How a $0.02/Call Model Scored 78.2% on SWE-bench Verified — Beating Every Model on the Leaderboard

1. 1Add architectural context to AI coding agents using MCP
2. 2Test the model on SWE-bench to evaluate its performance
3. 3Compare the results with other models on the leaderboard
4. 4Fine-tune the model to optimize its score
5. 5Apply the technique to other coding benchmarks to verify its effectiveness

AI engineers and researchers can benefit from this technique to enhance their models' performance on coding benchmarks, while software engineers can explore the potential of AI-powered coding tools

💡 Adding architectural context to AI coding agents can significantly improve their performance on coding benchmarks

💡 AI coding agent scores 78.2% on SWE-bench with architectural context via MCP! 🚀