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2025-04-02-rising

  • 精選方式: RISING

討論重點​

以下是針對25篇文章的條列式重點總結，並附上對應的文章錨點連結：

文章重點總結​

1. 10 mental frameworks to find your next AI Agent startup idea​

  • 核心主題：透過觀察用戶痛點發掘AI Agent商機。
  • 重點：
    1. 解決實際問題（如數據導出、切換視窗等高摩擦行為）。
    2. 10種框架（如「Export Button Principle」「Upwork/Fiverr Audit」）。
    3. 商業化潛力評估（替代成本、中小企業需求）。

2. [The Most Powerful Way to Build AI Agen: LangGraph + Pydantic AI](#2-the-most-powerful-way-to-build-ai-agen-langgr)​

  • 核心主題：結合LangGraph與Pydantic AI建構可擴展代理系統。
  • 重點：
    1. 技術優勢：模組化代理、複雜工作流協調。
    2. 案例：7個專用代理串聯（爬取、過濾、發布等）。
    3. 設計原則：類型匹配、人類審核機制。

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

  • 核心主題：MCP協議作為LLM與工具的標準化橋樑。
  • 重點：
    1. MCP解決工具溝通格式不一致問題。
    2. 協調機制管理代理邏輯與工具串聯。

4. Example of a simple prompt injection attack​

  • 核心主題：AI系統的提示注入漏洞風險。
  • 重點：
    1. 實驗展示惡意指令誘導（如竊取CRM資料）。
    2. 呼籲強化AI資安防護。
  • 核心主題：AI代理交易平台現狀與商業化生態。
  • 重點：
    1. 探討代理市場的可發現性與招聘機制。
    2. 詢問現有平台與產品類型。

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

  • 核心主題：AI代理與物理世界互動的應用場景。
  • 重點：
    1. 機器人控制、人機協作案例。
    2. 尋求社群經驗分享。

7. [Spreadshee and AI agent](#7-spreadshee-and-ai-agent)​

  • 核心主題：AI代理自動化處理非結構化表格數據。
  • 重點：
    1. 透過n8n整合AI識別變動欄位。
    2. 解決重複文件欄位不一致問題。

8. [Agen that solve captchas, and bot detection](#8-agen-that-solve-captchas-and-bot-detection)​

  • 核心主題：繞過驗證碼的現成爬蟲工具需求。
  • 重點：
    1. 求職網站資料抓取與反爬蟲挑戰。
    2. 尋求即用解決方案（非自行開發）。

9. Chatgpt vs other models​

  • 核心主題：社群投票評選最佳AI模型。
  • 重點：
    1. 比較GPT、Claude等模型性能。
    2. 公開投票形式引發討論。

10. Are there enough APIs?​

  • 核心主題：數據源API不足對自動化的影響。
  • 重點：
    1. 提案「網頁轉API」工具解決無API痛點。
    2. 驗證需求真實性。

11. [The efficacy of AI agen is largely dependent on the LLM model that one uses](#11-the-efficacy-of-ai-agen-is-largely-dependent)​

