--- title: "DecryptPrompt vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dsxiangli-decryptprompt-vs-sindresorhus-awesome" tools: ["dsxiangli-decryptprompt", "sindresorhus-awesome"] --- # DecryptPrompt vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick DecryptPrompt when tags unique to DecryptPrompt: aigc, chain-of-thought, chatgpt, demonstration; pick awesome when tags unique to awesome: awesome, awesome-list, lists, resources. [DecryptPrompt](https://github.com/DSXiangLi/DecryptPrompt) reports 3.4k GitHub stars, 322 forks, and 1 open issues, last pushed May 6, 2026. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [DecryptPrompt's repository](https://github.com/DSXiangLi/DecryptPrompt) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [DecryptPrompt](/tools/dsxiangli-decryptprompt.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | 总结Prompt&LLM论文，开源数据&模型，AIGC应用 | 😎 Awesome lists about all kinds of interesting topics | | Stars | 3,422 | 484,026 | | Forks | 322 | 35,799 | | Open issues | 1 | 92 | | Language | - | - | | Adopt for | - | A curated collection of resources on a variety of technological topics, emphasizing hardware and robotics. | | Persona | - | - | | Runtime | - | - | | License | - | CC0-1.0 | | Categories | AI Agents, LLM Frameworks | Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DecryptPrompt](/tools/dsxiangli-decryptprompt.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 66d | 11d | | Open issues (now) | 1 | 92 | | Full report | [trust report](/tools/dsxiangli-decryptprompt/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Decision facts: awesome - **Adopt for:** A curated collection of resources on a variety of technological topics, emphasizing hardware and robotics. ## Choose when ### Choose DecryptPrompt if… - Tags unique to DecryptPrompt: aigc, chain-of-thought, chatgpt, demonstration. - Also covers AI Agents, LLM Frameworks. - Leaner open-issue backlog (1). ### Choose awesome if… - Tags unique to awesome: awesome, awesome-list, lists, resources. - Also covers Developer Tools. - When you need well-organized access to diverse technical subjects from IoT to robotics ## When NOT to use DecryptPrompt - 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. ## When NOT to use awesome - If seeking specific coding frameworks or libraries for software development rather than hardware-focused resources - In scenarios requiring real-time interactive support or forums, as the content is static lists without active discussion ## Common questions ### What is the difference between DecryptPrompt and awesome? DecryptPrompt: 总结Prompt&LLM论文，开源数据&模型，AIGC应用. awesome: 😎 Awesome lists about all kinds of interesting topics. See the comparison table for live GitHub stats and shared categories. ### When should I choose DecryptPrompt over awesome? Choose DecryptPrompt over awesome when Tags unique to DecryptPrompt: aigc, chain-of-thought, chatgpt, demonstration; Also covers AI Agents, LLM Frameworks; Leaner open-issue backlog (1). ### When should I choose awesome over DecryptPrompt? Choose awesome over DecryptPrompt when Tags unique to awesome: awesome, awesome-list, lists, resources; Also covers Developer Tools; When you need well-organized access to diverse technical subjects from IoT to robotics. ### When should I avoid DecryptPrompt? 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. ### When should I avoid awesome? If seeking specific coding frameworks or libraries for software development rather than hardware-focused resources In scenarios requiring real-time interactive support or forums, as the content is static lists without active discussion ### Is DecryptPrompt or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 3,422). Stars measure visibility, not whether either tool fits your constraints. ### Are DecryptPrompt and awesome open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DecryptPrompt or awesome? GraphCanon lists graph-backed alternatives at [DecryptPrompt alternatives](/tools/dsxiangli-decryptprompt/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([DecryptPrompt markdown twin](/tools/dsxiangli-decryptprompt/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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/dsxiangli-decryptprompt-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DecryptPrompt or awesome? DecryptPrompt: Steady. awesome: 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 DecryptPrompt and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DecryptPrompt trust report](/tools/dsxiangli-decryptprompt/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dsxiangli-decryptprompt`](/api/graphcanon/graph?tool=dsxiangli-decryptprompt) - 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/_