Comparison
LLMs-from-scratch vs harness-books
Verdict
Pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; harness-books is Python; pick harness-books when harness-books is primarily Python; LLMs-from-scratch is Jupyter Notebook.
Markdown twin · LLMs-from-scratch alternatives · harness-books alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | LLMs-from-scratch | harness-books |
|---|---|---|
| Maintenance | Steady (38d since push) As of 1d · github_public_v1 | Steady (83d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1d · github_public_v1 | Not a fork · Personal account As of 1d · github_public_v1 |
| Security (OSV) | No lockfile As of 1d · none | No lockfile As of 1d · none |
Tagline
- LLMs-from-scratch
- Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
- harness-books
- 📚 Two books on harness engineering — the design philosophies behind Claude Code & Codex: constraints, query loops, context governance, multi-agent verification. harness-books.agentway.dev
Stars
- LLMs-from-scratch
- 99k
- harness-books
- 2.6k
Forks
- LLMs-from-scratch
- 15k
- harness-books
- 308
Open issues
- LLMs-from-scratch
- 4
- harness-books
- 5
Language
- LLMs-from-scratch
- Jupyter Notebook
- harness-books
- Python
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.
- harness-books
- -
Persona
- LLMs-from-scratch
- -
- harness-books
- -
Runtime
- LLMs-from-scratch
- -
- harness-books
- -
License
- LLMs-from-scratch
- Other
- harness-books
- -
Last pushed
- LLMs-from-scratch
- Jun 2, 2026
- harness-books
- Apr 19, 2026
Categories
- LLMs-from-scratch
- LLM Frameworks, Model Training
- harness-books
- AI Agents, LLM Frameworks, Model Training
Trust and health
Days since push
- LLMs-from-scratch
- 38d
- harness-books
- 83d
Open issues (now)
- LLMs-from-scratch
- 4
- harness-books
- 5
Full report
- LLMs-from-scratch
- Trust report
- harness-books
- Trust report
Choose LLMs-from-scratch if…
- LLMs-from-scratch is primarily Jupyter Notebook; harness-books is Python.
- 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丰
- a deeper learning experience.
Choose harness-books if…
- harness-books is primarily Python; LLMs-from-scratch is Jupyter Notebook.
- Tags unique to harness-books: agentic-ai, ai-agents, ai-engineering, claude-code.
- Also covers AI Agents.
When NOT to use harness-books
- 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 (wquguru/harness-books) · observed Jul 11, 2026
- GitHub forks (wquguru/harness-books) · observed Jul 11, 2026
- Last push (wquguru/harness-books) · observed Apr 19, 2026
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMs-from-scratch 99k · harness-books 2.6k (synced Jul 11, 2026).
Common questions
- What is the difference between LLMs-from-scratch and harness-books?
- LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. harness-books: 📚 Two books on harness engineering — the design philosophies behind Claude Code & Codex: constraints, query loops, context governance, multi-agent verification. harness-books.agentway.dev. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMs-from-scratch over harness-books?
- Choose LLMs-from-scratch over harness-books when LLMs-from-scratch is primarily Jupyter Notebook; harness-books is Python; 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 harness-books over LLMs-from-scratch?
- Choose harness-books over LLMs-from-scratch when harness-books is primarily Python; LLMs-from-scratch is Jupyter Notebook; Tags unique to harness-books: agentic-ai, ai-agents, ai-engineering, claude-code; 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丰 a deeper learning experience.
- When should I avoid harness-books?
- 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 harness-books more popular on GitHub?
- LLMs-from-scratch has more GitHub stars (98,899 vs 2,618). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMs-from-scratch and harness-books open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LLMs-from-scratch or harness-books?
- GraphCanon lists graph-backed alternatives at LLMs-from-scratch alternatives and harness-books alternatives (LLMs-from-scratch markdown twin, harness-books 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 harness-books?
- LLMs-from-scratch: Steady. harness-books: 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 LLMs-from-scratch and harness-books?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMs-from-scratch trust report; harness-books trust report.