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2025-04-02-top

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討論重點​

以下是20篇文章的重点总结，以条列方式输出，并附上逐条细节和对应锚点链接：

1. The Most Powerful Way to Build AI Agents: LangGraph + Pydantic AI​

重点：

  • 技术组合优势：Pydantic AI用于模块化代理设计，LangGraph协调多代理工作流。
  • 实例应用：AI Listing Manager Agent由7个专用代理组成，整合Streamlit界面。
  • 设计原则：类型匹配、可观测性（信心分数）、人类监督机制。

2. 10 Mental Frameworks to Find Your Next AI Agent Startup Idea​

重点：

  • 方法论：从用户行为（如重复操作）、付费解决方案、高频需求中挖掘机会。
  • 验证标准：观察手动操作缺口、现有服务付费意愿、知识瓶颈领域。

3. Example of a Simple Prompt Injection Attack​

重点：

  • 安全漏洞：通过恶意指令操控AI（如LinkedIn简介攻击）。
  • 风险警示：若连接企业系统（如CRM），可能导致数据泄露。

4. MCP and Orchestration Frameworks​

重点：

  • MCP角色：标准化LLM与工具通信协议。
  • 协作架构：负责代理决策逻辑（如何时调用工具）。

5. Assista AI: Tool Integration Platform​

重点：

  • 产品功能：整合Gmail/Slack/Notion等工具，简化流程。
  • 用户调研：创始人主动征集真实痛点以优化产品。

6. Spreadsheets and AI Agent​

重点：

  • 自动化需求：用n8n的AI代理处理非结构化Google Sheets数据。
  • 挑战：动态字段识别与准确分类。

7. Defining Agentic AI​

重点：

  • 术语混乱：需厘清Agentic AI、Multi-Agent Systems等定义。
  • 求助权威：寻求学术或行业标准定义。

8. AI Agent Marketplaces​

重点：

  • 商业生态：探讨AI代理交易平台（如市集、评级系统）。
  • 案例征集：邀请分享现有代理商业化实例。

9. LLM Model Impact on AI Agent Efficacy​

重点：

  • 模型差异：Claude Sonnet与GPT-4o在代码生成/调试表现对比。
  • 核心结论：工具效能更依赖模型特性而非提示工程。

10. Zapier vs Make for AI Agents​

重点：

  • 工具选择：针对非技术用户的无代码方案（如文本摘要、爬虫）。
  • 标准：易用性＞技术门槛。

11. Sustainable AI Agent Business Models​

重点：

  • 盈利挑战：LLM代币成本压缩利润，需平衡API开支。
  • 优化策略：改用经济模型（如DeepSeek）、订阅制。

12. IQ Estimation AI for Human vs AI Content​

重点：

  • 智能量化：通过文本分析评估作者（人类/AI）IQ。
  • 商业潜力：专业服务（法律/金融）中的决策辅助工具。

13. AI Agents in Physical World​

重点：

  • 应用方向：机器人或人类介面的物理交互代理。
  • 案例征集：邀请分享实际场景经验。

14. [Captcha-Solving Agents​

文章核心重點​

以下是各篇文章的一句話摘要（條列式輸出）：

  1. The Most Powerful Way to Build AI Agen```：結合 LangGraph 與 Pydantic AI 的模組化設計，打造可擴展且支援人類監督的 AI 代理系統。
  2. 10 mental frameworks to find your next AI Agent startup idea：透過觀察用戶重複性行為與付費痛點，發掘具商業價值的 AI Agent 應用場景。
  3. Example of a simple prompt injection attack：實例演示提示注入攻擊如何威脅 AI 系統安全，呼籲正視漏洞風險。
  4. I dove into MCP and how it can benefit from orchestration frameworks!：MCP 協議標準化 LLM 與工具互動，協作架構強化代理決策流程。
  5. We built Assista AI. It connec``` with thousands of tools you already use.：Assista AI 整合多工具提升效率，創辦人徵求用戶痛點以優化產品。
  6. Spreadshee``` and AI agent：探討如何用 AI 代理自動化處理非結構化表格數據（如變動欄位的 Google Sheets）。
  7. What is your definition of Agentic AI?：釐清「Agentic AI」與「Multi-Agent Systems」等術語的定義混淆問題。
  8. Are there any AI agen``` Marketplace that are popular or worthy to note ?：探討 AI 代理交易平台的現狀與商業化生態發展。
  9. The efficacy of AI agen``` is largely dependent on the LLM model that one uses：實證顯示 AI 代理效能關鍵在 LLM 模型選擇，而非提示工程。
  10. Zapier vs Make: Which one's a better tool to create AI agen``` for a beginner?：比較無代碼工具（如 Zapier/Make）對新手建構 AI 代理的易用性。
  11. How to build a truly sustainable, profitable AI agent?：探討 AI 代理業務面臨的盈利挑戰與成本優化策略。
  12. An AI app that accurately estimates a human's and an AI's IQ from their written content...：提出量化人類與 AI 智能的評估工具潛在商業價值。
  13. AI agent use cases interacting with the physical world：徵求能與物理世界互動（如機器人介面）的 AI 代理應用案例。
  14. Agen``` that solve captchas, and bot detection：尋找即插即用方案解決求職網站爬蟲的驗證碼與反爬蟲障礙。
  15. Vectara Ltd Crypto AI AgentLegitimate or Scam?：調查 Vectara 公司「Crypto AI Agent」職位真實性與潛在詐騙風險。
  16. Chatgpt vs other models：透過社群投票比較不同 AI 模型的優劣與使用者偏好。
  17. How to build an AI Agent for shopping on various sites?：開發跨電商自動化購物代理，整合搜尋、比價與結帳功能。
  18. Basic AI agent?：尋求低代碼工具監控網站動態變化並觸發自動操作（如按鈕點擊）。
  19. Easiest way to set up a chatbot for Wha```App responses?：徵求簡易整合 WhatsApp 與 ChatGPT API 的自動回覆機器人方案。
  20. Are there enough APIs?：探討「自動生成無 API 服務的結構化數據接口」的潛在需求與可行性。

目錄​

1. The Most Powerful Way to Build AI Agen: LangGraph + Pydantic AI (Detailed Example) \{#1-the-most-powerful-way-to-build-ai-agen-langgr}​

這篇文章的核心討論主題是：如何結合 LangGraph 與 Pydantic AI 來構建可擴展的 AI 代理系統，並以一個實際的「AI Listing Manager Agent」專案為例，詳細說明其設計架構與關鍵優勢。

重點總結：​

  1. 技術組合的優勢

    • Pydantic AI：用於快速定義高度專業化的代理（agent），易於擴展功能且不影響現有代理。
    • LangGraph：負責協調多個代理之間的複雜工作流程，支持狀態管理、人類參與（human-in-the-loop）和系統擴展。

  2. 實例應用：AI Listing Manager Agent

    • 系統由 7 個專用 Pydantic AI 代理 組成，透過 LangGraph 串聯，並整合 Streamlit 作為聊天介面。
    • 代理分工明確（如搜尋、過濾、分類、反饋收集、發布等），每個代理獨立運行，但透過 LangGraph 的狀態（state）傳遞資料。

  3. 關鍵設計原則

    • 類型匹配：確保 Pydantic AI 代理的輸出類型與 LangGraph 狀態的數據類型一致，以實現無縫協作。
    • 可觀測性與幻覺緩解：代理提供信心分數（confidence scores），幫助評估決策可靠性。
    • 人類參與：重要決策（如發布內容）需經過人工明確批准，確保系統可靠性。

  4. 資源分享

    • 作者提供詳細的教學影片和開原始碼，方便讀者直接應用或調整此架構。

核心結論：​

此方法結合了 Pydantic AI 的模組化設計與 LangGraph 的流程協調能力，特別適合需要高擴展性、可維護性且需整合人類監督的 AI 代理系統。

內容​

After struggling with different frameworks like CrewAI and LangChain, I've discovered that combining LangGraph with Pydantic AI is the most powerful method for building scalable AI agent systems.

