--- title: "Made-With-ML vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-rasbt-llms-from-scratch" tools: ["gokumohandas-made-with-ml", "rasbt-llms-from-scratch"] --- # Made-With-ML vs LLMs-from-scratch *GraphCanon updated Jul 15, 2026* ## Verdict Pick Made-With-ML when license: Made-With-ML is MIT, LLMs-from-scratch is Other; pick LLMs-from-scratch when license: LLMs-from-scratch is Other, Made-With-ML is MIT. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. [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 [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | Learn how to develop, deploy and iterate on production-grade ML applications. | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 48,703 | 98,899 | | Forks | 7,661 | 15,183 | | Open issues | 27 | 4 | | Language | Jupyter Notebook | 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 | AI Agents, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 132d | 38d | | Open issues (now) | 27 | 4 | | Full report | [trust report](/tools/gokumohandas-made-with-ml/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 Made-With-ML if… - License: Made-With-ML is MIT, LLMs-from-scratch is Other. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, distributed-ml. - Also covers AI Agents. ### Choose LLMs-from-scratch if… - License: LLMs-from-scratch is Other, Made-With-ML is MIT. - Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, finetuning. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ## When NOT to use Made-With-ML - Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. - 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. ## 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. ## Common questions ### What is the difference between Made-With-ML and LLMs-from-scratch? Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. 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 Made-With-ML over LLMs-from-scratch? Choose Made-With-ML over LLMs-from-scratch when License: Made-With-ML is MIT, LLMs-from-scratch is Other; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, distributed-ml; Also covers AI Agents. ### When should I choose LLMs-from-scratch over Made-With-ML? Choose LLMs-from-scratch over Made-With-ML when License: LLMs-from-scratch is Other, Made-With-ML is MIT; Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, finetuning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### When should I avoid Made-With-ML? Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. 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. ### 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. ### Is Made-With-ML or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 48,703). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, LLMs-from-scratch: Other). ### Where can I find alternatives to Made-With-ML or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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/gokumohandas-made-with-ml-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, Made-With-ML or LLMs-from-scratch? Made-With-ML: Slowing. 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 Made-With-ML and LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml) - 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/_