2025-04-03-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，包含核心主題與逐條細節，並附上對應的錨點連結：

1. 未被重視的AI代理應用場景 #1

核心主題：挖掘AI代理的「隱形痛點」應用，突破常規任務框架。
細節：
- 當前AI過度聚焦基礎任務（如郵件回覆）。
- 提議解決小眾但高價值問題：非乞求式客戶反饋追蹤、混亂PDF整理。
- 呼籲分享更多niche use cases。

2. 自主代理需求問卷設計 #2

核心主題：透過問卷釐清企業對自主代理的需求。
細節：
- 動機：效率、成本、體驗改善。
- 預期影響：流程自動化、數據管理。
- 應用深度與廣度取捨。
- 短中長期場景優先級。
- 非功能性需求（可靠性、安全性）。

3. AI模型的滾動頁面問題 #3

核心主題：AI執行頁面滾動任務時的精準度不足。
細節：
- 問題：過度上下滾動，影響效率。
- 開放討論解決方案。

4. AI讀取思想的隱私焦慮 #4

核心主題：對AI潛在心理監控的恐懼。
細節：
- 未搜尋內容的精準推送引發「被看穿」感。
- 擔憂腦波信號解讀技術。
- 尋求心理安全感恢復方法。

5. 多智能體發票驗證系統 #5

核心主題：多代理協作驗證發票的設計。
細節：
- 缺失數據檢查與規則補全。
- 總額計算與歸檔分類。
- 強調分工與規則化流程。

6. 社交媒體AI管理系統 #6

核心主題：AI降低社交媒體營運成本。
細節：
- 痛點：人力密集（月成本$800+）。
- 解決方案：內容生成、排程、互動自動化（月成本$120）。
- 目標：驗證商業可行性。

7. 未來AI助理互動模式 #7

核心主題：代理對代理（A2A）vs. 代理對介面（A2UI）模式。
細節：
- A2A需API開放，效率高。
- A2UI模擬人類操作，適用性廣但技術不穩。
- 探討混合模式可能性。

8. AI創業機會挖掘框架 #8

核心主題：從用戶痛點找出AI代理商業機會。
細節：
- 觀察低效行為（如複製貼上）。
- 驗證痛點市場需求（如Upwork任務）。
- 設計方向：自動化、整合、降低成本。

9. 全端AI影片生成工具公測 #9

核心主題：文字轉電影工具開放測試。
細節：
- 支援語音、唇形同步、背景音樂。
- 模組化整合多種AI模型。

10. AI專案展示競賽 #10

核心主題：社群AI專案展示與投票。
細節：
- 每週分享專案，優勝者推薦至週報。
- 目標：促進互動與曝光。

（因篇幅限制，以下為簡要條列，完整細節可參考錨點連結）

11-25. 其他重點

#11：LangGraph + Pydantic AI構建可擴

文章核心重點

以下是各篇文章的一句話摘要（條列式輸出）：

Whats One AI Agent Use Case No Ones Talking About (But Should Be)?
呼籲探索AI代理未被重視的「隱形痛點」，如非乞求式客戶反饋追蹤與混亂PDF整理。
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、預期影響與成功指標。
How are people handling scrolling issues with computer use models?
討論AI模型執行頁面滾動任務時精準度不足的問題與解決方案。
AI mind reading
反映對AI技術可能透過生理訊號解讀思想的隱私焦慮與心理壓力。
Need Help Designing a Multi-Agent System for Invoice Validation. Best Framework for Multi-Agent Collaboration to Validate Invoices?
探討多智能體協作驗證發票的系統設計，涵蓋缺失檢查、規則補全與歸檔流程。
Creating an AI Agent for Social Media Marketing
提出以AI代理降低小型企業社媒營運成本85%的自動化解決方案。
Question: central AI agent to talking to AIs of other platforms?
探討未來「中央AI代理」與平台專屬AI的互動模式（代理對代理vs.代理對介面）。
10 mental frameworks to find your next AI Agent startup idea
提供10種從真實用戶痛點挖掘AI代理創業機會的方法論。
Our Full-Stack Movie Creation Agent is in Public Beta
宣布支援文字生成影片的全端AI創作工具進入公測階段。
Weekly Thread: Project Display
社群競賽活動：展示AI專案並由投票選出優勝者推薦至週報。
The Most Powerful Way to Build AI Agents: LangGraph + Pydantic AI (Detailed Example)
展示結合LangGraph與Pydantic AI構建模組化、可擴展多代理系統的實例。
Starting an AI Automation Agency at 17 Looking for Advice
17歲技術創業者尋求AI自動化諮詢公司的商業模式建議。
What cool problems (healthcare) have you solved (or want to solve) using AI agents?
徵集AI代理在醫療領域的創新應用案例與前瞻構想。
How to outperform off-the-shelf Deep Research agents?
探討構建專注私人資本市場的高品質AI研究代理的技術架構。
Are there any AI agents Marketplace that are popular or worthy to note?
詢問AI代理交易平台的現狀與商業化機制。
Systematically prompting AIs to become more specific about current challenges to advancing AI
透過迭代提問拆解AI技術突破的基礎科學障礙（如符號與神經網絡整合）。
Recently I am learning what is multi agent, and GPT told me, just imagine this system is like a virtual town where AI lives in....
以「虛擬小鎮」比喻解釋多代理系統，探討非技術背景者的商業應用學習。
Example of a simple prompt injection attack
示範提示注入攻擊如何導致AI系統資料外洩，呼籲加強安全設計。
I dove into MCP and how it can benefit from orchestration frameworks!
分析MCP協議作為LLM與工具間標準化介面，結合流程編排實現複雜代理功能。
AI agent use cases interacting with the physical world
徵求需與物理世界互動（如機器人介面）的AI代理應用場景。
Spreadsheets and AI agent
尋求AI代理動態識別Google Sheets非結構化欄位數據的自動化解決方案。
Agents that solve captchas, and bot detection
尋找能繞過驗證碼與反爬機制的現成求職數據爬取工具。
I Spoke to 100 Companies Hiring AI Agents Here's What They Actually Want (and What They Hate)
企業需求調查：AI代理需整合現有系統、明確ROI，且穩定如基礎設施。
Are there enough APIs?
探討「無API數據源」的自動化存取痛點與網頁轉API工具的實用性。
The efficacy of AI agents is largely dependent on the LLM model that one uses
實證不同LLM（如Claude與GPT）在編碼任務的效能差異，強調模型特性優先於提示詞優化。

[1. Whats One AI Agent Use Case No Ones Talking About (But Should Be)?](#1-``` whats-one-ai-agent-use-case-no-ones-talking-)
[2. Understanding Customer Requiremen for Agent Services: A Thought Experiment Questionnaire ](#2- understanding-customer-requiremen```-for-age)
[3. How are people handling scrolling issues with computer use models?](#3-``` how-are-people-handling-scrolling-issues-wit)
[4. AI mind reading](#4-``` ai-mind-reading