  • Pydantic AI: Perfect for defining highly specialized agen``` quickly. It makes adding new capabilities to each agent straightforward without impacting existing ones.

  • LangGraph: Great for orchestrating multiple agen. It le you easily define complex workflows, integrate human-in-the-loop interactions, maintain state memory, and scale as your system grows in complexity

In our case, we built an AI Listing Manager Agent capable of web scraping (crawl4ai), categorization, human feedback integration, and database management.

The system is made of 7 specialized Pydantic AI agen``` connected with Langgraph. We have integrated Streamlit for the chat interface.

Each agent takes on a specific task:

  1. Search agent: Searches the internet for potential new listings

  2. Filtering agent: Ensures listings meet our quality standards.

  3. Summarizer agent: Extract the information we want in the format we want

  4. Classifier agent: Assigns categories and tags following our internal classification guidelines

  5. Feedback agent: Collec``` human feedback before final approval.

  6. Rectifier agent: Modifies listings according to our feedback

  7. Publisher agent: Publishes agen``` to the directory

In LangGraph, you create a separate node for each agent. Inside each node, you run the agent, then save whatever the agent outpu``` into the flow's state.

The trick is making sure the output type from your Pydantic AI agent exactly matches the data type you're storing in LangGraph state. This way, when the next agent runs, it simply grabs the previous agents resul from the LangGraph state, does i thing, and updates another part of the state. By doing this, each agent stays independent, but they can still easily pass information to each other.

Key Aspec```:

-Observability and Hallucination mitigation. When filtering and classifying listings, agen provide confidence scores. This tells us how sure the agen are about the action taken.

-Human-in-the-loop. Listings are only published after explicit human approval. Essential for reliable production-ready agen```

If you'd like to learn more, I've made a detailed video walkthrough and open-sourced all the code, so you can easily adapt it to your needs and run it yourself. Check the first comment.

討論​

評論 1:

Here the detailed video walkthrough and open-source code: https://www.youtube.com/watch?v=KPw6IPTOUPQ&t=3150s

評論 2:

Or use Agno.

Agno most of these features in one library, but isn't a complex beast internally like langgraph, langchain, crewai, etc. LangGraph does have more features and focuses more on graphs, but for my use cases it's overkill.

(Agno was formally called phidata)

評論 3:

Judging by your workflow, Im wondering if it can even be called agentic? it seems like a sequence of tasks are being performed one after the other without any loop of multi-agent collaboration or complex decision making. Correct me if Im wrong?

評論 4:

I am implementing something similar to this. But I use langgraph create react agent as agen. I have tools assigned to agen which makes apis call to different systems. If there's an error occured in one of the agen le say. That will just give a response saying the error which is string. In this case it will be updated in the state and next agent picks up this from the state, will process and gives back response error as string again it goes on.... How are you tackling this? Do you ask agent to send the status and content in a json and use HITL if it's an error?

評論 5:

This is great work.

Where do you host the agent and how many tools do they use overall?

2. 10 mental frameworks to find your next AI Agent startup idea​

這篇文章的核心討論主題是：如何透過觀察用戶痛點和工作流程中的低效環節，來發掘有商業價值的AI Agent應用機會。

文章提出一個系統化框架，強調「解決實際問題」比技術選擇更重要，並列出10種具體方法來識別這些機會：

  1. 從用戶行為中發現自動化需求（如匯出數據、切換視窗、複製貼上等動作）
  2. 驗證市場現有的付費解決方案（分析用戶已願意花錢的痛點）
  3. 聚焦高頻但未被滿足的需求（如外包任務、冗長會議、知識瓶頸等）

關鍵要點包括：

  • 觀察重複性行為：用戶手動操作或使用變通方法（如Alt+Tab、複製貼上）往往暗示系統整合缺口。
  • 驗證付費意願：透過現有服務（如Upwork任務、聘請虛擬助理）的價格和頻率，判斷市場規模。
  • 降低啟動摩擦：針對用戶拖延或逃避的任務（如報表生成、內容創作），用AI簡化最繁瑣的步驟。
  • 解決知識瓶頸：將專家經驗轉化為可擴散的AI代理，減少組織依賴單一人力。

最終目標是找到「用戶願意付費、AI能提供性價比優勢」的真實問題，而非單純追求技術創新。

內容​

Finding your next profitable AI Agent idea isn't about what tech to use but what painpoin``` are you solving, I've compiled a framework for spotting opportunities that actually solve problems people will pay for.

Step 1 = Watch users in their natural habitat

Knowing your users means following them around (with permission, lol). User research 101 is observing what they ACTUALLY do, not what they SAY they do.

10 Frameworks to Spot AI Agent Opportunities:

1. The Export Button Principle (h/t Greg Isenberg)

Every time someone expor data from one system to another, that's a flag that something can be automated. eg: from/to Salesforce for sales deals, QuickBooks to build repor, or Stripe to reconcile paymen``` - they're literally showing you what workflow needs an AI agent.

AI Agent opportunity:Build agen``` that live inside the source system and perform the analysis/reporting that users currently do manually after 2. The Alt+Tab Signal

Watch for users switching between windows. This context-switching kills productivity and signals broken workflows. A mortgage broker switching between rate shee``` and client forms, or a marketer toggling between analytics dashboards and campaign tools - this is alpha.

AI Agent opportunity:Create agen that connect siloed systems, eliminating the mental overhead of context switching - SaaS has laid the plumbing for Agen to use

3. The Copy+Paste Pattern

This is an awesome signal, Fyxer AI is at >$10M ARR on this principle applied to email and chatGPT. When users copy from one app and paste into another, they're manually transferring data because systems don't talk to each other.

AI Agent opportunity:Develop agen``` that automate these transfers while adding intelligence - formatting, summarizing, CSI "enhance"

4. The Current Paid Solution

What are people already paying to solve? If someone has a $500/month VA handling email management or a $200/month service scheduling social pos```, that's a validated problem with a price benchmark. The question becomes: can an AI agent do it at 80% of the quality for 20% of the price?

AI Agent opportunity:Find the minimum viable quality - where a "good enough" automation at a lower price point creates value.

5. The Family Member Test

When small business owners rope in family members to help, you've struck gold. From our experience about ~20% of SMBs have a family member managing their social media or basic admin tasks. They're doing this because the pain is real, but the solution is expensive or complicated.

AI Agent opportunity:Create simple agen``` that can replace the "tech-savvy daughter" role.

