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# AGENTS.md
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**Last Updated:** April 9, 2026
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## Research Project: Coding Agent Harness Analysis
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### Objective
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# Coding Harness Feedback Analysis
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**Last Updated:** April 9, 2026
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Research analyzing four coding agent harnesses (opencode, pi, hermes, forgecode) to understand what works best for local/smaller models (7B-27B parameters).
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## What Was Done
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# AGENTS.md
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**Last Updated:** April 9, 2026
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## Research/Analysis Folder for forgecode
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This is the research and analysis folder for the **forgecode** coding harness.
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# ForgeCode Research & Analysis Folder
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**Last Updated:** April 9, 2026
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This folder contains comprehensive research and analysis of the **ForgeCode** coding harness from antinomyhq.
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# AGENTS.md
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**Last Updated:** April 9, 2026
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## Research/Analysis Folder for hermes
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This is the research and analysis folder for the **hermes** coding harness.
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# AGENTS.md
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**Last Updated:** April 9, 2026
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## Research/Analysis Folder for opencode
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This is the research and analysis folder for the **opencode** coding harness.
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# OpenCode Feedback Summary
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**Last Updated:** April 9, 2026
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## Executive Overview
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This document provides a comprehensive summary of community feedback, benchmark results, and performance observations for **OpenCode** AI coding agent. Data sourced from Reddit, GitHub issues, benchmark dashboards, community blogs, and technical documentation.
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# Local LLM Feedback for OpenCode
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**Last Updated:** April 9, 2026
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## Overview
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This document compiles community feedback, benchmark results, and performance observations for **local LLM models** used with OpenCode. Data sourced from Reddit, GitHub issues, benchmark dashboards, and community blogs.
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# AGENTS.md
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**Last Updated:** April 9, 2026
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## Research/Analysis Folder for pi (pi-mono)
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This is the research and analysis folder for the **pi** coding harness.
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# pi-mono Comprehensive Feedback Summary
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**Last Updated:** April 9, 2026
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## Executive Summary
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This document consolidates all community feedback and benchmark data for the pi-mono coding agent across local and frontier models. The data was collected from GitHub issues, Reddit discussions, benchmark leaderboards, and community blog posts.
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