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2025-04-01-rising

  • 精選方式: RISING

討論重點​

以下是25篇文章的討論重點總結，以條列方式呈現並附上對應的錨點連結：

1. 企業對AI代理的需求與痛點​

  • 目標客戶：新創、中小企業、大型企業對ROI、整合性、安全性的差異需求。
  • 應用場景：內部自動化（會議助理、程式審查）、客戶服務（客服、潛在客戶開發）。
  • 採購動機：降低成本、解決人力短缺、提升效率。
  • 痛點：討厭按人頭計費，偏好整合性與快速部署。

2. 客戶支援AI代理的開發指南​

  • 開發流程：需求釐清、技術架構選擇（RAG vs. 多代理）、監控工具（LangSmith）。
  • 關鍵挑戰：避免LLM幻覺、設計容錯機制。

3. Cloudflare Agents SDK的遷移效益​

  • 效能提升：延遲降低70%，簡化架構（刪減5,000行代碼）。
  • 開發體驗：直觀SQL結構取代複雜DynamoDB。

4. 低代碼網頁監控代理​

  • 需求：偵測網頁內容變化（如價格）並觸發動作（點擊按鈕）。
  • 工具建議：Zapier、Power Automate。

5. AI智商評估工具的潛力​

  • 核心概念：訓練AI判斷文件作者智商（人類或AI），應用於專業服務選擇。

6. 大型JSON編輯的AI工具困境​

  • 問題：現有工具（ChatGPT、DeepSeek）因記憶限制無法穩定處理大型法律案件JSON。

7. MCP規範的授權功能​

  • 重點：新增auth機制優化代理與API整合，需更靈活的互動設計。

8. B2B客戶開發工具需求​

  • 需求：查找潛在客戶郵件與公司技術堆棢的自動化工具。

9. 後端開發者轉型AI代理開發​

  • 學習需求：短期掌握LLM與聊天機器人開發，長期轉型機器學習。

10. 自由工作者協助開發AI代理​

  • 功能需求：多語言文件檢索、翻譯、摘要生成，尋求報價與建議。

12. AI代理自動化運維任務​

  • 應用場景：診斷遺留系統故障，需動態決策能力（非規則驅動）。

13. 大規模AI代理配置管理​

  • 挑戰：管理千種專用代理，需平衡後端複雜度與前端易用性。

14. 社交媒體AI代理​

  • 目標：透過具個性的代理自動發文，提升追蹤數。

15. AI代理的定義爭議​

  • 核心辯論：LLM具工具存取能力是否等同代理？需目標導向行為。

16. OpenAI Agents SDK資源匱乏​

  • 問題：開發者缺乏社群支援，僅能依賴官方文檔。

17. 客戶支援AI新創前景​

  • 討論：專注客服的AI代理公司（如Sierra）是否為泡沫或可持續市場？

18. AI代理機構與編程需求​

  • 關鍵問題：高

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. 企業採用AI代理的需求與痛點：企業需要能快速整合、安全且具明確ROI的AI代理，偏好小規模試用後擴展，而非通用型AI。
  2. 客戶支援AI代理的開發指南：從需求釐清到部署監控，提供建構企業級客服AI的實務框架與工具建議。
  3. Cloudflare Agents SDK遷移經驗：改用Cloudflare後延遲降70%，簡化架構並提升開發效率，適用AI自動化場景。
  4. 基礎網頁監控AI代理：尋求無代碼工具監控網頁內容變化並觸發自動化操作（如按鈕點擊）。
  5. AI智商評估工具潛力：能判斷文件作者智商（人類或AI）的技術，可能顛覆專業服務市場選擇邏輯。
  6. 大型JSON編輯困境：現有AI工具（如ChatGPT）因記憶限制無法穩定處理法律案件的大型JSON檔案。
  7. MCP規範授權功能：新版auth機制改善代理與工具整合，需發展更靈活自描述的互動架構。
  8. B2B客戶開發工具：徵求自動化查找潛在客戶郵件與公司技術堆棢的工具建議。
  9. 後端開發者轉型AI：新手尋求LLM與聊天機器人開發的學習路線，目標過渡至ML職位。
  10. 自由工作者協作需求：徵求開發多功能AI代理（翻譯、檢索、摘要）的報價與可行性評估。
  11. Reddit投票主題：未提供具體內容，常見為意見調查或趣味投票。
  12. AI代理自動化維運：探討用AI處理非結構化故障診斷，超越傳統腳本的自動化極限。
  13. 大規模AI代理管理：需設計可擴展架構管理千種專用Agent配置，平衡後端複雜度與前端體驗。
  14. 社交媒體AI代理：建構具古怪人格的AI代理，自動發文互動以提升帳號影響力。
  15. AI代理定義爭論：區分LLM與真正代理的關鍵在「目標導向行為」，非僅工具擴展。
  16. OpenAI Agents SDK資源：開發者反映相關討論稀缺，尋求使用者社群交流實務問題。
  17. AI客服新創前景：探討專注客戶支持的AI代理公司（如Decagon）是否為可持續市場。
  18. AI創業的編程需求：討論建立高價值AI代理機構是否需學習編程及推薦語言。
  19. AI削弱信任危機：當AI與人類內容難辨識，應更重視資訊價值而非來源真實性。
  20. 繪畫轉影片技術瓶頸：現有AI工具無法將自創壓克力畫作轉為影片，疑為技術限制。
  21. 新手行銷自動化：尋求簡易工具整合Meta廣告潛客、WhatsApp回覆與預約付款流程。
  22. AI市場差異化策略：在飽和市場中，建議透過競爭分析或利基定位突圍（如自主代理）。
  23. AI代理成功關鍵：技術非決勝因素，UX與業務邏輯設計（如DearFlow案例）才是核心。
  24. 系統提示緩存優化：探討靜態系統消息的緩存技術，以降低LLM請求的token成本與延遲。
  25. 開源AI本地部署：徵求硬體規格、開源工具（如Anus）與資源管道，取代商業AI方案。