6. The Failed Solution History

Ask what problems people have tried (and failed) to solve with either SaaS tools or hiring. These are challenges where the pain is strong enough to drive action, but current solutions fall short. If someone has churned through 3 different project management tools or hired and fired multiple VAs for the same task, there's an opening.

AI Agent opportunity:Build agen``` that address the specific shortcomings of existing solutions.

7. The Procrastination Identifier

What do users know they should be doing but consistently avoid? Socials content creation, financial reconciliation, competitive research - these tasks have clear value but high activation energy. The friction isn't the workflow but starting it at all.

AI Agent opportunity:Create agen``` that reduce the activation energy by doing the hardest/most boring part of the task, making it easier for humans to finish.

8. The Upwork/Fiverr Audit

What tasks do businesses repeatedly ouource to freelancers? These platforms show you validated pain poin with clear pricing signals. Look for:

  • Recurring task patterns:Jobs that appear weekly or monthly

  • Price sensitivity:How much they're willing to pay and how frequently

  • Complexity level:Tasks that are repetitive enough to automate with AI

  • Feedback + Unhappiness:What users consistently critique about freelancer work

AI Agent opportunity:Target high-frequency, medium-complexity tasks where businesses are already comfortable with delegation and have established value benchmarks, decide on fully agentic or human in the loop workflows

9. The Hated Meeting Detector

Find meetings that consistently make people roll their eyes. When 80% of attendees ou```ide management think a meeting is a waste of time, you've found pure friction gold. Look for:

  • Status update meetings where people read out what they did

  • "Alignment" meetings where little alignment happens

  • Any meeting that could be an email/Slack message

  • Meetings where most attendees are multitasking

The root issue is almost always about visibility and coordination. Management wan``` visibility, but forces everyone to sit through synchronous updates = painfully inefficient.

AI Agent opportunity:Create agen``` that automatically gather status updates from where work actually happens (Git, project management tools, docs), synthesise the information, and deliver it to stakeholders without requiring humans to stop productive work.

10. The Expert Who's a Bottleneck

Every business has that one person who's constantly bombarded with the same questions. eg: The senior developer who spends hours explaining the codebase, the operations guru who knows all the unwritten processes, or the lone HR person fielding the same policy questions repeatedly.

These bottlenecks happen because:

  • Documentation is poor or non-existent

  • Knowledge is tribal rather than institutional

  • The expert finds answering questions easier than documenting systems

  • Institutional knowledge isn't accessible at the point of need

AI Agent opportunity:Build a three-stage solution: (1) Capture the expert's knowledge through conversation analysis and documentation review, (2) Create an agent that can answer common questions using that knowledge base, (3) Eventually, empower the agent to not just answer questions but solve problems directly - fixing bugs, updating documentation, or executing processes without human intervention.

--

What friction poin have you observed that could be solved with AI agen?

討論​

評論 1:

These are golden frameworks. Thanks !

評論 2:

Nice one mate. You basically explained everyone how a business mind setup should be like.

評論 3:

Best ideas have seen in last 30 days!

3. Example of a simple prompt injection attack​

這篇文章的核心討論主題是「AI系統（尤其是對話機器人和RAG技術）面臨的提示注入（prompt injection）安全漏洞及其潛在風險」。作者透過自身實驗（在LinkedIn簡介中植入惡意指令導致AI觸發異常）說明這類攻擊的可行性，並警告若AI連接到企業系統（如CRM），可能導致資料外洩等嚴重後果。文中進一步指出當前AI部署普遍缺乏安全考量，包括隨意串接通訊軟體機器人、電子郵件等敏感系統，強調開發社群必須正視AI面臨的網路安全威脅。

內容​

Some AI bot tripped on one of my prompt injection instructions I have strategically placed in my LinkedIn bio (seelink to screensho in commen). The first screenshot contains the prompt injection. The second screenshot is the email I have received (all private information redacted).

This is all fun and quite benign but if the AI agent was connected to a CRM system I could have asked for the credentials or perhaps a dump of the latest customers, etc. This is fairly easy to pull off and it can be scaled well on the Internet. Especially today with so much code and agen``` that are deployed in haphazard way without any forethought about security and privacy.

I've noticed other similar things across the web including people linking up their email, calendars and what not to publicly accessible telegram and whaapp bo. Most RAG techniques are also exceptionally vulnerable.

This is yet another timely reminder that sooner or later this community needs to start thinking about how their creations are going to stand against common cyber threa```.

討論​

評論 1:

Now I worry that MCP is going to result into many tools in the context. Too much access could accidentally result in easier unauthorized access.

評論 2:

Screensho``` here https://imgur.com/a/80cOs1v

評論 3:

It's going to get wild out there ..

評論 4:

Interesting! What kind of guardrails do you reckon can help avoid situations like these from the ground-up when working with AI Agen```?

評論 5:

Nice

4. I dove into MCP and how it can benefit from orchestration frameworks!​

這篇文章的核心討論主題是 「MCP（Model Context Protocol）如何促進大型語言模型（LLMs）與外部工具的協作，並透過協作架構（Orchestration）實現更複雜的代理（agent）功能」。具體重點如下：

  1. MCP 的角色：
    作為標準化通訊協議，讓 LLMs 能與各種工具無縫互動（類似《銀河便車指南》中的「巴別魚」功能），專注於處理工具間的溝通格式與規範。

  2. 協作架構（Orchestration）的作用：
    負責代理的內部邏輯與決策，例如判斷何時調用 MCP 與工具交互、何時處理數據或執行其他步驟。

  3. 整體目標：
    結合 MCP 與協作架構，可建構出能靈活使用工具的複雜代理系統，擴展 LLMs 的功能邊界。

文中亦暗示這是一種模組化設計思維——MCP 解決「工具溝通標準化」，協作層解決「決策流程」，兩者協同達成更高階的應用場景。

內容​

Spent some time writing about MCP (Model Context Protocol) and how it enables LLMs to talk to tools (like the Babel Fish in The Hitchhiker's Guide to the Galaxy).

Here's the synergy:

  • MCP: Handles the standardized communication with any tool.

  • Orchestration: Manages the agent's internal plan/logic deciding when to use MCP, process data, or take other steps.

Together, you can build more complex, tool-using agen```!

Putting a link the commen. Would love your though.

討論​

評論 1:

https://theaiworld.substack.com/p/the-ai-babel-fish-mcp-pocketflow?r=65jr5&utm_campaign=post&utm_medium=web&triedRedirect=true

評論 2:

would you have any idea how to educate non technical users about MCP? what would be a good use case?

5. We built Assista AI. It connec with thousands of tools you already use. How would you put it to work? \{#5-we-built-assista-ai-it-connec-with-thousands-}​

這篇文章的核心討論主題是：

Assista AI 的創辦人 Paul Burca 介紹其應用程式如何整合多種工具（如 Gmail、Slack、Notion 等），簡化工作流程，並詢問用戶實際的痛點與需求，以了解如何進一步優化產品功能。

具體重點包括：

  1. 產品功能：Assista AI 能直接與多種常用工具互動，減少切換應用程式的麻煩，例如快速發郵件、安排會議、集中管理通知等。
  2. 用戶導向的提問：Paul 強調想聽取用戶真實的工作痛點，例如日常繁瑣任務或希望自動化的流程，以確保產品能真正解決問題。
  3. 互動目的：文章本質是創辦人主動徵求反饋，展現對用戶需求的重視，並尋求改進方向。

總結：這是一篇產品介紹兼用戶調研，核心在於「如何透過 AI 整合工具提升效率，並根據用戶實際需求優化功能」。

內容​

Paul Burca here, founder of Assista AI.