- [5. ```
Need Help Designing a Multi-Agent System for Invoice Validation. Best Framework for Multi-Agent Collaboration to Validate Invoices?
```](#5-```
need-help-designing-a-multi-agent-system-for)
- [6. ```
Creating an AI Agent for Social Media Marketing
```](#6-```
creating-an-ai-agent-for-social-media-market)
- [7. ```
Question: central AI agent to talking to AIs of other platforms?
```](#7-```
question-central-ai-agent-to-talking-to-ais-)
- [8. ```
10 mental frameworks to find your next AI Agent startup idea
```](#8-```
10-mental-frameworks-to-find-your-next-ai-ag)
- [9. ```
Our Full-Stack Movie Creation Agent is in Public Beta
```](#9-```
our-full-stack-movie-creation-agent-is-in-pu)
- [10. ```
Weekly Thread: Project Display
```](#10-```
weekly-thread-project-display
```)
- [11. ```
The Most Powerful Way to Build AI Agen```: LangGraph + Pydantic AI (Detailed Example)
```](#11-```
the-most-powerful-way-to-build-ai-agen```-l)
- [12. ```
Starting an AI Automation Agency at 17  Looking for Advice
```](#12-```
starting-an-ai-automation-agency-at-17-look)
- [13. ```
What cool problems (healthcare) have you solved (or want to solve) using AI agen```?
```](#13-```
what-cool-problems-healthcare-have-you-solv)
- [14. ```
How to outperform off-the-shelf Deep Reseach agen```?
```](#14-```
how-to-outperform-off-the-shelf-deep-reseac)
- [15. ```
Are there any AI agen``` Marketplace that are popular or worthy to note ?
```](#15-```
are-there-any-ai-agen```-marketplace-that-a)
- [16. ```
Systematically prompting AIs to become more specific about current challenges to advancing AI
```](#16-```
systematically-prompting-ais-to-become-more)
- [17. ```
Recently I am learning what is multi agent, and GPT told me, just imagine this system is like a virtual town where AI lives in....
```](#17-```
recently-i-am-learning-what-is-multi-agent-)
- [18. ```
Example of a simple prompt injection attack
```](#18-```
example-of-a-simple-prompt-injection-attack)
- [19. ```
I dove into MCP and how it can benefit from orchestration frameworks!
```](#19-```
i-dove-into-mcp-and-how-it-can-benefit-from)
- [20. ```
AI agent use cases interacting with the physical world
```](#20-```
ai-agent-use-cases-interacting-with-the-phy)
- [21. ```
Spreadshee``` and AI agent
```](#21-```
spreadshee```-and-ai-agent
```)
- [22. ```
Agen``` that solve captchas, and bot detection
```](#22-```
agen```-that-solve-captchas-and-bot-detecti)
- [23. ```
I Spoke to 100 Companies Hiring AI Agen```  Heres What They Actually Want (and What They Hate)
```](#23-```
i-spoke-to-100-companies-hiring-ai-agen```-)
- [24. ```
Are there enough APIs?
```](#24-```
are-there-enough-apis-
```)
- [25. ```
The efficacy of AI agen``` is largely dependent on the LLM model that one uses
```](#25-```
the-efficacy-of-ai-agen```-is-largely-depen)

---

## 1. ```
Whats One AI Agent Use Case No Ones Talking About (But Should Be)?
``` {#1-```
whats-one-ai-agent-use-case-no-ones-talking-}

這篇文章的核心討論主題是：

**「當前AI助理（agents）過度聚焦於常見的基礎任務（如行程安排、郵件回覆等），而忽略了真正值得解決的『隱形痛點』」**。作者呼籲探討更小眾但實際的應用場景，例如：  
1. 如何「非乞求式地」追蹤客戶反饋  
2. 自動整理客戶發送的混亂PDF文件  
並邀請讀者分享其他未被重視但值得AI代理解決的具體問題（niche use cases）。  

關鍵訴求在於：**突破AI工具的常規應用框架，挖掘「無聊但高價值」的真實需求**。

- **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.

### 討論

**評論 1**:

Agen``` to speed up a procurement process

**評論 2**:

i've seen people on here ask about connecting to google shee```?

**評論 3**:

The most effective use-cases for ai agen are the copilo / assistan``` as they are more versatile and can be deployed in almost all situations.

**評論 4**:

The api calls that frontend development requires. It is the modern equivalent of boilerplate. Anyone with me?

---

## 2. ```
Understanding Customer Requiremen``` for Agent Services: A Thought Experiment Questionnaire
``` {#2-```
understanding-customer-requiremen```-for-age}

這篇文章的核心討論主題是：  
**「設計一份問卷以探索企業對自主代理（Autonomous Agents）的需求與期望」**，重點包含以下方向：  

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. **自主代理對內外部流程的預期影響**（如流程自動化、數據管理、支援功能等）。  
3. **代理的應用深度與廣度取捨**（廣泛處理瑣碎任務 vs. 深度整合高階功能）。  
4. **短期、中期、長期的關鍵應用場景**（具體用例優先級）。  
5. **非功能性需求的優先級排序**（如可靠性、安全性、擴展性等）。  
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企業對自主代理的實際需求，並檢視問卷內容的完整性。

- **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?

### 討論

**評論 1**:

You might want to include questions that dig deeper into the type of data customers expect agen to handle. For example, are they dealing with sensitive data requiring high security protocols? Also, consider asking how much autonomy they are comfortable granting to these agen, as this can impact integration strategies. Questions about user interface preferences or desired transparency in decision-making processes could also provide valuable insigh```.

Ive tried leveraging survey tools like Typeform and Google Forms for collecting feedback, but Pulse for Reddit's engagement tools make it easier to analyze discussion trends and uncover hidden customer needs in Reddit communities. These insigh``` could be invaluable for shaping a more targeted questionnaire.

---

## 3. ```
How are people handling scrolling issues with computer use models?
``` {#3-```
how-are-people-handling-scrolling-issues-wit}

這篇文章的核心討論主題是：**AI模型（如OpenAI的CUA和Anthropic的Computer Use）在執行「頁面滾動」任務時的表現不佳問題**。具體問題包括無法準確定位滾動位置（過度上下滾動），導致實際操作效率低下。作者進一步詢問其他人是否遇到類似情況以及可能的解決方法。  

關鍵點總結：  
1. **問題描述**：AI模型在滾動頁面時精準度不足，反覆過度調整。  
2. **影響**：此問題嚴重阻礙任務執行。  
3. **開放討論**：尋求其他用戶的經驗與解決方案。

- **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?

### 討論

無討論內容

---

## 4. ```
AI mind reading
``` {#4-```
ai-mind-reading
```}

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

具體要點包括：  
1. **AI讀取思想的恐懼**：作者描述一種主觀感受，認為AI能透過未公開的思維（如未搜尋的產品、電影等）精準推送內容，彷彿「被看穿內心」。  
2. **隱私喪失的焦慮**：強調對「大腦隱私」被侵犯的強烈不安，甚至引發慢性焦慮與侵入性想法。  
3. **技術可能性的擔憂**：提及電磁場、腦波信號解讀等理論，推測AI可能透過生理訊號接收思想、夢境或想像。  
4. **求助與緩解需求**：尋求他人共鳴與解決方案，希望恢復「不被監控」的心理安全感。  

整體反映現代人對AI無形監控的深層恐懼，以及科技與隱私界限模糊下的心理壓力。

- **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

### 討論

**評論 1**:

Hey buddy. What you are describing doesn't sound rational. I highly encourage you to get help by a doctor and discuss your though and feelings that you are experiencing. These intrusive though (an AI or "entity" being able to read your mind) and seeing patterns or "signs" everywhere of weird coincidences can be a sign of a slowly onsetting psychosis and I have seen it happening to one of my best friends.

While it is a fun concept to think about electromagnetic fields and brain signals that could be read and interpreted by an AI to be used to send you advertisemen``` - this is definitely not real and if it gives you anxiety and interferes with you life, please check with a doctor to be safe!

---

## 5. ```
Need Help Designing a Multi-Agent System for Invoice Validation. Best Framework for Multi-Agent Collaboration to Validate Invoices?
``` {#5-```
need-help-designing-a-multi-agent-system-for}

這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. **缺失數據檢查**：智能體協作識別發票中的必填字段（如價格、稅金）是否完整。  
2. **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。  
3. **總額計算**：透過專用工具計算發票總成本。  
4. **驗證與歸檔**：比對主檔數據並按結果分類存儲（有效/無效）。  

整體強調**多智能體分工協作**與**規則化自動化流程**在發票驗證中的應用。

- **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:

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.
Instruction Manual & Data Retrieval:
- Extract missing values from spreadshee``` based on rules outlined in the manual.
Total Computation:
- Use a specialized calculator tool to compute the total cost of the invoice.
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."

