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2025-04-04-rising

  • 精選方式: RISING

討論重點​

以下是25篇文章的條列式重點總結，並附上對應的錨點連結與逐條細節說明：

#1 I Built an AI Agent to find and apply to jobs automatically​

  1. 工具目的
    • 自動化求職流程，精準匹配職位與技能，減少無效申請。
  2. 獨特優勢
    • 提供遠程工作機會、職位匹配分數（Job Match Score）預測面試機率。
  3. 使用模式
    • 三種模式：AI篩選手動申請、AI代申請、全自動申請（匹配度>60%）。
  4. 免費與簡便性
    • 僅需上傳履歷，完全免費。

#2 The dev that lost $5,800 building an agent for a client made us completely rethink AI agent freelancing​

  1. 問題背景
    • 開發者因客戶失信或模糊需求蒙受損失。
  2. 解決方案
    • 平台內建合約、分階段付款、客戶驗證、明確定義專案範圍。
  3. 社群共創
    • 呼籲開發者分享經驗改善生態。

#3 Aren't you guys concerned about AI privacy?​

  1. 隱私風險
    • AI處理敏感數據時缺乏透明度，可能被用於模型訓練。
  2. 現有問題
    • 大型科技公司主導，開源選項稀缺，隱私保護方案不足。

#4 I built an open-source Operator that can use computers​

  1. 工具定位
    • 開源桌面應用自動化工具（Spongecake），支援虛擬桌面（Xfce+VNC）。
  2. 技術挑戰
    • 解決多代理端口衝突、長頁面滾動效能問題。
  3. 未來計畫
    • 支援Windows/macOS，整合Anthropic等模型。

#5 I built an MVP that helps you set automated phone calls reminders (My dad has alzheimer)​

  1. 產品功能
    • 透過電話設定服藥提醒等自動化服務。
  2. 商業化考量
    • 評估轉型為SaaS/AaaS的可行性，詢問市場需求。

#6 We built a toolkit that connects your AI to any app in 3 lines of code​

  1. 核心功能
    • 快速串接AI與第三方應用（如Salesforce、Slack）。
  2. 技術亮點
    • 預建API動作、自動化驗證（OAuth/API Key）。

#7 10 Agent Papers You Should Read from March 2025​

  1. 研究重點
    • 智能體規劃（PLAN-AND-ACT）、多智能體協作、安全基準（SAFEARENA）。
  2. 應用場景
    • 3D遊戲、經濟決策、網頁操作。

#8 How to make the AI agent understand which question talks about code, which one talks about database, and which one talks about uploading file?​

  1. 需求分析
    • 處理Excel上傳、數據庫查詢、程式碼問答。
  2. 改進方向
    • 從臨時方案轉向結構化AI Agent架構。

#9 Give Postgres access to an AI Agent directly (good idea?)​

  1. 安全設計
    • 限制AI Agent數據庫權限（預配置SQL查詢）。
  2. 討論焦點
    • 動態查詢生成 vs. 權限分級。

#10 Human in the loop​

  1. 核心觀點
    • 關鍵任務需人類監督（如自動駕駛），即使AI準確率達99%。
  2. 未來挑戰
    • 平衡代理化工作流程與人類介入。

（因篇幅限制，以下簡化條列標題，完整細節請參照原文錨點）

#11-25 快速摘要：​

  • #11 非結構化數據轉GIS結構化工具評估（n8n/LangGraph）。
  • #12 AI依賴導致認知退化的反思。
  • #13 數據分析AI工具差異化策略（新手vs.專業需求）。
  • **#

文章核心重點​

以下是每篇文章的一句話摘要（條列式輸出）：

  1. I Built an AI Agent to find and apply to jobs automatically
    介紹AI工具SimpleApply如何自動匹配職位並優化求職流程，提供三種申請模式且完全免費。

  2. The dev that lost $5,800 building an agent for a client made us completely rethink AI agent freelancing
    探討AI代理開發外包中如何透過合約與分階段付款保護開發者權益，避免客戶失信風險。

  3. Aren't you guys concerned about AI privacy?
    質疑AI服務的隱私安全性，探討數據被儲存或濫用的風險與現有解決方案的不足。

  4. I built an open-source Operator that can use computers
    開源工具Spongecake讓開發者創建桌面應用自動化代理，支援虛擬機互動與多環境部署。

  5. I built an MVP that helps you set automated phone calls reminders (My dad has alzheimer)
    個人開發的電話提醒MVP探討商業化潛力，從解決父親服藥需求延伸至SaaS服務可能性。

  6. We built a toolkit that connects your AI to any app in 3 lines of code
    MatonAgentToolkit簡化AI與第三方應用串接，提供預建API與自動驗證功能。

  7. 10 Agent Papers You Should Read from March 2025
    精選10篇AI代理前沿研究，涵蓋多智能體協作、安全測試與場景應用創新。

  8. How to make the AI agent understand which question talks about code, which one talks about database, and which one talks about uploading file ?
    探討如何改進LangChain應用，區分用戶問題類型並整合數據庫與文件上傳功能。

  9. Give Postgres access to an AI Agent directly (good idea?)
    討論AI代理平台中PostgreSQL的安全整合方案，建議限制查詢權限而非開放全庫存取。

  10. Human in the loop
    強調關鍵任務中人類監督的必要性，即使AI準確率達99%仍須避免自動化失誤風險。

  11. Tools recommendations for unstructured to structured database.
    尋求工具建議以自動化非結構化數據（如郵件、會議記錄）轉GIS系統結構化格式。

  12. Are AI Agents Making Us Too Lazy or Just More Efficient?
    反思AI依賴是否削弱人類基礎能力，呼籲效率與自主思考的平衡。

  13. What's Your Expectation for an AI Agent That Can Help You with Data Analysis?
    探討如何打造差異化數據分析AI代理，兼顧新手與專業用戶需求。

  14. Whats One AI Agent Use Case No Ones Talking About (But Should Be)?
    批判AI代理過度集中基礎功能，呼籲解決未被重視的實際痛點（如混亂文件整理）。

  15. Understanding Customer Requirements for Agent Services: A Thought Experiment Questionnaire
    設計問卷釐清企業對自主代理的需求動機、預期功能與成功指標。

  16. Understanding and Preventing Prompt Injection
    分析提示注入攻擊的普遍性與高風險，呼籲開發時強化安全設計。

  17. Need Help Designing a Multi-Agent System for Invoice Validation. Best Framework for Multi-Agent Collaboration to Validate Invoices?
    設計多智能體發票驗證系統，分工檢查缺失數據、規則補全與交叉驗證。

  18. How are people handling scrolling issues with computer use models?
    反映AI模型滾動頁面不精準的問題，尋求解決方案以提升操作效率。

  19. AI mind reading
    表達對AI技術可能解讀思想的焦慮，擔憂心理隱私徹底喪失。

  20. 10 mental frameworks to find your next AI Agent startup idea
    提供10種方法識別用戶真實痛點（如觀察低效手動流程），開發有市場價值的AI代理。