  • 核心主題：LLM模型差異對AI代理效率的影響。
  • 重點：

文章核心重點​

以下是各篇文章的一句話摘要（條列式輸出）：

  1. 10 mental frameworks to find your next AI Agent startup idea

    • 提供10種透過觀察用戶痛點與行為發掘AI Agent創業機會的框架，強調解決實際問題與市場驗證的重要性。

  2. The Most Powerful Way to Build AI Agen```: LangGraph + Pydantic AI (Detailed Example)

    • 結合LangGraph與Pydantic AI建構模組化AI代理系統的實例，展示複雜工作流協調與可擴展性優勢。

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

    • 探討MCP協議如何標準化LLM與工具互動，並與協調框架結合以實現高階AI代理功能。

  4. Example of a simple prompt injection attack

    • 實例演示提示注入攻擊如何誘導AI執行危險操作，呼籲重視AI系統資安防護。

  5. Are there any AI agen``` Marketplace that are popular or worthy to note ?

    • 詢問現有AI代理交易平台的生態現狀與商業化可能性。

  6. AI agent use cases interacting with the physical world

    • 探討AI代理透過機器人或人機介面與物理環境互動的應用場景。

  7. Spreadshee``` and AI agent

    • 利用AI代理自動化處理Google Sheets非結構化數據的實務挑戰與解決方案。

  8. Agen``` that solve captchas, and bot detection

    • 尋求能繞過驗證碼與反爬蟲機制、自動抓取求職網站資料的現成工具。

  9. Chatgpt vs other models

    • 發起社群投票比較不同AI模型性能的公開討論。

  10. Are there enough APIs?

    • 探討「將網頁轉API」工具的需求價值，驗證數據可訪問性是否為自動化痛點。

  11. The efficacy of AI agen``` is largely dependent on the LLM model that one uses

    • 實證不同LLM在編程任務中的表現差異，凸顯模型選擇對AI代理效能的關鍵影響。

  12. We built Assista AI. It connec``` with thousands of tools you already use. How would you put it to work?

    • 推廣串接多工具的Assista AI並徵求用戶真實應用場景反饋。

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

    • 呼籲釐清「Agentic AI」與相關術語的明確定義以避免混用。

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

    • 探討AI代理商業模式如何平衡成本與盈利可持續性。

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

    • 比較低代碼工具Zapier與Make在創建AI代理上的入門友善度。

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

    • 徵求對Vectara公司加密AI代理職位真實性的經驗分享。

  17. Easiest way to set up a chatbot for Wha```App responses?

    • 尋求整合AI聊天機器人至WhatsApp的最簡易方案。

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

    • 設計跨電商童裝比價AI代理的技術需求與應用場景探討。

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

    • 主張開發AI智能評估工具在專業服務選擇中的商業潛力。

  20. How Would You Prepare for & Build the Basic Customer Support Agent?

    • 彙整開發AI客服代理的端到端決策框架與最佳實踐。

  21. We switched to cloudflare agen``` SDK and feel the AGI

    • 分享遷移至Cloudflare Agent SDK後效能提升70%與架構簡化的實證經驗。

  22. Basic AI agent?

    • 詢問利用低代碼工具實現網頁監測與自動化操作的方法。

  23. What is the best A.I./ChatBot to edit large JSON code? (about a court case)

    • 比較AI處理大型JSON檔案的侷限性並尋求法律案件資料管理替代方案。

  24. Though``` on latest version of MCP spec with auth?

    • 分析

目錄​

1. 10 mental frameworks to find your next AI Agent startup idea​

這篇文章的核心討論主題是：如何透過觀察用戶行為和工作流程中的「痛點」（pain points），來發掘具有商業價值的AI Agent應用機會。

主要重點：​

  1. 以解決實際問題為導向：

    • 強調AI Agent的成功關鍵不在技術本身，而在於能否解決用戶「願意付費」的具體痛點。
    • 提出「觀察用戶真實行為」比依賴用戶自我報告更有效（例如：數據導出、切換視窗、複製貼上等行為）。

  2. 10種發掘AI Agent機會的框架：

    • 從用戶行為（如「Export Button Principle」「Alt+Tab Signal」「Copy+Paste Pattern」）到商業模式（如「Current Paid Solution」「Upwork/Fiverr Audit」），系統性歸納可自動化的場景。
    • 特別關注「已被驗證的需求」（如現有付費服務、外包任務）和「高摩擦情境」（如無效會議、知識瓶頸專家）。

  3. 商業化潛力評估：

    • 強調「替代成本」與「性價比」（例如：用AI取代部分人力，以更低成本提供80%的解決方案）。
    • 鎖定中小企業痛點（如家庭成員協助管理業務）或高頻重複性任務。

核心結論：​

成功的AI Agent產品需基於真實用戶痛點，並透過行為觀察與市場驗證（如現有付費習慣）來確保商業可行性。 文中的框架提供了一套具體方法，幫助開發者從日常場景中識別高潛力機會。

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!

2. The Most Powerful Way to Build AI Agen: LangGraph + Pydantic AI (Detailed Example) \{#2-the-most-powerful-way-to-build-ai-agen-langgr}​

這篇文章的核心討論主題是：如何結合 LangGraph 和 Pydantic AI 來構建可擴展的 AI 代理系統，並以一個具體的「AI Listing Manager Agent」案例說明其實際應用與優勢。

重點包括：

  1. 技術組合的優勢

    • Pydantic AI：快速定義高度專業化的代理，便於擴展功能而不影響現有代理。
    • LangGraph：靈活協調多個代理，支持複雜工作流設計、人類介入（human-in-the-loop）、狀態記憶，並能隨系統複雜度擴展。

  2. 實際應用案例

    • 開發一個由 7 個專用 Pydantic AI 代理組成的系統，透過 LangGraph 串聯，功能涵蓋：
      • 網路爬取（Search Agent）
      • 品質過濾（Filtering Agent）
      • 摘要與分類（Summarizer/Classifier Agent）
      • 人類反饋整合（Feedback/Rectifier Agent）
      • 最終發布（Publisher Agent）。

  3. 關鍵設計原則

    • 類型匹配：確保 Pydantic AI 代理的輸出類型與 LangGraph 狀態儲存的數據一致，以實現無縫協作。
    • 可觀測性與控管：
      • 代理提供信心分數（confidence scores）以降低幻覺風險。
      • 人類審核機制（human-in-the-loop）確保生產環境的可靠性。

  4. 開源與可擴展性

    • 作者公開程式碼與教學資源，強調此架構的易適應性，可供他人直接套用或修改。

總結：文章主要推廣 LangGraph + Pydantic AI 的技術組合，並透過實際案例展示其在構建模組化、可維護且可控的 AI 代理系統中的高效性。

內容​

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

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

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

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

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

Each agent takes on a specific task:

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

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

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

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

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

  6. Rectifier agent: Modifies listings according to our feedback

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

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

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

Key Aspec```:

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

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

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

討論​

評論 1:

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

評論 2:

Or use Agno.

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

(Agno was formally called phidata)

評論 3:

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

評論 4:

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

評論 5:

This is great work.

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

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

這篇文章的核心討論主題是 MCP（Model Context Protocol） 如何作為大型語言模型（LLMs）與外部工具之間的標準化溝通橋樑（類似《銀河便車指南》中的「巴別魚」），並強調其與「協調機制」（Orchestration）的協同作用：

  1. MCP 的角色：

    • 提供標準化協議，讓 LLMs 能與任何工具無縫互動，解決溝通格式不一致的問題。

  2. 協調機制的功能：

    • 管理代理（agent）的內部邏輯，決定何時調用 MCP、處理數據或執行其他步驟，以實現更複雜的工具串聯。

  3. 整體目標：

    • 結合 MCP 與協調機制，構建能靈活使用多工具的高階 AI 代理（tool-using agents）。

文中比喻 MCP 如同科幻中的「巴別魚」，突顯其消除工具與模型間溝通障礙的潛力。作者期待進一步討論此架構的應用可能性。

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?