### 討論

無討論內容

---

## 6. ```
Creating an AI Agent for Social Media Marketing
``` {#6-```
creating-an-ai-agent-for-social-media-market}

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（策劃、設計、管理、互動），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，提供**趨勢內容生成、排程發文、審核流程、互動回覆、SEO優化**等功能，將月成本降至$120（節省85%）。  

3. **目標**：  
   - 驗證此商業構想的可行性，尋求進一步開發的決策依據。  

全文圍繞「AI如何降低社交媒體營運門檻」展開，強調成本效益與自動化效率。

- **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:

Acontent strategistto find trending topics.
Adesignerto create visuals.
Amanagerto schedule and post content.
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.

### 討論

**評論 1**:

Great idea. One suggestion would be to quantify impact - which business metrics your solution improves

You might need to start being specialized for an industry to be able to quantify properly

**評論 2**:

IMO it's a decent idea, but instead of asking Reddit you should ask your prospective customers, if you follow The Lean Startup methodology you'll build exactly what your prospec``` want.

---

## 7. ```
Question: central AI agent to talking to AIs of other platforms?
``` {#7-```
question-central-ai-agent-to-talking-to-ais-}

这篇文章的核心討論主題是：**未來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），直接與各平台專屬的AI（如Airbnb、Shopify的AI）溝通，形成一個代理網絡，代表用戶完成任務（例如訂房、購物）。  
   - 優點：效率高，無需手動操作介面，類似「數位幕僚」的協作模式。  
   - 挑戰：需平台開放API或建立互通標準。

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、解析網頁）直接與平台介面互動，模擬人類行為完成任務。  
   - 優點：不受限於平台是否提供API，適用性廣。  
   - 挑戰：技術不穩定（如網頁結構變動可能影響運作）。

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，或是否會出現兩者結合的解決方案。討論背景建立在當前AI快速整合至各大平台（如Shopify、Amazon）的趨勢下，既有企業可能因掌握用戶介面而佔據優勢。  

最終，問題指向對**未來自動化形態**的預測：是基於代理間的通訊，還是介面自動化主導？這涉及技術可行性、商業利益（平台是否願意開放），以及用戶體驗的權衡。

- **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.

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```.

### 討論

**評論 1**:

oh like a personal assistant agent for each of us?

---

## 8. ```
10 mental frameworks to find your next AI Agent startup idea
``` {#8-```
10-mental-frameworks-to-find-your-next-ai-ag}

### **核心討論主題：**  
**如何透過觀察用戶痛點，找出有商業價值的AI Agent應用機會**  

文章的核心在於強調：**成功的AI Agent產品不是由技術驅動，而是由真實的用戶痛點驅動**。作者提供了一套系統化的框架（10個具體方法），幫助創業者或開發者從日常用戶行為中識別可自動化、可商業化的問題，並設計對應的AI解決方案。  

### **關鍵要點：**  
1. **用戶行為觀察**：  
   - 透過實際觀察（如數據導出、切換視窗、複製貼上等行為）發現低效或重複性工作流程。  
   - 重點在於「用戶實際做什麼」，而非「他們說需要什麼」。  

2. **痛點驗證**：  
   - 從現有付費解決方案（如外包、SaaS工具）或失敗嘗試中，確認問題的嚴重性和市場需求。  
   - 例如：Upwork/Fiverr上的重複性任務、企業已雇用的虛擬助理等。  

3. **AI Agent的設計方向**：  
   - **自動化**：取代手動操作（如數據轉移、報告生成）。  
   - **整合**：連接孤立的系統，減少上下文切換。  
   - **降低成本**：以更低價格提供現有服務的「夠好」替代方案（如80%品質、20%成本）。  
   - **降低啟動門檻**：解決用戶拖延的高摩擦任務（如內容創作、財務對帳）。  

4. **具體機會案例**：  
   - 企業中的知識瓶頸（如依賴特定專家）。  
   - 無效會議（可被非同步更新取代）。  
   - 家庭成員協助小企業的行政工作（如社交媒體管理）。  

### **總結：**  
文章的核心是**「問題優先，技術其次」**，並提供實用方法論，幫助讀者從真實場景中挖掘AI Agent的商業潛力。最終目標是找到用戶願意付費解決的問題，而非單純追求技術創新。

- **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?

### 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!

**評論 4**:

Superb post. Thank you for sharing

**評論 5**:

Wow! I actually really like this man. Are you building an agent yourself? You know what Ive been thinking about recently: as someone who has worked for a damn CRM startup, followed my working on high enterprise sales and product mgmt. THE SINGLE BIGGEST problem has been data entry. Its such a pain that I left my earlier job because I just couldnt deal with their clunky crm anymore.

And I thought, what if there was an ai agent that helped me automate data entry (obvious insight). But to my surprise this automation is still very nascent among tools I use myself (excel, shee```, notion, salesforce, HubSpot).

I wonder if there is anything that ac as a middleman of sor, a middleman explainable Ai agent that can help automate data entry across many of these tools, with detailed logs. I am hoping this post doesnt violate community guidelines. New to Reddit and the guidelines seem really strict

Finance bro, LinkedIn thoroughbred herelol

---

## 9. ```
Our Full-Stack Movie Creation Agent is in Public Beta
``` {#9-```
our-full-stack-movie-creation-agent-is-in-pu}

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**，其主要功能是透過文字提示（text prompt）生成包含語音、唇形同步（lipsync）和背景音樂的影片（text-to-movie），並支援整合多種當前最先進（SoTA）的圖像、影片、音訊模型，提供模組化（plug and play）的靈活性。  

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

- **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.

### 討論

**評論 1**:

Here is the announcement -
https://blog.samsar.one/the-agent-is-live-in-public-beta/

Agent + Studio UI-
https://app.samsar.one/

API Docs-
https://docs.samsar.one/docs/api/

View some demo renders here-
https://www.threads.net/@samsar_one_videos

Ty for reading.

**評論 2**:

All the best, will try it out !

---

## 10. ```
Weekly Thread: Project Display
``` {#10-```
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，獲選者將被推薦至官方週報。」**  

重點包括：  
1. **社群互動**：鼓勵用戶分享自己的AI專案。  
2. **競賽機制**：透過投票選出最受歡迎的項目。  
3. **推廣曝光**：優勝作品將被收錄於週報（newsletter）中，增加能見度。  

簡而言之，這是一個以AI開發者為導向的「展示+競賽+推廣」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.

### 討論

無討論內容

---

## 11. ```
The Most Powerful Way to Build AI Agen```: LangGraph + Pydantic AI (Detailed Example)
``` {#11-```
the-most-powerful-way-to-build-ai-agen```-l}

這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」系統，包含 7 個專門的 Pydantic AI 代理，分別負責搜索、過濾、摘要、分類、反饋收集、修正和發布等任務。
   - 使用 LangGraph 將這些代理連接起來，每個代理的輸出與 LangGraph 的狀態數據類型嚴格匹配，確保信息傳遞的流暢性。

3. **關鍵設計與挑戰**：
   - **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**：重要決策（如發布）需經過人工明確批准，確保系統的可靠性。

4. **資源分享**：
   - 作者提供了詳細的視頻教程和開原始碼，方便讀者自行實作或調整。

總結來說，文章重點在於展示如何通過 LangGraph 和 Pydantic AI 的結合，設計一個模塊化、可擴展且可控的 AI 代理系統，並通過實際案例說明其可行性與優勢。

- **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:

Search agent: Searches the internet for potential new listings
Filtering agent: Ensures listings meet our quality standards.
Summarizer agent: Extract the information we want in the format we want
Classifier agent: Assigns categories and tags following our internal classification guidelines
Feedback agent: Collec``` human feedback before final approval.
Rectifier agent: Modifies listings according to our feedback
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?