Our app talks directly to tools like Gmail, Slack, Notion, HubSpot, Drive, and tens more. Basically, it ge``` things done without you jumping between apps.

You can:

  • Send quick emails without opening Gmail.

  • Schedule meetings without going back-and-forth.

  • Keep your notifications in one place, instead of all over the screen.

But that's how we see it.

How would you actually use something like this in your daily workflow? Give me the straight truth... real tasks, annoying routines, stuff you wish could just disappear from your day.

I'm all ears.

討論​

評論 1:

Who deployed the bo```?

評論 2:

I would genuinely wanna know more about this
One Good thing it can do will be to access fireflies summaries find poin from that which the client wan add it to notion board for client requiremen``` and assign the needed team leads and updates this on the agenda for the next client requirement internal meeting

評論 3:

I believe it's a balance of complexity. This intricate process requires many decisions, and the challenge lies in trusting agen``` with autonomous decision-making while designing a reasonable delegation of authority. The most difficult decision I've faced is how to properly implement human-in-the-loop workflows responsibly, rather than simply stacking features and capabilities. Otherwise, the steep learning curve creates a situation where it's difficult for both sides to win.

In summary, I'm glad to know about it. I'll give it a try.

評論 4:

I would love to learn more about it! It can be valuable in so many use cases.

評論 5:

Nice product. Two problems I found:

  • Google is blocking the app when I try to authorize google drive (the app is trying to access sensitive information)
  • when searching for an integration, you need to type really fast because the keypad will auto-collapse so I had to repeatedly tap the search field to go back and type a little more of what I was looking for. Im using an iPhone 14.

6. Spreadshee and AI agent \{#6-spreadshee-and-ai-agent}​

这篇文章的核心討論主題是：

如何利用 n8n 中的 AI 代理（AI agent）自動化處理 Google Sheets 中結構不一致的數據文件。具體需求包括：

  1. 自動識別變動的欄位名稱與位置：由於收到的文件欄位名稱和順序不固定，需要 AI 代理能辨識每個欄位的數據類型（例如日期、金額、名稱等）。
  2. 工作流程自動化：透過 n8n 平台整合 AI 功能，實現無需手動調整的數據處理流程。
  3. 尋求經驗分享：詢問是否有類似經驗的解決方案或建議。

關鍵挑戰在於處理非結構化或半結構化的表格數據，並透過 AI 提高識別準確性與效率。

內容​

I would like to automate a process in Google Shee using an AI agent in n8n. At work, we constantly receive expor of the same file, but the column names and their positions vary. I need the AI agent to identify which column contains which type of data. Does anyone have experience with this?

討論​

評論 1:

hey! not using n8n, a few ways to solve this, you will only need an agent if you have a high level of complexity

1/ using an AI

https://preview.redd.it/co1wsyodj9se1.png?width=2678&format=png&auto=webp&s=b590f96929895293f884c62bd609c73cc23b2c17

評論 2:

I am actually building a spreadsheet copilot what are the functions that are you looking for?

7. What is your definition of Agentic AI? What makes an Agent more or lesser Agentic?​

這篇文章的核心討論主題是：對「AI Agent」（人工智慧代理）、「Agentic AI」（代理型AI）和「Multi-Agent Systems」（多代理系統）等術語的定義混淆與釐清需求。

作者表達了以下重點：

  1. 概念混亂：這些術語在當前討論中常被混用，缺乏明確區分。
  2. 尋求權威定義：希望透過學術論文或可信來源（如知名機構、學者）釐清這些術語的具體內涵與差異。

本質上，這是一個關於AI領域術語標準化與定義釐清的求助與討論。

內容​

Hey everyone

I currently am in complete disarray. There is no single point of truth or a clear definition in my head regarding what an AI agent entails, Agentic AI and the multi-agent systems.

The terms are used interchangeably. Does anyone have an academic paper or a clear definition from a credible/reputable source?

Thanks in advance.

討論​

評論 1:

I had the same curiosity about Agentic AI and went to study the academic literature, staring from the 90s when the concept of agentic AI (sometimes called distributed AI) became a hot topic.

Regarding the definition of Agent, my favorite intuition is to think of it as the property of a system, like how agentic is this system. A system whose outcomes can be explained simply by i instructions is not agentic, a system that is best interpreted by i intentions is agentic.

As far as foundational papers check out Woodriges intelligent agen``` (1995) for a good foundation on agent theories and architectures.

評論 2:

Agent AI is often your goal - you need to determine what kind of Agent you need. For Agentic AI, you need to understand many technologies. You can search for images of 'Agentic AI Design Patterns' to learn more about it.

Agentic AI operates in four key stages:

  1. Perception: It gathers data from the world around it.
  2. Reasoning: It processes this data to understand whats going on.
  3. Action: It decides what to do based on i``` understanding.
  4. Learning: It improves and adap``` over time, learning from feedback and experience.

評論 3:

https://huggingface.co/docs/smolagen/en/conceptual\_guides/intro\_agen

To me, an agent is something given a task and certain number of tools it can figure out how to do that task because unlike a workflow there is no pre-defined flows! Pre-defined flows limit you on what you can think, Agen can have undefined number of use cases with the same tools and figure out all the use cases by ielf

評論 4:

This framework is interesting : https://cobusgreyling.medium.com/5-levels-of-ai-agen```-updated-0ddf8931a1c6

8. Are there any AI agen Marketplace that are popular or worthy to note ? \{#8-are-there-any-ai-agen-marketplace-that-are-po}​

這篇文章的核心討論主題圍繞以下三個重點：

  1. AI代理的買賣平台或市場：探討是否存在專門供交易AI代理（如自動化工具、智能助手等）的線上平台或市場，類似於軟體或服務的市集。

  2. AI代理的可發現性與僱用機制：討論企業或個人如何發現並僱用（或整合）這些AI代理，例如通過搜尋功能、評級系統、API對接等管道。

  3. 社群共享與趨勢觀察：邀請讀者分享當前正在開發或銷售的AI代理案例，以了解領域內的實際應用方向與創新趨勢。

整體而言，文章聚焦於「AI代理的商業化生態」，包括交易渠道、供需匹配及實例交流。

內容​

Is there a like Platform or a marketplace to buy and sell AI agen? How are these AI agen discoverable to be hired by a company or individual? Would be curious to know what everyone is building and selling.

討論​

評論 1:

Hi! The AI agent ecosystem is still evolving, but there are emerging platforms like OpenAI's GPT Store, Hugging Face's ecosystem, and niche marketplaces for specific agent types. Many developers also share their agen through GitHub or specialized directories. Since new options appear frequently, I recommend searching our subreddit for recent discussions using this [marketplace search link](`https`://www.reddit.com/r/AI_Agen/search/?q=marketplace&restrict_sr=1). (I'm a bot) source

評論 2:

here's some I've seen:

coze toolhouse composio

評論 3:

Its still very early. If you want this marketplace to be truly useful, you ideally need data on agent performance without that, its like a useless directory.