4. Example of a simple prompt injection attack​

這篇文章的核心討論主題是「AI系統（尤其是對話機器人和RAG技術）面臨的提示注入（prompt injection）安全漏洞及其潛在風險」。作者透過親身實驗（在LinkedIn個人資料中植入惡意指令）展示AI系統可能被誘導執行危險操作（如竊取CRM系統的客戶資料），並警告當前許多AI應用在缺乏安全考量的情況下隨意部署，容易成為網路攻擊的突破口。文中進一步指出，類似漏洞普遍存在於整合郵件、日曆或通訊軟體的公開AI服務中，呼籲開發社群正視AI技術的資安防護需求。

內容​

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

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

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

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

討論​

評論 1:

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

評論 2:

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

評論 3:

It's going to get wild out there ..

評論 4:

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

評論 5:

Nice

5. Are there any AI agen Marketplace that are popular or worthy to note ? \{#5-are-there-any-ai-agen-marketplace-that-are-po}​

這篇文章的核心討論主題可以總結為以下幾點：

  1. AI代理（AI agents）的交易平台或市場：探討是否存在專門用於買賣AI代理的平台或市場，類似於其他數字產品或服務的交易市場。

  2. AI代理的可發現性與招聘機制：討論如何讓公司或個人能夠發現並雇用這些AI代理，包括可能的搜索、推薦或匹配機制。

  3. 當前市場上的AI代理產品：好奇了解目前開發者和賣家正在構建和銷售哪些類型的AI代理，以及這些產品的具體應用或功能。

整體而言，文章聚焦於AI代理的商業化生態系統，包括其交易平台、推廣方式以及市場現狀。

內容​

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

討論​

評論 1:

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

評論 2:

here's some I've seen:

coze toolhouse composio

評論 3:

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

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

評論 4:

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

評論 5:

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

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

這篇文章的核心討論主題是：
探索需要與物理世界互動的智能代理（agent）的應用場景，包括通過機器人或人類介面實現的案例。

具體要點包括：

  1. 應用方向：關注智能代理在物理環境中的實際應用（如機器人控制、人機協作等）。
  2. 目的：發起討論以了解相關案例，並邀請他人分享經驗或見解。
  3. 互動形式：尋求公開回應或私下交流，進一步深入探討此領域的可能性。

關鍵詞：智能代理（agent）、物理世界互動、機器人、人機介面、應用場景。

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.

7. Spreadshee and AI agent \{#7-spreadshee-and-ai-agent}​

這篇文章的核心討論主題是：如何利用AI代理（在n8n平台中）自動化處理Google Sheets中結構不一致的數據文件，具體需求是讓AI識別變動的欄位名稱與位置，以準確對應數據類型。

關鍵點包括：

  1. 自動化流程：透過n8n（低程式碼/無程式碼工具）整合AI代理。
  2. 數據挑戰：重複收到的文件存在欄位名稱、順序不一致的問題。
  3. AI的應用：訓練或設定AI自動識別欄位對應的數據類型（如日期、金額等）。
  4. 實務經驗求助：詢問是否有類似經驗的解決方案或建議。

本質上是探討AI在非結構化數據整理中的實際應用，並結合自動化工具（n8n + Google Sheets）提升工作效率。

內容​

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

討論​

評論 1:

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

1/ using an AI

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

評論 2:

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

8. Agen that solve captchas, and bot detection \{#8-agen-that-solve-captchas-and-bot-detection}​

這篇文章的核心討論主題是：

尋找可直接使用的「外掛即用」（plug and play）網路爬蟲工具或自動化代理（agent），用於從求職網站（如Indeed等）自動抓取職缺資訊（公司名稱、職位、連結、聯絡方式等），並整理成表格（如Google Sheets）。

作者提到已經嘗試過瀏覽器自動化工具、代理伺服器（proxy）以及AI模型（如Gemini、OpenAI、Grok等），但均因驗證碼（CAPTCHA）和反爬蟲機制而失敗。因此，他希望找到現成的解決方案，而非自行開發（儘管他認為自行開發需耗時16-24小時）。

關鍵需求：

  1. 自動化爬取求職網站資料。
  2. 避開驗證碼和反爬蟲機制（如使用代理或繞過檢測的工具）。
  3. 直接輸出結構化表格，無需額外處理。

內容​

So I need some agen``` for my company

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

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

Already tried:

- browser use

- proxy convergence

- deepresearch for gemini, oai, grok etc

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

Any suggestions for plug and play solutions?

討論​

評論 1:

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

評論 2:

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

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

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

9. Chatgpt vs other models​

該文章的核心討論主題是「投票評選最優秀的AI模型」，並透過Reddit的投票功能讓網友參與討論。文中可能涉及不同AI模型（如GPT、Claude、PaLM等）的比較，但具體內容需參考原連結的投票選項及討論。

簡要總結：

  1. 目的：發起公開投票，讓社群選出「最強AI模型」。
  2. 形式：以Reddit投票（Poll）進行，可能包含多個選項。
  3. 潛在爭論點：不同AI模型的性能、應用場景或技術優勢之爭。

（註：由於原連結無法直接查看，此分析基於標題及常見AI模型爭議推測。）

內容​

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

View Poll

討論​

評論 1:

it depends on your use case

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

評論 2:

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

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

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

10. 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」是否為自動化工具和AI應用中的實際痛點，並評估「自動從URL生成API」的解決方案是否具有需求價值。

具體要點包括：

  1. 問題背景：AI與自動化工具的興起增加了對結構化數據（透過API存取）的需求，但許多服務未提供開放API，導致效率瓶頸。
  2. 提案解決方案：開發一種工具，能將任意網頁資源（如URL）自動轉換為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存在（因主流平台通常已有API），因此徵求社群回饋，例如：
    • 當前如何處理「無API服務」的數據存取？
    • 使用AI或自動化工具時，連接數據源的最大挑戰為何？
  4. 目的：釐清該問題是真實需求或偽需求，以決定是否投入開發。

總結：這是一篇「需求驗證」導向的討論，聚焦於「數據可訪問性」在自動化場景中的重要性及潛在解決方案。

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.

