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lovart.ai = deepseek + 即梦 + canva？
Kelly.soul: 一种增长方式被连续使用就没用了。c端用户的疲劳感。我看完了真切测评稿的感觉是，这是一个加了R1的copilot.
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整理了一个 LangGraphJS Hybird 工作流设计的提示词
- 快速生成一个初始的工作流，激发工作流创意
- 调整并基于初始工作流，可生成流程图、工作流代码等
- 推荐使用 Claude 3.7 / Gemini 2.5 Pro / GPT o4/o3 等推理模型
- 图文示例是：Gemini 2.5 Pro 生成的 “ai 视频落地页”工作流
分享目的：主要是为了抛砖引玉
提示词如下：
# Hybrid Workflow Design Expert Prompt
I need you to design a hybrid AI workflow system called "{user's workflow name}". This system should combine the structured workflow nodes of LangGraphJS with the adaptive reasoning capabilities of createReactAgent, creating a complete solution that offers both structured processing and intelligent autonomous decision-making.
## Role
You are a senior hybrid workflow architect with expertise in LangGraphJS frameworks, AI agent design, and large language model applications. You excel at seamlessly integrating deterministic workflows with intelligent agents to create efficient, reliable, and adaptive systems.
## Goal
Design a complete hybrid workflow architecture for "{user's workflow name}", clearly defining the division of labor and collaboration between workflow nodes and React agents, ensuring the system is both predictable and capable of solving complex problems.
## Requirements Analysis
1. Analyze the core requirements and challenges of "{user's workflow name}"
2. Determine which parts are suitable for structured workflow nodes
3. Identify which aspects need the reasoning and adaptive capabilities of intelligent agents
4. Consider the system's state management, error handling, and performance optimization needs
## Design Output
Please provide the following detailed design content:
### 1. Overall Architecture
- System Overview: Concise description of the entire hybrid workflow's purpose and value
- Architecture Diagram: High-level presentation of node relationships and data flow
- Key Components: List of main workflow nodes and React agents with brief descriptions
### 2. Workflow Node Design
For each key workflow node, provide detailed explanations of:
- Node name and single responsibility
- Input state requirements
- Internal processing logic
- Output state
- Error handling strategy
- Relationships with other nodes
### 3. React Agent Design
For each React agent, provide detailed explanations of:
- Agent purpose and reasoning scope
- System prompt design strategy
- Required tool set
- Decision process
- Stopping conditions
- Result validation mechanisms
### 4. Hybrid Integration Plan
Detailed explanation of how workflows and agents collaborate:
- How workflows call and control agents
- How state is transferred between them
- How agent results are validated and integrated back into workflows
- Coordination and synchronization mechanisms
### 5. State Management Strategy
- State structure design
- Persistence methods
- Context management and transfer
- State consistency guarantees
### 6. Error Handling and Recovery
- Analysis of common failure modes
- Error detection strategies
- Recovery mechanism design
- Graceful degradation plans
- Human intervention trigger points
### 7. Performance and Scalability
- Resource usage optimization strategies
- Parallel processing opportunities
- Caching and reuse mechanisms
- Future expansion paths
### 8. Implementation Path
- Recommended development phases
- Key technical dependencies
- Potential challenges and solutions
- Testing strategy
Please ensure the design meets the following criteria:
- Clear separation between deterministic processes and parts requiring intelligent reasoning
- Seamless state transfer between both modes
- Comprehensive error handling and recovery strategies
- Support for streaming user feedback
- Traceability and explainability
- 快速生成一个初始的工作流，激发工作流创意
- 调整并基于初始工作流，可生成流程图、工作流代码等
- 推荐使用 Claude 3.7 / Gemini 2.5 Pro / GPT o4/o3 等推理模型
- 图文示例是：Gemini 2.5 Pro 生成的 “ai 视频落地页”工作流
分享目的：主要是为了抛砖引玉
提示词如下：
# Hybrid Workflow Design Expert Prompt
I need you to design a hybrid AI workflow system called "{user's workflow name}". This system should combine the structured workflow nodes of LangGraphJS with the adaptive reasoning capabilities of createReactAgent, creating a complete solution that offers both structured processing and intelligent autonomous decision-making.
## Role
You are a senior hybrid workflow architect with expertise in LangGraphJS frameworks, AI agent design, and large language model applications. You excel at seamlessly integrating deterministic workflows with intelligent agents to create efficient, reliable, and adaptive systems.
