--- title: "LLMForEverybody vs reasoning-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/luhengshiwo-llmforeverybody-vs-rasbt-reasoning-from-scratch" tools: ["luhengshiwo-llmforeverybody", "rasbt-reasoning-from-scratch"] --- # LLMForEverybody vs reasoning-from-scratch Neutral, constraint-first comparison with live GitHub stats. | | [LLMForEverybody](/tools/luhengshiwo-llmforeverybody.md) | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | | --- | --- | --- | | Tagline | Learning LLM is all you need. | Implement a reasoning LLM in PyTorch from scratch, step by step | | Stars | 6,884 | 4,707 | | Forks | 639 | 703 | | Open issues | 0 | 2 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | LLMForEverybody is a repository primarily focused on sharing knowledge about large language models, with content that includes interview practice, research paper studies (from foundational Transformer papers to more up-t | reasoning-from-scratch is a PyTorch-based repository for building reasoning models from scratch, as outlined in the book *Build a Reasoning Model (From Scratch)*. It focuses on hands-on development of a small but fully-f | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT License, permissive open source license allowing for commercial and non-commercial uses, as long as original licensing terms are met. | | Categories | AI Agents, LLM Frameworks, Developer Tools | Model Training, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMForEverybody](/tools/luhengshiwo-llmforeverybody.md) | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 38d | 2d | | Open issues (now) | 0 | 2 | | Security scan | No lockfile | 15 low (15 low) | | Full report | [trust report](/tools/luhengshiwo-llmforeverybody/trust.md) | [trust report](/tools/rasbt-reasoning-from-scratch/trust.md) | **Typed relationship:** LLMForEverybody _(alternative)_ reasoning-from-scratch Both repositories aim at making the process of learning Large Language Models approachable for everyone, focusing on educational and from-scratch model implementation. ## Decision facts: LLMForEverybody - **Adopt for:** LLMForEverybody is a repository primarily focused on sharing knowledge about large language models, with content that includes interview practice, research paper studies (from foundational Transformer papers to more up-t ## Decision facts: reasoning-from-scratch - **Hosting:** unknown - **Pricing:** freemium - The code itself is free under the Apache-2.0 license. However, purchasing the book *Build a Reasoning Model (From Scratch)* might be necessary to fully leverage the repository. - **Requirements:** Min 8 GB RAM; Python environment with PyTorch libraries and Jupyter Notebook setup required; Refer Chapter 2 for initial setup tips - **Adopt for:** reasoning-from-scratch is a PyTorch-based repository for building reasoning models from scratch, as outlined in the book *Build a Reasoning Model (From Scratch)*. It focuses on hands-on development of a small but fully-f - **License detail:** MIT License, permissive open source license allowing for commercial and non-commercial uses, as long as original licensing terms are met. ## Choose when ### Choose LLMForEverybody if… - Both repositories aim at making the process of learning Large Language Models approachable for everyone, focusing on educational and from-scratch model implementation. - Tags unique to LLMForEverybody: interview-practice, learnllm, rag. - Also covers AI Agents, Developer Tools. - If you are preparing for job interviews in the field of LLMs or related technologies and want access to practical questions and answers. ### Choose reasoning-from-scratch if… - Pricing: The code itself is free under the Apache-2.0 license. However, purchasing the book *Build a Reasoning Model (From Scratch)* might be necessary to fully leverage the repository.. - Requirements: Min 8 GB RAM; Python environment with PyTorch libraries and Jupyter Notebook setup required; Refer Chapter 2 for initial setup tips. - Both repositories aim at making the process of learning Large Language Models approachable for everyone, focusing on educational and from-scratch model implementation. - Tags unique to reasoning-from-scratch: inference-time-scaling, deep-learning, chain-of-thought, llm. - Also covers Model Training. - Use if you are interested in implementing and understanding reasoning capabilities for LLMs step-by-step using PyTorch. ## When NOT to use LLMForEverybody - If your learning preference leans towards a different language or if the Chinese-specific resources don't align with your needs. - For individuals looking for comprehensive open-source tools or frameworks to build upon directly; this is more about educational content than concrete implementations. ## When NOT to use reasoning-from-scratch - Not recommended if you need immediate deployment or large-scale application; it focuses on educational purposes and hands-on learning. - Avoid if your objective is to work with a pre-built, fine-tuned model that requires minimal custom development. - Do not use if you are looking for support beyond what is provided by community-driven resources or the book. ## Common questions ### What is the difference between LLMForEverybody and reasoning-from-scratch? LLMForEverybody: Learning LLM is all you need.. reasoning-from-scratch: Implement a reasoning LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMForEverybody over reasoning-from-scratch? Choose LLMForEverybody over reasoning-from-scratch when Both repositories aim at making the process of learning Large Language Models approachable for everyone, focusing on educational and from-scratch model implementation; Tags unique to LLMForEverybody: interview-practice, learnllm, rag; Also covers AI Agents, Developer Tools; If you are preparing for job interviews in the field of LLMs or related technologies and want access to practical questions and answers. ### When should I choose reasoning-from-scratch over LLMForEverybody? Choose reasoning-from-scratch over LLMForEverybody when Pricing: The code itself is free under the Apache-2.0 license. However, purchasing the book *Build a Reasoning Model (From Scratch)* might be necessary to fully leverage the repository.; Requirements: Min 8 GB RAM; Python environment with PyTorch libraries and Jupyter Notebook setup required; Refer Chapter 2 for initial setup tips; Both repositories aim at making the process of learning Large Language Models approachable for everyone, focusing on educational and from-scratch model implementation; Tags unique to reasoning-from-scratch: inference-time-scaling, deep-learning, chain-of-thought, llm; Also covers Model Training; Use if you are interested in implementing and understanding reasoning capabilities for LLMs step-by-step using PyTorch. ### When should I avoid LLMForEverybody? If your learning preference leans towards a different language or if the Chinese-specific resources don't align with your needs. For individuals looking for comprehensive open-source tools or frameworks to build upon directly; this is more about educational content than concrete implementations. ### When should I avoid reasoning-from-scratch? Not recommended if you need immediate deployment or large-scale application; it focuses on educational purposes and hands-on learning. Avoid if your objective is to work with a pre-built, fine-tuned model that requires minimal custom development. Do not use if you are looking for support beyond what is provided by community-driven resources or the book. ### Is LLMForEverybody or reasoning-from-scratch more popular on GitHub? LLMForEverybody has more GitHub stars (6,884 vs 4,707). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMForEverybody and reasoning-from-scratch open source? Yes - both are open-source projects on GitHub (LLMForEverybody: Apache-2.0, reasoning-from-scratch: Apache-2.0). ### Where can I find alternatives to LLMForEverybody or reasoning-from-scratch? GraphCanon lists graph-backed alternatives at /tools/luhengshiwo-llmforeverybody/alternatives and /tools/rasbt-reasoning-from-scratch/alternatives (/tools/luhengshiwo-llmforeverybody/alternatives.md, /tools/rasbt-reasoning-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 /compare/luhengshiwo-llmforeverybody-vs-rasbt-reasoning-from-scratch.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMForEverybody or reasoning-from-scratch? LLMForEverybody: Steady. reasoning-from-scratch: 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 LLMForEverybody and reasoning-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMForEverybody: /tools/luhengshiwo-llmforeverybody/trust; reasoning-from-scratch: /tools/rasbt-reasoning-from-scratch/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=luhengshiwo-llmforeverybody`](/api/graphcanon/graph?tool=luhengshiwo-llmforeverybody) - 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/_