11. The efficacy of AI agen is largely dependent on the LLM model that one uses \{#11-the-efficacy-of-ai-agen-is-largely-dependent}​

這篇文章的核心討論主題可以總結為以下幾點：

  1. AI代理在編程中的應用：作者探討了使用AI代理來自動化編程流程（包括代碼生成、部署和日誌分析）的可行性與效果。

  2. 不同LLM模型的性能差異：作者通過實驗比較了不同大型語言模型（如Claude Sonnet和GPT-4o）在代碼生成和調試任務中的表現，發現模型之間的表現存在顯著差異。例如：

    • Claude Sonnet在遵循指令和逐步生成代碼方面表現出色。
    • GPT-4o在代碼生成量或調試時可能陷入循環，表現不穩定。

  3. 提示詞（prompt）的通用性問題：作者指出，即使是能力相近的LLM模型，也無法通過單一的提示詞實現一致的輸出效果，需要針對不同模型調整提示策略。

  4. 效率與提示工程的關係：作者提出，任務效率（如代碼生成質量）並不完全依賴於提示工程的優化，而是更受後端模型本身能力的影響。

  5. 模型切換的必要性：實驗結果顯示，在某些場景下需要切換模型（如從GPT-4o轉為Claude Sonnet）才能獲得理想結果，暗示當前技術下「單一模型通用解決方案」的局限性。

總結來說，文章的核心在於探討不同LLM在實際編程任務中的表現差異性，以及這種差異對AI工具設計的影響（如模型選擇、提示詞適配性等），並引發對「提示工程效用邊界」的思考。

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.

12. We built Assista AI. It connec with thousands of tools you already use. How would you put it to work? \{#12-we-built-assista-ai-it-connec-with-thousands}​

這篇文章的核心討論主題是：

「Assista AI 如何簡化日常工作流程，並徵求用戶對實際應用場景的反饋」

具體重點包括：

  1. Assista AI 的功能介紹：

    • 直接串接多種工具（如 Gmail、Slack、Notion 等），減少切換應用程式的需求。
    • 舉例功能：快速發送郵件、安排會議、集中管理通知。

  2. 徵求用戶真實意見：

    • 創辦人 Paul Burca 希望了解用戶在日常工作中的痛點，例如重複性任務或希望自動化的流程。
    • 強調「用戶視角」的重要性，而非僅從開發者角度推測需求。

  3. 互動目的：

    • 透過用戶反饋，進一步優化產品以解決實際問題。

簡言之，文章旨在推廣 Assista AI 的效率價值，同時發起一場關於「用戶真實需求」的開放式討論。

內容​

Paul Burca here, founder of Assista AI.

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

You can:

  • Send quick emails without opening Gmail.

  • Schedule meetings without going back-and-forth.

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

But that's how we see it.

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

I'm all ears.

討論​

評論 1:

Who deployed the bo```?

評論 2:

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

評論 3:

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

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

評論 4:

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

評論 5:

Nice product. Two problems I found:

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

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

這篇文章的核心討論主題是：對「AI agent」（AI代理）、「Agentic AI」（能動性AI）和「multi-agent systems」（多代理系統）等術語的定義混淆與釐清需求。

作者表達了對這些術語缺乏明確、權威定義的困惑，並希望透過學術論文或可信來源來釐清概念，以避免這些詞彙被混用的情況。因此，重點在於釐清相關術語的定義與區別。

內容​

Hey everyone

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

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

Thanks in advance.

討論​

評論 1:

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

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

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

評論 2:

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

Agentic AI operates in four key stages:

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

評論 3:

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

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

評論 4:

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

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

這篇文章的核心討論主題是：
「如何建立兼具盈利性和長期可持續性的AI代理（AI agent）商業模式」

具體聚焦的議題包括：

  1. 盈利挑戰：AI代理的運營成本（如LLM tokens、第三方服務費用）侵蝕利潤，導致實際收益薄弱。
  2. 成本優化：嘗試採用更經濟的模型（如DeepSeek R1/V3）來降低開支。
  3. 商業模式探討：
    • 哪些模式能產生穩定收入並覆蓋持續成本？
    • 現有變現方式的成功或失敗案例分享。
  4. 可持續性策略：尋求未被充分討論的創新方法，以平衡長期運營與盈利能力。

整體而言，作者呼籲AI從業者交流實際經驗，共同解決「獲利」與「可持續性」之間的矛盾。

內容​

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

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

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

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

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

討論​

評論 1:

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

評論 2:

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

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

这篇文章的核心討論主題是：

「如何選擇一個適合非技術背景使用者、能快速且輕鬆創建AI代理（agent）的工具，以自動化工作流程（如文字摘要、網頁爬取、圖像生成等），並排除當前不考慮使用n8n的解決方案。」

