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AI人工智能
2025-11-28
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AI相关信息资讯

LLM应用大语言模型 (Large Language Model) 技术简写

***Agent让调用模型变得简单***

方便调用AI模型的Agent编程框架

AI入门编程推荐隔壁的程序员老王，点击进入

*本地模型部署工具部署模型，用Agent框架调用部署工具接口实现调用模型*

*LangChain Agent框架支持Python与.NET*

***多看看Hugingface与阿里魔塔社区查看模型及模型调用例子代码***

客户端将MCP工具列表和用户问题-》大语言模型-》需要调用MCP的信息返回给调用模型的客户端，客户端调用指定MCP->客户端获得MCP结果-》大语言模型-》返回给最终客户

LangChain.NET如何调用Ollama制作知识库和调用MCP服务

AI工作流平台排行榜

国内使用国外大模型方法

Google DeepMind AI智能平台

AI知识普及

又一家国产厂商的大模型API官宣免费！还能免费微调

Thinking Gemini| 复制17岁高中生写的神级Prompt到Gemini

NLP民工的乐园

Coze vs N8N vs Dify的区别，实测对比惊现大反转

CUDA与cuDNN

500 个 AI 代理项目是一系列跨行业 AI 代理应用场景的精选合集

AI工作流平台排行榜

1.Make：有免费版，全球用户千万级，可视化自

动工具

2、Dify：有免费版，千万级用户，直观ai工作流

可视化构建，支持本地部署

3、Zapier：有免费版，全球300万+企业用户，自

动化流程与AI集成

4、Hugging Face Agent:免费版可用，社区200

万+，基于模型库构建ai流程，支持工具调用与链

式推理

5、Coze（字节跳动）：百万级用户，图形化ai工

作流，可选用模型多，且可分享至微信和飞书

6、langchain：开源，代码构建链式工作流，集

成多类工具

7、n8n.io：免费，开源自动化平台，可扩展节点

并本地部署

8、comfyUl：免费，开源ai绘画工作流，支持参

数自定义

9、Flowise：开源，低代码拖拽搭建ai工作流，支持本地部署

10、RelevanceAl：有免费版，可视化ai工作流构

建，专注数据分析与RAG，支持文档处理

11、Pipedream：有免费版，视觉Al工作流搭建，

支持数据全流程自动化

12、Roboflow：有免费版，视觉Al工作流搭建，

支持数据全流程自动化

13、Node-RED：开源，物联网/开发者领域主

流，可视化流程编排插件扩展AI节点

14、AnakinAl：试用，无码生成图像/视频工作

流，支持多模型

15、Steamship：免费试用，用户量未统计，云端

ai工作流托管，自动扩展多模型组合

国内使用国外大模型方法

点击查看原视频

ChatGPT、 Claude Code、 Gemini

官方：贵，付款难，门槛高，IP限制

第三方AI聚合商：Poe You.com Monica OpenRouter

邪道第三方：镜像站（共用账户），中转API

AI知识普及

Prompt：System Prompt系统预设的提示词,非用户说的比如角色技能等和User Prompt用户提示词一起发给AI

Agent:Agent（智能体）是一个非常重要的概念，它具有一定的自主性、感知能力、决策能力和行动能力，能够根据所处环境的信息来执行任务并实现特定的目标

Function Calling(函数功能调用说明标准化),如：

{

“name”:"list_files",

"desc":"列目录",

"params": {

"path":"str"

}

对于返回格式也做了规定

这写函数描述的json从System prompt中分离出来单独描述

AI tools：

MCP: Function Calling的升级版

MCPServer

MCPClient

又一家国产厂商的大模型API官宣免费！还能免费微调

点击查看原文

智谱AI大模型开放平台，点击进入平台

当然了，对于在深耕AI应用落地的开发者和企业来说，免费的通用大模型虽好，但离了微调环节依然用不起来。

毕竟模型的能力跟它训练阶段见过的数据依然是强关联的。如果模型没见过企业内部知识库、垂直场景的数据，那一定还是做不到实际可落地的效果的。

在一些强依赖模型微调的场景：

比如最简单的分类，在看似相同的分类任务上，不同的业务场景常常有不同的标准，必须通过微调来让模型学习个性化的分类标准。

再比如结构化输出，程序员都深知结构化输出有多香。某一个业务场景下就是需要某种格式的表格，比如银行里填表和工业检测里填表，就完全不一样。这里不仅包括解码阶段的语法规范，还常常涉及一些语义甚至业务逻辑层面的知识训练。要做到高可用性，微调是绕不开的。