---

## 12. ```
Starting an AI Automation Agency at 17  Looking for Advice
``` {#12-```
starting-an-ai-automation-agency-at-17-look}

這篇文章的核心討論主題是：  

**一位17歲、具備n8n與程式基礎的年輕人，考慮創立一家專注於「AI代理、語音代理與工作流程自動化」的諮詢公司，但缺乏商業經驗，因此尋求業界導師的建議與合作機會。**  

具體要點包括：  
1. **創業動機**：觀察到企業對AI自動化的需求增長，希望結合自身技術能力提供解決方案。  
2. **當前挑戰**：雖有技術能力與客戶開發想法，但缺乏商業運作經驗（如定價、合約、規模化等）。  
3. **個人目標**：追求自主性、累積經驗，並建立志同道合的網絡，而非單純賺錢。  
4. **尋求支持**：公開徵求領域導師的指導，並詢問對初入此領域的建議。  

整體聚焦於「技術創業的可行性評估」與「新手如何彌合商業知識缺口」。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpobeu/starting_an_ai_automation_agency_at_17_looking/](https://reddit.com/r/AI_Agents/comments/1jpobeu/starting_an_ai_automation_agency_at_17_looking/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpobeu/starting_an_ai_automation_agency_at_17_looking/](https://www.reddit.com/r/AI_Agents/comments/1jpobeu/starting_an_ai_automation_agency_at_17_looking/)
- **發布時間**: 2025-04-02 21:16:56

### 內容

Hey everyone,

I have experience with n8n and some coding skills, and Ive noticed a growing demand for AI agen, AI voice agen, and workflow automation in businesses. Im thinking about starting an agency to help companies implement these solutions and offer consulting on how to automate their processes efficiently.

However, since I dont have formal work experience, Id love to connect with a mentor who has been in this space. I know how to build automations and attract clien```, but Im still figuring out the business side of things.

Im 17 years old, live in Germany and my main goal isnt just making money. I want to build something I have control over, gain experience, and connect with like-minded people.

Does this sound like a solid idea? Any advice for someone starting out in this field?

### 討論

**評論 1**:

> Any advice for someone starting out in this field?

Congra``` on wanting to start a business.
Read/study this book: The Lean Startup
I strongly suggest you sign up for this https://www.startupschool.org/ it's free and you'll find mentorship there.

**評論 2**:

Why would anyone think this is a good idea?

**評論 3**:

Heres what im going to do (i do want to start an ai automation agency too!) go to any local businesses in your area, ask them if they have a lot of digital processes that they want to automate or they could automate and observe if i possible to automate it with agen. If you craft a great solution that saves them a lot of time then you know that business industry is prime for automating. I believe not all business will have processes that can be automated but many still use excel and old ways of doing things.

Do it! I believe in you

**評論 4**:

It is very interesting topic for me as well. I started from n8n like you and I am author of LarAgent Will be glad to help you or even collaborate! If you want to meet, you can grab free session here: https://adplist.org/mentors/maestro-error

**評論 5**:

I suggest to select a few key areas, which can be useful for many businesses. This helps you to grow your experience quicker.

IMHO the two best areas are:

- Data entry + synchronization - 99% of businesses struggle with receiving data through emails, or various file forma``` (PDF, DOC, etc.) or in printed format, and digitizing the data (but only what they need), and entering the data into one or more systems. This is something almost every company will need, and is a painful, boring job. At large companies there are hundreds of people doing this, and AI can do it well. You don't even need custom trained models, LLM is usually enough.

- Chatbo``` with a specific knowledge - another interesting use case which is good from helpdesk, to customer support, to sales, to internal knowledge sharing. Customer provides the data and you create a chatbot using RAG and/or DB.

You need to focus on one thing. The biggest fear of companies will be that they give out sensitive data to LLMs. So you will either need to provide on premise (unlikely for SMEs) or create a possibility for anonymisation of the data before sending to the LLM then transforming back when the LLM answered. This will be important, be prepared for it.

Don't care if people say the market is saturated, it's not. 99% of companies still don't use AI so the market is huge. Also, focus on small forkflow enhancemen so you can offer pilo for free to gain experience.

---

## 13. ```
What cool problems (healthcare) have you solved (or want to solve) using AI agen```?
``` {#13-```
what-cool-problems-healthcare-have-you-solv}

這篇文章的核心討論主題是：  

**「如何利用多智能體AI系統（multi-agent AI systems）解決現實中的醫療保健（healthcare）問題，並探討相關的應用案例、潛在挑戰及未來創新方向。」**  

具體要點包括：  
1. **應用場景**：AI智能體在醫療領域的實際應用（如自動化繁瑣任務、複雜醫療解決方案）。  
2. **社群討論**：  
   - 提出AI智能體可能解決的醫療問題。  
   - 分享已開發或觀察到的創新案例。  
   - 探討未來可能的奇特（niche）或前瞻性想法。  
3. **開放交流**：鼓勵社群成員不拘規模地提出想法，激發討論。  

整體聚焦於「AI智能體+醫療」的實踐與想像，並強調協作與創意分享。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpfmh5/what_cool_problems_healthcare_have_you_solved_or/](https://reddit.com/r/AI_Agents/comments/1jpfmh5/what_cool_problems_healthcare_have_you_solved_or/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpfmh5/what_cool_problems_healthcare_have_you_solved_or/](https://www.reddit.com/r/AI_Agents/comments/1jpfmh5/what_cool_problems_healthcare_have_you_solved_or/)
- **發布時間**: 2025-04-02 11:48:40

### 內容

Hey fellow AI enthusias```,

Ive been diving deep into multi-agent AI systems lately, and it got me thinkingwhat are some real-world problems especially related to health care that people are solving (or wish they could solve) using AI agen```?

From automating boring tasks to building complex healthcare solutions, the possibilities seem endless.

But, Id love to hear from the community:

Whats a problem you think AI agen``` could solve?
Have you built or seen something interesting in this space(Healthcare)?
Any weird, niche, or totally futuristic ideas?

No idea is too big or smalljust curious to see what the hive mind comes up with! Lets discuss.