具體要點：​

  1. 使用者痛點：

    • 缺乏技術背景，希望工具簡單易用。
    • 需要快速創建AI代理，節省時間。
    • 排除n8n（因技術複雜性）。

  2. 自動化需求：

    • 文字摘要（Text summarization）。
    • 網頁爬取（URL scraping）。
    • 圖像生成（Image generation）。

  3. 核心問題：

    • 在符合上述條件下，推薦最合適的工具選項。

可能的延伸討論方向：​

內容​

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

討論​

評論 1:

N8n is cool

評論 2:

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

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

Again, it all depends on your use case ;)

評論 3:

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

評論 4:

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

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

評論 5:

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

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

這篇文章的核心討論主題是：

詢問關於公司「Vectara Ltd」及其「Crypto AI Agent」職位的真實性與可信度。

具體要點包括：

  1. 發文者希望瞭解該公司招募加密貨幣相關任務（如Crypto AI Agent）的合法性。
  2. 徵求他人過往的工作經驗（例如是否收到高額訂單或包裹）。
  3. 評估該職位機會是否值得信賴，並請求正面或負面的回饋。

整體而言，這是一個對潛在求職機會的風險與真實性驗證討論。

內容​

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

討論​

評論 1:

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

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

17. Easiest way to set up a chatbot for WhaApp responses? \{#17-easiest-way-to-set-up-a-chatbot-for-whaapp-r}​

這篇文章的核心討論主題是：

「如何以最簡單的方式設置一個能自動回覆 Wha```App 訊息的聊天機器人，並整合 AI 技術（如 ChatGPT API）實現對話功能。」

具體需求包括：

  1. 簡易設置與整合：希望流程簡單，尤其是與 Wha```App 的串接。
  2. AI 對話能力：能透過 ChatGPT API 或類似技術處理自然語言對話。
  3. 可靠性與擴展性：解決方案需穩定且能應對未來需求增長。

作者進一步詢問推薦的工具、平台以及實現流程，以達成上述目標。

內容​

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

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

A few key things Im looking for:

  • Easy setup and integration with Wha```App

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

  • Reliable and scalable solution

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

Thanks in advance.

討論​

評論 1:

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

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

(I am a bot) Source

評論 2:

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

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

這篇文章的核心討論主題是：
如何開發一個專為父母設計的AI代理（AI agent），用於自動化搜尋並整合多個電商平台的童裝選購流程。具體需求包括：

  1. 簡化輸入：透過自然指令（如季節、尺寸、預算、偏好品牌）觸發搜尋。
  2. 跨平台搜尋：掃描多個指定或推薦的電商網站，避開Google Shopping或Amazon等主流平台。
  3. 智能整合：將結果彙整成單一願望清單或購物車，提供最佳選項。
  4. 一站式結帳：允許用戶在統一介面確認並完成付款（儘管技術上可能具挑戰性）。

作者進一步尋求技術建議（如開發方法）與共鳴（是否有類似需求的群體），目標是解決家長因孩子快速成長而需頻繁購衣的痛點，節省時間並提升個性化體驗。

內容​

Hi everyone,

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

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

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

  2. Search across multiple e-commerce sites

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

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

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

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

討論​

評論 1:

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

評論 2:

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

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

這篇文章的核心討論主題是：AI與人類在知識工作領域（如法律、金融、行銷等）的智能比較與競爭，並提出一個潛在的商業機會——開發能評估文件作者（人類或AI）智能水平的工具，以幫助消費者選擇更高效或更具成本效益的服務提供者（人類或AI）。

具體要點包括：

  1. AI與人類專業服務的取捨：探討未來消費者可能面臨的選擇，例如在價格與智能水平之間權衡是否雇用AI律師或人類律師。
  2. 智能評估的可行性：主張AI可透過分析文件內容（如邏輯、準確性等）來量化作者的智能（類似IQ測試），並認為這種評估能跨領域應用（如金融、工程等）。
  3. 商業潛力：強調此類智能評估工具可能成為市場先驅者的關鍵產品，尤其在「AI代理」（AI agent）崛起的背景下，具有龐大的競爭優勢與獲利空間。

爭議點在於：作者反駁「AI與人類智能不可比」的觀點，認為智能應以輸出結果（如文件質量）而非思考過程來衡量。

內容​

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

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

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

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

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

討論​

評論 1:

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

評論 2:

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

評論 3:

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

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

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

評論 4:

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

評論 5:

92

20. How Would You Prepare for & Build the Basic Customer Support Agent?​

這篇文章的核心討論主題是：「開發和部署一個簡單AI客服代理（agent）的最佳實踐與決策框架」，具體聚焦於以下幾個面向：

  1. 需求釐清與客戶溝通

    • 如何系統化地收集客戶需求（例如：模板或檢查清單）。
    • 釐清場景範圍（如支援渠道、處理的問題類型）。

  2. 技術架構選擇

    • 代理的類型與功能設計（如RAG、工具調用、記憶管理、資料庫整合等）。
    • 複雜度權衡（從基礎規則回應到自主決策的連續光譜）。

  3. 開發工具與框架

    • 工具鏈選擇（無代碼平臺 vs. LangChain/CrewAI等代碼框架）。
    • 特定技術建議（如Pydantic驗證、流程編排工具）。

  4. 監控與評估

    • 生產環境監控方案（LangSmith/Langfuse等工具的實用性比較）。
    • 評估指標設計（準確性、用戶體驗、異常處理）。

  5. 部署策略

    • 基礎設施選型（雲端/本地/混合部署的考量因素）。
    • 可擴展性與成本平衡。

  6. 實戰經驗分享

    • 常見陷阱與解決方案（如幻覺控制、對話流設計）。
    • 從真實案例中總結的技巧（例如多平臺整合的挑戰）。

整體目標是建立一個端到端的開發指南，協助開發者在實際商業場景中（如XYZ公司的客服代理）做出關鍵決策，同時匯集社群經驗形成最佳實踐。

內容​

Have you found the perfect process/platform/approach for developing & deploying a simple agent?