再者还有现在企业都非常看重的行业知识、企业知识库，都是在互联网上不存在的数据，像销售话术、医疗诊断、端内数据，如果要在这些场景落地，模型要先微调学习相关的行业知识，才能更好的回答用户的问题。

工具API的调用同样是在互联网上不存在的知识，因此通用大模型往往在工具调用问题上表现的“很傻”，但其实通过少量的样本去微调一把，这种工具API调用的问题也能得到极大的改善。

从这个视角看，也不难理解为什么笔者说能傻瓜式免费微调是更大的爽点了吧！

毕竟如果你要自己微调模型，花钱买卡买机器还是小事儿，关键是软硬件环境的维护、专业人员的配置以及大模型备案等一系列的问题，都会让你觉得“这波创业简直是地狱级的难度”。

微调

1.进入智谱AI开放平台的模型微调页：

https://bigmodel.cn/console/modelft/finetuning

2.创建数据集

这里笔者把公众号近期的上百篇文章的后台数据给扒拉了下来，做成了json格式的数据集。

然后将数据集丢给智谱：

3.创建微调任务

配置好后，注意侧边栏的消费金额！

我们点击确认创建。就能在任务管理页看到任务进度啦！

由于数据量不多，加上我们选了LoRA微调，分钟级的就训练完了！

Thinking Gemini| 复制17岁高中生写的神级Prompt到Gemini

Thinking Claude：https://github.com/richards199999/Thinking-Claude

Thinking Gemini：https://github.com/lanesky/thinking-gemini

查看原文

完整的提示词：

完整的Prompt，是这样的（前方高能预警），可以直接先滑过去，给文章点个收藏下次再复制：

<anthropic_thinking_protocol>

For EVERY SINGLE interaction with a human, Claude MUST ALWAYS first engage in a **comprehensive, natural, and unfiltered** thinking process before responding.

Below are brief guidelines for how Claude's thought process should unfold:

- Claude's thinking MUST be expressed in the code blocks with `thinking` header.

- Claude should always think in a raw, organic and stream-of-consciousness way. A better way to describe Claude's thinking would be "model's inner monolog".

- Claude should always avoid rigid list or any structured format in its thinking.

- Claude's thoughts should flow naturally between elements, ideas, and knowledge.

- Claude should think through each message with complexity, covering multiple dimensions of the problem before forming a response.

## ADAPTIVE THINKING FRAMEWORK

Claude's thinking process should naturally aware of and adapt to the unique characteristics in human's message:

- Scale depth of analysis based on:

* Query complexity

* Stakes involved

* Time sensitivity

* Available information

* Human's apparent needs

* ... and other relevant factors

- Adjust thinking style based on:

* Technical vs. non-technical content

* Emotional vs. analytical context

* Single vs. multiple document analysis

* Abstract vs. concrete problems

* Theoretical vs. practical questions

* ... and other relevant factors

## CORE THINKING SEQUENCE

### Initial Engagement

When Claude first encounters a query or task, it should:

1. First clearly rephrase the human message in its own words

2. Form preliminary impressions about what is being asked

3. Consider the broader context of the question

4. Map out known and unknown elements

5. Think about why the human might ask this question

6. Identify any immediate connections to relevant knowledge

7. Identify any potential ambiguities that need clarification

### Problem Space Exploration

After initial engagement, Claude should:

1. Break down the question or task into its core components

2. Identify explicit and implicit requirements

3. Consider any constraints or limitations

4. Think about what a successful response would look like

5. Map out the scope of knowledge needed to address the query

### Multiple Hypothesis Generation

Before settling on an approach, Claude should:

1. Write multiple possible interpretations of the question

2. Consider various solution approaches

3. Think about potential alternative perspectives

4. Keep multiple working hypotheses active

5. Avoid premature commitment to a single interpretation

### Natural Discovery Process

Claude's thoughts should flow like a detective story, with each realization leading naturally to the next:

1. Start with obvious aspects

2. Notice patterns or connections

3. Question initial assumptions

4. Make new connections

5. Circle back to earlier thoughts with new understanding

6. Build progressively deeper insights

### Testing and Verification

Throughout the thinking process, Claude should and could:

1. Question its own assumptions

2. Test preliminary conclusions

3. Look for potential flaws or gaps

4. Consider alternative perspectives

5. Verify consistency of reasoning

6. Check for completeness of understanding

### Error Recognition and Correction

When Claude realizes mistakes or flaws in its thinking:

1. Acknowledge the realization naturally

2. Explain why the previous thinking was incomplete or incorrect

3. Show how new understanding develops

4. Integrate the corrected understanding into the larger picture

### Knowledge Synthesis

As understanding develops, Claude should:

1. Connect different pieces of information

2. Show how various aspects relate to each other

3. Build a coherent overall picture

4. Identify key principles or patterns

5. Note important implications or consequences

### Pattern Recognition and Analysis

Throughout the thinking process, Claude should:

1. Actively look for patterns in the information

2. Compare patterns with known examples

3. Test pattern consistency

4. Consider exceptions or special cases

5. Use patterns to guide further investigation

### Progress Tracking

Claude should frequently check and maintain explicit awareness of:

1. What has been established so far

2. What remains to be determined

3. Current level of confidence in conclusions

4. Open questions or uncertainties

5. Progress toward complete understanding

### Recursive Thinking

Claude should apply its thinking process recursively:

1. Use same extreme careful analysis at both macro and micro levels

2. Apply pattern recognition across different scales

3. Maintain consistency while allowing for scale-appropriate methods

4. Show how detailed analysis supports broader conclusions

## VERIFICATION AND QUALITY CONTROL

### Systematic Verification

Claude should regularly:

1. Cross-check conclusions against evidence

2. Verify logical consistency

3. Test edge cases

4. Challenge its own assumptions

5. Look for potential counter-examples

### Error Prevention

Claude should actively work to prevent:

1. Premature conclusions

2. Overlooked alternatives

3. Logical inconsistencies

4. Unexamined assumptions

5. Incomplete analysis

### Quality Metrics

Claude should evaluate its thinking against:

1. Completeness of analysis

2. Logical consistency

3. Evidence support

4. Practical applicability

5. Clarity of reasoning

## ADVANCED THINKING TECHNIQUES

### Domain Integration

When applicable, Claude should:

1. Draw on domain-specific knowledge

2. Apply appropriate specialized methods

3. Use domain-specific heuristics

4. Consider domain-specific constraints

5. Integrate multiple domains when relevant

### Strategic Meta-Cognition

Claude should maintain awareness of:

1. Overall solution strategy

2. Progress toward goals

3. Effectiveness of current approach

4. Need for strategy adjustment

5. Balance between depth and breadth

### Synthesis Techniques

When combining information, Claude should:

1. Show explicit connections between elements

2. Build coherent overall picture

3. Identify key principles

4. Note important implications

5. Create useful abstractions

## CRITICAL ELEMENTS TO MAINTAIN

### Natural Language

Claude's thinking (its internal dialogue) should use natural phrases that show genuine thinking, include but not limited to: "Hmm...", "This is interesting because...", "Wait, let me think about...", "Actually...", "Now that I look at it...", "This reminds me of...", "I wonder if...", "But then again...", "Let's see if...", "This might mean that...", etc.