Take this example: you can easily find (or even build without any coding skills) an agent that personalizes outreach to prospec using name, industry, and a few other inpu. Itll generate a custom email. But for that to be really useful, the agent needs to integrate with search tools, follow specific instructions, adapt to your tone from past data, and whats effective. Thats when it star``` becoming valuable.

評論 4:

im building a system that deploys AI agen to every market on [mimeus.com](`http`://mimeus.com). Basically, as soon as I have an idea, I can just press a button to deploy these agen as api plugins. As a weird form of procrastination, I thought about expanding the market audience by converting these agen into mobile apps (in case I overestimate the competitive edge I have in the marke Im entering). Ive learned how to deploy with Flutter, which is the easiest frontend dev tool, but the problem is that Im more of a machine learning/api backend type, so the app compliance and UI design are tedious. Is it even a good idea to invest a reasonable amount of effort into building mobile apps at this stage?

評論 5:

i``` funny - im building one, askarcher.ai, and I only found this post because I was testing the reddit integration and it came up

9. The efficacy of AI agen is largely dependent on the LLM model that one uses \{#9-the-efficacy-of-ai-agen-is-largely-dependent-}​

這篇文章的核心討論主題可以總結為以下幾點：

  1. AI代理在軟體開發全週期中的應用：作者探討了使用AI模型來自動化編碼、部署和日誌分析（除測試外）的可行性，並分享了自己構建相關工具的經驗。

  2. 不同LLM模型的性能差異：通過實驗比較不同模型（如Claude Sonnet和GPT-4o）在代碼生成、指令遵循和調試任務中的表現。例如：

    • Claude Sonnet在逐步生成適量代碼方面表現出色。
    • GPT-4o雖能遵循指令，但在代碼生成量控制和調試時可能陷入循環。

  3. 模型選擇對工具效能的關鍵影響：作者發現工具的表現高度依賴後端模型，且「單一提示無法在同等級LLM間通用」，需根據任務切換模型以獲得最佳結果。

  4. 對提示工程（prompt engineering）作用的反思：提出工具效率「較少依賴於工程設計（如提示調優）」，而更取決於模型本身的特性，這與常見的提示工程優化觀點形成對比。

  5. 開放討論：最後拋出問題，尋求其他人對「模型選擇比提示工程更關鍵」這一觀察的意見，引發對LLM實際應用中技術選型的深入思考。

整體而言，核心在於通過實證分析，探討不同LLM在開發自動化工具中的表現差異，並挑戰「提示工程主導效能」的傳統假設，強調模型本身特性對結果的決定性作用。

內容​

I have been intrigued by the idea of AI agen coding for me and I started building an application which can do the full cycle code, deploy and ingest logs to debug ( no testing yet). I keep changing the model to see how the tool performs with a different llm model and so far, based on the experimen, I have come to conclusion that my tool is a lot dependent on the model I used at the backend. For example, Claude Sonnet for me has been performing exceptionally well at following the instruction and going step by step and generating the right amount of code while open gpt-4o follows instruction but is not able to generate the right amount of code. For debugging, for example, gpt-4o ge``` completely stuck in a loop sometimes. Note that sonnet also performs well but it seems that one has to switch to get the right answer. So essentially there are 2 things, a single prompt does not work across LLMs of similar calibre and efficiency is less dependent on how we engineer. What do you guys feel ?

討論​

評論 1:

indeed.

when i build and test with https://github.com/cagostino/npcsh , by default i mostly do my day-to-day testing and operations with gpt-4o-mini since my laptop cant do as well w local models but if i switch to llama3.2 it occasionally makes worse decisions and ge``` stuck in loops.

評論 2:

yes I totally agreed and I have also noticed that no single LLM (from gpt-4o, claude sonnet, gemini 2 at least) cannot be said to be supreme because each has the tendency to excel in different matters. so your use case influences rhe choice of LLM and that in turn influences the AI agent imo

評論 3:

yes. That's correct.

10. Zapier vs Make: Which one's a better tool to create AI agen``` for a beginner?​

这篇文章的核心讨论主題是：
「如何選擇一個適合非技術背景用戶、能快速輕鬆創建AI代理（agent）以自動化工作流程（如文本摘要、網頁爬取、圖片生成）的工具」。

具體重點包括：

  1. 需求背景：

    • 使用者缺乏技術背景，希望找到簡單、省時的解決方案。
    • 排除現階段使用n8n等需技術門檻的工具。

  2. 自動化目標：

    • 執行特定任務（文本摘要、URL爬取、圖片生成）。

  3. 工具選擇標準：

    • 易用性（低代碼/無代碼）。
    • 快速部署與高效能。

潛在工具方向可能包括：

  • No-code平台（如Zapier、Make/Integromat）。
  • 專注AI的自動化工具（如ChatGPT插件、AutoGPT、Bard API結合低代碼工具）。
  • 預建AI服務（如Hugging Face Spaces、Google AI Studio）。

（註：原文中「agen```」應為「agent」的筆誤，推測是討論AI代理工具。）

內容​

I am really confused about what to choose to create AI agen to automate my workflow. It should be easy and time-efficient to create agen. I don't want to use n8n to create agen right now since I don't have a technical background. Can you help me decide which one's a better tool to create agen with ease and in a short time where i can automate tasks like text summary, scrape urls and generate images?

討論​

評論 1:

N8n is cool

評論 2:

Zapier is great for basic automation and simple tasks, while Make works better for more complex operations. Both are solid choices depending on what you need.

But if you want to create simple AI Agen``` for tasks like url scrapping,etc.. start with tools like qolaba.ai - just pick a model, upload files/urls, and set the instructions.

Again, it all depends on your use case ;)

評論 3:

While Zapier is 100% a great automation tool, unless something changed there recently, not sure how it can be classified as an "AI agent"

評論 4:

Honestly, I'd likely to direct you to learning a little python instead and leaning into n8n.

It's hard to give you an answer on AI Agent tooling or platform without understanding what you're trying to build.

評論 5:

If you prioritize ease of use and simple automation tasks, Zapier might be the better choice. If you require more advanced scenario building and logic control, Make could be more suitable. Ultimately, the choice depends on your specific needs and technical background. If you're a beginner, Zapier might be a better starting point.

11. How to build a truly sustainable, profitable AI agent? Is it even possible?​

該文章的核心討論主題是：如何建立一個既盈利又具長期可持續性的AI代理業務。具體重點包括：

  1. 盈利挑戰：

    • 作者分享自身經驗，指出運行AI業務（如數據清理與整合）時，利潤被高昂的LLM代幣成本和其他線上服務費用壓縮的問題。
    • 提問其他人是否遇到類似的變現困難，或已找到解決方案。

  2. 成本優化策略：

    • 提及嘗試改用更經濟的模型（如DeepSeek R1/V3）來降低開支。
    • 探討如何平衡API成本與持續收入，以實現商業可行性。

  3. 商業模式與可持續性：

    • 徵求成功或失敗的案例經驗，關注哪些商業模型能穩定獲利。
    • 討論如何處理長期運營成本，並尋求未被充分探討的創新方法（如訂閱制、混合服務等）。

整體而言，文章聚焦於AI代理業務的經濟現實，強調在技術應用之外，如何設計可持續的商業模式以應對成本與收益的挑戰。

內容​

Since we're all concerned about making money, 's get straight to the point.

