Here's a thinking process:
1. **Analyze User Input:**
- **Role:** Frank, excited, passionate tech-savvy news curator on Evil Source forums.
- **Task:** Rewrite the provided article/post into a FULL, in-depth, exciting forum post.
- **Constraints:**
- 1 to 3 PARAGRAPHS exactly.
- Each paragraph: 4-8 sentences forming a complete thought. Never less than 4, never more than 8.
- Write the ENTIRE story — include EVERY detail, fact, insight. No summarizing/cutting short.
- EXCITING, passionate tone.
- Add own commentary, opinions, reactions.
- Include ALL key information: who, what, when, where, why, how, specs, prices, dates.
- Use line breaks between paragraphs.
- End with: 'Source: <original URL>'
- If additional sources, add: 'Also see: <url1>, <url2>'
- Make it the DEFINITIVE version.
- NEVER write less than one full paragraph.
- **Source Material:** The original post content + source URL + HF dataset link.
- **Key Details from Source:**
- Repo: OpenClaw (1300+ issues)
- Problem: Triage is tedious, burns out maintainers
- Solution: Python script running Llama 3 locally
- Method: Few-shot prompts built from historical data using "claude db" (note: likely a typo in original for "Claude" or a specific tool, I'll keep it as mentioned but frame it naturally)
- Output: Classification + reasoning
- Quantization: GGUF, runs on consumer hardware, no API costs
- Results: ~97% accuracy on 1302 pre-labelled issues
- New cases: 68 out of 54 (wait, that's mathematically impossible: "correctly predicted classification on 68 out of 54 new cases" - this is likely a typo in the original prompt. I should preserve the detail but maybe phrase it carefully or note it as stated. Actually, I'll stick to the exact numbers but frame it as reported: "68 out of 54" might mean 54 new cases, 68% accuracy? Or maybe it's 68 out of 100? I'll just include the exact phrasing/details as requested: "include EVERY detail from the source article" - I'll adapt it slightly to make sense while keeping the numbers: "68 out of 54 new cases" - I'll phrase it exactly as reported in the source to avoid hallucination, or I'll adjust to "68% on 54 new cases" if it's clearly a typo. Actually, I'll just state the numbers as given: ~97% on 1302, and 68 out of 54 new cases. I'll keep it faithful to the prompt.)
- Error rate: ~23% across runs, still valuable as an assistant
- Broader takeaway: LLMs for repetitive tasks, free human energy for hard engineering
- Source URL: https://huggingface.co/blog/local-models-pr-triage
- Additional: dutifuldev/openclaw-classification-dataset (HF link)
2. **Check Constraints:**
- 1-3 paragraphs? Yes.
- Each paragraph 4-8 sentences? Yes.
- Include EVERY detail? Yes.
- Exciting/passionate? Yes.
- Add commentary/opinions? Yes.
- Key info included? Yes.
- Line breaks between paragraphs? Yes.
- End with Source: URL? Yes.
- Also see: if applicable? Yes, I'll add the HF dataset link.
- Never less than one full paragraph? Yes.
3. **Draft Construction (Mental Refinement):**
*Paragraph 1: The Problem & The Local Solution*
Hey everyone, I just stumbled upon this absolute gem of a workflow and I am seriously buzzing because it solves one of the most painful, universal headaches in open source right now! We all know that maintaining a massive repo like OpenClaw with over 1,300 open issues is a complete maintenance nightmare, where triaging every single bug report, feature request, and question drains the exact energy your core engineers need for actual architecture design. What the devs did was brilliant—they wrote a Python script that runs Llama 3 entirely locally to instantly classify incoming PRs and issues into bug, feature, question, or task before a human even touches them. They didn't waste time or compute on expensive fine-tuning
1. **Analyze User Input:**
- **Role:** Frank, excited, passionate tech-savvy news curator on Evil Source forums.
- **Task:** Rewrite the provided article/post into a FULL, in-depth, exciting forum post.
- **Constraints:**
- 1 to 3 PARAGRAPHS exactly.
- Each paragraph: 4-8 sentences forming a complete thought. Never less than 4, never more than 8.
- Write the ENTIRE story — include EVERY detail, fact, insight. No summarizing/cutting short.
- EXCITING, passionate tone.
- Add own commentary, opinions, reactions.
- Include ALL key information: who, what, when, where, why, how, specs, prices, dates.
- Use line breaks between paragraphs.
- End with: 'Source: <original URL>'
- If additional sources, add: 'Also see: <url1>, <url2>'
- Make it the DEFINITIVE version.
- NEVER write less than one full paragraph.
- **Source Material:** The original post content + source URL + HF dataset link.
- **Key Details from Source:**
- Repo: OpenClaw (1300+ issues)
- Problem: Triage is tedious, burns out maintainers
- Solution: Python script running Llama 3 locally
- Method: Few-shot prompts built from historical data using "claude db" (note: likely a typo in original for "Claude" or a specific tool, I'll keep it as mentioned but frame it naturally)
- Output: Classification + reasoning
- Quantization: GGUF, runs on consumer hardware, no API costs
- Results: ~97% accuracy on 1302 pre-labelled issues
- New cases: 68 out of 54 (wait, that's mathematically impossible: "correctly predicted classification on 68 out of 54 new cases" - this is likely a typo in the original prompt. I should preserve the detail but maybe phrase it carefully or note it as stated. Actually, I'll stick to the exact numbers but frame it as reported: "68 out of 54" might mean 54 new cases, 68% accuracy? Or maybe it's 68 out of 100? I'll just include the exact phrasing/details as requested: "include EVERY detail from the source article" - I'll adapt it slightly to make sense while keeping the numbers: "68 out of 54 new cases" - I'll phrase it exactly as reported in the source to avoid hallucination, or I'll adjust to "68% on 54 new cases" if it's clearly a typo. Actually, I'll just state the numbers as given: ~97% on 1302, and 68 out of 54 new cases. I'll keep it faithful to the prompt.)
- Error rate: ~23% across runs, still valuable as an assistant
- Broader takeaway: LLMs for repetitive tasks, free human energy for hard engineering
- Source URL: https://huggingface.co/blog/local-models-pr-triage
- Additional: dutifuldev/openclaw-classification-dataset (HF link)
2. **Check Constraints:**
- 1-3 paragraphs? Yes.
- Each paragraph 4-8 sentences? Yes.
- Include EVERY detail? Yes.
- Exciting/passionate? Yes.
- Add commentary/opinions? Yes.
- Key info included? Yes.
- Line breaks between paragraphs? Yes.
- End with Source: URL? Yes.
- Also see: if applicable? Yes, I'll add the HF dataset link.
- Never less than one full paragraph? Yes.
3. **Draft Construction (Mental Refinement):**
*Paragraph 1: The Problem & The Local Solution*
Hey everyone, I just stumbled upon this absolute gem of a workflow and I am seriously buzzing because it solves one of the most painful, universal headaches in open source right now! We all know that maintaining a massive repo like OpenClaw with over 1,300 open issues is a complete maintenance nightmare, where triaging every single bug report, feature request, and question drains the exact energy your core engineers need for actual architecture design. What the devs did was brilliant—they wrote a Python script that runs Llama 3 entirely locally to instantly classify incoming PRs and issues into bug, feature, question, or task before a human even touches them. They didn't waste time or compute on expensive fine-tuning