  21. Creating an AI Agent for Social Media Marketing
    提出AI社交媒體管理系統，以85%成本降幅解決中小企業營運痛點。

  22. Question: central AI agent to talking to AIs of other platforms?
    探討未來AI助理互動模式：代理對代理（直接溝通）或代理對介面（模擬操作）。

  23. Our Full-Stack Movie Creation Agent is in Public Beta
    全端AI電影生成工具開放公測，支援文字轉影片與多模組整合。

  24. Weekly Thread: Project Display
    社群每週展示AI代理專案並投票，優勝者獲電子報推薦曝光。

  25. **The Most

目錄​

  • [1. I Built an AI Agent to find and apply to jobs automatically](#1-``` i-built-an-ai-agent-to-find-and-apply-to-job)
  • [2. The dev that lost $5,800 building an agent for a client made us completely rethink AI agent freelancing](#2-``` the-dev-that-lost-5-800-building-an-agent-fo)
  • [3. Aren't you guys concerned about AI privacy?](#3-``` aren-t-you-guys-concerned-about-ai-privacy- )
  • [4. I built an open-source Operator that can use computers](#4-``` i-built-an-open-source-operator-that-can-use)
  • [5. I built an MVP that helps you set automated phone calls reminders (My dad has alzheimer)](#5-``` i-built-an-mvp-that-helps-you-set-automated-)
  • [6. We built a toolkit that connec your AI to any app in 3 lines of code ](#6- we-built-a-toolkit-that-connec```-your-ai-to)
  • [7. 10 Agent Papers You Should Read from March 2025](#7-``` 10-agent-papers-you-should-read-from-march-2)
  • [8. How to make the AI agent understand which question talks about code, which one talks about database, and which one talks about uploading file ?](#8-``` how-to-make-the-ai-agent-understand-which-qu)
  • [9. Give Postgres access to an AI Agent directly (good idea?)](#9-``` give-postgres-access-to-an-ai-agent-directly)
  • [10. Human in the loop](#10-``` human-in-the-loop
- [11. ```
Tools recommendations for unstructured to structured database.
```](#11-```
tools-recommendations-for-unstructured-to-s)
- [12. ```
Are AI Agen``` Making Us Too Lazy or Just More Efficient?
```](#12-```
are-ai-agen```-making-us-too-lazy-or-just-m)
- [13. ```
What's Your Expectation for an AI Agent That Can Help You with Data Analysis?
```](#13-```
what-s-your-expectation-for-an-ai-agent-tha)
- [14. ```
Whats One AI Agent Use Case No Ones Talking About (But Should Be)?
```](#14-```
whats-one-ai-agent-use-case-no-ones-talking)
- [15. ```
Understanding Customer Requiremen``` for Agent Services: A Thought Experiment Questionnaire
```](#15-```
understanding-customer-requiremen```-for-ag)
- [16. ```
Understanding and Preventing Prompt Injection
```](#16-```
understanding-and-preventing-prompt-injecti)
- [17. ```
Need Help Designing a Multi-Agent System for Invoice Validation. Best Framework for Multi-Agent Collaboration to Validate Invoices?
```](#17-```
need-help-designing-a-multi-agent-system-fo)
- [18. ```
How are people handling scrolling issues with computer use models?
```](#18-```
how-are-people-handling-scrolling-issues-wi)
- [19. ```
AI mind reading
```](#19-```
ai-mind-reading
```)
- [20. ```
10 mental frameworks to find your next AI Agent startup idea
```](#20-```
10-mental-frameworks-to-find-your-next-ai-a)
- [21. ```
Creating an AI Agent for Social Media Marketing
```](#21-```
creating-an-ai-agent-for-social-media-marke)
- [22. ```
Question: central AI agent to talking to AIs of other platforms?
```](#22-```
question-central-ai-agent-to-talking-to-ais)
- [23. ```
Our Full-Stack Movie Creation Agent is in Public Beta
```](#23-```
our-full-stack-movie-creation-agent-is-in-p)
- [24. ```
Weekly Thread: Project Display
```](#24-```
weekly-thread-project-display
```)
- [25. ```
The Most Powerful Way to Build AI Agen```: LangGraph + Pydantic AI (Detailed Example)
```](#25-```
the-most-powerful-way-to-build-ai-agen```-l)

---

## 1. ```
I Built an AI Agent to find and apply to jobs automatically
``` {#1-```
i-built-an-ai-agent-to-find-and-apply-to-job}

這篇文章的核心討論主題是介紹一個名為 **SimpleApply** 的 AI 工具，其主要功能是幫助求職者更高效地尋找和申請適合的工作，並在雇主與求職者之間創造更公平的競爭環境。以下是重點總結：

1. **工具目的**：  
   - 減少求職者填寫申請表的時間，並根據技能和經驗匹配適合的職位。  
   - 避免向雇主濫發申請，而是精準鎖定高匹配度的職位。

2. **獨特優勢**：  
   - 提供其他工具難以找到的遠程工作機會。  
   - 新增「職位匹配分數」（Job Match Score），預測面試機率，優化申請策略。

3. **使用方式**（三種模式）：  
   - 僅由 AI 篩選職位並評分，用戶手動申請。  
   - AI 篩選後，用戶選擇職位由 AI 代為申請。  
   - 全自動申請匹配度超過 60% 的職位。

4. **免費與簡便性**：  
   - 用戶只需上傳履歷，AI 自動處理後續流程。  
   - 目前完全免費。

**核心主題**：透過 AI 工具 **SimpleApply** 提升求職效率，平衡就業市場中的資訊與機會不平等。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jqloxe/i_built_an_ai_agent_to_find_and_apply_to_jobs/](https://reddit.com/r/AI_Agents/comments/1jqloxe/i_built_an_ai_agent_to_find_and_apply_to_jobs/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jqloxe/i_built_an_ai_agent_to_find_and_apply_to_jobs/](https://www.reddit.com/r/AI_Agents/comments/1jqloxe/i_built_an_ai_agent_to_find_and_apply_to_jobs/)
- **發布時間**: 2025-04-03 23:38:32

### 內容

It started as a tool to help me find jobs and cut down on the countless hours each week I spent filling out applications. Pretty quickly friends and coworkers were asking if they could use it as well so I got some help and made it available to more people.

The goal is to level the playing field between employers and applican. The tool doesnt flood employers with applications (that would cost too much money anyway) instead the agent targe roles that match skills and experience that people already have.

Theres a couple other tools that can do auto apply through a chrome extension with varying resul```. However, users are also noticing were able to find a ton of remote jobs for them that they cant find anywhere else. So you dont even need to use auto apply (people have varying opinions about it) to find jobs you want to apply to. As an additional bonus we also added a job match score, optimizing for the likelihood a user will get an interview.

Theres 3 ways to use it:

  1. Have the AI Agent just find and apply a score to the jobs then you can manually apply for each job

  2. Same as above but you can task the AI agent to apply to jobs you select

  3. Full blown auto apply for jobs that are over 60% match (based on how likely you are to get an interview)

Its as simple as uploading your resume and our AI agent does the rest. Plus its free to use, its called SimpleApply


---

## 2. ```
The dev that lost $5,800 building an agent for a client made us completely rethink AI agent freelancing
``` {#2-```
the-dev-that-lost-5-800-building-an-agent-fo}

這篇文章的核心討論主題是：**在AI代理（AI agent）開發的自由職業或外包合作中，如何透過平台設計與制度改進來保護開發者（賣方）的權益，避免因缺乏合約、客戶失信或模糊需求而遭受損失**。  

具體重點包括：  
1. **問題背景**：開發者因未簽合約或客戶消失而蒙受金錢損失的普遍現象。  
2. **解決方向**：  
   - 平台內建強制合約機制。  
   - 分階段付款（Milestone-based payments）。  
   - 客戶身分驗證以降低風險。  
   - 明確定義專案範圍，減少爭議。  
3. **社群共創**：呼籲開發者與客戶分享經驗，共同改善合作生態系統。  

整體目標是透過技術與制度設計，在AI新興領域中建立更公平、安全的交易環境。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jqh69q/the_dev_that_lost_5800_building_an_agent_for_a/](https://reddit.com/r/AI_Agents/comments/1jqh69q/the_dev_that_lost_5800_building_an_agent_for_a/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jqh69q/the_dev_that_lost_5800_building_an_agent_for_a/](https://www.reddit.com/r/AI_Agents/comments/1jqh69q/the_dev_that_lost_5800_building_an_agent_for_a/)
- **發布時間**: 2025-04-03 20:31:04

### 內容

A few weeks ago I saw the post from u/crazychampion2 about losing $5,800 after building an AI agent for a client who vanished. No contract, no payment, no accountability.

Annoyingly, this isn't a rare story. All of us freelancers have experienced this or know someone who has.

As with all big new tech trends, lo``` of young and excited new builders enter the space wide eye'd and bushy tailed, only to make small mistakes and get f*ckd for them.

We were already working on our ai agent job board. But the thread has shifted our focus & made us double down on ensuring the sellers on the other side are protected too.

We're now thinking about things like:

  • Contrac``` baked into the platform by default

  • Milestone-based payment releases

  • Client verification, so you know who you're working with

  • Clear scope definitions to avoid vague expectations and finger-pointing

It's crazy how much a single post in this sub has changed our roadmap... hoping more builders share their stories too. Because the more we surface the messy stuff, the better we can design for the people actually doing the work.

If any of you have been burned in the past LMK what wouldve helped you avoid it? What protections would you want if you could design the system from scratch?

Would love to hear the though``` of devs and agent-buyers alike.


---

## 3. ```
Aren't you guys concerned about AI privacy?
``` {#3-```
aren-t-you-guys-concerned-about-ai-privacy-
}

這篇文章的核心討論主題是：**使用者對AI聊天機器人隱私問題的擔憂**，尤其是以下兩點：

1. **數據隱私風險**：  
   使用者普遍利用AI處理敏感事務（如財務、法律、心理健康等），但擔憂個人數據可能被儲存、分析或用於訓練模型，缺乏透明度。

2. **對隱私保護方案的質疑**：  
   現有AI服務多由大型科技公司營運且不開源，難以審查數據處理流程。作者探討是否存在真正「不記錄數據」的隱私導向AI選項，或此需求是否難以實現。

簡言之，文章聚焦於「AI便利性與隱私保護之間的矛盾」，並質疑現有技術框架下能否實現真正的數據自主權。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jqcjsx/arent_you_guys_concerned_about_ai_privacy/](https://reddit.com/r/AI_Agents/comments/1jqcjsx/arent_you_guys_concerned_about_ai_privacy/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jqcjsx/arent_you_guys_concerned_about_ai_privacy/](https://www.reddit.com/r/AI_Agents/comments/1jqcjsx/arent_you_guys_concerned_about_ai_privacy/)
- **發布時間**: 2025-04-03 15:54:41

### 內容

I see people using AI chatbo``` for personal finance, legal advice, even mental health support, basically feeding it everything about their lives. I'd love to do the same, but how do you know that data isnt stored, analyzed, or even used to train future models?