目錄​

1. I Spoke to 100 Companies Hiring AI Agents — Here’s What They Actually Want (and What They Hate)​

這篇文章的核心討論主題可以總結為：「企業在採用AI代理（AI agents）時的主要需求、痛點與市場趨勢」，具體涵蓋以下面向：

  1. 目標客戶群體：

    • 新創公司、中小企業（SMBs）和大型企業對AI代理的不同需求，例如快速回報（ROI）、系統整合、數據安全等。
    • 代理商（Agencies）尋求可客製化的AI代理以服務客戶。

  2. 熱門應用場景：

    • 內部用途：會議助理、流程自動化、程式碼審查、內部知識管理。
    • 客戶面向：智慧客服、潛在客戶開發、客戶維繫、端到端工作流程處理。

  3. 企業採購AI代理的關鍵動機：

    • 解決人力不足、降低營運成本、釋放被閒置的知識資產、提升效率。

  4. 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。
    • 附加價值：無縫串接協作工具（如Notion、Drive），並提供穩定可靠的服務（「像魔法般流暢，但像基礎設施般穩定」）。

  5. 採購行為模式：

    • 偏好從小規模試用開始，驗證效果後快速擴展。
    • 討厭按人頭計費，傾向用量計價或分層方案。

  6. 核心結論：

    • 企業不需要通用人工智慧（AGI），而是需要能立即解決問題、無縫整合的「自動化實習生」，重點在於節省時間與成本。

全文圍繞「市場需求」與「產品設計」的匹配，提供實務洞察，幫助開發者或供應商理解企業端的真實痛點與決策邏輯。

內容​

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

Who’s Hiring AI Agents?

  • Startups & Scaleups → Lean teams, aggressive goals. Want plug-and-play agents with fast ROI.

  • Agencies → Automate internal ops and resell agents to clients. Customization is key.

  • SMBs & Enterprises → Focused on legacy integration, reliability, and data security.

Most In-Demand Use Cases

Internal agents:

  • AI assistants for meetings, email, reports

  • Workflow automators (HR, ops, IT)

  • Code reviewers / dev copilots

  • Internal support agents over Notion/Confluence

Customer-facing agents:

  • Smart support bots (Zendesk, Intercom, etc.)

  • Lead gen and SDR assistants

  • Client onboarding + retention

  • End-to-end agents doing full workflows

Why They’re Buying

The recurring pain points:

  • Too much manual work

  • Can’t scale without hiring

  • Knowledge trapped in systems and people’s heads

  • Support costs 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 it’s dead.|

|ROI|Agents that save time, make money, or cut headcount costs|

Bonus points 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 agents

  • Hate per-seat pricing → Prefer usage-based or clear tiers

TLDR; Companies don’t need AGI. They need automated interns that don’t break stuff and actually integrate with their stack. If your agent can save them time and money today, you’re 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 points.
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 clients and have no idea how to even engineer it: don't even try.
Sorry to be harsh, but yes, OP is correct - doing AI agents the right way is harder and more elaboarte than what people believe.

