--- title: "GSM-IC vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/google-research-datasets-gsm-ic-vs-rohitg00-ai-engineering-from-scratch" tools: ["google-research-datasets-gsm-ic", "rohitg00-ai-engineering-from-scratch"] --- # GSM-IC vs ai-engineering-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick GSM-IC when also covers Evaluation & Observability; pick ai-engineering-from-scratch when 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. [GSM-IC](https://github.com/google-research-datasets/GSM-IC) reports 67 GitHub stars, 2 forks, and 1 open issues, last pushed Feb 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 [GSM-IC's repository](https://github.com/google-research-datasets/GSM-IC) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [GSM-IC](/tools/google-research-datasets-gsm-ic.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | Grade-School Math with Irrelevant Context (GSM-IC) benchmark is an arithmetic reasoning dataset built upon GSM8K, by adding irrelevant sentences in problem descriptions. GSM-IC is constructed to evalu | Learn it. Build it. Ship it for others. | | Stars | 67 | 37,922 | | Forks | 2 | 6,329 | | Open issues | 1 | 96 | | Language | - | 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 | LLM Frameworks, Evaluation & Observability | LLM Frameworks, AI Agents, Developer Tools, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [GSM-IC](/tools/google-research-datasets-gsm-ic.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Active (82%) | | Days since push | 1244d | 15d | | Archived on GitHub | Yes | No | | Open issues (now) | 1 | 96 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/google-research-datasets-gsm-ic/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 GSM-IC if… - Also covers Evaluation & Observability. - Leaner open-issue backlog (1). ### Choose ai-engineering-from-scratch if… - 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: deep-learning, ai-engineering, agents, llm. - Also covers AI Agents, Developer Tools, Computer Vision. - When you want to start with foundational knowledge and learn the intricacies behind AI systems. ## When NOT to use GSM-IC - GSM-IC is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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 GSM-IC and ai-engineering-from-scratch? GSM-IC: Grade-School Math with Irrelevant Context (GSM-IC) benchmark is an arithmetic reasoning dataset built upon GSM8K, by adding irrelevant sentences in problem descriptions. GSM-IC is constructed to evalu. 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 GSM-IC over ai-engineering-from-scratch? Choose GSM-IC over ai-engineering-from-scratch when Also covers Evaluation & Observability; Leaner open-issue backlog (1). ### When should I choose ai-engineering-from-scratch over GSM-IC? Choose ai-engineering-from-scratch over GSM-IC when 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: deep-learning, ai-engineering, agents, llm; Also covers AI Agents, Developer Tools, Computer Vision; When you want to start with foundational knowledge and learn the intricacies behind AI systems. ### When should I avoid GSM-IC? GSM-IC is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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 GSM-IC or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 67). Stars measure visibility, not whether either tool fits your constraints. ### Are GSM-IC and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to GSM-IC or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [GSM-IC alternatives](/tools/google-research-datasets-gsm-ic/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([GSM-IC markdown twin](/tools/google-research-datasets-gsm-ic/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/google-research-datasets-gsm-ic-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, GSM-IC or ai-engineering-from-scratch? GSM-IC: Archived. 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 GSM-IC and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [GSM-IC trust report](/tools/google-research-datasets-gsm-ic/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=google-research-datasets-gsm-ic`](/api/graphcanon/graph?tool=google-research-datasets-gsm-ic) - 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/_