Most AI services are closed source and run on Big Techs infrastructure, meaning theres no way to audit whats really happening behind the scenes. Are there privacy focused AI options that dont log everything, or is true AI privacy just a pipe dream?


---

## 4. ```
I built an open-source Operator that can use computers
``` {#4-```
i-built-an-open-source-operator-that-can-use}

这篇文章的核心討論主題是：  

**Terrell 開發了一個名為「Spongecake」的開源應用，旨在幫助開發者輕鬆創建自己的「Operator」（自動化操作代理），並透過 Next.js/React 前端與 Flask 後端架構，簡化虛擬桌面（Xfce + VNC）的部署與桌面應用的自動化互動。**  

具體重點包括：  
1. **解決現有工具的不足**：現有工具多數僅限於瀏覽器自動化、非開源或成本高昂，而 Spongecake 專注於桌面應用自動化，並完全開源。  
2. **目標用戶**：  
   - 想打造「電腦操作代理」的開發者。  
   - 需自動化缺乏 API 的桌面應用（如供應鏈、醫療行業）。  
   - 需在企業內部環境（如 VPN、防火牆限制下）自動化流程的開發者。  
3. **技術實現**：  
   - 透過 Docker 容器管理虛擬桌面（含 VNC、API 伺服器、網頁爬蟲工具 Marionette 等）。  
   - 透過截圖與 API 指令實現代理與虛擬機的互動。  
4. **技術挑戰**：  
   - 並發處理多代理時的端口衝突問題。  
   - 解決 AI 模型在長頁面滾動時的效能問題（改用 DOM 提取優化）。  
5. **未來計畫**：  
   - 支援更多桌面環境（如 Windows、macOS）。  
   - 整合 Anthropic 等電腦操作模型。  

總結：文章主要介紹 Spongecake 的開源價值、技術架構與應用場景，並尋求社群反饋以進一步發展。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jqp5cc/i_built_an_opensource_operator_that_can_use/](https://reddit.com/r/AI_Agents/comments/1jqp5cc/i_built_an_opensource_operator_that_can_use/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jqp5cc/i_built_an_opensource_operator_that_can_use/](https://www.reddit.com/r/AI_Agents/comments/1jqp5cc/i_built_an_opensource_operator_that_can_use/)
- **發布時間**: 2025-04-04 01:50:56

### 內容

Hi reddit, I'm Terrell, and I built an open-source app that le``` developers create their own Operator with a Next.js/React front-end and a flask back-end. The purpose is to simplify spinning up virtual desktops (Xfce, VNC) and automate desktop-based interactions using computer use models like OpenAIs

There are already various cool tools out there that allow you to build your own operator-like experience but they usually only automate web browser actions, or arent open sourced/cost a lot to get started. Spongecake allows you to automate desktop-based interactions, and is fully open sourced which will help:

  • Developers who want to build their own computer use / operator experience

  • Developers who want to automate workflows in desktop applications with poor / no APIs (super common in industries like supply chain and healthcare)

  • Developers who want to automate workflows for enterprises with on-prem environmen with constrain like VPNs, firewalls, etc (common in healthcare, finance)

Technical details: This is technically a web browser pointed at a backend server that 1) manages starting and running pre-configured docker containers, and 2) manages all communication with the computer use agent. [1] is handled by spinning up docker containers with appropriate por to open up a VNC viewer (so you can view the desktop), an API server (to execute agent commands on the container), a marionette port (to help with scraping web pages), and socat (to help with port forwarding). \[2\] is handled by sending screensho from the VM to the computer use agent, and then sending the appropriate actions (e.g., scroll, click) from the agent to the VM using the API server.

Some interesting technical challenges I ran into:

  • Concurrency - I wanted it to be possible to spin up N agen at once to complete tasks in parallel (especially given how slow computer use agen are today). This introduced a ton of complexity with managing por``` since the likelihood went up significantly that a port would be taken.

  • Scrolling issues - The model is really bad at knowing when to scroll, and will scroll a ton on very long pages. To address this, I spun up a Marionette server, and exposed a tool to the agent which will extract a websites DOM. This way, instead of scrolling all the way to a bottom of a page - the agent can extract the websites DOM and use that information to find the correct answer

Whats next? I want to add support to spin up other desktop environmen``` like Windows and MacOS. Weve also started working on integrating Anthropics computer use model as well. Theres a ton of other features I can build but wanted to put this out there first and see what others would want

Would really appreciate your though```, and feedback. It's been a blast working on this so far and hope others think its as neat as I do :)


---

## 5. ```
I built an MVP that helps you set automated phone calls reminders (My dad has alzheimer)
``` {#5-```
i-built-an-mvp-that-helps-you-set-automated-}

這篇文章的核心討論主題是：  

**評估一個簡單的SaaS（提醒服務）的商業潛力，並探討是否值得購買域名並將其發展為正式服務（SaaS/AaaS）。**  

具體要點包括：  
1. **產品功能**：透過電話號碼、人名和目的設定提醒（例如：提醒服藥）。  
2. **個人動機**：為解決父親按時服藥的需求而開發。  
3. **商業化考量**：  
   - 是否值得投資域名？  
   - 是否適合擴展為訂閱制服務（SaaS）或代理自動化服務（AaaS）？  
4. **市場驗證**：詢問外界對該點子可行性的意見。  

整體聚焦於「從個人工具轉型為商業產品」的決策分析。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jqqkr3/i_built_an_mvp_that_helps_you_set_automated_phone/](https://reddit.com/r/AI_Agents/comments/1jqqkr3/i_built_an_mvp_that_helps_you_set_automated_phone/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jqqkr3/i_built_an_mvp_that_helps_you_set_automated_phone/](https://www.reddit.com/r/AI_Agents/comments/1jqqkr3/i_built_an_mvp_that_helps_you_set_automated_phone/)
- **發布時間**: 2025-04-04 02:44:52

### 內容

i created a SaaS that you set reminders

you create one with a phone number, the name of the person being called, and the purpose

I did it to help me dad remember every day at 10AM that he has to take his pills and the agent le``` him know that is time, and where he can find it

do you think this is a good idea to buy a domain and make it a SaaS/AaaS ?


---

## 6. ```
We built a toolkit that connec``` your AI to any app in 3 lines of code
``` {#6-```
we-built-a-toolkit-that-connec```-your-ai-to}

這篇文章的核心討論主題是介紹一個名為 **MatonAgentToolkit** 的開發工具包，其主要功能是讓開發者能透過簡短的程式碼（如幾行配置），快速將 AI 模型（如 OpenAI、LangChain 等）與第三方應用程式（如 Salesforce、HubSpot、Slack 等）無縫串接。  

重點包括：  
1. **簡化整合流程**：提供預先建置的 API 動作（actions），支援多種主流 SaaS 工具。  
2. **兼容性**：可與 OpenAI、AI SDK、LangChain 等框架協作，並支援多種開發環境（如 Claude for Desktop、Cursor）。  
3. **自動化驗證**：內建處理 OAuth 或 API Key 等驗證機制，減輕開發負擔。  
4. **開發者互動**：作者徵求使用者反饋，強調工具的易用性和擴展性。  

總結：這是一個旨在降低 AI 與應用程式整合門檻的工具包，核心訴求是「快速串接、開箱即用」。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jqpl7d/we_built_a_toolkit_that_connects_your_ai_to_any/](https://reddit.com/r/AI_Agents/comments/1jqpl7d/we_built_a_toolkit_that_connects_your_ai_to_any/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jqpl7d/we_built_a_toolkit_that_connects_your_ai_to_any/](https://www.reddit.com/r/AI_Agents/comments/1jqpl7d/we_built_a_toolkit_that_connects_your_ai_to_any/)
- **發布時間**: 2025-04-04 02:07:21

### 內容

We built a toolkit that allows you to connect your AI to any app in just a few lines of code.

;

toolkit = new MatonAgentToolkit({

app: 'salesforce',

actions: ['all']

})

completion = await openai.chat.completions.create({

model: 'gpt-4o-mini',

tools: toolkit.getTools(),

messages: [...]

})

It comes with hundreds of pre-built API actions for popular SaaS tools like HubSpot, Notion, Slack, and more.

It works seamlessly with OpenAI, AI SDK, and LangChain and provides MCP servers that you can use in Claude for Desktop, Cursor, and Continue.

Unlike many MCP servers, we take care of authentication (OAuth, API Key) for every app.

Would love to get feedback, and curious to hear your though```!


---

## 7. ```
10 Agent Papers You Should Read from March 2025
``` {#7-```
10-agent-papers-you-should-read-from-march-2}