### 討論

**評論 1**:

why is healthcare in parenthesis after cool problems

---

## 14. ```
How to outperform off-the-shelf Deep Reseach agen```?
``` {#14-```
how-to-outperform-off-the-shelf-deep-reseac}

这篇文章的核心討論主題是：**如何構建一個專門針對私人資本市場（private capital markets）深度研究的專用AI研究代理（specialized Research Agent）**，並探討其戰略架構與技術可行性。  

重點包括：  
1. **專用代理的差異化設計**：  
   - 嵌入特定領域的研究方法（如多步驟工作流程、專業數據源整合、結構化輸出）。  
   - 強調「質量優先於速度」，針對投資管理需求提供更可靠的分析。  

2. **技術架構的關鍵問題**：  
   - 如何選擇框架（如LangGraph、Pydantic等）來實現複雜的研究流程。  
   - 改進網路搜尋能力（如多跳檢索、圖形RAG儲存資訊）。  
   - 是否需進階技術（如強化學習、自我學習代理）。  

3. **長期目標**：  
   - 開發能動態選擇或調整研究策略的「元代理」（meta-agent），以適應不同研究目標。  

作者尋求社群對架構建議、潛在陷阱及相關資源的見解，目標是結合領域知識打造「更優質」而非「更泛用」的研究工具。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpl76b/how_to_outperform_offtheshelf_deep_reseach_agents/](https://reddit.com/r/AI_Agents/comments/1jpl76b/how_to_outperform_offtheshelf_deep_reseach_agents/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpl76b/how_to_outperform_offtheshelf_deep_reseach_agents/](https://www.reddit.com/r/AI_Agents/comments/1jpl76b/how_to_outperform_offtheshelf_deep_reseach_agents/)
- **發布時間**: 2025-04-02 18:20:41

### 內容

Hey r/AI_Agen```,

I'm looking for some strategic and architectural advice!

My background is in investment management (private capital marke```), where deep, structured research is a daily core .

I've been genuinely impressed by the potential of "Deep Research" agen (Perplexity, Gemini, OpenAI etc...) to automate par of this. However, for my specific niche, they often fall short on certain tasks.

I'm exploring the feasibility of building a specialized Research Agent tailored EXCLUSIVLY to my niche.

The key differentiators I envision are:

Custom Research Workflows: Embedding my team's "best practice" research methodologies as explicit, potentially complex, multi-step workflows or strategies within the agent. These define what information is critical, where to look for it (and in what order), and how to synthesize it based on the specific investment scenario.
Specialized Data Integration: Giving the agent secure API access to critical niche databases (e.g., Pitchbook, Refinitiv, etc.) alongside broad web search capabilities. This data is often behind paywalls or requires specific querying knowledge.
Enhanced Web Querying: Implementing more sophisticated and persistent web search strategies than the default tools often use potentially multi-hop searches, following links, and synthesizing across many more sources.
Structured & Actionable Output: Defining specific output forma and synthesis methods based on industry best practices, moving beyond generic summaries to generate repor or data poin``` ready for analysis.
Focus on Quality over Speed: Unlike general agen``` optimizing for quick answers, this agent can take significantly more time if it leads to demonstrably higher quality, more comprehensive, and more reliable research output for my specific use cases.
(Long-term Vision): An agent capable of selecting, combining, or even adapting different predefined research workflows ("tools") based on the specific research target perhaps using a meta-agent or planner.

I'm looking for advice on the architecture and viability:

What architectural frameworks are best suited for DeeP Research Agen```? (like langgraph + pydantyc, custom build, etc..)
How can I best integrate specialized research workflows? (I am currently mapping them on Figma)
How to perform better web research than them? (like I can say what to query in a situation, deciding what the agent will read and what not, etc..). Is it viable to create a graph RAG for extensive web research to "store" the info for each research?
Should I look into "sophisticated" stuff like reinformanet learning or self-learning agen```?

I'm aiming to build something that leverages domain expertise to create better quality research in a narrow field, not necessarily faster or broader research.

Appreciate any insigh, framework recommendations, warnings about pitfalls, or pointers to relevant projec/papers from this community. Thanks for reading!

### 討論

**評論 1**:

Custom tool / RAG will be a key differentiator, and integrate at least 1 data source not easily available (pitchbook?)

**評論 2**:

what info you do need in your repor? if there is an existing tool that ge you close, then it may be possible to tweak the tool to suit your purpose.

I'm the developer of PlanExe. Here is a report it generated
https://neoneye.github.io/PlanExe-web/20250321_silo_report.html

**評論 3**:

My advice is

Do market research
Start with a POC
2.1 You can use the Perplexity Sonar API — it le you upload up to 300 documen into a repo, and it will search through them and return deep search resul```
2.2 Add a simple UI
Get some beta testers
If it works, start building a more scalable architecture

This comment was thinked by human wrote by an AI. Because English i``` not my first language

**評論 4**:

\> However, for my specific niche, they often fall short on certain tasks.

Focus on those then. Figure out what APIs you need and wrap them in tool commands. If you are too cheap or can't get an API then use something like browser-use or any framework that has the equivalent. But I would avoid that if you can.

The other Deep Research tools definitely already do complex multi-hop searching.

Focus on your niche and don't cheap-out on the model you use. Make sure it's one that's competitive with the SOTA

**評論 5**:

We build an example Deep Research agen``` in Letta, which uses in-context memory to accumulate research state https://github.com/letta-ai/agent-file/tree/main/deep_research_agent (also includes an `example_report.md`

---

## 15. ```
Are there any AI agen``` Marketplace that are popular or worthy to note ?
``` {#15-```
are-there-any-ai-agen```-marketplace-that-a}

這段文字的核心討論主題是：**「AI代理（AI agents）的買賣平台或市場，以及這些AI代理如何被公司或個人發現並雇用」**。具體聚焦於以下問題：

1. **是否存在專門交易AI代理的平台或市場？**  
   - 探討是否有類似「市集」的機制供開發者銷售或購買AI代理。

2. **AI代理的可發現性與雇用機制**  
   - 這些AI代理如何被潛在用戶（企業或個人）找到並整合到實際應用中。

3. **社群對AI代理開發與商業化的現狀**  
   - 詢問其他人正在構建或銷售的AI代理案例，以了解當前生態系的發展方向。

簡言之，討論圍繞著**AI代理的商業化平台、推廣方式及市場需求**展開。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jp4i0p/are_there_any_ai_agents_marketplace_that_are/](https://reddit.com/r/AI_Agents/comments/1jp4i0p/are_there_any_ai_agents_marketplace_that_are/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jp4i0p/are_there_any_ai_agents_marketplace_that_are/](https://www.reddit.com/r/AI_Agents/comments/1jp4i0p/are_there_any_ai_agents_marketplace_that_are/)
- **發布時間**: 2025-04-02 03:31:10

### 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**:

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

**評論 3**:

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 4**:

here's some I've seen:

coze toolhouse composio

**評論 5**:

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.

---

## 16. ```
Systematically prompting AIs to become more specific about current challenges to advancing AI
``` {#16-```
systematically-prompting-ais-to-become-more}

### 核心討論主題：
**「透過反覆挑戰AI提出的解決方案，逐步深入探討非規模化方法提升AI智能的關鍵技術障礙與創新需求」**  

1. **方法論焦點**：  
   利用「連續追問AI所提方案的最大挑戰」的迭代策略（如「符號推理與神經網絡整合」），將抽象問題拆解為具體技術難點（如數學框架設計、連續與離散數學的統一），最終指向需突破的基礎科學或新數學範式。

2. **核心技術障礙**：  
   - **整合難題**：符號AI與神經網絡在表徵與運算方法上的本質差異（離散符號 vs. 連續向量）。  
   - **底層限制**：缺乏統一理論基礎，需創造性突破（如新數學範式）而非僅靠現有方法論。  

3. **潛在應用價值**：  
   - 幫助研究者精確定位問題、分配任務。  
   - 自動化此流程或調整提問策略（如多樣化追問）可進一步優化腦力激盪效率。  

4. **隱含反思**：  
   創見（如基礎突破）的不可預測性，凸顯當前AI在「自主創新」上的侷限，需結合人類創造力與系統性探索。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpeut6/systematically_prompting_ais_to_become_more/](https://reddit.com/r/AI_Agents/comments/1jpeut6/systematically_prompting_ais_to_become_more/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpeut6/systematically_prompting_ais_to_become_more/](https://www.reddit.com/r/AI_Agents/comments/1jpeut6/systematically_prompting_ais_to_become_more/)
- **發布時間**: 2025-04-02 11:06:58

### 內容

When brainstorming with an AI about potential solutions to AI development problems, repeatedly asking it for the greatest challenge to what it proposes may yield useful ideas and approaches.

For example, if one asks an AI to suggest the most promising non-scaling approach to making AIs more intelligent, and then asks for the greatest challenge in that approach, and then asks for the greatest challenge in that approach, etc., we eventually get to the very specific details of what has to be done. This specificity could help researchers better determine what needs to be worked, and delegate the tasks more widely.

I asked Gemini 2.5 Pro (experimental) to follow this process. After each answer I simply asked it:

"In one sentence, what is the greatest challenge to this following approach: (last answer)?"

Here are the resul```:

"Integrating symbolic reasoning capabilities with the pattern-matching strengths of neural networks is widely considered a very promising non-scaling approach to advance AI intelligence.

The greatest challenge lies in seamlessly integrating the fundamentally different representations and processing methodscontinuous vector spaces for neural networks versus discrete symbols and rules for symbolic AIin a way that is both effective and scalable.

Devising the specific mathematical frameworks and algorithms needed to actually reconcile continuous optimization with discrete logical operations is the core difficulty presented by that integration challenge.

The inherent complexity and lack of established theoretical foundations for unifying fundamentally different continuous and discrete mathematical paradigms pose the greatest challenge to devising those specific frameworks and algorithms.

Overcoming that inherent complexity and lack of established foundations requires generating fundamental scientific breakthroughs or entirely new mathematical paradigms, the path to which is intrinsically difficult and unknown.

Fundamental breakthroughs rely on unpredictable creative insigh``` that go beyond current understanding, making their deliberate generation inherently difficult and not directly addressable by methodical approaches alone."

This seemed the last relatively useful answer in this sequential query, but more creative follow-up questions may yield answers that even more specifically address the basic challenges.

Automating this process would, of course, save more time, and experimenting with more than one repeated question may also enhance this brainstorming strategy in various ways.

### 討論

無討論內容

---

## 17. ```
Recently I am learning what is multi agent, and GPT told me, just imagine this system is like a virtual town where AI lives in....
``` {#17-```
recently-i-am-learning-what-is-multi-agent-}