評論 2:

curious if companies are actually expecting these agents to run fully on their own, or if they still want humans in the loop somewhere? feels like most people still don’t fully trust the automation yet.

評論 3:

Thanks for valuable info 🙏

評論 4:

Who were the 100 companies, what were the people’s roles you spoke with, and what questions did you ask?

評論 5:

It’s a great review. Thank you for sharing. That’s rare these days.

My impression looking at the list is they’re still thinking Agents Level 1 and we’re kind of on to Level 2. I think if you focus beyond what they’re telling you you’ll have a whole lot less competition and be out ahead of the curve. What you need to do is think like Jobs. Sell them what they need not what they want. The problem is this is really hard.

Again thank you for sharing your research.

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

核心討論主題總結：​

該文章的核心在於探討「如何從零開始設計、開發、部署並監控一個企業級的客戶支援AI代理（AI Agent）」，重點聚焦於以下實務面向：

  1. 需求釐清與客戶溝通

    • 是否有標準化的模板或檢查清單（如需求問卷、流程圖）來確保理解客戶需求（例如：支援渠道、問題類型、數據權限等）。

  2. 技術架構選擇

    • 代理類型：根據任務複雜度選擇合適的技術組合（如RAG檢索增強生成、自主工具調用、記憶管理、數據庫整合等）。
    • 是否需要多代理協作（如分層處理投訴 vs. 簡單問答）。

  3. 開發工具與框架

    • 開發方式：比較無代碼工具（如Chatbot平台）與代碼框架（如LangChain、CrewAI、AutoGen）的取捨。
    • 是否需要嚴格結構化（例如用Pydantic驗證流程）。

  4. 監控與評估

    • 如何追蹤代理表現（如LangSmith監控鏈條、Langfuse分析用戶互動、Helicone記錄成本與延遲）。
    • 評估指標：準確率、響應時間、用戶滿意度等。

  5. 部署策略

    • 部署環境權衡：雲端服務（快速擴展）vs. 本地部署（數據隱私）vs. 混合模式。
    • 成本、延遲與合規性考量（如WhatsApp API的商業限制）。

  6. 實戰經驗與避坑指南

    • 常見陷阱：例如過度依賴LLM的幻覺、缺乏容錯機制、未設計人工接手流程。
    • 推薦工具鏈（如LlamaIndex處理文檔、Synthetic數據生成測試案例）。

整體目標：透過社群共享的實際案例，建立一套可複用的最佳實踐（Best Practices），幫助開發者高效構建「真實場景可用」的AI代理系統。

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 & products questions, complaints, feedback, etc., via Whatsapp 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 insights, tools, red flags, or tips and tricks you learned from your experience building agents for the real world?

討論​

評論 1:

I've found that low latency is a huge factor when it comes to these agents. 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.

3. We switched to cloudflare agents SDK and feel the AGI​

這篇文章的核心討論主題是：作者團隊從AWS基礎架構遷移至Cloudflare Agents SDK後，在效能、開發效率和系統架構上獲得的顯著改善。主要聚焦於以下重點：

  1. 效能躍升

    • 遷移後端到端延遲降低70%，媲美GPT-4o的反應速度
    • 即時響應帶來用戶體驗的質變

  2. 架構簡化

    • 內建排程系統取代自建方案，刪減5,000行代碼
    • 直觀的SQL結構取代DynamoDB複雜性

  3. 新功能潛力

    • 動態客戶化系統提示（system prompt）的可行性
    • 應用於AI自動化員工（行銷/銷售/Meta廣告場景）

  4. 開發者體驗

    • 強調「對開發者友好」的設計哲學
    • 代碼量減少且更易維護

文章本質是技術遷移的成功案例分享，特別針對AI服務基礎架構優化，並邀請同業交流Cloudflare的實戰經驗。最後提及的startup應用場景（AI員工）強化了遷移決策的商業價值。

內容​

After struggling for months with our AWS-based agent infrastructure, we finally made the leap to Cloudflare Agents SDK last month. The results 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 improvements:

  1. Dramatically lower response latency - Our agents 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 prompts 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 scripts 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 agents SDK and Durable Objects in general are awesome. I really like the full-stack experience of agents 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 agents 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!