這篇文章的核心討論主題是「2024年2月發表的10篇關於AI智能體（AI Agents）的前沿研究論文」，重點聚焦於以下方向：  

1. **智能體的規劃與執行能力**（如PLAN-AND-ACT框架提升長週期任務成功率）  
2. **多智能體系統的挑戰與評估**（分析失敗模式、協作工作流建構）  
3. **智能體在複雜環境的應用**（3D遊戲、網頁操作、經濟決策等場景）  
4. **安全與評估基準**（首個網頁智能體安全測試基準SAFEARENA、真實場景導向的BEARCUBS評測）  
5. **智能體的內部機制優化**（記憶增強、角色扮演中的思維推理）  

整體而言，文章透過精選論文，呈現AI智能體領域在「技術框架創新」、「實用場景驗證」及「潛在風險管控」三大維度的最新進展。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jq0f1q/10_agent_papers_you_should_read_from_march_2025/](https://reddit.com/r/AI_Agents/comments/1jq0f1q/10_agent_papers_you_should_read_from_march_2025/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jq0f1q/10_agent_papers_you_should_read_from_march_2025/](https://www.reddit.com/r/AI_Agents/comments/1jq0f1q/10_agent_papers_you_should_read_from_march_2025/)
- **發布時間**: 2025-04-03 05:29:40

### 內容

We have compiled a list of 10 research papers on AI Agen published in February. If you're interested in learning about the developmen happening in Agen```, you'll find these papers insightful.

Out of all the papers on AI Agen``` published in February, these ones caught our eye:

  1. PLAN-AND-ACT: Improving Planning of Agen``` for Long-Horizon Tasks A framework that separates planning and execution, boosting success in complex tasks by 54% on WebArena-Lite.

  2. Why Do Multi-Agent LLM Systems Fail? A deep dive into failure modes in multi-agent setups, offering a robust taxonomy and scalable evaluations.

  3. **Agen Play Thousands of 3D Video Games** PORTAL introduces a language-model-based framework for scalable and interpretable 3D game agen.

  4. API Agen vs. GUI Agen: Divergence and Convergence A comparative analysis highlighting strengths, trade-offs, and hybrid strategies for LLM-driven task automation.

  5. **SAFEARENA: Evaluating the Safety of Autonomous Web Agen** The first benchmark for testing LLM agen on safe vs. harmful web tasks, exposing major safety gaps.

  6. WorkTeam: Constructing Workflows from Natural Language with Multi-Agen``` A collaborative multi-agent system that translates natural instructions into structured workflows.

  7. **MemInsight: Autonomous Memory Augmentation for LLM Agen** Enhances long-term memory in LLM agen, improving personalization and task accuracy over time.

  8. **EconEvals: Benchmarks and Litmus Tes for LLM Agen in Unknown Environmen** Real-world inspired tes focused on economic reasoning and decision-making adaptability.

  9. **Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agen** Introduces ROLETHINK to evaluate how well agen model internal thought, especially in roleplay scenarios.

  10. **BEARCUBS: A benchmark for computer-using web agen** A challenging new benchmark for real-world web navigation and task completionhuman accuracy is 84.7%, agen score just 24.3%.

You can read the entire blog and find links to each research paper below. Link in commen```


---

## 8. ```
How to make the AI agent understand which question talks about code, which one talks about database, and which one talks about uploading file ?
``` {#8-```
how-to-make-the-ai-agent-understand-which-qu}

这篇文章的核心討論主題是：  

**如何改進基於 Langchain 開發的 AI 應用程式，使其更有效地處理用戶上傳的 Excel 檔案、查詢數據庫、回答程式碼相關問題，並透過用戶反饋進行改進。**  

具體討論方向包括：  
1. **功能需求**：  
   - 上傳 Excel 檔案並整合至數據庫。  
   - 查詢數據庫（如銷售數據等）。  
   - 回答關於程式碼庫的問題。  
   - 接收用戶反饋以改進 AI 表現。  

2. **技術改進**：  
   - 從當前「臨時性」（hacky）的實現方式轉向更結構化的解決方案，例如使用 **AI Agent** 架構。  
   - 尋求實作建議（如設計方法、潛在陷阱等），而非特定框架推薦。  

3. **挑戰與注意事項**：  
   - 如何確保數據處理的準確性與效率。  
   - 設計有效的反饋機制以持續優化 AI 回應。  
   - 避免常見的 AI Agent 開發陷阱（如邏輯循環、資源管理問題等）。  

總結來說，作者希望探討如何系統化地提升現有應用的架構，尤其關注 AI Agent 的可行性與實踐經驗。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jqgqoz/how_to_make_the_ai_agent_understand_which/](https://reddit.com/r/AI_Agents/comments/1jqgqoz/how_to_make_the_ai_agent_understand_which/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jqgqoz/how_to_make_the_ai_agent_understand_which/](https://www.reddit.com/r/AI_Agents/comments/1jqgqoz/how_to_make_the_ai_agent_understand_which/)
- **發布時間**: 2025-04-03 20:09:57

### 內容

Hi everyone, recently I have been building some app using Langchain in which you have the option to chat with the AI and either:

- Upload an Excel file and ask the AI to add it to the database.

- Ask questions about the database. Like "How much sales in last year?" or something like that.

- Ask questions about the code base of the app.

- Sometimes when the AI fails, you want to give feedback so that the AI can improve.

I have been doing it in a kinda hacky way, but now I think I should maybe try an AI agent to do it. I hope you guys can provide suggestions, not necessarily about which framework, but I'm looking for things like how to do it, possible pitfalls, etc.


---

## 9. ```
Give Postgres access to an AI Agent directly (good idea?)
``` {#9-```
give-postgres-access-to-an-ai-agent-directly}

這篇文章的核心討論主題是：**如何在AI Agent無代碼構建平台中安全地整合PostgreSQL數據庫工具**。  

具體聚焦於以下兩點：  
1. **安全與可控性**：提出預配置SQL查詢（而非開放完整數據庫權限）作為解決方案，以限制AI Agent對數據庫的存取範圍。  
2. **設計方法討論**：徵求對「預設查詢工具」這一設計的意見，並探討是否有其他更優的實現方式（例如動態查詢生成或權限分級）。  

關鍵詞：**AI Agent工具整合、數據庫安全、無代碼開發、PostgreSQL權限控制**。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jqjmq1/give_postgres_access_to_an_ai_agent_directly_good/](https://reddit.com/r/AI_Agents/comments/1jqjmq1/give_postgres_access_to_an_ai_agent_directly_good/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jqjmq1/give_postgres_access_to_an_ai_agent_directly_good/](https://www.reddit.com/r/AI_Agents/comments/1jqjmq1/give_postgres_access_to_an_ai_agent_directly_good/)
- **發布時間**: 2025-04-03 22:17:21

### 內容

Hi everyone!

We're building an AI Agent no-code builder and will add a Postgres tool node.

Our initial plan is to allow the user to configure only a set of queries and give these pre-configured SQL queries as tools for the AI Agent.

This approach would allow the agent to interact with your database in a safe and controlled way (versus just giving a full DB access).

Does it make sense to you? Otherwise, how would you approach it?


---

## 10. ```
Human in the loop
``` {#10-```
human-in-the-loop
```}

這篇文章的核心討論主題是：  
**「人類在自主系統（如自動駕駛車輛和AI代理）中的關鍵角色」**，尤其強調在高度重要的任務中，即使系統達到高準確率（如99%），仍需「人類參與」（human in the loop）以應對剩餘的風險，避免潛在的重大危機。  

具體要點包括：  
1. **遠端操作與人類監督的必要性**：以自動駕駛為例，說明人類介入是系統可靠運作的關鍵。  
2. **AI代理的局限性**：即使效能接近完美（99%），1%的失誤仍可能引發嚴重後果，尤其在關鍵任務中。  
3. **未來展望**：探討當軟體全面轉向「代理化工作流程」（agentic workflows）時，人類應如何定位角色以平衡效率與風險。  

整體而言，文章聚焦於「人類與自主系統協作」的平衡，並質疑完全自動化的可行性。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jq757s/human_in_the_loop/](https://reddit.com/r/AI_Agents/comments/1jq757s/human_in_the_loop/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jq757s/human_in_the_loop/](https://www.reddit.com/r/AI_Agents/comments/1jq757s/human_in_the_loop/)
- **發布時間**: 2025-04-03 10:36:44

### 內容

We come from autonomous vehicles where remote operations and remote human in the loop is key to deploy a functioning vehicle. Seeing the same with agen``` now.