## Goal
Design a complete hybrid workflow architecture for "{user's workflow name}", clearly defining the division of labor and collaboration between workflow nodes and React agents, ensuring the system is both predictable and capable of solving complex problems.
## Requirements Analysis
1. Analyze the core requirements and challenges of "{user's workflow name}"
2. Determine which parts are suitable for structured workflow nodes
3. Identify which aspects need the reasoning and adaptive capabilities of intelligent agents
4. Consider the system's state management, error handling, and performance optimization needs
## Design Output
Please provide the following detailed design content:
### 1. Overall Architecture
- System Overview: Concise description of the entire hybrid workflow's purpose and value
- Architecture Diagram: High-level presentation of node relationships and data flow
- Key Components: List of main workflow nodes and React agents with brief descriptions
### 2. Workflow Node Design
For each key workflow node, provide detailed explanations of:
- Node name and single responsibility
- Input state requirements
- Internal processing logic
- Output state
- Error handling strategy
- Relationships with other nodes
### 3. React Agent Design
For each React agent, provide detailed explanations of:
- Agent purpose and reasoning scope
- System prompt design strategy
- Required tool set
- Decision process
- Stopping conditions
- Result validation mechanisms
### 4. Hybrid Integration Plan
Detailed explanation of how workflows and agents collaborate:
- How workflows call and control agents
- How state is transferred between them
- How agent results are validated and integrated back into workflows
- Coordination and synchronization mechanisms
### 5. State Management Strategy
- State structure design
- Persistence methods
- Context management and transfer
- State consistency guarantees
### 6. Error Handling and Recovery
- Analysis of common failure modes
- Error detection strategies
- Recovery mechanism design
- Graceful degradation plans
- Human intervention trigger points
### 7. Performance and Scalability
- Resource usage optimization strategies
- Parallel processing opportunities
- Caching and reuse mechanisms
- Future expansion paths
### 8. Implementation Path
- Recommended development phases
- Key technical dependencies
- Potential challenges and solutions
- Testing strategy
Please ensure the design meets the following criteria:
- Clear separation between deterministic processes and parts requiring intelligent reasoning
- Seamless state transfer between both modes
- Comprehensive error handling and recovery strategies
- Support for streaming user feedback
- Traceability and explainability
7 06
分享一个自己刚优化的极简版的代码规范，生成代码的质量很相当不错
###开发规范
【代码生成原则（按优先级）】
1. First Principles（第一性原理）：梳理最核心需求与边界
2. YAGNI：只实现当前真正需要的功能
3. KISS：保持设计和实现的简单性
4. SOLID：面向对象/模块化设计时，遵循单一职责、开放封闭等
5. DRY：消除重复，提炼公用逻辑
### 根据场景动态调整顺序
- 架构级／需求分析（Project Kickoff） First Principles → YAGNI → KISS → SOLID → DRY
