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Musa Ceylanleft a comment
Hey Product Hunt 👋 I built OrchestrAI because most AI coding workflows still rely on one model doing everything: 🧠 planning 💻 coding 🧪 testing 🔍 reviewing ⚖️ judging That felt like the wrong architecture. So I created OrchestrAI — an open-source MCP server for multi-model software engineering. Instead of forcing one model to handle the whole workflow, OrchestrAI lets multiple models collaborate...
OrchestrAITagline MCP server for multi-model software engineering
OrchestrAI is an open-source MCP server that lets Claude, OpenAI/Codex, Gemini, and local models work together on coding tasks. It routes work by role, supports parallel collaboration, and verifies outputs with lint, tests, and type checks. Built for hybrid local/cloud AI workflows with inspectable traces and artifacts.
OrchestrAITagline MCP server for multi-model software engineering
Musa Ceylanleft a comment
Hey Product Hunt 👋✨ Super excited to share LoopLens MCP 🔁🔍 I built this around one frustrating problem in AI coding workflows: The first fix fails… and then the next few attempts often become noisy, repetitive, and hard to reason about 😵💫 So instead of just storing logs, LoopLens is meant to help answer: 🔍 What already got tried? 🧩 What changed between attempts? 🚨 Is the same failure repeating?...
LoopLens MCPSee the loop. Break the loop. ⚡
LoopLens MCP helps AI coding workflows track failed fixes, detect retry loops, compare attempts, and choose the next best debugging step. Built for Claude Code, it turns repeated repair attempts into structured insight so agents can stop blindly retrying and start learning from iteration history. 🔁🔍
LoopLens MCPSee the loop. Break the loop. ⚡