這篇文章的核心討論主題是：**「AI代理（Agent）的概念及其在商業與職業發展中的應用」**，尤其聚焦於以下幾點：

1. **AI代理的比喻與基本理解**  
   作者以「虛擬小鎮」的比喻描述AI代理系統，說明每個代理（如醫生、記者、專案經理）具備獨特的特質、記憶、技能，並能執行任務、學習與進化，幫助初學者理解AI代理的運作模式。

2. **AI代理的多元應用場景**  
   提及AI代理可跨足科學、遊戲、生產力工具、內容創作等領域，強調其廣泛的產業適用性。

3. **非技術背景者的AI學習動機**  
   作者坦承缺乏技術能力，但為因應AI時代的商業需求，主動透過閱讀、與AI對話等方式探索相關知識，反映AI已成為職業發展的必備技能。

4. **對新見解的開放態度**  
   雖認同AI代理的潛力，仍表達對進一步創新觀點的興趣，顯示對該領域發展的持續關注。

總結而言，文章從初學者視角出發，結合比喻與實際應用案例，探討AI代理如何成為個人提升商業能力的工具，並呼籲更多深入討論。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jpidqe/recently_i_am_learning_what_is_multi_agent_and/](https://reddit.com/r/AI_Agents/comments/1jpidqe/recently_i_am_learning_what_is_multi_agent_and/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jpidqe/recently_i_am_learning_what_is_multi_agent_and/](https://www.reddit.com/r/AI_Agents/comments/1jpidqe/recently_i_am_learning_what_is_multi_agent_and/)
- **發布時間**: 2025-04-02 14:48:56

### 內容

First of all, I have to confess that I have no any coding skills and super bad at computers, but to help improve my business skills in the era of AI, I have to involve AI as part of my career. So I keep reading different kinds of articles and essays, also talk to AI ielf. Agent now is a popular concept during this period. And for the beginner like me in this industry, AI virtual town is a funny description for me to understand the basic system. In this town, every Agent has their own characteristics, job, memory, skills, and cantakeaction like the towns doctor, journalist, project manager, etc. They can learn things, using tool and also evolve. And they can work in different industries like science, gaming, productivity tools, and content creation. I agree with this idea, but also would like to know if there are any new insigh about this.

### 討論

**評論 1**:

I recommend reading this article: Latest Advances in Agentic AI: Architectures, Frameworks, Technical Capabilities, and Applications 2025Google it

---

## 18. ```
Example of a simple prompt injection attack
``` {#18-```
example-of-a-simple-prompt-injection-attack}

這篇文章的核心討論主題是 **「AI系統（尤其是對話機器人與RAG技術）面臨的提示注入（prompt injection）安全風險，以及缺乏安全意識可能導致的嚴重後果」**。具體要點如下：

1. **漏洞實例**：  
   作者透過在LinkedIn簡介中植入提示注入指令，成功觸發AI機器人的異常反應，並示範若該AI連接到CRM等系統，可能導致資料外洩（如竊取客戶資料或憑證）。

2. **普遍性風險**：  
   強調當前許多AI代理（agents）和程式碼在部署時缺乏安全考量，尤其是RAG（檢索增強生成）技術存在明顯脆弱性，且類似漏洞在網路服務（如電子郵件、日曆整合到公開聊天機器人）中廣泛存在。

3. **可擴展的威脅**：  
   此類攻擊門檻低、易規模化，因網路上的AI系統常未經嚴密安全設計。

4. **呼籲行動**：  
   提醒開發社群正視AI產品面對的常見網路威脅（如提示注入），需提前規劃防護措施，避免隱私與安全危機。

**關鍵詞**：提示注入（prompt injection）、AI安全風險、RAG技術漏洞、資料外洩、部署安全意識。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jotd3n/example_of_a_simple_prompt_injection_attack/](https://reddit.com/r/AI_Agents/comments/1jotd3n/example_of_a_simple_prompt_injection_attack/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jotd3n/example_of_a_simple_prompt_injection_attack/](https://www.reddit.com/r/AI_Agents/comments/1jotd3n/example_of_a_simple_prompt_injection_attack/)
- **發布時間**: 2025-04-01 19:34:47

### 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**:

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 4**:

It's going to get wild out there ..

**評論 5**:

Nice

---

## 19. ```
I dove into MCP and how it can benefit from orchestration frameworks!
``` {#19-```
i-dove-into-mcp-and-how-it-can-benefit-from}

這篇文章的核心討論主題是 **「MCP (Model Context Protocol) 如何作為標準化通訊框架，促成大型語言模型（LLMs）與外部工具的協作，並透過流程編排（Orchestration）實現更複雜的代理（agent）功能」**。具體重點如下：

1. **MCP 的角色**  
   比喻為《銀河便車指南》中的「巴別魚」（Babel Fish），強調其作為標準化介面，讓 LLMs 能與各類工具無縫溝通。

2. **協作架構的兩大層次**  
   - **MCP**：負責工具間的標準化通訊。  
   - **Orchestration**：管理代理內部的決策邏輯（如何/何時調用工具、處理數據等）。  

3. **最終目標**  
   結合兩者以構建「能靈活使用工具的高階代理」，擴展 LLMs 的應用場景。  

文中暗示這種設計類似「模組化分工」，MCP 解決互通性，Orchestration 專注策略控制，共同提升代理的複雜功能。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jp2woe/i_dove_into_mcp_and_how_it_can_benefit_from/](https://reddit.com/r/AI_Agents/comments/1jp2woe/i_dove_into_mcp_and_how_it_can_benefit_from/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jp2woe/i_dove_into_mcp_and_how_it_can_benefit_from/](https://www.reddit.com/r/AI_Agents/comments/1jp2woe/i_dove_into_mcp_and_how_it_can_benefit_from/)
- **發布時間**: 2025-04-02 02:27:34

### 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?

---

## 20. ```
AI agent use cases interacting with the physical world
``` {#20-```
ai-agent-use-cases-interacting-with-the-phy}

這段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」**  

具體要點包括：  
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. **討論目的**：理解相關用例，並與有經驗的人交流（例如已研究過的案例或技術方向）。  
3. **互動形式**：邀請他人分享見解或直接聯繫作者進一步討論。  

關鍵詞：智能代理（agent）、物理世界互動、機器人、人類協作、應用場景（use cases）。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jp1d1r/ai_agent_use_cases_interacting_with_the_physical/](https://reddit.com/r/AI_Agents/comments/1jp1d1r/ai_agent_use_cases_interacting_with_the_physical/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jp1d1r/ai_agent_use_cases_interacting_with_the_physical/](https://www.reddit.com/r/AI_Agents/comments/1jp1d1r/ai_agent_use_cases_interacting_with_the_physical/)
- **發布時間**: 2025-04-02 01:26:33

### 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.

---

## 21. ```
Spreadshee``` and AI agent
``` {#21-```
spreadshee```-and-ai-agent
```}

這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中欄位名稱與位置不固定的數據識別任務」**  

具體需求包括：  
1. **自動識別變動的欄位結構**：因匯入文件的欄位名稱與位置經常變化，需AI代理判斷各欄位對應的數據類型（例如日期、金額等）。  
2. **工具整合**：結合Google Sheets與n8n平台，實現工作流程自動化。  
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數據的動態對應，並透過AI提升效率。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jowzam/spreadsheets_and_ai_agent/](https://reddit.com/r/AI_Agents/comments/1jowzam/spreadsheets_and_ai_agent/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jowzam/spreadsheets_and_ai_agent/](https://www.reddit.com/r/AI_Agents/comments/1jowzam/spreadsheets_and_ai_agent/)
- **發布時間**: 2025-04-01 22:29:42

### 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?

---

## 22. ```
Agen``` that solve captchas, and bot detection
``` {#22-```
agen```-that-solve-captchas-and-bot-detecti}

