AI编程进入无屏时代：阿里云Qoder眼镜版重新定义开发者交互

# Qoder眼镜版架构概念示例
class QoderGlassesAgent:
    """Qoder眼镜版AI编程Agent"""

    def __init__(self, glasses_device: str = "qwen_ai_glasses"):
        self.device = glasses_device
        self.wake_word = "Qoder"
        self.cloud_agent = CloudCodeAgent(model="qwen-coder-max")
        self.local_cache = LocalContextCache(max_size=4096)

    async def listen_and_execute(self):
        """监听语音指令并执行"""
        while True:
            # 1. 语音唤醒检测（本地执行，低延迟）
            audio = await self.device.capture_audio(duration=0.5)
            if not self._detect_wake_word(audio):
                continue

            # 2. 语音识别和意图理解
            command_audio = await self.device.capture_audio(duration=10)
            transcript = await self._stt(command_audio)
            intent = await self._understand_intent(transcript)

            # 3. 根据意图执行不同操作
            if intent.type == "code_generation":
                result = await self.cloud_agent.generate_code(
                    prompt=intent.payload,
                    context=self.local_cache.get_context()
                )
                await self.device.display_code(result.code)

            elif intent.type == "debug":
                result = await self.cloud_agent.debug_code(
                    error=intent.payload,
                    codebase=self.local_cache.get_current_file()
                )
                await self.device.display_fix(result.suggestion)

            elif intent.type == "doc_parse":
                # 通过眼镜摄像头捕获文档
                image = await self.device.capture_image()
                doc_text = await self.cloud_agent.ocr_and_parse(image)
                await self.device.display_text(doc_text)

class EdgeCloudRouter:
    """端云协同路由器：决定请求在本地还是云端处理"""

    def __init__(self):
        self.local_models = {
            "wake_detection": WakeWordModel(),      # 唤醒词检测
            "intent_classify": IntentClassifier(),   # 意图分类
            "code_snippet": CodeSnippetDB(),         # 代码片段检索
            "note_taking": NoteTakerModel()           # 语音笔记
        }
        self.cloud_agent = CloudAgent()
        self.confidence_threshold = 0.85

    async def route(self, request: VoiceRequest) -> Response:
        """路由决策：本地处理 or 云端处理"""
        # 第一步：本地意图分类
        intent = self.local_models["intent_classify"].classify(request.text)

        if intent.confidence < self.confidence_threshold:
            # 低置信度：交给云端处理
            return await self.cloud_agent.handle(request)

        # 高置信度简单任务：本地处理
        if intent.type in ["note", "snippet_search"]:
            return self.local_models[intent.handler].handle(request)

        # 复杂任务：云端处理
        if intent.type in ["code_gen", "debug", "refactor"]:
            # 本地预处理上下文
            context = self.local_models["code_snippet"].get_context()
            enriched_request = request.enrich(context)
            return await self.cloud_agent.handle(enriched_request)

        return await self.cloud_agent.handle(request)