Comparison
LLMs-from-scratch vs Awesome-LLMSecOps
Verdict
Pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; Awesome-LLMSecOps is HTML; pick Awesome-LLMSecOps when awesome-LLMSecOps is primarily HTML; LLMs-from-scratch is Jupyter Notebook.
Markdown twin · LLMs-from-scratch alternatives · Awesome-LLMSecOps alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | LLMs-from-scratch | Awesome-LLMSecOps |
|---|---|---|
| Maintenance | Steady (38d since push) As of 4d · github_public_v1 | Very active (1d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4d · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 4d · osv@v1 | No lockfile (source not queried) As of today · 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
- LLMs-from-scratch
- Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
- Awesome-LLMSecOps
- LLM | Agentic | Security | Operations in one github repo with good links and pictures.
Stars
- LLMs-from-scratch
- 99k
- Awesome-LLMSecOps
- 144
Forks
- LLMs-from-scratch
- 15k
- Awesome-LLMSecOps
- 51
Open issues
- LLMs-from-scratch
- 4
- Awesome-LLMSecOps
- 8
Language
- LLMs-from-scratch
- Jupyter Notebook
- Awesome-LLMSecOps
- HTML
Adopt for
- 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.
- Awesome-LLMSecOps
- -
Persona
- LLMs-from-scratch
- -
- Awesome-LLMSecOps
- -
Runtime
- LLMs-from-scratch
- -
- Awesome-LLMSecOps
- -
License
- LLMs-from-scratch
- Other
- Awesome-LLMSecOps
- -
Last pushed
- LLMs-from-scratch
- Jun 2, 2026
- Awesome-LLMSecOps
- Jul 13, 2026
Categories
- LLMs-from-scratch
- LLM Frameworks, Model Training
- Awesome-LLMSecOps
- AI Agents, LLM Frameworks, Model Training
Trust and health
Maintenance
- LLMs-from-scratch
- Steady (60%)
- Awesome-LLMSecOps
- Very active (96%)
Days since push
- LLMs-from-scratch
- 38d
- Awesome-LLMSecOps
- 1d
Open issues (now)
- LLMs-from-scratch
- 4
- Awesome-LLMSecOps
- 8
Full report
- LLMs-from-scratch
- Trust report
- Awesome-LLMSecOps
- Trust report
Choose LLMs-from-scratch if…
- LLMs-from-scratch is primarily Jupyter Notebook; Awesome-LLMSecOps is HTML.
- Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, deep-learning.
- - 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.
Choose Awesome-LLMSecOps if…
- Awesome-LLMSecOps is primarily HTML; LLMs-from-scratch is Jupyter Notebook.
- Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, ai-red-team, ai-safety-supply-chain-security.
- Also covers AI Agents.
When NOT to use Awesome-LLMSecOps
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (wearetyomsmnv/Awesome-LLMSecOps) · observed Jul 15, 2026
- GitHub forks (wearetyomsmnv/Awesome-LLMSecOps) · observed Jul 15, 2026
- Last push (wearetyomsmnv/Awesome-LLMSecOps) · observed Jul 13, 2026
- License file (unknown) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: LLMs-from-scratch 99k · Awesome-LLMSecOps 144 (synced Jul 11, 2026).
Common questions
- What is the difference between LLMs-from-scratch and Awesome-LLMSecOps?
- LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. Awesome-LLMSecOps: LLM | Agentic | Security | Operations in one github repo with good links and pictures.. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMs-from-scratch over Awesome-LLMSecOps?
- Choose LLMs-from-scratch over Awesome-LLMSecOps when LLMs-from-scratch is primarily Jupyter Notebook; Awesome-LLMSecOps is HTML; Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, deep-learning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.
- When should I choose Awesome-LLMSecOps over LLMs-from-scratch?
- Choose Awesome-LLMSecOps over LLMs-from-scratch when Awesome-LLMSecOps is primarily HTML; LLMs-from-scratch is Jupyter Notebook; Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, ai-red-team, ai-safety-supply-chain-security; Also covers AI Agents.
- 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.
- When should I avoid Awesome-LLMSecOps?
- 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.
- Is LLMs-from-scratch or Awesome-LLMSecOps more popular on GitHub?
- LLMs-from-scratch has more GitHub stars (98,899 vs 144). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMs-from-scratch and Awesome-LLMSecOps open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LLMs-from-scratch or Awesome-LLMSecOps?
- GraphCanon lists graph-backed alternatives at LLMs-from-scratch alternatives and Awesome-LLMSecOps alternatives (LLMs-from-scratch markdown twin, Awesome-LLMSecOps 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, LLMs-from-scratch or Awesome-LLMSecOps?
- LLMs-from-scratch: Steady. Awesome-LLMSecOps: 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 LLMs-from-scratch and Awesome-LLMSecOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMs-from-scratch trust report; Awesome-LLMSecOps trust report.