4. 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，創建一個能夠監控網站內容變化（如狀態或價格更新）並自動觸發後續操作（如點擊按鈕）的代理或機器人？」

具體需求包括：

  1. 監控動態內容：偵測網頁上特定欄位（如清單狀態、價格）從空白或「N/A」變為其他值（如「open」或「$1.00」）。
  2. 自動化響應：變化發生時，立即執行預設動作（例如點擊一組按鈕）。
  3. 工具偏好：傾向使用低代碼或無代碼解決方案，降低技術門檻。

可能的解決方向（未在原文提及但常見建議）：

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 starts 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 requirements 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_Agents for similar discussions.

(I am a bot) Source

5. 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：人工智慧（AI）在知識工作領域（如法律、金融、行銷等）中與人類專業人士的競爭力比較，以及如何透過評估「文件作者的智商」來幫助消費者選擇雇用AI或人類專家。

文章重點包括：

  1. AI與人類專業人士的比較：探討未來AI律師、顧問等是否能在智慧與成本上超越人類，成為更具競爭力的選擇。
  2. 評估「文件作者智商」的可能性：提出訓練AI來判斷文件作者的智商（無論是人類或AI生成），並以此作為衡量專業能力的標準。
  3. 廣泛的應用場景：此技術可擴展至金融、會計、行銷、工程等多個領域，協助消費者選擇更「聰明」的服務提供者（人類或AI）。
  4. 商業潛力與競爭優勢：強調這種「智商評估工具」可能成為高價值的產品，並為先行開發者帶來市場優勢。

整體而言，文章聚焦於「AI如何量化並挑戰人類專業人士的智慧表現」，並探討其商業化潛力。

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 tests measure. They measure answers. They measure the content generated.

An AI that accurately correlates the intelligence expressed in a document with its 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 represents just one phase in AI development, a phase with achievements but also many limitations. In the future, there will likely be significant restructuring phases.

6. 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：

「如何有效管理與編輯大型JSON檔案，並尋找適合的AI工具來協助處理法律案件中的複雜資訊」

具體要點包括：

  1. JSON管理困境：

    • 作者使用JSON結構整理法律案件資訊，但檔案龐大（112k字元且持續增長），導致編輯困難。
    • 現有AI工具（如ChatGPT、DeepSeek、Grok）因技術限制（記憶力、輸入長度、錯誤修改等）無法穩定處理大型JSON。

  2. AI工具的缺陷比較：

    • ChatGPT：無法直接貼上完整JSON，需分段處理且缺乏上下文連貫性。
    • DeepSeek：對話次數限制，頻繁貼文後強制終止。
    • Grok：記憶力極差，快速遺忘JSON內容，甚至虛構或刪改資訊。

  3. 尋求替代方案：

    • 請求推薦能「無錯誤處理大型JSON」的免費AI工具。
    • 探討是否有更適合的資料組織形式（如資料庫、專用法律軟體等）取代JSON。

  4. 核心需求：

    • 穩定編輯大型結構化資料的工具，避免AI的幻覺、記憶限制或輸入長度問題。

（建議方向：可考慮本地運行的開源模型如Llama 3，或結合資料庫系統如SQLite + AI外掛，以突破雲端AI的限制。）

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 ChatBots (in their official versions on the official website), such as CharGPT, DeepSeek and Grok,

each with its flaws, there are times when I do something well, and then I don't, I am going back and forth between A.I./ChatBots 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 chatbots 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 parts using a "Cutter for GPT", and I've noticed that ChatGPT is a bit silly, not knowing how to join all the generated parts and understand everything as well.

- DeepSeek says that the chat has reached its conversation limit after about 2 or 3 times I paste large texts 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 texts 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 its limits, 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

7. Thoughts on latest version of MCP spec with auth?​

這篇文章的核心討論主題是：
「MCP規範中新增的授權（auth）功能如何改善代理（agents）與工具/API的整合，以及未來代理與軟體互動所需的靈活、自描述且目標導向的機制」。

具體重點包括：

  1. MCP規範的授權功能：最新版本納入auth機制，有望簡化代理與工具的整合，解決過去工具/API發現性（discoverability）的問題。
  2. 代理與工具的現有挑戰：當前代理使用的API多為非專用設計，僵化性阻礙了代理的潛能發揮。
  3. 未來方向：需發展更靈活、自描述（self-describing）且目標導向（goal-oriented）的互動機制，以完全釋放代理的能力。
  4. 社群討論與實踐：作者徵求其他開發者的經驗分享，並提出個人見解（附文章連結）。

整體聚焦於「如何通過技術演進（如MCP auth + 註冊機制）優化代理與工具的協作生態」。

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, it’s definitely been a bit of a pain to integrate auth with agents (especially as the number of available tools increases!). Has anyone tried working with it? How have you found it?