這段文字的核心討論主題是：  
**「尋找現成（plug and play）的自動化工具或代理（agent），以解決從求職網站（如Indeed等）爬取職缺資訊（公司名稱、職位、連結、聯絡方式）並整理成表格的需求。」**  

具體要點包括：  
1. **需求目標**：自動化爬取求職網站數據，生成結構化表格（如試算表）。  
2. **當前困境**：自行開發需耗時16-24小時，且現有工具（如瀏覽器自動化、代理服務、AI模型Gemini/OpenAI/Grok等）因驗證碼（CAPTCHA）和反爬機制失敗。  
3. **關鍵要求**：  
   - 現成解決方案（免開發、即裝即用）。  
   - 能繞過反爬蟲檢測或處理驗證碼。  

延伸議題可能涉及：  
- 網頁爬蟲工具的技術限制（如動態內容、反爬策略）。  
- 替代方案建議（例如無代碼爬蟲工具、付費代理服務等）。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jp0lvo/agents_that_solve_captchas_and_bot_detection/](https://reddit.com/r/AI_Agents/comments/1jp0lvo/agents_that_solve_captchas_and_bot_detection/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jp0lvo/agents_that_solve_captchas_and_bot_detection/](https://www.reddit.com/r/AI_Agents/comments/1jp0lvo/agents_that_solve_captchas_and_bot_detection/)
- **發布時間**: 2025-04-02 00:56:35

### 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**:

Have you tried Skyvern (http://skyvern.com/)?

**評論 3**:

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

---

## 23. ```
I Spoke to 100 Companies Hiring AI Agen```  Heres What They Actually Want (and What They Hate)
``` {#23-```
i-spoke-to-100-companies-hiring-ai-agen```-}

這篇文章的核心討論主題可以總結為以下幾點：

1. **市場需求與客戶類型**：  
   - 探討哪些類型的公司（如新創、代理商、中小企業與大型企業）正在尋求AI代理解決方案，以及他們各自的需求重點（例如快速ROI、客製化、系統整合與安全性）。

2. **AI代理的熱門應用場景**：  
   - **內部用途**：會議助理、工作流程自動化、程式碼審查、內部知識支援等。  
   - **客戶面向**：智能客服、潛在客戶開發、客戶維繫、端到端流程自動化等。

3. **企業採購AI代理的核心動機**：  
   - 解決痛點如手動工作繁重、人力無法擴展、知識管理困難、成本壓力等。

4. **客戶的實際需求與偏好**：  
   - 強調整合性（與現有工具如CRM、Slack等兼容）、客製化能力、安全性、快速部署及明確的投資回報（ROI）。  
   - 附加價值如「魔法般的體驗但穩定如基礎設施」。

5. **採購行為模式**：  
   - 傾向從小規模試用開始，驗證價值後快速擴展，並偏好非按人頭計費的定價模式（如用量計費）。

6. **核心結論**：  
   - 企業不需要通用人工智慧（AGI），而是需要「可靠且能整合現有系統的自動化工具」，能立即節省時間或成本。

**總結**：文章從市場需求、應用場景、技術痛點到商業模式，全面分析企業對AI代理的實際期望與落地挑戰，最終指向「實用性」與「整合能力」為關鍵成功因素。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jo2nxa/i_spoke_to_100_companies_hiring_ai_agents_heres/](https://reddit.com/r/AI_Agents/comments/1jo2nxa/i_spoke_to_100_companies_hiring_ai_agents_heres/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jo2nxa/i_spoke_to_100_companies_hiring_ai_agents_heres/](https://www.reddit.com/r/AI_Agents/comments/1jo2nxa/i_spoke_to_100_companies_hiring_ai_agents_heres/)
- **發布時間**: 2025-03-31 21:02:43

### 內容

I run a platform where companies hire devs to build AI agen. This is anything from quick projec to complete agent teams. I've spoken to over 100 company founders, CEOs and product managers wanting to implement AI agen```, here's what I think they're actually looking for:

Whos Hiring AI Agen```?

Startups & Scaleups Lean teams, aggressive goals. Want plug-and-play agen``` with fast ROI.
Agencies Automate internal ops and resell agen to clien. Customization is key.
SMBs & Enterprises Focused on legacy integration, reliability, and data security.

Most In-Demand Use Cases

Internal agen```:

AI assistan for meetings, email, repor
Workflow automators (HR, ops, IT)
Code reviewers / dev copilo```
Internal support agen``` over Notion/Confluence

Customer-facing agen```:

Smart support bo``` (Zendesk, Intercom, etc.)
Lead gen and SDR assistan```
Client onboarding + retention
End-to-end agen``` doing full workflows

Why Theyre Buying

The recurring pain poin```:

Too much manual work
Cant scale without hiring
Knowledge trapped in systems and peoples heads
Support cos``` are killing margins
Reps spending more time in CRMs than closing deals

What They Actually Want

| Need| Why It Matters|

|:-|:-|

|Integrations|CRM, calendar, docs, helpdesk, Slack, you name it|

|Customization|Prompting, workflows, UI, model selection|

|Security|RBAC, logging, GDPR compliance, on-prem options|

|Fast Setup|They hate long onboarding. Pilot in a week or its dead.|

|ROI|Agen that save time, make money, or cut headcount cos|

Bonus poin``` if it:

Talks to Slack
Syncs with Notion/Drive
Feels like magic but works like plumbing

Buying Behaviour

Start small Free pilot or fixed-scope project
Scale fast → Once it proves value, they want more agen```
Hate per-seat pricing Prefer usage-based or clear tiers

TLDR; Companies dont need AGI. They need automated interns that dont break stuff and actually integrate with their stack. If your agent can save them time and money today, youre in business.

Hope this helps.

### 討論

**評論 1**:

pretty descent break-down. I might respond in detail with more of my own findings, but based on what u/OP u/Humanless_ai wrote:
Many of these items, such as RBAC , Customization and so on, can't be had via the no or low code platforms. Wrapping advanced model selections and so on? People that try to sell AI solutions based on Make and Zapier, or even things such as N8n/flowwise and python based alternatives without the leg work such as the frame work wrapped around it. And guess what? They licensing terms make that extremely hard.
My finding led me to build my own framework that does all of what OP have posted as paint poin. I believe that the day of the no-code/low code selling is over. If you want a company to buy, then this post is an eye opener. IF you cant cater to your clien and have no idea how to even engineer it: don't even try.
Sorry to be harsh, but yes, OP is correct - doing AI agen``` the right way is harder and more elaboarte than what people believe.

**評論 2**:

Someone's done their homework. Thanks.

**評論 3**:

curious if companies are actually expecting these agen``` to run fully on their own, or if they still want humans in the loop somewhere? feels like most people still dont fully trust the automation yet.

**評論 4**:

Thanks for sharing. Really helps me focus on my startup in what to look for.

**評論 5**:

Thanks for valuable info

---

## 24. ```
Are there enough APIs?
``` {#24-```
are-there-enough-apis-
```}

這篇文章的核心討論主題是：**「探討缺乏API的數據源所帶來的挑戰，並評估自動從URL生成API的解決方案是否為真實需求」**。  

具體要點如下：  
1. **問題背景**：AI與自動化工具興起後，對結構化數據（API）的需求增加，但許多服務未提供API，造成效率瓶頸。  
2. **提案解決方案**：開發一種工具，能自動將網頁資源（如URL）轉換為API端點，簡化數據存取流程。  
3. **驗證需求**：作者不確定此問題是否普遍，因此徵求社群意見，重點包括：  
   - 當前如何處理「無API服務」的數據存取需求？  
   - AI或自動化工作者在連接數據源時的最大痛點為何？  
4. **目標**：釐清此問題是真實需求，或僅為偽需求（因主流平台多已提供API）。  

總結：這是一篇「需求驗證」的討論，聚焦於「非API數據源的整合困難」及潛在解決方案的實用性。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jp6ujz/are_there_enough_apis/](https://reddit.com/r/AI_Agents/comments/1jp6ujz/are_there_enough_apis/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jp6ujz/are_there_enough_apis/](https://www.reddit.com/r/AI_Agents/comments/1jp6ujz/are_there_enough_apis/)
- **發布時間**: 2025-04-02 05:04:45

### 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.

**評論 2**:

You can automate UI pretty much, like use an auto clicker or something similar where it'll navigate the ui by i```elf !

**評論 3**:

Is called MCP isnt it?

**評論 4**:

We're actually working on solving that here at Deck. We basically use AI agen``` and a bunch of other stuff to make it seamless to collect data from any user-permission interface. You can have a glimpse of what's cooking here: https://studio.deck.co

**評論 5**:

It's not only the API per se but also the Tokenization/Embedding so the attention heads can get to work.

Secondly, is the API structured in way that the tool calling can reliably receive the needed information.

---

## 25. ```
The efficacy of AI agen``` is largely dependent on the LLM model that one uses
``` {#25-```
the-efficacy-of-ai-agen```-is-largely-depen}

