-2.0).
- Where can I find alternatives to ai-getting-started or Rapid-MLX?
- GraphCanon lists graph-backed alternatives at ai-getting-started alternatives and Rapid-MLX alternatives (ai-getting-started markdown twin, Rapid-MLX 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, ai-getting-started or Rapid-MLX?
- ai-getting-started: Dormant. Rapid-MLX: 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 ai-getting-started and Rapid-MLX?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ai-getting-started trust report; Rapid-MLX trust report.