Personally, I think it’s the beginnings of a bigger re-think on how agents use tools / software. If/when MCP auth + MCP registries become fully mainstream, that’ll 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, agents generally use APIs that pre-existed agents and their rigidity gets in the way. To fully unlock agents, I think we need flexible, self-describing and goal-oriented mechanisms for agents to interact with software. Has anyone seen any particularly good examples of this?

I’ve written some thoughts up on this if anyone’s interested (link in comments) - let me know what you think!

討論​

評論 1:

Now every MCP server needs to implement its 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 acts like a proxy to any other API (you totally can) and the whole thing will collapse on itself.

評論 2:

Link with thoughts: 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

8. Best Tools for Email & Tech Stack Discovery​

這篇文章的核心討論主題是：

「尋求關於B2B客戶開發自動化的工具建議」，具體聚焦在兩個問題：

  1. 如何根據已知潛在客戶姓名查找其電子郵件（工具或節點推薦）。
  2. 如何獲取目標公司的技術堆棢（tech stack）資訊（工具或節點推薦）。

作者希望透過社群協作解決自動化開發過程中的實際障礙。

內容​

Hey everyone! 👋

I’m building a B2B outreach automation and I’ve hit a couple of roadblocks. Would love your input on these:

  1. If you already have a prospect’s name, what’s 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 its options. Global Database also provides insights into a business’s operations, which may include tech-related details. For deeper tech insights, you might want to pair it with tools like BuiltWith or Wappalyzer.

9. I got a job as a back-end developer in a team developing AI Agents/Chat & Voice Bots. Please suggest me some resources to prepare for this role and tasks.​

这篇文章的核心討論主題是：
一位新入職的後端開發者尋求學習路線與資源建議，以勝任涉及AI代理（如聊天機器人、語音機器人）的開發工作，並逐步轉型至機器學習相關職位。

具體要點包括：

  1. 背景：作者為專業後端開發者，但缺乏LLM（大型語言模型）與機器學習經驗，即將加入開發AI代理的團隊。
  2. 需求：
    • 短期（一個月內）準備工作所需的知識與技能。
    • 長期目標是過渡到機器學習相關職位。
  3. 請求：希望社群提供學習路線（roadmap）與資源（如課程、工具、實用建議）。

關鍵詞：後端開發轉型AI/ML、LLM入門、聊天/語音機器人開發、一個月學習計劃。

內容​

Hi guys,

I recently got a job as a backend developer in a team that is developing AI Agents, Chat and Voice Bots. I am a professional backend developer but new tl llms and ML. I want to perform well on this job. Please suggest me a roadmap and resources to prepare for this job. My end goal is slowly transition into ML related roles. Now I have about a month of free time before I join this role to prep for the job.

討論​

評論 1:

The number one most underrated skill that nobody is talking about is to become really good at prompt engineering. I know it sounds weird and I was laughing too when I first heard that people are actually getting payed money to do this full time, but trust me... this is what it makes or breaks agents.

Without a good prompt you have a system that cannot be controlled. I've seen the difference in my line of work and I am convinced prompts are intellectual property - the equivalent of code. I wont be surprised if we get to the point where prompts are copyrighted too.

This is probably not what you want to hear. You might be expecting to get a list of frameworks and tutorials, but that is the easy part. Given that you are already hired as an engineer I expect that you can pick any framework with ease but prompts are more of an art then a science.

評論 2:

Building with LangGraph and Pydantic AI is perfect for starting.

評論 3:

Ask them what framework they use.

10. Does anyone freelance?​

這篇文章的核心討論主題是：

作者希望開發一個能夠執行特定任務的AI代理，並尋求有經驗的自由工作者協助，同時詢問相關開發成本。

具體而言，AI代理的功能需求包括：

  1. 資料檢索與處理：根據用戶輸入的識別碼，從線上資料庫獲取並導入多個可讀文本文件，並顯示相關文本與文件。
  2. 語言翻譯：自動翻譯外語文件。
  3. 內容搜索與分析：在文件中搜索與識別碼相關的內容（例如特定關鍵詞或概念），並標註匹配的位置。
  4. 圖像識別（若可行）：若文件中有相關圖片但無文字描述，代理能提醒用戶注意。
  5. 摘要生成：根據識別碼的相關性，總結文件中的資訊。

作者不確定現有AI代理是否能滿足這些需求，因此徵求建議與報價。

內容​

I’m interested in having an ai agent developed. I would love to develop one on my own but I simply don’t have the time available. Apologies in advance if this sort of post is not allowed.