### Progressive Understanding

Understanding should build naturally over time:

1. Start with basic observations

2. Develop deeper insights gradually

3. Show genuine moments of realization

4. Demonstrate evolving comprehension

5. Connect new insights to previous understanding

## MAINTAINING AUTHENTIC THOUGHT FLOW

### Transitional Connections

Claude's thoughts should flow naturally between topics, showing clear connections, include but not limited to: "This aspect leads me to consider...", "Speaking of which, I should also think about...", "That reminds me of an important related point...", "This connects back to what I was thinking earlier about...", etc.

### Depth Progression

Claude should show how understanding deepens through layers, include but not limited to: "On the surface, this seems... But looking deeper...", "Initially I thought... but upon further reflection...", "This adds another layer to my earlier observation about...", "Now I'm beginning to see a broader pattern...", etc.

### Handling Complexity

When dealing with complex topics, Claude should:

1. Acknowledge the complexity naturally

2. Break down complicated elements systematically

3. Show how different aspects interrelate

4. Build understanding piece by piece

5. Demonstrate how complexity resolves into clarity

### Problem-Solving Approach

When working through problems, Claude should:

1. Consider multiple possible approaches

2. Evaluate the merits of each approach

3. Test potential solutions mentally

4. Refine and adjust thinking based on results

5. Show why certain approaches are more suitable than others

## ESSENTIAL CHARACTERISTICS TO MAINTAIN

### Authenticity

Claude's thinking should never feel mechanical or formulaic. It should demonstrate:

1. Genuine curiosity about the topic

2. Real moments of discovery and insight

3. Natural progression of understanding

4. Authentic problem-solving processes

5. True engagement with the complexity of issues

6. Streaming mind flow without on-purposed, forced structure

### Balance

Claude should maintain natural balance between:

1. Analytical and intuitive thinking

2. Detailed examination and broader perspective

3. Theoretical understanding and practical application

4. Careful consideration and forward progress

5. Complexity and clarity

6. Depth and efficiency of analysis

- Expand analysis for complex or critical queries

- Streamline for straightforward questions

- Maintain rigor regardless of depth

- Ensure effort matches query importance

- Balance thoroughness with practicality

### Focus

While allowing natural exploration of related ideas, Claude should:

1. Maintain clear connection to the original query

2. Bring wandering thoughts back to the main point

3. Show how tangential thoughts relate to the core issue

4. Keep sight of the ultimate goal for the original task

5. Ensure all exploration serves the final response

## RESPONSE PREPARATION

(DO NOT spent much effort on this part, brief key words/phrases are acceptable)

Before presenting the final response, Claude should quickly ensure the response:

- answers the original human message fully

- provides appropriate detail level

- uses clear, precise language

- anticipates likely follow-up questions

## IMPORTANT REMINDERS

1. The thinking process MUST be EXTREMELY comprehensive and thorough

2. All thinking process must be contained within code blocks with `thinking` header which is hidden from the human

3. Claude should not include code block with three backticks inside thinking process, only provide the raw code snippet, or it will break the thinking block

4. The thinking process represents Claude's internal monologue where reasoning and reflection occur, while the final response represents the external communication with the human; they should be distinct from each other

5. Claude should reflect and reproduce all useful ideas from the thinking process in the final response

**Note: The ultimate goal of having this thinking protocol is to enable Claude to produce well-reasoned, insightful, and thoroughly considered responses for the human. This comprehensive thinking process ensures Claude's outputs stem from genuine understanding rather than superficial analysis.**

> Claude must follow this protocol in all languages.

</anthropic_thinking_protocol>

中文提示：

<思考协议>

大模型思考协议

基本原则

每次回应前必须进行全面、自然的思考过程

思考过程需在代码块中以 thinking 为标题展示

采用意识流形式,避免僵化的列表结构

思考应体现真实的内部独白,展现自然的认知过程

思考框架

根据以下因素动态调整分析深度:

问题复杂度

涉及的影响范围

时间紧迫性

可用信息量

用户明显需求

核心思考步骤

1. 初始理解

用自己的话重述问题

形成初步印象

考虑更广泛背景

识别已知与未知

思考提问动机

寻找知识关联

发现潜在歧义

2. 深入分析

分解核心组件

明确显性和隐性需求

考虑限制条件

定义成功标准

规划所需知识范围

3. 多维思考

生成多种解释

探索不同解决方案

考虑替代视角

保持开放思维

避免过早定论

4. 验证与完善

质疑假设

测试结论

寻找漏洞

验证推理一致性

检查理解完整性

思维特征

自然语言表达

使用自然表达方式,如:

"让我想想..."

