Comparison
mobilegym vs LLMs-from-scratch
Verdict
Pick mobilegym when mobilegym is primarily Python; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; mobilegym is Python.
Markdown twin · mobilegym alternatives · LLMs-from-scratch alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | mobilegym | LLMs-from-scratch |
|---|---|---|
| Maintenance | Active (14d since push) As of today · github_public_v1 | Steady (38d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of today · osv@v1 | No lockfile (source not queried) As of 4d · 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
- mobilegym
- MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research · 浏览器里运行的安卓模拟器 · Browser-hosted Android Simulator · Verifiable Evaluation · Scalable Online RL Training
- LLMs-from-scratch
- Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Stars
- mobilegym
- 721
- LLMs-from-scratch
- 99k
Forks
- mobilegym
- 116
- LLMs-from-scratch
- 15k
Open issues
- mobilegym
- 5
- LLMs-from-scratch
- 4
Language
- mobilegym
- Python
- LLMs-from-scratch
- Jupyter Notebook
Adopt for
- mobilegym
- -
- LLMs-from-scratch
- LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions.
Persona
- mobilegym
- -
- LLMs-from-scratch
- -
Runtime
- mobilegym
- -
- LLMs-from-scratch
- -
License
- mobilegym
- Apache-2.0
- LLMs-from-scratch
- Other
Last pushed
- mobilegym
- Jul 1, 2026
- LLMs-from-scratch
- Jun 2, 2026
Categories
- mobilegym
- AI Agents, LLM Frameworks, Model Training
- LLMs-from-scratch
- LLM Frameworks, Model Training
Trust and health
Maintenance
- mobilegym
- Active (82%)
- LLMs-from-scratch
- Steady (60%)
Days since push
- mobilegym
- 14d
- LLMs-from-scratch
- 38d
Open issues (now)
- mobilegym
- 5
- LLMs-from-scratch
- 4
Full report
- mobilegym
- Trust report
- LLMs-from-scratch
- Trust report
Choose mobilegym if…
- mobilegym is primarily Python; LLMs-from-scratch is Jupyter Notebook.
- License: mobilegym is Apache-2.0, LLMs-from-scratch is Other.
- Tags unique to mobilegym: agent, agents, android, automation.
- Also covers AI Agents.
When NOT to use mobilegym
- 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.
Choose LLMs-from-scratch if…
- LLMs-from-scratch is primarily Jupyter Notebook; mobilegym is Python.
- License: LLMs-from-scratch is Other, mobilegym is Apache-2.0.
- Tags unique to LLMs-from-scratch: artificial-intelligence, attention-mechanism, deep-learning, finetuning.
- - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.
When NOT to use LLMs-from-scratch
- - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work.
- - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers
- a deeper learning experience.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Purewhiter/mobilegym) · observed Jul 15, 2026
- GitHub forks (Purewhiter/mobilegym) · observed Jul 15, 2026
- Last push (Purewhiter/mobilegym) · observed Jul 1, 2026
- License file (Apache-2.0) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (rasbt/LLMs-from-scratch) · observed Jul 11, 2026
- GitHub forks (rasbt/LLMs-from-scratch) · observed Jul 11, 2026
- Last push (rasbt/LLMs-from-scratch) · observed Jun 2, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mobilegym 721 · LLMs-from-scratch 99k (synced Jul 15, 2026).
Common questions
- What is the difference between mobilegym and LLMs-from-scratch?
- mobilegym: MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research · 浏览器里运行的安卓模拟器 · Browser-hosted Android Simulator · Verifiable Evaluation · Scalable Online RL Training. LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories.
- When should I choose mobilegym over LLMs-from-scratch?
- Choose mobilegym over LLMs-from-scratch when mobilegym is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: mobilegym is Apache-2.0, LLMs-from-scratch is Other; Tags unique to mobilegym: agent, agents, android, automation; Also covers AI Agents.
- When should I choose LLMs-from-scratch over mobilegym?
- Choose LLMs-from-scratch over mobilegym when LLMs-from-scratch is primarily Jupyter Notebook; mobilegym is Python; License: LLMs-from-scratch is Other, mobilegym is Apache-2.0; Tags unique to LLMs-from-scratch: artificial-intelligence, attention-mechanism, deep-learning, finetuning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.
- When should I avoid mobilegym?
- 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.
- When should I avoid LLMs-from-scratch?
- - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers a deeper learning experience.
- Is mobilegym or LLMs-from-scratch more popular on GitHub?
- LLMs-from-scratch has more GitHub stars (98,899 vs 721). Stars measure visibility, not whether either tool fits your constraints.
- Are mobilegym and LLMs-from-scratch open source?
- Yes - both are open-source projects on GitHub (mobilegym: Apache-2.0, LLMs-from-scratch: Other).
- Where can I find alternatives to mobilegym or LLMs-from-scratch?
- GraphCanon lists graph-backed alternatives at mobilegym alternatives and LLMs-from-scratch alternatives (mobilegym markdown twin, LLMs-from-scratch 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, mobilegym or LLMs-from-scratch?
- mobilegym: Active. LLMs-from-scratch: Steady. 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 mobilegym and LLMs-from-scratch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mobilegym trust report; LLMs-from-scratch trust report.