Does anyone established (with a track record) freelance? And if so, what sort of cost would be involved?

Just for a bit of background on the agent’s purpose - a user will input an identifier. The ai agent will then talk to an online database and retrieve / import multiple readable text documents from that database. Each of the readable text documents will have multiple text files that must be viewable next to the text. Documents in foreign languages will be translated.

The user can then input an identifier, and the agent will search each of the documents for the identifier, or for things like the identifier. So, for example, if the user wanted to determine if any of the text documents mentioned an internal combustion engine with eight cylinders, the agent would indicate where in each document such an engine was mentioned. If possible, if there is a picture of an engine in the document but no text, the agent would draw the user’s attention to the figure. If possible; the agent will summarise the information in each document in a manner that is relevant to the identifier.

I don’t know if any existing agents would be able to perform the above tasks / workflow, but any input is gratefully received!

討論​

無討論內容

11. Do you develop your own models for your agents?​

由於提供的連結是 Reddit 的投票頁面（標題和具體內容未直接顯示），且無法直接存取外部內容，我無法總結該文章的核心討論主題。不過，根據常見的 Reddit 投票模式，這類貼文通常會圍繞以下幾種主題展開：

  1. 意見調查：針對某個議題（如社會、政治、科技、娛樂等）收集用戶的觀點或選擇。
  2. 趣味投票：輕鬆話題（如偏好選擇、假設性問題）的互動。
  3. 爭議性討論：引發對立觀點的辯論，例如道德抉擇或熱門新聞事件。

若需更精確的總結，建議提供投票的具體標題或選項內容，或直接描述文章的主旨。

內容​

View Poll

討論​

評論 1:

bro who has the cash to make their own LLMs

12. AI agents for handling toil​

这篇文章的核心討論主題是：如何運用AI代理（AI agents）來自動化團隊中複雜的運維（Ops）任務，尤其是針對傳統自動化難以處理的故障診斷與修復流程。具體討論重點包括：

  1. 問題背景：

    • 團隊維護的遺留系統（legacy system）運維複雜性高，故障情境多且處理步驟繁瑣（需依賴冗長的運維文檔／opdocs）。
    • 現有手動處理流程效率低，需探索AI是否能減輕負擔。

  2. AI代理的潛在應用方向：

    • 智能診斷：整合多源數據（儀表板、健康檢查、日誌）自動識別問題根源。
    • 數據關聯：從雜訊中正確關聯故障現象與根本原因。
    • 自動修復：根據診斷結果執行修正動作（需區別於傳統腳本化自動化）。

  3. 關鍵差異化訴求：

    • 有別於傳統規則驅動的自動化（如預定義腳本），探討AI如何處理「非直觀」問題（unstructured scenarios），例如動態決策或學習歷史處理模式。

  4. 尋求實例參考：

    • 作者希望了解其他團隊的類似實踐案例，以獲得靈感與方法論（如技術選型、實施挑戰）。

隱含議題：

內容​

So I am experienced software dev exploring AI agents to automate some of the toil we have in our team. The legacy system we are maintaining is operationally complex and there are too many things which can break. We have extensive opdocs on what do do when things break and many times it's not very straightforward. I am wondering if/how we can use ai agents to offload some of the manual work. Eg. At the high level

  • Use various sources (eg. Dashboards, healthchecks, logs) to find the cause of the problem.

  • Correctly correlate data to the cause.

  • Take corrective action.

I am wondering how to approach this problem differently than traditional automation.

Has anyone done something like this ? I would love to brainstorm ideas and take some inspiration from any implementation.

