--- title: "Best_AI_paper_2020 vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/louisfb01-best-ai-paper-2020-vs-rasbt-llms-from-scratch" tools: ["louisfb01-best-ai-paper-2020", "rasbt-llms-from-scratch"] --- # Best_AI_paper_2020 vs LLMs-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick Best_AI_paper_2020 when license: Best_AI_paper_2020 is MIT, LLMs-from-scratch is Other; pick LLMs-from-scratch when license: LLMs-from-scratch is Other, Best_AI_paper_2020 is MIT. [Best_AI_paper_2020](https://www.louisbouchard.ai/2020-a-year-full-of-amazing-ai-papers-a-review/) reports 2.2k GitHub stars, 240 forks, and 0 open issues, last pushed Jan 28, 2022. [LLMs-from-scratch](https://amzn.to/4fqvn0D) has 99k stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [Best_AI_paper_2020's repository](https://github.com/louisfb01/Best_AI_paper_2020) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [Best_AI_paper_2020](/tools/louisfb01-best-ai-paper-2020.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 2,241 | 98,899 | | Forks | 240 | 15,183 | | Open issues | 0 | 4 | | Language | - | Jupyter Notebook | | Adopt for | - | 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 | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | LLM Frameworks, Model Training, Computer Vision | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Best_AI_paper_2020](/tools/louisfb01-best-ai-paper-2020.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1624d | 38d | | Open issues (now) | 0 | 4 | | Full report | [trust report](/tools/louisfb01-best-ai-paper-2020/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | ## Decision facts: LLMs-from-scratch - **Adopt for:** 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. ## Choose when ### Choose Best_AI_paper_2020 if… - License: Best_AI_paper_2020 is MIT, LLMs-from-scratch is Other. - Tags unique to Best_AI_paper_2020: artificialintelligence, deep-neural-networks, 2020, deeplearning. - Also covers Computer Vision. ### Choose LLMs-from-scratch if… - License: LLMs-from-scratch is Other, Best_AI_paper_2020 is MIT. - Tags unique to LLMs-from-scratch: attention-mechanism, from-scratch, generative-ai, finetuning. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ## When NOT to use Best_AI_paper_2020 - Last GitHub push was 1625 days ago (dormant maintenance, Jan 28, 2022). Validate activity before betting a new project on Best_AI_paper_2020. - 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 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. ## Common questions ### What is the difference between Best_AI_paper_2020 and LLMs-from-scratch? Best_AI_paper_2020: A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code. 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 Best_AI_paper_2020 over LLMs-from-scratch? Choose Best_AI_paper_2020 over LLMs-from-scratch when License: Best_AI_paper_2020 is MIT, LLMs-from-scratch is Other; Tags unique to Best_AI_paper_2020: artificialintelligence, deep-neural-networks, 2020, deeplearning; Also covers Computer Vision. ### When should I choose LLMs-from-scratch over Best_AI_paper_2020? Choose LLMs-from-scratch over Best_AI_paper_2020 when License: LLMs-from-scratch is Other, Best_AI_paper_2020 is MIT; Tags unique to LLMs-from-scratch: attention-mechanism, from-scratch, generative-ai, finetuning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### When should I avoid Best_AI_paper_2020? Last GitHub push was 1625 days ago (dormant maintenance, Jan 28, 2022). Validate activity before betting a new project on Best_AI_paper_2020. 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丰 a deeper learning experience. ### Is Best_AI_paper_2020 or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 2,241). Stars measure visibility, not whether either tool fits your constraints. ### Are Best_AI_paper_2020 and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (Best_AI_paper_2020: MIT, LLMs-from-scratch: Other). ### Where can I find alternatives to Best_AI_paper_2020 or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [Best_AI_paper_2020 alternatives](/tools/louisfb01-best-ai-paper-2020/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([Best_AI_paper_2020 markdown twin](/tools/louisfb01-best-ai-paper-2020/alternatives.md), [LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/alternatives.md)), 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](/compare/louisfb01-best-ai-paper-2020-vs-rasbt-llms-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Best_AI_paper_2020 or LLMs-from-scratch? Best_AI_paper_2020: Dormant. 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 Best_AI_paper_2020 and LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Best_AI_paper_2020 trust report](/tools/louisfb01-best-ai-paper-2020/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=louisfb01-best-ai-paper-2020`](/api/graphcanon/graph?tool=louisfb01-best-ai-paper-2020) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_