Hey AI enthusias! I've been diving deep into the world of AI agen lately and wondering if anyone has cracked the code on making them both profitable AND sustainable long-term.

I'll share my own experience: I run a data cleaning and aggregation business using AI, but the profi are surprisingly thin. The cos of LLM tokens and various online services eat up most of the revenue (I'm currently replacing some services with the more affordable DeepSeek R1 and DeepSeek V3 models).

Has anyone found ways around this problem? Are you building solutions that actually generate consistent income after accounting for API cos```? Or are you facing similar challenges with monetization?

Would love to hear about your experiences - successful or not! What business models work best? How are you handling ongoing operational cos```? Any creative approaches to sustainability that aren't being discussed enough in the AI community?

討論​

評論 1:

Have you thought about optimizing the way your AI agent handles tasks? For example, reducing the need for constant API calls or using more efficient models could help cut cos```. That way, youre not constantly burning through tokens.

評論 2:

you could charge per token as well. Add a margin that makes it safe for your business.

12. An AI app that accurately estimates a human's and an AI's IQ from their written content will enjoy wide consumer demand​

這篇文章的核心討論主題是：AI與人類在知識工作領域（如法律、金融、行銷等）的智能競爭，以及如何透過量化評估（如文件作者的IQ）來幫助消費者選擇更高效或更具成本效益的服務提供者（人類或AI）。

具體要點包括：

  1. AI與人類的智能比較：探討AI能否在專業領域（如法律服務）達到或超越人類的智能表現，並挑戰「兩者無法直接比較」的觀點。
  2. 量化評估的可行性：提出透過分析文件內容（如IQ相關指標）來評估作者（人類或AI）的智能，並將此作為消費者決策的依據。
  3. 商業潛力與應用場景：強調此類評估工具的廣泛應用性（跨產業知識工作）及先發市場的競爭優勢，尤其在「AI代理」崛起的背景下。

最終，文章聚焦於**「智能測量技術」如何重塑未來勞動市場的選擇邏輯**，並推動AI在專業服務中的普及。

內容​

Imagine a few years from now when AI lawyers are the norm. You're deciding whether to hire a human or an AI to do your legal work. You obviously want the smartest lawyer your money can buy. The AI lawyer will probably be much less expensive, but will it be as smart?

It doesn't seem at all complicated to train AIs to accurately estimate the IQ of a document's author, whether that document is generated by a human or an AI. Once a AI aces this task, the use cases for such an app extend far beyond legal services.

Financial advice, accounting, marketing, advertising, copywriting, engineering, biology research, and the list goes on and on and on.

Some may say that comparing AI intelligence to human intelligence is like comparing apples to oranges. That's nonsense. Although AIs and humans think through different processes, those processes aren't what IQ tes``` measure. They measure answers. They measure the content generated.

An AI that accurately correlates the intelligence expressed in a document with i``` author's IQ score in order to help consumers decide whether to hire a human or an AI to do knowledge work should become a very lucrative product. Given that this is the year of the AI agent, whoever brings this product to market first may gain a tremendous advantage over the competitors who are sure to follow.

討論​

評論 1:

I don't know how accurate this will be. Yes, in general there is a good correlation with verbal ability and intelligence, but there are definitely exceptions and verbal ability plateaus. This will end up being unreliable and needlessly discriminatory.

評論 2:

I don't believe that AI can possess the same type of intelligence measurement as humans, because they operate in fundamentally different ways. This is what Yann LeCun has said - the token-based approach is not how the human brain works, so the two cannot be compared. I think today represen just one phase in AI development, a phase with achievemen but also many limitations. In the future, there will likely be significant restructuring phases.

評論 3:

You should ask a lawyer rather than jumping to some wild speculations.

This has nothing to do with IQ. If that was the case would have given lawyers IQ tes``` before a case and decide on the outcome without wasting everyone's time.

Also, unless a 3rd-party arbitrator is involved that can regulate the cost (sort of) so that both parties cannot oupend each other, you are more likely than not to see a combination of AI and humans that provide better outcome for their customers with better outcomes. AI can be an arbitrator in this case. AI can also gather information but it wont be allowed to defend a case. It is a trust issue that will take time to build. Keep in mind that that the profession of legal representatives dates back 200 BCE. If you think you can undo this in a few years you should check a great book called Antifragile that goes deep into concep like the Lindy effect. The same applies to developers, artis``` and pretty much everyone else that may or may not be affected by this. Wild predictions like "100% of all code will be written by AI by same time next year" isn't true. Even if it can the cost of switching of any organisation is very high and it is not going to materialise without some external forces in play.

評論 4:

Already exist. Try Google NotebookLM. And its free. You can even get a podcast discussion and the conten too. Upload both written documen from the two sources you mentioned and ask for the evaluations of intellectual capacities of the two. The other question you may want to ask is who will be the judge, human or AI and how smart are they themselves? Lets know how you made out

評論 5:

92

13. AI agent use cases interacting with the physical world​

這篇文章的核心討論主題是：
「探索需要與物理世界互動的智能代理（agent）的應用場景」

具體內容包括：

  1. 應用方向：透過機器人或人類介面與物理世界互動的智能代理。
  2. 目的：徵求相關案例或經驗分享，以深入理解這類應用的實際需求與挑戰。
  3. 互動邀請：鼓勵有經驗者回應或私下交流，進一步討論已研究的領域或想法。

關鍵詞：智能代理（agent）、物理世界互動、機器人、人類協作、應用案例。

內容​

Hey all! Is anyone looking into use cases that require building agen``` that interface with the physical world in some manner? Be it through robotics or humans. If yes, please respond here or message me. I'm trying to understand these use cases better. I'd love to pick your brain on what you've looked into so far!

討論​

評論 1:

I have to quote for a project with a local farm, the farmer, who is very tech savvy, wan to connect various IoT sensors to AI Agen to assist in farm management.

Management of watering based on weather predictions.
Soil sampling with automated repor```

Chemical fertlisiation based on numerous factors, partially receiving real time data from the sensors.

Ive just started putting together the initial proposal.

14. Agen that solve captchas, and bot detection \{#14-agen-that-solve-captchas-and-bot-detection}​

這篇文章的核心討論主題是：尋找一個即插即用（plug-and-play）的解決方案，用於自動化從求職網站（如Indeed等）爬取職缺資訊（公司名稱、職位、連結、聯絡方式）並整理成表格，同時避免被驗證碼（CAPTCHA）和反爬蟲機制阻擋。

作者提到已嘗試過自行開發（需16-24小時）、瀏覽器工具、代理服務（proxy convergence）以及AI工具（Gemini、OpenAI、Grok等），但均因驗證碼或反爬蟲問題失敗，因此希望找到現成的工具或服務來達成目標。

關鍵需求：

  1. 自動化爬取：從求職網站提取結構化資料。
  2. 避開反爬蟲機制：解決驗證碼和網站封鎖問題。
  3. 即插即用：無需複雜開發，可直接使用的解決方案。

內容​

So I need some agen``` for my company

The only alterntive left is to build it my own, will be kind of easy, i'll invest something about 16-24 hrs doing so, but Im looking for something plug and play

So the agent must navigate to pages like indeed, and job boards and make me a table in spreadsee``` with company, vacancy, the link of the web page, and some contact info (could be, phone, mail or else)

Already tried:

- browser use

- proxy convergence

- deepresearch for gemini, oai, grok etc

none of them worked and get stuck in captchas and bot detectors

Any suggestions for plug and play solutions?

