--- title: "reasoning-from-scratch vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rasbt-reasoning-from-scratch-vs-wangrongsheng-awesome-llm-resources" tools: ["rasbt-reasoning-from-scratch", "wangrongsheng-awesome-llm-resources"] --- # reasoning-from-scratch vs awesome-LLM-resources *GraphCanon updated Aug 17, 2026* ## Verdict Pick reasoning-from-scratch if a step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a. [reasoning-from-scratch](https://mng.bz/lZ5B) reports 5.0k GitHub stars, 759 forks, and 2 open issues, last pushed Aug 4, 2026. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [reasoning-from-scratch's repository](https://github.com/rasbt/reasoning-from-scratch) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Implement a reasoning LLM in PyTorch from scratch, step by step | Summary of the world's best LLM resources. | | Stars | 4,998 | 8,845 | | Forks | 759 | 950 | | Open issues | 2 | 23 | | Language | Jupyter Notebook | - | | Adopt for | A step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization. | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | Apache-2.0 | | Categories | LLM Frameworks, Model Training | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 12d | 2d | | Open issues (now) | 2 | 23 | | Stars delta | +252 (30d) | +142 (30d) | | Open issues delta | 0 (30d) | -13 (30d) | | Full report | [trust report](/tools/rasbt-reasoning-from-scratch/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: reasoning-from-scratch - **Requirements:** Automatic GPU utilization where available, though not strictly necessary for the early chapters. - **Adopt for:** A step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization. - **License detail:** Apache-2.0 License ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### Choose reasoning-from-scratch if… - Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.. - Tags unique to reasoning-from-scratch: ai, artificial-intelligence, chain-of-thought, deep-learning. - When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS. ### Choose awesome-LLM-resources if… - Tags unique to awesome-LLM-resources: awesome-list, book, course, llama. - Also covers AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## When NOT to use reasoning-from-scratch - Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components. - If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU. ## When NOT to use awesome-LLM-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between reasoning-from-scratch and awesome-LLM-resources? reasoning-from-scratch: Implement a reasoning LLM in PyTorch from scratch, step by step. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose reasoning-from-scratch over awesome-LLM-resources? Choose reasoning-from-scratch over awesome-LLM-resources when Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.; Tags unique to reasoning-from-scratch: ai, artificial-intelligence, chain-of-thought, deep-learning; When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS. ### When should I choose awesome-LLM-resources over reasoning-from-scratch? Choose awesome-LLM-resources over reasoning-from-scratch when Tags unique to awesome-LLM-resources: awesome-list, book, course, llama; Also covers AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### When should I avoid reasoning-from-scratch? Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components. If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU. ### When should I avoid awesome-LLM-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is reasoning-from-scratch or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 4,998). Stars measure visibility, not whether either tool fits your constraints. ### Are reasoning-from-scratch and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (reasoning-from-scratch: Apache-2.0, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to reasoning-from-scratch or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [reasoning-from-scratch alternatives](/tools/rasbt-reasoning-from-scratch/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([reasoning-from-scratch markdown twin](/tools/rasbt-reasoning-from-scratch/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/rasbt-reasoning-from-scratch-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, reasoning-from-scratch or awesome-LLM-resources? reasoning-from-scratch: Active. awesome-LLM-resources: 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 reasoning-from-scratch and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [reasoning-from-scratch trust report](/tools/rasbt-reasoning-from-scratch/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rasbt-reasoning-from-scratch`](/api/graphcanon/graph?tool=rasbt-reasoning-from-scratch) - 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/_