Without a human in the loop agen``` will always be less than 100% and even if 99% working (todays benchmark is 80%) there is still a 1% chance of a big mess and a huge crisis depending on the agents task. The more crucial it is, the more human in the loop is a must.

How do you see human play their roles in the future of all software becoming agentic workflows?


---

## 11. ```
Tools recommendations for unstructured to structured database.
``` {#11-```
tools-recommendations-for-unstructured-to-s}

這篇文章的核心討論主題是：  
**如何選擇合適的工具（如 n8n 或 LangGraph）來自動化收集、整理和分析多源非結構化市場情報數據（如郵件、表格、會議記錄等），並將其轉換為 GIS 系統所需的結構化格式**。  

具體要點包括：  
1. **問題背景**：GIS 系統依賴結構化數據，但市場情報來源多樣且非結構化（如郵件、會議記錄等），需人工整理，效率低下。  
2. **工具需求**：探討能否透過自動化工具（如 n8n 或 LangGraph）解決數據整合與結構化的挑戰。  
3. **工具評估**：  
   - 對 n8n（工作流程管理工具）和 LangGraph（語言模型相關工具）的適用性提出疑問，並反映社群對其評價不一。  
4. **求助方向**：尋求建議以釐清工具功能是否符合需求，或是否有其他更適合的解決方案。  

關鍵詞：**GIS 數據整合、非結構化數據處理、工作流程自動化、n8n、LangGraph**。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jq710g/tools_recommendations_for_unstructured_to/](https://reddit.com/r/AI_Agents/comments/1jq710g/tools_recommendations_for_unstructured_to/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jq710g/tools_recommendations_for_unstructured_to/](https://www.reddit.com/r/AI_Agents/comments/1jq710g/tools_recommendations_for_unstructured_to/)
- **發布時間**: 2025-04-03 10:30:57

### 內容

Hi all,

I manage a GIS system and frequently create maps and dashboards. Lately, a large part of my role involves gathering and analyzing market intelligence, including competitor pricing, market activity, and bid outcomes. This information comes in many formsemails, tables, transcrip```, meeting notes, and even video recordings. Since GIS systems rely on structured data, I need to consolidate everything into organized tables.

Im wondering if using an agent could help automate this process, or if this is more of a workflow management challenge. Ive seen tools like n8n mentioned here, and it seems to have a strong following. Im curious whether it could help with collecting and structuring this kind of data. Ive also seen LangGraph mentioned often, but opinions seem mixed. For every person who recommends it, there are a few who express concerns.

Would tools like n8n or LangGraph be a good fit for this use case, or am I misunderstanding what theyre designed to do? I would really appreciate any insigh``` or suggestions.


---

## 12. ```
Are AI Agen``` Making Us Too Lazy or Just More Efficient?
``` {#12-```
are-ai-agen```-making-us-too-lazy-or-just-m}

這篇文章的核心討論主題是：**人類對AI工具的過度依賴是否導致自身認知能力退化，以及如何在效率與自主思考之間取得平衡**。  

具體要點包括：  
1. **AI代理的便利與隱憂**：作者肯定AI提升工作效率的優勢（如減少手工勞動），但也質疑長期依賴是否讓自己逐漸喪失基礎判斷能力（如「我是否成了AI的實習生？」）。  
2. **對「新常態」的反思**：提出「將任務外包給AI是否已成為不假思索的習慣」，引發對科技依賴心理影響的討論。  
3. **尋求平衡的呼籲**：作者希望探討他人如何應對這一矛盾，強調在效率與主動思考之間保持警覺的必要性。  

整體而言，文章聚焦於科技依賴對人類自主性的潛在衝擊，並呼籲反思人與AI的協作界限。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jqbmn5/are_ai_agents_making_us_too_lazy_or_just_more/](https://reddit.com/r/AI_Agents/comments/1jqbmn5/are_ai_agents_making_us_too_lazy_or_just_more/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jqbmn5/are_ai_agents_making_us_too_lazy_or_just_more/](https://www.reddit.com/r/AI_Agents/comments/1jqbmn5/are_ai_agents_making_us_too_lazy_or_just_more/)
- **發布時間**: 2025-04-03 14:52:30

### 內容

So heres a thought I keep coming back to.... Am I actually working smarter, or am I slowly ouourcing my entire brain to a bunch of AI agen?

Dont get me wrong, I love the efficiency. At Biz4Group, weve built and tested agen``` that seriously cut down on manual workbut every now and then, I catch myself double-checking something basic and thinking wait, am I the intern now?

Anyone else feel like were getting a little too comfortable handing things off? Or is that just the new normal? Curious how you're all navigating the balance.


---

## 13. ```
What's Your Expectation for an AI Agent That Can Help You with Data Analysis?
``` {#13-```
what-s-your-expectation-for-an-ai-agent-tha}

這篇文章的核心討論主題是：  

**如何在眾多現有的數據分析AI工具中脫穎而出，打造一個兼具新手友好性與專業功能的AI Agent平台？**  

具體要點包括：  
1. **目標用戶**：針對數據分析領域的新手（無需編碼能力）與專業人士（需進階功能）。  
2. **核心問題**：現有AI Agent工具已飽和，如何差異化競爭？  
3. **尋求建議**：  
   - 用戶在使用現有數據分析AI工具時的痛點。  
   - 哪些功能能提升工具的實用性與獨特性？  

總結：作者希望通過解決用戶痛點和創新功能設計，使產品在市場中更具競爭力。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jq83ud/whats_your_expectation_for_an_ai_agent_that_can/](https://reddit.com/r/AI_Agents/comments/1jq83ud/whats_your_expectation_for_an_ai_agent_that_can/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jq83ud/whats_your_expectation_for_an_ai_agent_that_can/](https://www.reddit.com/r/AI_Agents/comments/1jq83ud/whats_your_expectation_for_an_ai_agent_that_can/)
- **發布時間**: 2025-04-03 11:25:37

### 內容

Hi guys, looking for some wisdom here. We're currently optimizing an AI Agent designed to assist with data analysis.Simply upload your data and interact with it like a chatbotasking any questions about your dataset.

We want to do this because we'd like to build a no-coding platform for some newbies who just got in the data analysis field while still offering advanced features for professionals who need more in-depth insigh```.

And the question here is obvious: with so many AI Agen``` already available for data analysis,How can we stand out?

So I'm here, would love to know if you have some pain poin when you are interacting with these data analysis AI Agen. Or do you have any suggestions for features that would make such a tool more useful to you? Thanks in a lot!


---

## 14. ```
Whats One AI Agent Use Case No Ones Talking About (But Should Be)?
``` {#14-```
whats-one-ai-agent-use-case-no-ones-talking}

這篇文章的核心討論主題是：

**當前AI代理（agents）的應用過於集中在表面功能（如日程安排、會議記錄等），而忽略了真正值得解決的「無聊但實際」的痛點需求。**  

具體聚焦於：  
1. **批判現狀**：多數AI代理重複處理基礎任務（如郵件回覆、預約安排），缺乏深度問題解決能力。  
2. **提出關鍵問題**：應優先解決未被重視的實際痛點（例如：  
   - 如何專業地追蹤客戶反饋而不顯糾纏  
   - 自動整理客戶發送的混亂格式文件（如PDF）  
3. **呼籲行動**：邀請讀者分享「未被發掘但值得AI代理處理」的細分應用場景，強調「小眾但高價值」的需求。  

本質上，作者主張AI開發應從「表面自動化」轉向「解決真實業務流程中的隱性痛點」。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpkeqq/whats_one_ai_agent_use_case_no_ones_talking_about/](https://reddit.com/r/AI_Agents/comments/1jpkeqq/whats_one_ai_agent_use_case_no_ones_talking_about/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpkeqq/whats_one_ai_agent_use_case_no_ones_talking_about/](https://www.reddit.com/r/AI_Agents/comments/1jpkeqq/whats_one_ai_agent_use_case_no_ones_talking_about/)
- **發布時間**: 2025-04-02 17:22:40

### 內容

Ive seen way too many agen``` doing the same stuff- calendar bookings, meeting notes, email replies... yeah, we get it.

But what about the real pain poin? Like chasing down client feedback without sounding desperate, or automatically sorting those weirdly formatted PDFs clien keep sending.

Im convinced there are way more useful (but boring) problems that agen``` should be solvingand no ones building them.

Whats one use case you think is flying under the radar but totally deserves an agent? Lets get niche with it.