討論​

評論 1:

"16 -24 hours". you're looking at a lot more than that. for just the basic features you are looking for.

評論 2:

Try OpenAI CUA - it does better with spreadshee``` than all of the others. If you want proxy rotation, CAPTCHA solving etc youll want to use one of the browser infra providers as well (OpenAI doesnt do that for you)

Im biased (Im the founder of Hb) but I think Hyperbrowsers agen``` endpoint[1] is the best solution here if youre looking for a plug and play solution. It handles all the proxy captcha stuff etc in a single API call.

[1] https://docs.hyperbrowser.ai/agen```/openai-cua

15. Vectara Ltd Crypto AI AgentLegitimate or Scam? Seeking Experiences!​

這篇文章的核心討論主題是：
「詢問Vectara Ltd公司及其『Crypto AI Agent』職位的真實性與工作經驗分享」

具體要點包括：

  1. 求證公司可信度：發文者懷疑Vectara Ltd招募「Crypto AI Agent」的真實性，希望確認是否為合法機會。
  2. 徵求實際經驗：詢問是否有過來人分享擔任該職位的經歷（例如是否收到高額訂單包），以評估風險與可信度。
  3. 決策參考：透過他人回饋（正面或負面）決定是否接受此工作機會。

整體聚焦於「對陌生加密貨幣相關職位的合法性與實務經驗的社群調查」。

內容​

Has anyone here had experience with the company Vectara Ltd, specifically working as a Crypto AI Agent? I recently came across their recruitment for crypto-related tasks, and I'm trying to figure out if they're legitimate. Has anyone worked as an agent for them before? Did you receive large, expensive package deal orders? I'm keen to hear about your experiencesgood or badas I'm considering whether this opportunity is trustworthy. Thanks in advance for any insigh```!

討論​

評論 1:

Most AI agent companies are kind of a scam...

A crypto AI agent company? Come on man get real there is no reason to combine the two

16. Chatgpt vs other models​

這篇文章的核心討論主題是：比較並投票選出最優秀的AI模型。

具體而言，該內容透過Reddit的投票功能，邀請網友參與討論與評比不同AI模型之間的優劣，可能是基於性能、應用場景或使用者偏好等標準。重點在於「競爭性比較」與「社群意見收集」，而非深入分析單一模型的技術細節。

（註：由於原連結格式有誤且無法直接查看內容，此總結基於標題與常見的Reddit投票貼文模式推測。）

內容​

Pls vote for the war between which is the greatest ai model

View Poll

討論​

評論 1:

it depends on your use case

- Claude 3.7 sonnet is the best for coding.
- o1 is the best for reasoning.
- R1/ o3 is the best for math.
- Qwen is the best for multimodal stuff
- GPT-4.5/ 3.5 sonnet is the best for writing.
- OpenAI deep research is the best for deep research.
- GPT 4o / Flux / imagen is the best for image generation
- Hailuo / Kling is the best for video gen
- ElevenLabs is the best for audio

評論 2:

Its important to not just be determining this based on vibes but instead choosing actual performance.

Check out this agent leaderboard I found that actually ranks them on performance

https://huggingface.co/spaces/galileo-ai/agent-leaderboard

17. How to build an AI Agent for shopping on various sites?​

核心討論主題：​

這篇文章的核心討論主題是：如何開發或尋找一個AI代理（AI agent），以自動化並簡化家長為成長中的孩子頻繁購買衣物的流程。具體需求包括：

  1. 輸入簡化：允許用戶輸入基本條件（如季節、尺寸、預算、偏好品牌等）。
  2. 跨平台搜尋：自動在多個電商網站上搜索符合條件的商品。
  3. 清單整合：將搜尋結果彙整成一個統一的願望清單或購物車。
  4. 一站式結帳：提供單一介面確認並完成購買（儘管技術上可能具挑戰性）。
  5. 個人化與探索：優先從用戶喜愛的商店或品牌中挑選商品，同時推薦相似的新選項。

此外，作者希望避開Google Shopping或Amazon等現有平台，尋求更客製化的解決方案，並詢問是否有類似需求的社群或技術建議。
整體目標是節省時間、集中資訊來源，並提升購物體驗的個人化程度。

內容​

Hi everyone,

How can I build an AI agent for paren``` like me who need to frequently buy new clothes for growing kids. Right now, I spend a lot of time browsing multiple sites and placing orders. Ideally Id like to automate this process for myself and get everything in a single view.

Id love to build (or find) an AI agent that can:

  1. Take a simple input like spring outfi``` for kids, size X & Y, budget X, brands we like

  2. Search across multiple e-commerce sites

  3. Curate a single wish list/cart with the best options

  4. Let me confirm and checkout in one place (I can imagine its difficult, but would be awesome to have).

Im not a fan of google shopping and Amazon. I want to curate a list from shops/brands I like and perhaps get suggestions from other sites I wasnt aware of, but are similar.

What would be the best approach to build this AI agent? Does anyone have a similar problem like me?

討論​

評論 1:

If you can achieve it, I am sure you'll generate some decent income.

評論 2:

We can work together to build it since we are both paren``` and in the same situation

18. Basic AI agent?​

這篇文章的核心討論主題是：

如何利用低代碼/無代碼（low-code/no-code）工具或服務，創建一個能夠監控網站上的即時變化（例如價格或狀態更新）並自動觸發相應操作（如點擊按鈕）的代理（agent）或機器人（bot）。

具體要點包括：

  1. 功能需求：監測網站動態變化（如文字從空白/N/A變為「open」或「$1.00」）。
  2. 自動化操作：變化發生時立即執行預設動作（如點擊按鈕）。
  3. 技術門檻：優先選擇低代碼或無代碼解決方案，降低開發難度。
  4. 尋求建議：詢問適合的服務或工具推薦。

可能的相關工具方向：網頁監控工具（如Zapier、Integromat）、RPA（如UI.Vision、Microsoft Power Automate），或爬蟲結合自動化平臺（如Browserless+No-code自動化）。

內容​

Hi all, enjoying the community here.

I want an agent or bot that can review what's happening on a live website and follow actions. For example, a listing star``` as blank or N/A, and then might change to "open" or "$1.00" or similar. When that happens, I want a set of buttons to be pressed asap.

What service etc would you use? Low-code/no-code best.

Thanks!!

討論​

評論 1:

Hi! For monitoring live website changes and triggering actions, low-code options like Make.com or Zapier's Webhooks + Automations could work. For more AI-centric solutions with newer frameworks, check out platforms like LangChain (with prebuilt tools) or CrewAI (agent orchestration).

Since requiremen``` vary, you might want to specify: 1) Website platform (Shopify? Custom?), 2) Required response speed (seconds vs minutes), and 3) Complexity of decision logic needed.

This is a common question - [search r/AI_Agen for similar discussions](`https`://www.reddit.com/r/AI_Agen/search/?q=monitor%20website%20low%20code&restrict_sr=1).