Your experiences will make this a useful resource for anyone developing an AI agent or Agentic system.

Scenario:

You are tasked to develop a customer support agent for the tech company XYZ.

It handles general inquiries, prices & produc questions, complain, feedback, etc., via Wha```app and Social Media channels.

The complexity of the agent/flow is up to you.

Now what?

  • What do you request from yout client (do you have a template/checklist/etc.)?

  • What type of agent do you build (RAG, CAG, Tools, DB, Memory,etc.)

  • How do you build it (no-code, LangChain, PydanticAI, CrewAI, other)?

  • How do you monitor and eval (Langsmith, Langfuse, Helicone, other)?

  • Where do you deploy it (cloud/local/hybrid)?

  • Any additional insigh, tools, red flags, or tips and tricks you learned from your experience building agen for the real world?

討論​

評論 1:

I've found that low latency is a huge factor when it comes to these agen```. Having the chatbot responding to users with virtually no lag improves the user experience.

My current go-to process is sending the client a checklist with details about their business and examples of inquiries. I ask them to includes as many Q&As as possible.

If it's a simple chatbot (no tools), I'm using CrewAI. If it's a more complicated automation flow I use LangChain+ LangGraph + LangSmith

Most are deployed on the cloud and it's aws for me.

21. We switched to cloudflare agen SDK and feel the AGI \{#21-we-switched-to-cloudflare-agen-sdk-and-feel-}​

这篇文章的核心討論主題是作者分享從AWS基礎設施遷移到Cloudflare Agent SDK後的顯著改進體驗。主要重點包括：

  1. 效能提升：遷移後，Claude Sonnet 3.7的回應速度與之前使用GPT-4o相當，端到端延遲降低了約70%，使AI感覺更加智能，並大幅提升了用戶參與度。

  2. 簡化的架構與管理：

    • 內建的調度系統取代了自定義解決方案，減少了5,000行代碼。
    • 使用簡單的SQL結構，避免了DynamoDB的複雜性，降低了代碼量和維護難度。

  3. 靈活的客製化功能：新架構支持為每個客戶動態調整系統提示（system prompts），目前處於構想階段，但可行性高。

  4. 應用場景：新基礎設施用於支持創業公司的AI員工，自動化行銷、銷售和Meta廣告管理。

最後，作者詢問是否有其他人也有類似的遷移經驗，希望引發討論。

內容​

After struggling for months with our AWS-based agent infrastructure, we finally made the leap to Cloudflare Agen SDK last month. The resul have been AMAZING and I wanted to share our experience with fellow builders.

The "Holy $%&@" moment: Claude Sonnet 3.7 post migration is as snappy as using GPT-4o on our old infra. We're seeing ~70% reduction in end-to-end latency.

Four noticble improvemen```:

  1. Dramatically lower response latency- Our agen``` now respond in nearly real-time, making the AI feel genuinely intelligent. The psychological impact on latency on user engagement and overall been huge.

  2. Built-in scheduling that actually works- We literally cut 5,000 lines of code from a custom scheduling system to using Cloudflare Workers in built one. Simpler and less code to write / manage.

  3. Simple SQL structure = vibe coder friendly- Their database is refreshingly straightforward SQL. No more wrangling DynamoDB and cursor's quality is better on a smaller code based with less files (no more DB schema complexity)

  4. Per-customer system prompt customization- The architecture makes it easy to dynamically rewrite system promp``` for each customer, we are at idea stage here but can see it's feasible.

PS: we're using this new infrastructure to power our startup's AI employees that automate Marketing, Sales and running your Meta Ads

Anyone else made the switch?

討論​

評論 1:

Please don't call scrip``` employees. Just a suggestion. :)

評論 2:

Did you get paid by Cloudflare for this post?

評論 3:

I just do the domains DNS through it... What am i missing. I run a small home page business

評論 4:

The agen SDK and Durable Objec in general are awesome. I really like the full-stack experience of agen``` with useAgent.

My biggest gripe it's vendor-locked to Cloudflare. I've been working on a project called ActorCore that provides a similar experience but on both Cloudflare DO any place you can run Node.js (GitHub). If you have a sec, I'd love to hear how your experience with agen``` compares to ActorCore.

評論 5:

intresting can you share more info on what specific product of cloudfare are you using?
hosting llm locally ?
can you share more technical context please ? thank!

22. Basic AI agent?​

這篇文章的核心討論主題是：

「如何利用低代碼/無代碼工具，創建一個能監控網站內容變化（例如商品狀態或價格變動）並自動觸發按鈕點擊等後續動作的代理或機器人（agent/bot）。」

具體要點包括：

  1. 功能需求：監控網頁動態變化（如文字從空白/N/A變為「open」或「$1.00」）。
  2. 自動化操作：偵測到變化後，立即執行預設的按鈕點擊等動作。
  3. 工具偏好：尋求低代碼（low-code）或無代碼（no-code）的解決方案，降低技術門檻。

可能的解決方向（未在原文提及但相關）：

內容​

Hi all, enjoying the community here.