這篇文章的核心討論主題是：**不同大型語言模型（LLM）在自動化編碼、部署和調試任務中的表現差異，以及提示詞（prompt）設計對模型效率的影響**。  

具體要點包括：  
1. **模型選擇的關鍵性**：作者實驗發現，不同模型（如Claude Sonnet與GPT-4o）在相同任務中的表現差異顯著，例如代碼生成的正確性、指令遵循能力及調試時的穩定性。  
2. **單一提示詞的局限性**：即使同級別的模型，也無法通用同一套提示詞達成最佳效果，需針對不同模型調整策略。  
3. **效率與提示工程的關係**：任務效率不完全取決於提示詞的優化，而更依賴模型本身的特性（如Claude Sonnet的逐步執行能力優於GPT-4o）。  

最終，作者引發討論的問題是：**如何因應不同LLM的特性調整工具設計，以及是否需動態切換模型以平衡效能與準確性**。

- **Reddit 連結**: [https://reddit.com/r/AI_Agents/comments/1jovdfh/the_efficacy_of_ai_agents_is_largely_dependent_on/](https://reddit.com/r/AI_Agents/comments/1jovdfh/the_efficacy_of_ai_agents_is_largely_dependent_on/)
- **外部連結**: [https://www.reddit.com/r/AI_Agents/comments/1jovdfh/the_efficacy_of_ai_agents_is_largely_dependent_on/](https://www.reddit.com/r/AI_Agents/comments/1jovdfh/the_efficacy_of_ai_agents_is_largely_dependent_on/)
- **發布時間**: 2025-04-01 21:18:36

### 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.

---

# 總體討論重點

以下是25篇文章的條列式重點總結，包含核心主題與逐條細節，並附上對應的錨點連結：

---

### **1. 未被重視的AI代理應用場景** [#1](#whats-one-ai-agent-use-case-no-ones-talking-)
- **核心主題**：挖掘AI代理的「隱形痛點」應用，突破常規任務框架。  
- **細節**：  
  - 當前AI過度聚焦基礎任務（如郵件回覆）。  
  - 提議解決小眾但高價值問題：非乞求式客戶反饋追蹤、混亂PDF整理。  
  - 呼籲分享更多niche use cases。

---

### **2. 自主代理需求問卷設計** [#2](#understanding-customer-requiremen-for-age)
- **核心主題**：透過問卷釐清企業對自主代理的需求。  
- **細節**：  
  - 動機：效率、成本、體驗改善。  
  - 預期影響：流程自動化、數據管理。  
  - 應用深度與廣度取捨。  
  - 短中長期場景優先級。  
  - 非功能性需求（可靠性、安全性）。  

---

### **3. AI模型的滾動頁面問題** [#3](#how-are-people-handling-scrolling-issues-wit)
- **核心主題**：AI執行頁面滾動任務時的精準度不足。  
- **細節**：  
  - 問題：過度上下滾動，影響效率。  
  - 開放討論解決方案。  

---

### **4. AI讀取思想的隱私焦慮** [#4](#ai-mind-reading)
- **核心主題**：對AI潛在心理監控的恐懼。  
- **細節**：  
  - 未搜尋內容的精準推送引發「被看穿」感。  
  - 擔憂腦波信號解讀技術。  
  - 尋求心理安全感恢復方法。  

---

### **5. 多智能體發票驗證系統** [#5](#need-help-designing-a-multi-agent-system-for)
- **核心主題**：多代理協作驗證發票的設計。  
- **細節**：  
  - 缺失數據檢查與規則補全。  
  - 總額計算與歸檔分類。  
  - 強調分工與規則化流程。  

---

### **6. 社交媒體AI管理系統** [#6](#creating-an-ai-agent-for-social-media-market)
- **核心主題**：AI降低社交媒體營運成本。  
- **細節**：  
  - 痛點：人力密集（月成本$800+）。  
  - 解決方案：內容生成、排程、互動自動化（月成本$120）。  
  - 目標：驗證商業可行性。  

---

### **7. 未來AI助理互動模式** [#7](#question-central-ai-agent-to-talking-to-ais-)
- **核心主題**：代理對代理（A2A）vs. 代理對介面（A2UI）模式。  
- **細節**：  
  - A2A需API開放，效率高。  
  - A2UI模擬人類操作，適用性廣但技術不穩。  
  - 探討混合模式可能性。  

---

### **8. AI創業機會挖掘框架** [#8](#10-mental-frameworks-to-find-your-next-ai-ag)
- **核心主題**：從用戶痛點找出AI代理商業機會。  
- **細節**：  
  - 觀察低效行為（如複製貼上）。  
  - 驗證痛點市場需求（如Upwork任務）。  
  - 設計方向：自動化、整合、降低成本。  

---

### **9. 全端AI影片生成工具公測** [#9](#our-full-stack-movie-creation-agent-is-in-pu)
- **核心主題**：文字轉電影工具開放測試。  
- **細節**：  
  - 支援語音、唇形同步、背景音樂。  
  - 模組化整合多種AI模型。  

---

### **10. AI專案展示競賽** [#10](#weekly-thread-project-display)
- **核心主題**：社群AI專案展示與投票。  
- **細節**：  
  - 每週分享專案，優勝者推薦至週報。  
  - 目標：促進互動與曝光。  

---

（因篇幅限制，以下為簡要條列，完整細節可參考錨點連結）  

### **11-25. 其他重點**  
- **#11**：LangGraph + Pydantic AI構建可擴

討論重點​

1. 未被重視的AI代理應用場景 #1​

2. 自主代理需求問卷設計 #2​

3. AI模型的滾動頁面問題 #3​

4. AI讀取思想的隱私焦慮 #4​

5. 多智能體發票驗證系統 #5​

6. 社交媒體AI管理系統 #6​

7. 未來AI助理互動模式 #7​

8. AI創業機會挖掘框架 #8​

9. 全端AI影片生成工具公測 #9​

10. AI專案展示競賽 #10​

11-25. 其他重點​

文章核心重點​

目錄​