--- title: "HuChenFeng vs Awesome-Prompt-Engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/olcmyk-huchenfeng-vs-promptslab-awesome-prompt-engineering" tools: ["olcmyk-huchenfeng", "promptslab-awesome-prompt-engineering"] --- # HuChenFeng vs Awesome-Prompt-Engineering *GraphCanon updated Jul 11, 2026* ## Verdict Pick HuChenFeng when license: HuChenFeng is MIT, Awesome-Prompt-Engineering is Apache-2.0; pick Awesome-Prompt-Engineering when license: Awesome-Prompt-Engineering is Apache-2.0, HuChenFeng is MIT. [HuChenFeng](https://huchenfeng.live) reports 1.6k GitHub stars, 180 forks, and 23 open issues, last pushed Feb 17, 2026. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) has 6.2k stars, 723 forks, and 88 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [HuChenFeng's repository](https://github.com/Olcmyk/HuChenFeng) and [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering). | | [HuChenFeng](/tools/olcmyk-huchenfeng.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Tagline | 收集户晨风的所有内容 | This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | | Stars | 1,630 | 6,150 | | Forks | 180 | 723 | | Open issues | 23 | 88 | | Language | - | TypeScript | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Speech & Audio | LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HuChenFeng](/tools/olcmyk-huchenfeng.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 144d | 0d | | Open issues (now) | 23 | 88 | | Owner type | User | Organization | | Full report | [trust report](/tools/olcmyk-huchenfeng/trust.md) | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | ## Choose when ### Choose HuChenFeng if… - License: HuChenFeng is MIT, Awesome-Prompt-Engineering is Apache-2.0. - Tags unique to HuChenFeng: archiving, content-analysis, datasets, internet-culture. - Leaner open-issue backlog (23). ### Choose Awesome-Prompt-Engineering if… - License: Awesome-Prompt-Engineering is Apache-2.0, HuChenFeng is MIT. - Tags unique to Awesome-Prompt-Engineering: chatgpt, chatgpt-api, deep-learning, few-shot-learning. - Also covers LLM Frameworks, Model Training. ## When NOT to use HuChenFeng - Last GitHub push was 145 days ago (slowing maintenance, Feb 17, 2026). Validate activity before betting a new project on HuChenFeng. ## 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 HuChenFeng and Awesome-Prompt-Engineering? HuChenFeng: 收集户晨风的所有内容. 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. See the comparison table for live GitHub stats and shared categories. ### When should I choose HuChenFeng over Awesome-Prompt-Engineering? Choose HuChenFeng over Awesome-Prompt-Engineering when License: HuChenFeng is MIT, Awesome-Prompt-Engineering is Apache-2.0; Tags unique to HuChenFeng: archiving, content-analysis, datasets, internet-culture; Leaner open-issue backlog (23). ### When should I choose Awesome-Prompt-Engineering over HuChenFeng? Choose Awesome-Prompt-Engineering over HuChenFeng when License: Awesome-Prompt-Engineering is Apache-2.0, HuChenFeng is MIT; Tags unique to Awesome-Prompt-Engineering: chatgpt, chatgpt-api, deep-learning, few-shot-learning; Also covers LLM Frameworks, Model Training. ### When should I avoid HuChenFeng? Last GitHub push was 145 days ago (slowing maintenance, Feb 17, 2026). Validate activity before betting a new project on HuChenFeng. ### 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 HuChenFeng or Awesome-Prompt-Engineering more popular on GitHub? Awesome-Prompt-Engineering has more GitHub stars (6,150 vs 1,630). Stars measure visibility, not whether either tool fits your constraints. ### Are HuChenFeng and Awesome-Prompt-Engineering open source? Yes - both are open-source projects on GitHub (HuChenFeng: MIT, Awesome-Prompt-Engineering: Apache-2.0). ### Where can I find alternatives to HuChenFeng or Awesome-Prompt-Engineering? GraphCanon lists graph-backed alternatives at [HuChenFeng alternatives](/tools/olcmyk-huchenfeng/alternatives) and [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) ([HuChenFeng markdown twin](/tools/olcmyk-huchenfeng/alternatives.md), [Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/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/olcmyk-huchenfeng-vs-promptslab-awesome-prompt-engineering.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, HuChenFeng or Awesome-Prompt-Engineering? HuChenFeng: Slowing. Awesome-Prompt-Engineering: 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 HuChenFeng and Awesome-Prompt-Engineering? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HuChenFeng trust report](/tools/olcmyk-huchenfeng/trust); [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=olcmyk-huchenfeng`](/api/graphcanon/graph?tool=olcmyk-huchenfeng) - 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/_