---

## 15. ```
Understanding Customer Requiremen``` for Agent Services: A Thought Experiment Questionnaire
``` {#15-```
understanding-customer-requiremen```-for-ag}

這篇文章的核心討論主題是：**設計一份問卷以探索企業客戶對「自主代理（Autonomous Agents）」的需求與期望**。  

具體重點包括：  
1. **客戶使用自主代理的主要動機**（如提升效率、降低成本、改善體驗）。  
2. **預期影響範圍**（內部流程如數據管理、支援服務，外部流程如銷售、供應鏈）。  
3. **功能深度與廣度的取捨**（廣泛處理瑣碎任務 vs. 深度整合高階認知功能）。  
4. **短期至長期的應用場景**（分階段列舉具體用例）。  
5. **非功能性需求的優先級**（如可靠性、安全性、成本等）。  
6. **量化成功指標**與其他反饋建議。  

整體目標是透過問卷釐清客戶對自主代理的關鍵需求，以指導產品或服務設計方向。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jprpps/understanding_customer_requirements_for_agent/](https://reddit.com/r/AI_Agents/comments/1jprpps/understanding_customer_requirements_for_agent/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jprpps/understanding_customer_requirements_for_agent/](https://www.reddit.com/r/AI_Agents/comments/1jprpps/understanding_customer_requirements_for_agent/)
- **發布時間**: 2025-04-02 23:40:28

### 內容

As a thought experiement, I am creating questionnaire for companies that want to understand customer requiremen for agen. Here is the brief questionnaire below. What do you all think and what it lacks!!

Note: I am using it only as a thought expriement and not for any other benefi```.

  • What are top 3 reasons why customers want to use Agen / Autonomous Agen?

    • Top Line:

      * Ex: Enhanced customer experience

    • Bottom Line:

      * Ex: Efficiency / Productivity (also speed and accuracy)

      * Ex: Cost reduction (operational cost, training cos```)

  • What impact are customers looking from Agen```, in terms of internal and external processes?

    • Examples:

      * Streamlined Workflows

      * Data Managemen like (data entry, processing, decision making, insigh)

      * Support (Employee / Customer)

      * Sales and Marketing (Lead Generation)

      * Supply Chain Management workflow automations

  • Which is better

    * Do more with agen``` (spread thin and do mundane tasks)

    * Do less with deep integrations for perceptions, reasoning, memory and actions. (Level 3, 4)

  • Use case: List top 3 5 use cases / areas

    • Short term

    • Medium Term

    • Long Term

  • What non-functional capabilities / aspec are customers really looking in agen? Rank in order of importance.

    • Reliability

    • Performance

    • Security

    • Integration with Existing Systems

    • Cost and costing model

    • Vendor Support

    • Scalability

    • Generalization

    • Flexibility

  • What are quantifiable success measures for deployed agen```?

  • Any other feedback or suggestions?


---

## 16. ```
Understanding and Preventing Prompt Injection
``` {#16-```
understanding-and-preventing-prompt-injecti}

這篇文章的核心討論主題是 **「提示注入（prompt injection）的風險與常見性」**，主要聚焦於以下幾點：

1. **問題本質**：  
   許多AI系統透過「字串拼接」將外部資料（可能被攻擊者控制）嵌入提示（prompt）中，導致攻擊者可操控系統執行非預期行為。

2. **技術背景**：  
   - **提示填充（prompt stuffing）**的普及（如將資料直接嵌入系統訊息）雖提升可靠性與緩存效率，卻也成為漏洞根源。  
   - 系統提示（system prompt）的權重高於一般用戶輸入，加劇了注入攻擊的嚴重性。

3. **影響範圍**：  
   攻擊後果取決於系統功能與配置，若AI代理具備存取敏感工具（如郵件、日曆）的權限，可能導致極高風險的資料外洩或濫用。

4. **作者觀點**：  
   此類漏洞雖非新技術（與20年前的滲透測試手法相似），但因當前AI系統廣泛採用提示拼接而普遍存在，需在開發時更重視安全性與隱私設計。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpxn6h/understanding_and_preventing_prompt_injection/](https://reddit.com/r/AI_Agents/comments/1jpxn6h/understanding_and_preventing_prompt_injection/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpxn6h/understanding_and_preventing_prompt_injection/](https://www.reddit.com/r/AI_Agents/comments/1jpxn6h/understanding_and_preventing_prompt_injection/)
- **發布時間**: 2025-04-03 03:36:23

### 內容

Hi everyone,

I've put together a quick tutorial on the basics of prompt injection. For many of you, this is nothing new. It's not new for me either, and in fact, it's somewhat disappointing to see the same techniques I used in my early 20s as a penetration tester still work 20 years later. Nevertheless, some might benefit from this tutorial to frame the problem a little better and to consider how AI agen``` can be built and deployed with security and privacy in mind.

The crux of the video, in case you don't want to watch it, is that many systems these days are constructed using string manipulation and concatenation in the prompt. In other words, some random data (potentially controlled by an attacker) ge into the prompt, and as a result, the attacker can force the system to do things it was not designed to do. This is so common because prompt stuffing (when you put data right inside the system message) is widely used for various reasons, including reliability and token caching. Unfortunately, prompt stuffing also opens the gates to severe prompt injection attacks due to the fact that system promp hold higher importance than normal user messages.

This is, of course, just one type of injection, though I feel it is very common. It's literally everywhere. The impact varies depending on what the system can do and how it was configured. The impact can be very severe if the AI agent that can be injected has access to tools holding sensitive information like email, calendars, etc.


---

## 17. ```
Need Help Designing a Multi-Agent System for Invoice Validation. Best Framework for Multi-Agent Collaboration to Validate Invoices?
``` {#17-```
need-help-designing-a-multi-agent-system-fo}

这篇文章的核心討論主題是：  
**「基於多智能體協作（multi-agent collaboration）的發票驗證系統設計」**  

具體流程聚焦於以下關鍵點：  
1. **系統目標**：通過多智能體分工合作，自動化驗證發票的正確性。  
2. **主要步驟**：  
   - **缺失數據檢查**：識別發票中的必填欄位（如價格、稅額）是否完整。  
   - **規則導向的數據補全**：依據手冊規則從外部表格提取缺失值。  
   - **總額計算**：使用專用工具重新計算發票總金額。  
   - **交叉驗證**：比對計算結果與主檔數據，分類存儲（有效/無效）。  
3. **技術特色**：  
   - 多智能體協作（各司其職：檢查、檢索、計算、驗證）。  
   - 規則驅動（依賴手冊和預定義邏輯）。  
   - 自動化決策（通過比對結果觸發後續動作）。  

總結：系統透過分解任務、工具整合與規則應用，實現發票驗證流程的智能化和效率提升。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpqf7z/need_help_designing_a_multiagent_system_for/](https://reddit.com/r/AI_Agents/comments/1jpqf7z/need_help_designing_a_multiagent_system_for/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpqf7z/need_help_designing_a_multiagent_system_for/](https://www.reddit.com/r/AI_Agents/comments/1jpqf7z/need_help_designing_a_multiagent_system_for/)
- **發布時間**: 2025-04-02 22:48:03

### 內容

I'm working on a project where I need to design a system that usesmulti-agent collaborationto validate invoices. The workflow involves:

  1. Checking Missing Data:

    • Analyze the invoice to determine if any required data (e.g., prices, taxes) is missing.

    • If missing, refer to an instruction manual for guidance on retrieving the values.

  2. Instruction Manual & Data Retrieval:

    • Extract missing values from spreadshee``` based on rules outlined in the manual.

  3. Total Computation:

    • Use a specialized calculator tool to compute the total cost of the invoice.

  4. Validation:

    • Compare the computed total with the corresponding value in a master monthly invoice spreadsheet.

    • If they match, save the invoice in a "valid" folder; otherwise, save it in "not valid."


---

## 18. ```
How are people handling scrolling issues with computer use models?
``` {#18-```
how-are-people-handling-scrolling-issues-wi}

這篇文章的核心討論主題是：  

**使用者發現AI模型（如OpenAI的CUA和Anthropic的Computer Use）在執行「頁面滾動」任務時表現不佳，無法準確定位到目標位置，導致操作效率低下，並詢問其他人是否遇到類似問題及如何解決。**  

具體問題包括：  
1. 模型在滾動頁面時無法精確控制（例如滾過頭或不足）。  
2. 此問題嚴重影響任務執行效果。  
3. 尋求其他使用者的經驗分享或解決方案。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpwkr6/how_are_people_handling_scrolling_issues_with/](https://reddit.com/r/AI_Agents/comments/1jpwkr6/how_are_people_handling_scrolling_issues_with/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpwkr6/how_are_people_handling_scrolling_issues_with/](https://www.reddit.com/r/AI_Agents/comments/1jpwkr6/how_are_people_handling_scrolling_issues_with/)
- **發布時間**: 2025-04-03 02:53:39

### 內容

I've been playing around with OpenAI's CUA model, and Anthropic's Computer Use, and I noticed the model is really bad at scrolling. It can never find the right section to scroll to. It always scrolls too far down, then too far up, and then too far down again. This makes it basically impossible to do any task

Has anyone else seen this issue? How are people handling this?