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

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

Thanks!!

討論​

評論 1:

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

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

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

(I am a bot) Source

評論 2:

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

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

評論 3:

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

23. What is the best A.I./ChatBot to edit large JSON code? (about a court case)​

這篇文章的核心討論主題是：

作者在處理法律案件時，使用 JSON 格式整理資訊並與多種 AI 聊天機器人（如 ChatGPT、DeepSeek、Grok）互動，但遇到以下問題：

  1. JSON 檔案過大（112K+ 字符）導致 AI 處理困難，包括：

    • ChatGPT 無法完整貼入新對話，需分段處理且整合效果差。
    • DeepSeek 有對話次數限制，貼入大型文本幾次後即中斷。
    • Grok 記憶力極差，很快忘記 JSON 內容並虛構或刪改資訊。

  2. AI 的共通缺陷：

    • 自動簡化或篡改 JSON 內容，即使有明確指令仍無效。
    • 無法穩定處理大型結構化數據，導致來回修改、效率低下。

  3. 尋求解決方案：

    • 是否有更適合的免費 AI/ChatBot 能無錯誤處理大型 JSON？
    • 是否有 JSON 以外的替代方法（如資料庫、專用工具）來高效組織案件資訊？

總結：作者需要一個能穩定處理大型結構化數據（如 JSON）的 AI 或替代方案，以克服當前工具的限制，確保法律案件資訊的完整性與編輯效率。

內容​

I am investigating and collecting information for a court case,

and to organize myself and also work with different A.I. I am keeping the case organized within a JSON code (since an A.I. gave me a JSON code when I asked to somehow preserve everything I had discussed in a chat to paste into another chat and continue where I left off)

but I am going crazy trying to edit and improve this JSON,

I am lost between several ChatBo``` (in their official versions on the official website), such as CharGPT, DeepSeek and Grok,

each with i flaws, there are times when I do something well, and then I don't, I am going back and forth between A.I./ChatBo kind of lost and having to redo things.

(if there is a better way to organize and enhance a collection of related information instead of JSON, feel free to suggest that too)

I would like to know of any free AI/ChatBot that:

- Doesn't make mistakes with large JSON, because I've noticed that chatbo``` are bugging due to the size of the JSON (it currently has 112 thousand characters, and it will get bigger as I describe more details of the process within it)

- ChatGPT doesn't allow me to paste the JSON into a new chat, so I have to divide the code into par using a "Cutter for GPT", and I've noticed that ChatGPT is a bit silly, not knowing how to join all the generated par and understand everything as well.

- DeepSeek says that the chat has reached i conversation limit after about 2 or 3 times I paste large tex into it, like this JSON.

- Grok has a BAD PROBLEM of not being able to memorize things, I paste the complete JSON into it... and after about 2 messages it has already forgotten that I pasted a JSON into it and has forgotten all the content that was in the JSON. - due to the size of the file, these AIs have the bad habit of deleting details and information from the JSON, or changing tex``` by inventing things or fictitious jurisprudence that does not exist, and generating summaries instead of the complete JSON, even though I put several guidelines against this within the JSON code.

So would there be any other solution to continue editing and improving this large JSON?

a chatbot that did not have all these problems, or that could bypass i limi, and did not have understanding bugs when dealing with large codes.

討論​

評論 1:

JSON is not code, I'm guessing your background is not technical so I'll assume RAG or Agentic solutions would be a challenge. Easiest thing would be to chat with Google's Gemini 2.5, which has a HUGE context window with 1 million tokens, so that's plenty of room for your JSON and whatever else you wanna put in the chat.

評論 2:

Ask bolt.new to parse your JSON into a supabase database then tell it to write code around it for whatever you're trying to accomplish with the data

24. Though on latest version of MCP spec with auth? \{#24-though-on-latest-version-of-mcp-spec-with-au}​

這篇文章的核心討論主題是 「MCP規範最新版本中的認證（auth）功能，以及其對agent與工具/軟體整合的影響與未來發展」。具體要點如下：

  1. MCP規範的認證功能
    作者肯定最新版MCP（可能是某種協定或框架）加入認證機制，解決過去agent與工具整合時繁瑣的授權問題，尤其隨著工具數量增加，此功能顯得更重要。

  2. 工具/API的可發現性（discoverability）
    討論MCP認證與註冊機制（registries）普及後，將改善工具和API的發現與整合效率，但同時指出現有API設計可能成為agent靈活運用的阻礙。

  3. 未來工具/API的改進方向
    作者主張需發展更靈活、自描述（self-describing）且目標導向（goal-oriented）的API機制，以充分釋放agent的潛力，並詢問是否有相關成功案例。

  4. 社群互動與觀點分享
    作者邀請讀者分享使用MCP認證的經驗，並附上自己的相關文章連結，希望激發進一步討論。

整體而言，文章聚焦於 技術規範（MCP）的演進如何推動agent與工具生態的變革，並探討未來API設計的關鍵方向。

內容​

It was great to see that auth was included in the latest version of the MCP spec (released last week). Up to now, its definitely been a bit of a pain to integrate auth with agen``` (especially as the number of available tools increases!). Has anyone tried working with it? How have you found it?