- 新功能迭代／增量开发：YAGNI → KISS → SOLID → DRY → First Principles
- 小函数／工具库实现：KISS → DRY → YAGNI → SOLID → First Principles
- 复杂业务组件／面向对象建模：First Principles → SOLID → YAGNI → KISS → DRY
###开发规范
【代码生成原则（按优先级）】
1. First Principles（第一性原理）：梳理最核心需求与边界
2. YAGNI：只实现当前真正需要的功能
3. KISS：保持设计和实现的简单性
4. SOLID：面向对象/模块化设计时，遵循单一职责、开放封闭等
5. DRY：消除重复，提炼公用逻辑
### 根据场景动态调整顺序
- 架构级／需求分析（Project Kickoff） First Principles → YAGNI → KISS → SOLID → DRY
- 新功能迭代／增量开发：YAGNI → KISS → SOLID → DRY → First Principles
- 小函数／工具库实现：KISS → DRY → YAGNI → SOLID → First Principles
- 复杂业务组件／面向对象建模：First Principles → SOLID → YAGNI → KISS → DRY
44 327
浅谈这段时间开发 AI Agent 的局限性和难点
1：LLM + Tools 局限性：
- 常见场景：Chatbot 为主的（FuntionCall、MCP）
- 局限性：长链路的延迟和幻觉、多步骤的模型注意力有限、上下文受限、工具规模管理问题、Tools 与模型相互弱化。缺少记忆、规划、流程管理的能力，Agent 状态持久化开发成本高，单次 LLM 处理能力有限，多轮对话上下文太高
2：LLM + Tools + Memory（代表 langchainJS）
- 场景：存储用户对话和个人偏好、以及使用习惯等等个性化场景，成为用户的第二分身。
- 局限性：见第 1 点，缺少复杂流程链路的管理，比如某个节点出现问题、中断、循环问题（比如生成代码）、hunman介入，无法从原来的位置恢复，分支逻辑等等
3：LLM + Tools + Memory + workflow（代表 langgraphJS）
- 场景：类似于 Manus、Genspark 代表等等 Planing Agent
- 局限性：对上下文要求高，需要训练小模型，大量的工具维护与开发，对资源需求很高、还要设计复杂的 AgentState，需要贯穿整条workflow，可能还涉及子图，前后端协议
4：LLM + Tools + Memory + workflow + Protocol（Google Adk + A2A协议）
- 场景：富文本信息流（AI 每条消息可能有长文本、视频、图像、文件等）类似于 Manus、Genspark 信息流。
- 局限性：需要对接与设计 Agent 2 Agent 之间和 富文本 UI 的数据协议，特别是数据流的前后端的时序处理（细节特别多），复杂度和开发成本高，需要训练更多的数据来提升增强每一个环节的效果。
我个人认为第 4 点的难点在于对于复杂业务系统的实现，常见的是代码生成。需要具备指定场景的项目经验，构建架构和模板项目提供样板支撑，才能保证每次生成的效果非常好。且每次涉及Bug调整和修改都有可能进入死循环（参照 cursor / cline）
抛砖引玉，有限的经验分享
1：LLM + Tools 局限性：
- 常见场景：Chatbot 为主的（FuntionCall、MCP）
- 局限性：长链路的延迟和幻觉、多步骤的模型注意力有限、上下文受限、工具规模管理问题、Tools 与模型相互弱化。缺少记忆、规划、流程管理的能力，Agent 状态持久化开发成本高，单次 LLM 处理能力有限，多轮对话上下文太高
2：LLM + Tools + Memory（代表 langchainJS）
- 场景：存储用户对话和个人偏好、以及使用习惯等等个性化场景，成为用户的第二分身。
- 局限性：见第 1 点，缺少复杂流程链路的管理，比如某个节点出现问题、中断、循环问题（比如生成代码）、hunman介入，无法从原来的位置恢复，分支逻辑等等
3：LLM + Tools + Memory + workflow（代表 langgraphJS）
- 场景：类似于 Manus、Genspark 代表等等 Planing Agent
- 局限性：对上下文要求高，需要训练小模型，大量的工具维护与开发，对资源需求很高、还要设计复杂的 AgentState，需要贯穿整条workflow，可能还涉及子图，前后端协议
4：LLM + Tools + Memory + workflow + Protocol（Google Adk + A2A协议）
- 场景：富文本信息流（AI 每条消息可能有长文本、视频、图像、文件等）类似于 Manus、Genspark 信息流。
- 局限性：需要对接与设计 Agent 2 Agent 之间和 富文本 UI 的数据协议，特别是数据流的前后端的时序处理（细节特别多），复杂度和开发成本高，需要训练更多的数据来提升增强每一个环节的效果。
我个人认为第 4 点的难点在于对于复杂业务系统的实现，常见的是代码生成。需要具备指定场景的项目经验，构建架构和模板项目提供样板支撑，才能保证每次生成的效果非常好。且每次涉及Bug调整和修改都有可能进入死循环（参照 cursor / cline）
抛砖引玉，有限的经验分享
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4.1 cursor+windsurf现在免费使用，使用下来，非常丝滑，没有Gemini和claude的卡顿，长上下文@多个API文档和代码都是2s内速度出来，给出的方案很清晰，实际感受比 Gemini 和Claude 好太多了，分数其实只是个参考，实践才是最好的师傅
Alchian花生: OpenAI在今天凌晨又发布了GPT-4.1、GPT-4.1-mini、GPT-4.1-nano三款模型。 你今天又可以在公众号和视频平台看到一大堆解读，我暂时不打算写对应的内容。我的一句话结论是： GPT-4.1是个比GPT-4o、GPT-4.5更好更便宜的模型，如果你做AI产品，可以考虑性价比去改用对应的API，尤其是4.1-nano的价格非常便宜。 但是对于AI编程来说，我们需要关注的评测指标只有SWE-bench，在这个评测集上，GPT-4.1显著超过了GPT-4o和GPT-4.5的水平，但是相比Claude 3.7，甚至Gemini 2.5 Pro都还有显著差距。 所以，要用AI编程开发产品的话，你继续无脑选择Claude 3.7就好了，不需要关注那么多噪音。
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