Ghost Mentor Match: Channeling Coding Spirits & a Git Ofrenda with Qwen & Gemma

说明与用途

Ghost Mentor Match 提供隐私保护、离线 AI 导师和已删除 Git 提交的数字祭坛，使用开放权重模型 Qwen 和 Gemma。

问题

Developers need a privacy‑preserving, offline‑capable mentorship tool that can analyze stack traces, architectural roadblocks, and historical git commits without exposing proprietary code to external APIs.

结果

The platform delivers privacy‑preserving, offline mentorship and a unique code‑ofrenda experience, demonstrating the benefits of open‑weight models for developer tooling.

工作流

• 1. User inputs tech stack, stack trace, or git log into the web UI.
• 2. The system calculates compatibility scores and matches the user with a specialized AI mentor persona.
• 3. The mentor diagnoses the issue and provides concrete fixes, presented as terminal poetry.
• 4. For git logs, the model reads the diff context and generates a heartfelt tribute on a digital altar.
• 5. Users can toggle between cloud‑hosted Qwen and local Gemma inference via a single UI switch.

证据

The primary reasoning and persona‑matching engine is powered by the open‑weight Qwen 3.8‑27B model running on ultra‑fast Cerebras Cloud inference, paired with a native local inference fallback using Google’s Gemma via Ollama

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Running Gemma locally or routing through self‑controlled open weights ensures sensitive source code isn’t harvested to train a closed vendor’s next commercial model

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By supporting local open weights on consumer hardware, developers can summon mentor diagnostics completely offline at $0.00 token cost

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