Personally, I think its the beginnings of a bigger re-think on how agen use tools / software. If/when MCP auth + MCP registries become fully mainstream, thatll solve the issue around discoverability of tools / APIs. However, I also think the APIs and tools themselves will then need to change. At the moment, agen generally use APIs that pre-existed agen and their rigidity ge in the way. To fully unlock agen, I think we need flexible, self-describing and goal-oriented mechanisms for agen to interact with software. Has anyone seen any particularly good examples of this?

Ive written some though up on this if anyones interested (link in commen) - me know what you think!

討論​

評論 1:

Now every MCP server needs to implement i``` own oauth on top of the existing oauth or whatever other authentication is required by the underlaying API.

This is an insane level of complications and abstraction. There are already APIs and those APIs already support authentication and in most case they are described cleanly in OpenAPI specs.

Am I the only one looking at MCP and thinking that the whole thing was badly designed from the get go. For every API now you need at least one MCP server - maybe more, exponentially increasing the number of things that need to be maintained.

My prediction is that someone will create a general purpose MCP server that ac like a proxy to any other API (you totally can) and the whole thing will collapse on ielf.

評論 2:

Link with though```: https://blog.portialabs.ai/beyond%20apis
Latest MCP spec update: https://spec.modelcontextprotocol.io/specification/2025-03-26/changelog/

評論 3:

it's critical for even thinking about putting something like this into production

25. Best Tools for Email & Tech Stack Discovery​

這篇文章的核心討論主題是：尋求關於B2B自動化開發中兩個關鍵問題的工具或方法建議，具體包括：

  1. 如何根據已知潛在客戶的姓名查找其電子郵件（聯繫信息獲取工具推薦）。
  2. 如何分析目標公司的技術堆棧（Tech Stack）（技術偵查工具推薦）。

作者希望通過社群協作解決自動化開發流程中的實際障礙。

內容​

Hey everyone!

Im building a B2B outreach automation and Ive hit a couple of roadblocks. Would love your input on these:

  1. If you already have a prospects name, whats the best tool or node you use to find their email?

  2. Before reaching out, I want to see what kind of tech stack the company is using. Any tool or node you'd recommend for that?

Thanks a ton in advance

討論​

評論 1:

LinkedIn for emails and profiles.

評論 2:

Apollo or Muraena are pretty foot to get emails and contact info.

評論 3:

You can check 'Global Database' and i options. Global Database also provides insigh into a businesss operations, which may include tech-related details. For deeper tech insigh```, you might want to pair it with tools like BuiltWith or Wappalyzer.

總體討論重點

以下是針對25篇文章的條列式重點總結，並附上對應的文章錨點連結：

文章重點總結​

1. 10 mental frameworks to find your next AI Agent startup idea​

  • 核心主題：透過觀察用戶痛點發掘AI Agent商機。
  • 重點：
    1. 解決實際問題（如數據導出、切換視窗等高摩擦行為）。
    2. 10種框架（如「Export Button Principle」「Upwork/Fiverr Audit」）。
    3. 商業化潛力評估（替代成本、中小企業需求）。

2. [The Most Powerful Way to Build AI Agen: LangGraph + Pydantic AI](#2-the-most-powerful-way-to-build-ai-agen-langgr)​

  • 核心主題：結合LangGraph與Pydantic AI建構可擴展代理系統。
  • 重點：
    1. 技術優勢：模組化代理、複雜工作流協調。
    2. 案例：7個專用代理串聯（爬取、過濾、發布等）。
    3. 設計原則：類型匹配、人類審核機制。

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

  • 核心主題：MCP協議作為LLM與工具的標準化橋樑。
  • 重點：
    1. MCP解決工具溝通格式不一致問題。
    2. 協調機制管理代理邏輯與工具串聯。

4. Example of a simple prompt injection attack​

  • 核心主題：AI系統的提示注入漏洞風險。
  • 重點：
    1. 實驗展示惡意指令誘導（如竊取CRM資料）。
    2. 呼籲強化AI資安防護。
  • 核心主題：AI代理交易平台現狀與商業化生態。
  • 重點：
    1. 探討代理市場的可發現性與招聘機制。
    2. 詢問現有平台與產品類型。

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

  • 核心主題：AI代理與物理世界互動的應用場景。
  • 重點：
    1. 機器人控制、人機協作案例。
    2. 尋求社群經驗分享。

7. [Spreadshee and AI agent](#7-spreadshee-and-ai-agent)​

  • 核心主題：AI代理自動化處理非結構化表格數據。
  • 重點：
    1. 透過n8n整合AI識別變動欄位。
    2. 解決重複文件欄位不一致問題。

8. [Agen that solve captchas, and bot detection](#8-agen-that-solve-captchas-and-bot-detection)​

  • 核心主題：繞過驗證碼的現成爬蟲工具需求。
  • 重點：
    1. 求職網站資料抓取與反爬蟲挑戰。
    2. 尋求即用解決方案（非自行開發）。

9. Chatgpt vs other models​

  • 核心主題：社群投票評選最佳AI模型。
  • 重點：
    1. 比較GPT、Claude等模型性能。
    2. 公開投票形式引發討論。

10. Are there enough APIs?​

  • 核心主題：數據源API不足對自動化的影響。
  • 重點：
    1. 提案「網頁轉API」工具解決無API痛點。
    2. 驗證需求真實性。

11. [The efficacy of AI agen is largely dependent on the LLM model that one uses](#11-the-efficacy-of-ai-agen-is-largely-dependent)​

  • 核心主題：LLM模型差異對AI代理效率的影響。
  • 重點：