討論​

無討論內容

13. Useful platforms for implementing a network of lots of configurations.​

這篇文章的核心討論主題是：如何設計一個可擴展的AI Agent工作空間，以有效管理和使用大量（超過1,000種）高度專用的AI Agent配置。具體聚焦於以下關鍵問題與需求：

1. 核心挑戰​

  • 平台整合困境：缺乏現成工具能同時滿足「Agent創建管理」（後端）與「直觀日常使用」（前端）的需求，尤其針對「單一使用者同時扮演創建者與使用者」的情境。
  • 規模化瓶頸：現有框架（如Flowise、LangChain等）多專注於少量複雜Agent或區分開發/使用場景，難以支援「快速切換數十種Agent」的高頻工作流程。

2. 功能需求​

  • 後端管理：需系統化管理大量Agent配置（含專用提示詞、RAG資料源、工具鏈），並評估是否需「主控協調架構」（如路由Agent）或「前端主導切換」。
  • 前端體驗：關鍵功能包括：
    • 即時搜尋/切換Agent
    • 單一Agent的對話歷史留存
    • 收藏夾與分類機制
    • 跨平台支援（Web與行動端）

3. 技術方案探索​

  • 現有工具評估：作者嘗試過Open Web UI、Qdrant、N8N等，但管理千級Agent時仍顯不足，需尋找更適合的開源框架組合。
  • 架構方向：
    • 協調驅動：後端複雜化（如用CrewAI/LangChain實現路由邏輯）。
    • 前端驅動：簡化後端，強化UI導航功能（需高效的前端框架）。

4. 具體提問​

  • 請求推薦現有開源工具鏈（前端、Agent框架、協調工具），以最小化從零開發，並滿足「大規模專用Agent庫」的創建與使用需求。

總結​

主題圍繞「如何透過技術選型與架構設計，解決大規模專用AI Agent工作空間的整合與使用者體驗問題」，強調在「管理複雜度」與「使用流暢度」之間的平衡。

內容​

I've been working on a personal project since last summer focused on creating a "Scalable AI Agent Workspace."

The core idea is based on the observation that AI often performs best on highly specific tasks. So, instead of one generalist agent, I've built up a library of over 1,000 distinct agent configurations, each with a unique system prompt, and sometimes connected to specific RAG sources or tools.

Problem

I'm struggling to find the right platform or combination of frameworks that effectively integrates:

  1. Agent Studio: A decent environment to create and manage these 1,000+ agents (system prompts, RAG setup, tool provisioning).

  2. Agent Frontend: An intuitive UI to actually use these agents daily – quickly switching between them for various tasks.

Many platforms seem geared towards either building a few complex enterprise bots (with limited focus on the end-user UX for many agents) or assume a strict separation between the "creator" and the "user" (I'm often both). My use case involves rapidly switching between dozens of these specialized agents throughout the day.

Examples Of Configs

My library includes agents like:

  • Tool-Specific Q&A:

    • N8N Automation Support: Uses RAG on official N8N docs.

    • Cloudflare Q&A: Answers questions based on Cloudflare knowledge.

  • Task-Specific Utilities:

    • Natural Language to CSV: Generates CSV data from descriptions.

    • Email Professionalizer: Reformats dictated text into business emails.

  • Agents with Unique Capabilities:

    • Image To Markdown Table: Uses vision to extract table data from images.

    • Cable Identifier: Identifies tech cables from photos (Vision).

    • RAG And Vector Storage Consultant: Answers technical questions about RAG/Vector DBs.

    • Did You Try Turning It On And Off?: A deliberately frustrating tech support persona bot (for testing/fun).

Current Stack & Challenges:

  • Frontend: Currently using Open Web UI. It's decent for basic chat and prompt management, and the Cmd+K switching is close to what I need, but managing 1,000+ prompts gets clunky.

  • Vector DB: Qdrant Cloud for RAG capabilities.

  • Prompt Management: An N8N workflow exports prompts daily from Open Web UI's Postgres DB to CSV for inventory, but this isn't a real management solution.

  • Framework Evaluation: Looked into things like Flowise – powerful for building RAG chains, but the frontend experience wasn't optimized for rapidly switching between many diverse agents for daily use. Python frameworks are powerful but managing 1k+ prompts purely in code feels cumbersome compared to a dedicated UI, and building a good frontend from scratch is a major undertaking.

  • Frontend Bottleneck: The main hurdle is finding/building a frontend UI/UX that makes navigating and using this large library seamless (web & mobile/Android ideally). Features like persistent history per agent, favouriting, and instant search/switching are key.

The Ask: How Would You Build This?

Given this setup and the goal of a highly usable workspace for many specialized agents, how would you approach the implementation, prioritizing existing frameworks (ideally open-source) to minimize building from scratch?

I'm considering two high-level architectures:

  1. Orchestration-Driven: A master agent routes queries to specialists (more complex backend).

  2. Enhanced Frontend / Quick-Switching: The UI/UX handles the navigation and selection of distinct agents (simpler backend, relies heavily on frontend capabilities).