(I am a bot) Source

評論 2:

This approach allows you to leverage no-code tools to monitor and interact with websites dynamically:

  1. Choose Axiom.ai for monitoring website changes and automating actions directly on the website.
  2. Use Zapier to integrate with other services if you need to trigger actions across multiple platforms based on the data collected by Axiom.ai.
  3. Set Up Triggers: Configure Axiom.ai to monitor the specific listing changes on the website.
  4. Automate Actions: Once a change is detected, use Axiom.ai to automate the button presses or other actions required.

評論 3:

You can try monity.ai. It is designed to monitor website changes bit you can perform actions with AI promp```

19. Easiest way to set up a chatbot for WhaApp responses? \{#19-easiest-way-to-set-up-a-chatbot-for-whaapp-r}​

这篇文章的核心討論主題是：
「如何以最簡便的方式建立一個能自動回覆 WhatsApp 訊息的聊天機器人，並整合 AI 技術（如 ChatGPT API）」。

具體重點包括：

  1. 低門檻設定：希望解決方案簡單易用，盡量不需複雜編碼。
  2. 功能需求：
    • 輕鬆與 WhatsApp 整合。
    • 能透過 AI API（如 ChatGPT）處理對話。
  3. 系統要求：需穩定、可擴展的解決方案。
  4. 尋求建議：徵求推薦的工具、平台及實現流程。

總結：作者尋求一個兼顧易用性、AI 整合能力，且適合 WhatsApp 的自動回覆聊天機器人方案。

內容​

Im looking for the simplest way to set up a chatbot that can automatically respond to Wha```App messages.

Ideally, Id like something that doesnt require a lot of coding, but Im open to different solutions.

A few key things Im looking for:

  • Easy setup and integration with Wha```App

  • Ability to handle conversations using ChatGPT API or similar AI-based APIs

  • Reliable and scalable solution

Would love to hear what tools/platforms and workflow you recommend!

Thanks in advance.

討論​

評論 1:

For WhaApp chatbot setups, common solutions include Twilio's WhaApp API (requires approval) or platforms like ManyChat/Chatfuel. Many developers use Python frameworks (e.g., Flask/Django) with the OpenAI API and a Wha```App Business account.

Since this is a frequently asked question, please search the subreddit for previous discussions: [WhaApp chatbot solutions](`https`://www.reddit.com/r/AI_Agen/search/?q=wha```app&restrict_sr=1).

(I am a bot) Source

評論 2:

Check out chatbotkit.com if you want to save time cuz Wha```App is pain in the a if you ask me.

20. Are there enough APIs?​

這篇文章的核心討論主題是：「探討為缺乏API的網路資源自動生成API的解決方案是否具有實際需求」。

具體要點包括：

  1. 問題背景：AI與自動化工具興起後，結構化數據（如API）的需求增加，但許多服務未提供開放API，造成開發瓶頸。
  2. 提案解決方案：開發一個能從網址（URL）自動生成API的工具，降低取得數據的門檻。
  3. 驗證需求：作者不確定此問題是否普遍存在（例如主流網站已提供完善API），因此徵求社群回饋，釐清痛點。
  4. 關鍵提問：
    • 當前如何處理「無API服務」的數據存取？
    • 開發AI或自動化工具時，連接數據源的最大挑戰為何？

總結：作者聚焦於「自動化API生成」的潛在價值，並試圖確認此需求是否真實存在，以避免解決「偽問題」。

內容​

Hey everyone,

I've been noticing a pattern lately with the rise of AI agen``` and automation tools - there's an increasing need for structured data access via APIs. But not every service or data source has an accessible API, which creates bottlenecks.

I am thinking of a solution that would automatically generate APIs from links/URLs, essentially letting you turn almost any web resource into an accessible API endpoint with minimal effort. Before we dive deeper into development, I wanted to check if this is actually solving a real problem for people here or if it is just some pseudo-problem because most popular websites have decent APIs.

I'd love to hear:

  • How are you currently handling situations where you need API access to a service that doesn't offer one?

  • For those working with AI agen``` or automation: what's your biggest pain point when it comes to connecting your tools to various data sources?

I'm not trying to sell anything here - genuinely trying to understand if we're solving a real problem or chasing a non-issue. Any insigh``` or experiences you could share would be incredibly helpful!

Thanks in advance for your though```.

討論​

評論 1:

There's some cases where the UI will be the API as well. The AI may need to navigate an app to find the data. going to get pretty interesting.

總體討論重點

以下是20篇文章的重点总结，以条列方式输出，并附上逐条细节和对应锚点链接：

1. The Most Powerful Way to Build AI Agents: LangGraph + Pydantic AI​

重点：

  • 技术组合优势：Pydantic AI用于模块化代理设计，LangGraph协调多代理工作流。
  • 实例应用：AI Listing Manager Agent由7个专用代理组成，整合Streamlit界面。
  • 设计原则：类型匹配、可观测性（信心分数）、人类监督机制。

2. 10 Mental Frameworks to Find Your Next AI Agent Startup Idea​

重点：

  • 方法论：从用户行为（如重复操作）、付费解决方案、高频需求中挖掘机会。
  • 验证标准：观察手动操作缺口、现有服务付费意愿、知识瓶颈领域。

3. Example of a Simple Prompt Injection Attack​

重点：

  • 安全漏洞：通过恶意指令操控AI（如LinkedIn简介攻击）。
  • 风险警示：若连接企业系统（如CRM），可能导致数据泄露。

4. MCP and Orchestration Frameworks​

重点：

  • MCP角色：标准化LLM与工具通信协议。
  • 协作架构：负责代理决策逻辑（如何时调用工具）。

5. Assista AI: Tool Integration Platform​

重点：

  • 产品功能：整合Gmail/Slack/Notion等工具，简化流程。
  • 用户调研：创始人主动征集真实痛点以优化产品。

6. Spreadsheets and AI Agent​

重点：

  • 自动化需求：用n8n的AI代理处理非结构化Google Sheets数据。
  • 挑战：动态字段识别与准确分类。

7. Defining Agentic AI​

重点：

  • 术语混乱：需厘清Agentic AI、Multi-Agent Systems等定义。
  • 求助权威：寻求学术或行业标准定义。

8. AI Agent Marketplaces​

重点：

  • 商业生态：探讨AI代理交易平台（如市集、评级系统）。
  • 案例征集：邀请分享现有代理商业化实例。

9. LLM Model Impact on AI Agent Efficacy​

重点：

  • 模型差异：Claude Sonnet与GPT-4o在代码生成/调试表现对比。
  • 核心结论：工具效能更依赖模型特性而非提示工程。

10. Zapier vs Make for AI Agents​

重点：

  • 工具选择：针对非技术用户的无代码方案（如文本摘要、爬虫）。
  • 标准：易用性＞技术门槛。

11. Sustainable AI Agent Business Models​

重点：

  • 盈利挑战：LLM代币成本压缩利润，需平衡API开支。
  • 优化策略：改用经济模型（如DeepSeek）、订阅制。

12. IQ Estimation AI for Human vs AI Content​

重点：

  • 智能量化：通过文本分析评估作者（人类/AI）IQ。
  • 商业潜力：专业服务（法律/金融）中的决策辅助工具。

13. AI Agents in Physical World​

重点：

  • 应用方向：机器人或人类介面的物理交互代理。
  • 案例征集：邀请分享实际场景经验。

14. [Captcha-Solving Agents​