--- title: "llm-lobbyist vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/johnnay-llm-lobbyist-vs-rohitg00-ai-engineering-from-scratch" tools: ["johnnay-llm-lobbyist", "rohitg00-ai-engineering-from-scratch"] --- # llm-lobbyist vs ai-engineering-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-lobbyist when llm-lobbyist is primarily Jupyter Notebook; ai-engineering-from-scratch is Python; pick ai-engineering-from-scratch when ai-engineering-from-scratch is primarily Python; llm-lobbyist is Jupyter Notebook. [llm-lobbyist](https://github.com/JohnNay/llm-lobbyist) reports 174 GitHub stars, 14 forks, and 0 open issues, last pushed Jan 13, 2023. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 38k stars, 6.3k forks, and 96 open issues, last pushed Jun 25, 2026. Figures are from public GitHub metadata via [llm-lobbyist's repository](https://github.com/JohnNay/llm-lobbyist) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | Code for the paper: "Large Language Models as Corporate Lobbyists" (2023). | Learn it. Build it. Ship it for others. | | Stars | 174 | 37,922 | | Forks | 14 | 6,329 | | Open issues | 0 | 96 | | Language | Jupyter Notebook | Python | | Adopt for | - | Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Evaluation & Observability, LLM Frameworks, Vector Databases | AI Agents, Computer Vision, Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 1275d | 15d | | Open issues (now) | 0 | 96 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/johnnay-llm-lobbyist/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Decision facts: ai-engineering-from-scratch - **Pricing:** freemium - The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up - **Adopt for:** Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. ## Choose when ### Choose llm-lobbyist if… - llm-lobbyist is primarily Jupyter Notebook; ai-engineering-from-scratch is Python. - Tags unique to llm-lobbyist: jupyter notebook. - Also covers Evaluation & Observability, Vector Databases. ### Choose ai-engineering-from-scratch if… - ai-engineering-from-scratch is primarily Python; llm-lobbyist is Jupyter Notebook. - Pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up. - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning. - Also covers AI Agents, Computer Vision, Developer Tools. - When you want to start with foundational knowledge and learn the intricacies behind AI systems. ## When NOT to use llm-lobbyist - Last GitHub push was 1276 days ago (dormant maintenance, Jan 13, 2023). Validate activity before betting a new project on llm-lobbyist. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use ai-engineering-from-scratch - If you are looking for a quick setup or ready-to-go solution without diving into the foundational understanding. - When your project requires immediate practical application with less emphasis on self-implemented solutions from scratch. ## Common questions ### What is the difference between llm-lobbyist and ai-engineering-from-scratch? llm-lobbyist: Code for the paper: "Large Language Models as Corporate Lobbyists" (2023).. ai-engineering-from-scratch: Learn it. Build it. Ship it for others.. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-lobbyist over ai-engineering-from-scratch? Choose llm-lobbyist over ai-engineering-from-scratch when llm-lobbyist is primarily Jupyter Notebook; ai-engineering-from-scratch is Python; Tags unique to llm-lobbyist: jupyter notebook; Also covers Evaluation & Observability, Vector Databases. ### When should I choose ai-engineering-from-scratch over llm-lobbyist? Choose ai-engineering-from-scratch over llm-lobbyist when ai-engineering-from-scratch is primarily Python; llm-lobbyist is Jupyter Notebook; Pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning; Also covers AI Agents, Computer Vision, Developer Tools; When you want to start with foundational knowledge and learn the intricacies behind AI systems. ### When should I avoid llm-lobbyist? Last GitHub push was 1276 days ago (dormant maintenance, Jan 13, 2023). Validate activity before betting a new project on llm-lobbyist. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid ai-engineering-from-scratch? If you are looking for a quick setup or ready-to-go solution without diving into the foundational understanding. When your project requires immediate practical application with less emphasis on self-implemented solutions from scratch. ### Is llm-lobbyist or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 174). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-lobbyist and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm-lobbyist or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [llm-lobbyist alternatives](/tools/johnnay-llm-lobbyist/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([llm-lobbyist markdown twin](/tools/johnnay-llm-lobbyist/alternatives.md), [ai-engineering-from-scratch markdown twin](/tools/rohitg00-ai-engineering-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/johnnay-llm-lobbyist-vs-rohitg00-ai-engineering-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-lobbyist or ai-engineering-from-scratch? llm-lobbyist: Dormant. ai-engineering-from-scratch: 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 llm-lobbyist and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-lobbyist trust report](/tools/johnnay-llm-lobbyist/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=johnnay-llm-lobbyist`](/api/graphcanon/graph?tool=johnnay-llm-lobbyist) - 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/_