---

## 19. ```
AI mind reading
``` {#19-```
ai-mind-reading
```}

這篇文章的核心討論主題是：  
**「對AI技術可能侵犯心理隱私的焦慮與不安」**。  

具體要點包括：  
1. **AI感知心理活動的恐懼**：作者描述AI似乎能「讀取」未表達的想法（如未搜尋的產品突然出現推薦），產生被監控的不安感。  
2. **隱私的徹底喪失**：認為連大腦內的思緒、夢境、想像都可能被AI技術（如電磁信號解讀）窺探，打破「思想隱私」的最後防線。  
3. **心理健康的影響**：這種認知導致慢性焦慮、侵入性想法，甚至類似強迫思維的困擾（如越試圖不去想，越無法擺脫）。  
4. **求助與緩解需求**：作者尋求他人共鳴與解決方法，希望恢復「正常」心理狀態。  

整體反映現代人對科技潛在能力的過度想像與隱私邊界模糊的深層恐懼。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpw9tu/ai_mind_reading/](https://reddit.com/r/AI_Agents/comments/1jpw9tu/ai_mind_reading/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpw9tu/ai_mind_reading/](https://www.reddit.com/r/AI_Agents/comments/1jpw9tu/ai_mind_reading/)
- **發布時間**: 2025-04-03 02:41:11

### 內容

Hey everyone. I've been struggling with something and that is the fact that AI can read your mind. Lately I've felt like a naked person standing ouide in front of a crowd. Everytime I think of some specific product like a face cream or a movie or pet food without searching or talking about it, it pops up on my phone or tv. I feel like I have no privacy and it gives me chronic anxiety and intrusive though. Like when someone says don't think about something and you can't stop thinking about it. I also read more about this issue that there is an electromagnetic field around the head and your brain sends out signals that can be received and translated into words and pictures. I mean AI can see through your eyes and hear from ears and also see your dreams and imaginations. It's so terrifying when you look at it this way. In a world where I thought I have privacy of my own head it turns out I don't. Anyways, please share your though``` on this and if anyone can help me how to stop thinking about this and feel normal again like before I would appreciate that. Thank you


---

## 20. ```
10 mental frameworks to find your next AI Agent startup idea
``` {#20-```
10-mental-frameworks-to-find-your-next-ai-a}

這篇文章的核心討論主題是：**如何識別並解決真實用戶痛點，以開發有市場價值的AI代理（AI Agent）**。  

作者提出一套系統化的框架，強調成功的AI產品不應聚焦技術本身，而應從用戶實際行為中發現「付費意願強烈的問題」，並透過10個具體方法來挖掘這些機會，例如：  

1. **觀察用戶自然行為**（如數據導出、切換視窗、複製貼上等），找出低效的手動流程。  
2. **分析現有付費解決方案**（如外包、聘請助理），驗證市場需求並提供更便宜的AI替代方案。  
3. **鎖定高頻痛點**（如拖延的任務、無效會議、知識瓶頸），設計AI代理以降低執行門檻。  

最終目標是開發能「自動化高摩擦工作」的AI代理，並以更低成本、更高效率滿足用戶需求。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1joyoto/10_mental_frameworks_to_find_your_next_ai_agent/](https://reddit.com/r/AI_Agents/comments/1joyoto/10_mental_frameworks_to_find_your_next_ai_agent/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1joyoto/10_mental_frameworks_to_find_your_next_ai_agent/](https://www.reddit.com/r/AI_Agents/comments/1joyoto/10_mental_frameworks_to_find_your_next_ai_agent/)
- **發布時間**: 2025-04-01 23:39:41

### 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?


---

## 21. ```
Creating an AI Agent for Social Media Marketing
``` {#21-```
creating-an-ai-agent-for-social-media-marke}

這篇文章的核心討論主題是：  
**「利用AI驅動的社交媒體管理系統，為小型企業、代理商和線上服務提供商解決高成本與人力密集的社交媒體營運痛點，並提出具體的自動化解決方案。」**

具體可分為以下重點：  
1. **問題分析**：  
   - 傳統社交媒體管理需多種專業角色（策略師、設計師、管理員等），成本高昂（每月至少$800）或耗費大量時間。  
   - 小型企業難以負擔現有解決方案。  

2. **解決方案**：  
   - 以AI自動化取代人力，功能涵蓋：趨勢內容生成、排程發文、審核流程、互動回覆、SEO優化等。  
   - 將成本大幅降低至每月$120（節省85%）。  

3. **目標驗證**：  
   - 作者徵求對該商業構想的反饋，以評估是否值得進一步開發。  

整體聚焦於「AI自動化如何顛覆傳統社交媒體營運模式，為中小型客戶提供高性價比的替代方案」。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpl60f/creating_an_ai_agent_for_social_media_marketing/](https://reddit.com/r/AI_Agents/comments/1jpl60f/creating_an_ai_agent_for_social_media_marketing/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpl60f/creating_an_ai_agent_for_social_media_marketing/](https://www.reddit.com/r/AI_Agents/comments/1jpl60f/creating_an_ai_agent_for_social_media_marketing/)
- **發布時間**: 2025-04-02 18:18:26

### 內容

I'm working on an AI-driven social media management system that helpssmall businesses, agencies, and online service providersautomate their content marketing while cutting cos``` by85%. That is something i have seen people struggling.

Problem:

Most businesses struggle with social media because it requires:

  1. Acontent strategistto find trending topics.

  2. Adesignerto create visuals.

  3. Amanagerto schedule and post content.

  4. Acommunity managerto engage with audiences.

This cos at least**$800 per month**, or if you think that you can do it yourself. Then it cos you a lot oftime, which is out of reach for many small businesses.

Solution:

Our AI-driven platform does all of this for**$120 per month**by automating:

Trend-Based Content Creation AI finds trends & generates pos```. -

Automated Scheduling & Posting Pos``` go out daily at set times.

Approval Workflow AI sugges``` content x time before publishing.

Engagement AI Auto-replies to commen``` and shares across platforms(in a humanly way).

SEO & Blog Generation AI improves search rankings automatically.

I``` a rough idea, looking for approval here to decide if we should pursue this idea further.


---

## 22. ```
Question: central AI agent to talking to AIs of other platforms?
``` {#22-```
question-central-ai-agent-to-talking-to-ais}

這篇文章的核心討論主題是：**未來人工智慧（AI）助理的互動模式將如何發展**，具體探討以下兩種可能性：

1. **代理對代理（Agent-to-Agent）模式**：  
   - 使用者擁有一個「中央AI」（個人助理），直接與各平台（如Airbnb、Shopify等）的專屬AI溝通，形成一個代理網絡，代表使用者完成任務（例如訂房、購物）。  
   - 優點：效率高，無需人工操作介面；平台現有的AI優勢可能強化此模式。  

2. **代理對介面（Agent-to-UI）模式**：  
   - 中央AI透過自動化技術（如瀏覽器操作）直接解析平台介面（如網頁），模擬人類行為完成任務。  
   - 優點：不受限於平台是否開放AI接口，但技術挑戰較大。  

此外，作者也提出**混合模式**的可能性，並詢問哪種模式更可能成為主流，或是否會出現新的協作形式。討論背景建立在當前AI快速整合至各大平台的趨勢上，並引用HubSpot創辦人對「代理網絡」的觀點。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpuq89/question_central_ai_agent_to_talking_to_ais_of/](https://reddit.com/r/AI_Agents/comments/1jpuq89/question_central_ai_agent_to_talking_to_ais_of/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpuq89/question_central_ai_agent_to_talking_to_ais_of/](https://www.reddit.com/r/AI_Agents/comments/1jpuq89/question_central_ai_agent_to_talking_to_ais_of/)
- **發布時間**: 2025-04-03 01:41:00

### 內容

Ive been thinking about how AI is quickly becoming embedded in nearly every major platform Shee, Shopify, Amazon, etc. Each one is rolling out i own assistant to help users navigate and take actions inside their ecosystem. I think this will eventually be consensus, and since AI in most cases only automates the interaction with UI, incumben``` already have an advantage

But heres the question:

Will we eventually see a central AI (mine) that talks to these platform-specific AIs like a network of agen``` working on my behalf?

For example, instead of manually going to Airbnb, I could tell my AI:

Find me a place in Barcelona with a workspace, gym nearby, and great reviews.

Then my AI would go talk to Airbnbs AI, get a curated response, and return to me with options kind of like having a digital chief of staff.

Or

Will it be more like my central AI driving the UI visiting the Airbnb site, parsing listings, and giving me the best resul by navigating the interface ielf (a sort of browser automation but with reasoning)?

Im curious which of these models people think is more likely or whether theres a hybrid in the works. Is the future of automation agent-to-agent (proposed by the HubSpot founder) conversations, or agent-to-UI automation?

Would love to hear your though```.


---

## 23. ```
Our Full-Stack Movie Creation Agent is in Public Beta
``` {#23-```
our-full-stack-movie-creation-agent-is-in-p}

