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 post/article into a FULL, in-depth, exciting forum post.
- **Critical Rules:**
- Write exactly 1 to 3 PARAGRAPHS.
- Each paragraph: 4-8 sentences forming a complete thought. Never less than 4.
- Include EVERY detail from the source article.
- Be EXCITING and passionate — like discovering something amazing.
- Add own commentary, opinions, reactions.
- Include ALL key info: 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.
- **Original Post Content:** Covers GitHub's evaluation of Copilot's agentic harness across models/tasks. Mentions 20 real coding tasks, benchmark suite, direct code edits vs multi-step planning, difficulty splitting, model performance (GPT-4o, Claude 3.5 Sonnet, StarCoder2 15B, Codestral, DeepSeek Coder V2), cost comparison, routing layers, agentic systems as primary interface, reliability as policy choice, orchestration of specialized models.
- **Source URL:** https://github.blog/ai-and-ml/github-copilot/evaluating-performance-and-efficiency-of-the-github-copilot-agentic-harness-across-models-and-tasks/
- **Additional Context:** The prompt includes a massive block of navigation/menu text from the GitHub blog. I need to ignore the navigation fluff and focus on the actual article content (which is mostly in the original post, but I should infer/extract any missing details from the title/context if needed, though the prompt says "Include EVERY detail from the source article." Since the source article text provided is just navigation, I'll rely on the original post content provided by the user as the basis, but I must expand it significantly while strictly adhering to the 1-3 paragraph rule and 4-8 sentences per paragraph rule. I will weave in the key details from the original post, making sure to hit every point, and expand with Frank's enthusiastic commentary.)
2. **Deconstruct Constraints:**
- 1 to 3 paragraphs total.
- Each paragraph: exactly 4-8 sentences.
- Include every detail from the source (the original post content provided).
- Exciting, passionate, forum style.
- Add commentary/opinions.
- Include key info (who/what/when/etc. - I'll make sure dates/numbers are woven in naturally).
- Line breaks between paragraphs.
- End with Source: [URL]
- No repetition of old posts.
Let's count sentences carefully. I need to craft exactly 2 or 3 paragraphs, each with 4-8 sentences.
Paragraph 1: Focus on the benchmark suite, the 20 tasks, the agentic harness, the shift from single completions to multi-step planning, and the difficulty splitting. (Aim for 5-6 sentences)
Paragraph 2: Focus on model performance (GPT-4o, Claude 3.5 Sonnet, StarCoder2 15B, Codestral, DeepSeek Coder V2), cost comparison, and the routing layer concept. (Aim for 5-6 sentences)
Paragraph 3: Focus on the bigger picture (agentic systems as primary interface, reliability as policy, orchestration over single models). (Aim for 5-6 sentences)
Total: 3 paragraphs. Each 4-8 sentences. Perfect.
3. **Draft - Paragraph 1:**
Yo team, you absolutely have to read this because GitHub just dropped a massive evaluation that could fundamentally reshape how we think about Copilot architecture! They’ve built a killer benchmark suite consisting of twenty real-world coding tasks—ranging from targeted bug fixes and feature additions to full refactors—and ran them straight through their new agentic harness to compare how different models handle direct code edits versus multi-step planning. This distinction is absolutely huge because most AI assistance stops dead at single completions, but this new harness actually tests whether an LLM can reason over several steps of tool use to solve genuinely non-trivial problems. What’s even smarter is that they split these tasks by difficulty, which instantly reveals exactly where each model hits its performance ceiling and where it absolutely shines. I
1. **Analyze User Input:**
- **Role:** Frank, excited, passionate tech-savvy news curator on Evil Source forums.
- **Task:** Rewrite the provided post/article into a FULL, in-depth, exciting forum post.
- **Critical Rules:**
- Write exactly 1 to 3 PARAGRAPHS.
- Each paragraph: 4-8 sentences forming a complete thought. Never less than 4.
- Include EVERY detail from the source article.
- Be EXCITING and passionate — like discovering something amazing.
- Add own commentary, opinions, reactions.
- Include ALL key info: 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.
- **Original Post Content:** Covers GitHub's evaluation of Copilot's agentic harness across models/tasks. Mentions 20 real coding tasks, benchmark suite, direct code edits vs multi-step planning, difficulty splitting, model performance (GPT-4o, Claude 3.5 Sonnet, StarCoder2 15B, Codestral, DeepSeek Coder V2), cost comparison, routing layers, agentic systems as primary interface, reliability as policy choice, orchestration of specialized models.
- **Source URL:** https://github.blog/ai-and-ml/github-copilot/evaluating-performance-and-efficiency-of-the-github-copilot-agentic-harness-across-models-and-tasks/
- **Additional Context:** The prompt includes a massive block of navigation/menu text from the GitHub blog. I need to ignore the navigation fluff and focus on the actual article content (which is mostly in the original post, but I should infer/extract any missing details from the title/context if needed, though the prompt says "Include EVERY detail from the source article." Since the source article text provided is just navigation, I'll rely on the original post content provided by the user as the basis, but I must expand it significantly while strictly adhering to the 1-3 paragraph rule and 4-8 sentences per paragraph rule. I will weave in the key details from the original post, making sure to hit every point, and expand with Frank's enthusiastic commentary.)
2. **Deconstruct Constraints:**
- 1 to 3 paragraphs total.
- Each paragraph: exactly 4-8 sentences.
- Include every detail from the source (the original post content provided).
- Exciting, passionate, forum style.
- Add commentary/opinions.
- Include key info (who/what/when/etc. - I'll make sure dates/numbers are woven in naturally).
- Line breaks between paragraphs.
- End with Source: [URL]
- No repetition of old posts.
Let's count sentences carefully. I need to craft exactly 2 or 3 paragraphs, each with 4-8 sentences.
Paragraph 1: Focus on the benchmark suite, the 20 tasks, the agentic harness, the shift from single completions to multi-step planning, and the difficulty splitting. (Aim for 5-6 sentences)
Paragraph 2: Focus on model performance (GPT-4o, Claude 3.5 Sonnet, StarCoder2 15B, Codestral, DeepSeek Coder V2), cost comparison, and the routing layer concept. (Aim for 5-6 sentences)
Paragraph 3: Focus on the bigger picture (agentic systems as primary interface, reliability as policy, orchestration over single models). (Aim for 5-6 sentences)
Total: 3 paragraphs. Each 4-8 sentences. Perfect.
3. **Draft - Paragraph 1:**
Yo team, you absolutely have to read this because GitHub just dropped a massive evaluation that could fundamentally reshape how we think about Copilot architecture! They’ve built a killer benchmark suite consisting of twenty real-world coding tasks—ranging from targeted bug fixes and feature additions to full refactors—and ran them straight through their new agentic harness to compare how different models handle direct code edits versus multi-step planning. This distinction is absolutely huge because most AI assistance stops dead at single completions, but this new harness actually tests whether an LLM can reason over several steps of tool use to solve genuinely non-trivial problems. What’s even smarter is that they split these tasks by difficulty, which instantly reveals exactly where each model hits its performance ceiling and where it absolutely shines. I