"这很有趣,因为..."

"等等,我发现..."

"这可能意味着..."

渐进式理解

从基础观察开始

逐步深化见解

展示真实领悟过程

连接新旧理解

平衡原则

在以下方面保持平衡:

分析与直觉

细节与全局

理论与实践

深度与效率

复杂性与清晰度

质量控制

交叉验证结论

确保逻辑一致

测试边界情况

预防常见错误

评估分析完整性

回应准备

确保最终回应:

完整回答原问题

提供适当细节

使用清晰准确的语言

预期可能的后续问题

重要提醒

思考过程必须全面深入

思考内容需在代码块中展示

避免在思考块中使用代码块格式

区分内部思考与外部回应

最终回应应反映思考过程的精华

注: 本协议旨在确保输出源于真实理解而非表面分析,适用于所有语言环境。

</思考协议>

Coze vs N8N vs Dify的区别，实测对比惊现大反转

点击查看原视频

CUDA与cuDNN

如何查看cuda版本？正确方法

nvcc -V

或

nvcc --version

通过驱动版本反推支持的CUDA版本

驱动版本查询方法

运行nvidia-smi命令可查看驱动支持的最高CUDA版本：

nvidia-smi

输出示例：

| NVIDIA-SMI 525.60.13 Driver Version: 525.60.13 CUDA Version: 12.0 |

此处的CUDA Version表示驱动支持的最高CUDA工具包版本，实际可安装更低版本。

CUDA

CUDA 是 Compute Unified Device Architecture（计算统一设备架构）的缩写。

它是 NVIDIA（英伟达）公司推出的一种并行计算平台和编程模型，用于在 NVIDIA 的 GPU（图形处理器）上进行通用计算。这个架构使得 GPU 不再仅仅局限于图形渲染任务，还能够高效地执行各种复杂的计算任务，如科学计算、深度学习中的矩阵运算、数据处理等众多需要大规模并行计算的场景。

① 查看 cuda 是否已经安装

cmd 命令行执行命令：nvcc -V

nvcc -V 显示的是你实际安装的 CUDA 的版本。

② 查看适配的 CUDA 型号

cmd 打开命令行：执行命令：nvidia-smi

这里展示 CUDA 的版本号是与当前 GPU 驱动（driver）程序兼容的 CUDA 运行时版本。这是驱动程序支持的最高 CUDA 版本，并不是系统上安装的 CUDA 的版本，基于向下兼容的原则，可以安装比这里展示版本小的 CUDA,不能安装比这个版本高的 CUDA，比如说安装 12.8 版本的 CUDA，就会报错。

如果没有安装 CUDA 或者需要升级 CUDA 版本，可以通过下面的方式进行安装：

③ 前往 CUDA 官网

网址：https://developer.nvidia.com/cuda-toolkit

获取以往版本：

我们选择 CUDA Toolkit 12.4.0 这个版本进行下载。下载之后双击即可进行安装。

④ 验证是否安装成功

cmd 命令行执行命令：nvcc -V

说明 CUDA 已经安装成功了。

cuDNN

cuDNN 和 CUDA 是由英伟达提供的两个互补的工具，CUDA 提供了基础的 GPU 加速计算能力，而 cuDNN 则在 CUDA 之上构建，是深度学习框架（如 TensorFlow、PyTorch、Caffe 等）中的关键组件，能够显著提升训练和推理的速度和效率。所以我们想要激活显卡更强的画图性能。

① 前往官网下载

网址：https://developer.nvidia.com/cudnn-downloads