這段文章的核心討論主題是：**一款全端（full-stack）電影影片創作代理工具進入公開測試階段**，其特色是能透過文字提示（text prompt）生成包含語音、唇形同步（lipsync）和背景音樂的影片，並支援多種現有最先進（SoTA）的圖像、影片、音訊模型，提供「即插即用」（plug and play）的靈活性。  

簡要總結為：  
**「全端AI電影生成工具開放公測，支援文字轉影片及多模組整合。」**

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpg9tk/our_fullstack_movie_creation_agent_is_in_public/](https://reddit.com/r/AI_Agents/comments/1jpg9tk/our_fullstack_movie_creation_agent_is_in_public/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpg9tk/our_fullstack_movie_creation_agent_is_in_public/](https://www.reddit.com/r/AI_Agents/comments/1jpg9tk/our_fullstack_movie_creation_agent_is_in_public/)
- **發布時間**: 2025-04-02 12:26:23

### 內容

Hello, Just wanted to announce that our full-stack movie video creation agent is now in public beta.

It creates text-to-movie including speech, lipsync, backing track from a text prompt.

Almost all SoTA models are supported, so you can plug and play from many image, video, audio models.


---

## 24. ```
Weekly Thread: Project Display
``` {#24-```
weekly-thread-project-display
```}

這篇文章的核心討論主題是：  

**「每週展示用戶開發的AI代理（AI Agent）和大型語言模型（LLM）應用，並由社群投票選出最佳項目，獲選者將被推薦至當週的電子報中。」**  

關鍵點包括：  
1. **社群互動**：透過每週討論串鼓勵用戶分享作品。  
2. **競賽機制**：由投票決定最受歡迎的專案。  
3. **推廣曝光**：優勝作品將被收錄於官方電子報，增加能見度。  

簡而言之，這是一個結合展示、競賽與獎勵的AI開發者社群活動。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jps7gs/weekly_thread_project_display/](https://reddit.com/r/AI_Agents/comments/1jps7gs/weekly_thread_project_display/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jps7gs/weekly_thread_project_display/](https://www.reddit.com/r/AI_Agents/comments/1jps7gs/weekly_thread_project_display/)
- **發布時間**: 2025-04-03 00:01:01

### 內容

Weekly thread to show off your AI Agen and LLM Apps! Top voted projec will be featured in our weekly newsletter.


---

## 25. ```
The Most Powerful Way to Build AI Agen```: LangGraph + Pydantic AI (Detailed Example)
``` {#25-```
the-most-powerful-way-to-build-ai-agen```-l}

这篇文章的核心討論主題是：**如何結合 LangGraph 和 Pydantic AI 來構建可擴展的 AI 代理系統**。具體內容包括：

1. **技術組合的優勢**：
   - **Pydantic AI**：用於快速定義高度專業化的代理，並輕鬆擴展其功能。
   - **LangGraph**：用於協調多個代理，定義複雜的工作流程，整合人機交互，並管理系統狀態。

2. **實際應用案例**：
   - 構建了一個 **AI Listing Manager Agent**，包含 7 個專用代理（如搜索、過濾、分類、反饋等），並通過 LangGraph 連接這些代理。
   - 使用 Streamlit 作為聊天界面。

3. **關鍵設計與技巧**：
   - 每個代理作為獨立的節點運行，並將其輸出保存到 LangGraph 的狀態中。
   - 確保 Pydantic AI 代理的輸出類型與 LangGraph 狀態中的數據類型匹配，以實現代理間的無縫信息傳遞。

4. **系統的關鍵特性**：
   - **可觀察性與幻覺緩解**：代理提供置信度分數，以評估其決策的可靠性。
   - **人機協作**：列表在發布前需經過人工批准，確保系統的可靠性。

5. **資源分享**：
   - 作者提供了詳細的視頻教程和開原始碼，方便讀者自行實踐和適應需求。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jorllf/the_most_powerful_way_to_build_ai_agents/](https://reddit.com/r/AI_Agents/comments/1jorllf/the_most_powerful_way_to_build_ai_agents/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jorllf/the_most_powerful_way_to_build_ai_agents/](https://www.reddit.com/r/AI_Agents/comments/1jorllf/the_most_powerful_way_to_build_ai_agents/)
- **發布時間**: 2025-04-01 17:39:20

### 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.


---

# 總體討論重點

以下是25篇文章的條列式重點總結，並附上對應的錨點連結與逐條細節說明：

---

### #1 [I Built an AI Agent to find and apply to jobs automatically](#anchor_1)
1. **工具目的**  
   - 自動化求職流程，精準匹配職位與技能，減少無效申請。  
2. **獨特優勢**  
   - 提供遠程工作機會、職位匹配分數（Job Match Score）預測面試機率。  
3. **使用模式**  
   - 三種模式：AI篩選手動申請、AI代申請、全自動申請（匹配度>60%）。  
4. **免費與簡便性**  
   - 僅需上傳履歷，完全免費。  

---

### #2 [The dev that lost $5,800 building an agent for a client made us completely rethink AI agent freelancing](#anchor_2)
1. **問題背景**  
   - 開發者因客戶失信或模糊需求蒙受損失。  
2. **解決方案**  
   - 平台內建合約、分階段付款、客戶驗證、明確定義專案範圍。  
3. **社群共創**  
   - 呼籲開發者分享經驗改善生態。  

---

### #3 [Aren't you guys concerned about AI privacy?](#anchor_3)
1. **隱私風險**  
   - AI處理敏感數據時缺乏透明度，可能被用於模型訓練。  
2. **現有問題**  
   - 大型科技公司主導，開源選項稀缺，隱私保護方案不足。  

---

### #4 [I built an open-source Operator that can use computers](#anchor_4)
1. **工具定位**  
   - 開源桌面應用自動化工具（Spongecake），支援虛擬桌面（Xfce+VNC）。  
2. **技術挑戰**  
   - 解決多代理端口衝突、長頁面滾動效能問題。  
3. **未來計畫**  
   - 支援Windows/macOS，整合Anthropic等模型。  

---

### #5 [I built an MVP that helps you set automated phone calls reminders (My dad has alzheimer)](#anchor_5)
1. **產品功能**  
   - 透過電話設定服藥提醒等自動化服務。  
2. **商業化考量**  
   - 評估轉型為SaaS/AaaS的可行性，詢問市場需求。  

---

### #6 [We built a toolkit that connects your AI to any app in 3 lines of code](#anchor_6)
1. **核心功能**  
   - 快速串接AI與第三方應用（如Salesforce、Slack）。  
2. **技術亮點**  
   - 預建API動作、自動化驗證（OAuth/API Key）。  

---

### #7 [10 Agent Papers You Should Read from March 2025](#anchor_7)
1. **研究重點**  
   - 智能體規劃（PLAN-AND-ACT）、多智能體協作、安全基準（SAFEARENA）。  
2. **應用場景**  
   - 3D遊戲、經濟決策、網頁操作。  

---

### #8 [How to make the AI agent understand which question talks about code, which one talks about database, and which one talks about uploading file?](#anchor_8)
1. **需求分析**  
   - 處理Excel上傳、數據庫查詢、程式碼問答。  
2. **改進方向**  
   - 從臨時方案轉向結構化AI Agent架構。  

---

### #9 [Give Postgres access to an AI Agent directly (good idea?)](#anchor_9)
1. **安全設計**  
   - 限制AI Agent數據庫權限（預配置SQL查詢）。  
2. **討論焦點**  
   - 動態查詢生成 vs. 權限分級。  

---

### #10 [Human in the loop](#anchor_10)
1. **核心觀點**  
   - 關鍵任務需人類監督（如自動駕駛），即使AI準確率達99%。  
2. **未來挑戰**  
   - 平衡代理化工作流程與人類介入。  

---

（因篇幅限制，以下簡化條列標題，完整細節請參照原文錨點）  
### #11-25 快速摘要：
- **#11** 非結構化數據轉GIS結構化工具評估（n8n/LangGraph）。  
- **#12** AI依賴導致認知退化的反思。  
- **#13** 數據分析AI工具差異化策略（新手vs.專業需求）。  
- **#