--- title: "Awesome-Prompt-Engineering vs auto-subs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/promptslab-awesome-prompt-engineering-vs-tmoroney-auto-subs" tools: ["promptslab-awesome-prompt-engineering", "tmoroney-auto-subs"] --- # Awesome-Prompt-Engineering vs auto-subs *GraphCanon updated Jul 11, 2026* ## Verdict Pick Awesome-Prompt-Engineering when license: Awesome-Prompt-Engineering is Apache-2.0, auto-subs is MIT; pick auto-subs when license: auto-subs is MIT, Awesome-Prompt-Engineering is Apache-2.0. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) reports 6.2k GitHub stars, 723 forks, and 88 open issues, last pushed Jul 11, 2026. [auto-subs](https://tom-moroney.com/auto-subs/) has 3.8k stars, 245 forks, and 216 open issues, last pushed Jul 4, 2026. Figures are from public GitHub metadata via [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering) and [auto-subs's repository](https://github.com/tmoroney/auto-subs). | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [auto-subs](/tools/tmoroney-auto-subs.md) | | --- | --- | --- | | Tagline | This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | On-device subtitle generation that connects directly to DaVinci Resolve, Premiere, and After Effects. | | Stars | 6,150 | 3,796 | | Forks | 723 | 245 | | Open issues | 88 | 216 | | Language | TypeScript | TypeScript | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training, Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [auto-subs](/tools/tmoroney-auto-subs.md) | | --- | --- | --- | | Days since push | 0d | 6d | | Open issues (now) | 88 | 216 | | Owner type | Organization | User | | Full report | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | [trust report](/tools/tmoroney-auto-subs/trust.md) | ## Choose when ### Choose Awesome-Prompt-Engineering if… - License: Awesome-Prompt-Engineering is Apache-2.0, auto-subs is MIT. - Tags unique to Awesome-Prompt-Engineering: gpt-3, chatgpt-api, deep-learning, few-shot-learning. - Also covers LLM Frameworks, Model Training. ### Choose auto-subs if… - License: auto-subs is MIT, Awesome-Prompt-Engineering is Apache-2.0. - Tags unique to auto-subs: davinci-resolve, ai, speech-to-text, cross-platform. ## When NOT to use Awesome-Prompt-Engineering - 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. ## Common questions ### What is the difference between Awesome-Prompt-Engineering and auto-subs? Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc. auto-subs: On-device subtitle generation that connects directly to DaVinci Resolve, Premiere, and After Effects.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Prompt-Engineering over auto-subs? Choose Awesome-Prompt-Engineering over auto-subs when License: Awesome-Prompt-Engineering is Apache-2.0, auto-subs is MIT; Tags unique to Awesome-Prompt-Engineering: gpt-3, chatgpt-api, deep-learning, few-shot-learning; Also covers LLM Frameworks, Model Training. ### When should I choose auto-subs over Awesome-Prompt-Engineering? Choose auto-subs over Awesome-Prompt-Engineering when License: auto-subs is MIT, Awesome-Prompt-Engineering is Apache-2.0; Tags unique to auto-subs: davinci-resolve, ai, speech-to-text, cross-platform. ### When should I avoid Awesome-Prompt-Engineering? 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. ### Is Awesome-Prompt-Engineering or auto-subs more popular on GitHub? Awesome-Prompt-Engineering has more GitHub stars (6,150 vs 3,796). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Prompt-Engineering and auto-subs open source? Yes - both are open-source projects on GitHub (Awesome-Prompt-Engineering: Apache-2.0, auto-subs: MIT). ### Where can I find alternatives to Awesome-Prompt-Engineering or auto-subs? GraphCanon lists graph-backed alternatives at [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) and [auto-subs alternatives](/tools/tmoroney-auto-subs/alternatives) ([Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/alternatives.md), [auto-subs markdown twin](/tools/tmoroney-auto-subs/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/promptslab-awesome-prompt-engineering-vs-tmoroney-auto-subs.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Prompt-Engineering or auto-subs? Awesome-Prompt-Engineering: Very active. auto-subs: 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 Awesome-Prompt-Engineering and auto-subs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust); [auto-subs trust report](/tools/tmoroney-auto-subs/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=promptslab-awesome-prompt-engineering`](/api/graphcanon/graph?tool=promptslab-awesome-prompt-engineering) - 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/_