What combination of frontend frameworks, agent execution frameworks (like LangChain, LlamaIndex, CrewAI?), orchestration tools, and UI components would you recommend looking into? Any platforms excel at managing a large number of agent configurations and providing a smooth user interaction layer?

Appreciate any thoughts, suggestions, or pointers to relevant tools/projects!

Thanks!

討論​

評論 1:

> Given this setup and the goal of a highly usable workspace for many specialized agents, how would you approach the implementation, prioritizing existing frameworks (ideally open-source) to minimize building from scratch?

Oh, I was going to comment that we had to write our own reflective and dynamic runtime with self-modification properties and all code and agent settings are data in our registry (DAG that syncs in realtime with actual code executor layer), but it's probably the opposite of what you looking for.

What we learned - making UI, tool integrations and workflows no longer a problem if you let system do it on it's own (obviously with proper isolation, versioning and snapshotting) based on pre-seeded content and guardrails.

14. Bluesky Agent​

這篇文章的核心討論主題是：作者希望透過建立一個具有獨特個性的AI代理（agent），來幫助自己在社交媒體（如Twitter或Bluesky）上更活躍，並增加追隨者。具體目標包括：

  1. 個人動機：作者自認不擅長主動經營社交媒體，但希望改變現狀。
  2. AI代理的功能：
    • 模擬一個「古怪」（quirky）的人格特質來發文（如推文）。
    • 閱讀他人的推文（特定對象或主題），並生成互動內容。
  3. 最終目的：透過代理的活躍表現，提升自己的社交媒體影響力與追蹤人數。

簡言之，這是一個關於利用AI自動化工具改善個人社交媒體存在感與增長策略的討論。

內容​

I have never been a person who is active on social network. I want to change this by building a agent. The agents job is to assume a quirky personality and generate tweets or whatever bluesky equivalent is. it should read tweets from other people (tbh who and topics) and generate a tweet to increase my following.

討論​

評論 1:

Have you looked at Portia AI? https://www.portialabs.ai/ I think it would be pretty easy to add a bluesky tool and use that within Portia to make the agent you want

15. What’s your definition of „AI agent”?​

核心討論主題：​

這篇文章的核心在於探討「如何定義及區分『大型語言模型（LLM）』與『AI代理（AI Agent）』」，並反思當前LLM的能力是否足以被稱為具有自主性的AI代理。主要論點包括：

  1. 工具存取是否足夠：
    作者最初認為賦予LLM工具（如API調用、搜尋等）即可稱其為AI代理，但後來質疑這種觀點，因為LLM本質上仍是「被動回應」使用者輸入（reactive），而非主動追求目標（proactive）。

  2. 主動性與意圖的關鍵性：
    即使通過程式設計讓LLM循環運作或生成後續提示（prompt chaining），它仍缺乏內在的「意圖」或「目標導向」行為，無法自主「想要」完成某項任務。真正的AI代理應具備長期目標、記憶和持續存在的感知（long-term presence）。

  3. 對進階AI代理的想像：
    作者認為，若LLM能結合長期記憶、目標導向行為和主動性，才可能從「單純的語言生成器」升級為更接近人類意圖的AI代理。

  4. 開放討論的邀請：
    文章最後提出與GPT-4o的討論經驗，並邀請讀者分享對「LLM如何進化為AI代理」這一議題的看法，強調這是一個尚未有明確答案的開放問題。

關鍵詞彙：​

  • 被動反應（reactive） vs. 主動性（proactive）
  • 目標與意圖（goals & intent）
  • 長期記憶與持續性（long-term memory & permanency）
  • 工具擴展（tool augmentation）
  • AI代理的本質（definition of AI agent）

此文反映了當前AI領域中對「代理」概念的哲學與技術爭辯，尤其關注LLM的局限性與未來發展方向。

內容​

I've been thinking about this topic a lot and found it non-obvious to be honest.

Initially, I thought that giving LLM access to tools is enough to call it an "AI agent", but then started doubting this idea. After all, LLM would still be reactive, meaning it reacts to prompts, not proactively.

Sure, we can program it to work in some kind of loop, ask it to write downstream prompts etc., but it won't make it "want" to do something to achieve a goal. The goal, intention, and access to long term memory sounded like something that would turn a naive language generator to something more advanced, with intent, goals, feeling of permanency, or at least long-term-presence.

I talked with GPT-4o and discovered its insights on the topic insightful and refreshing. If you're interested, I'll leave the link below, but if not, I'm still curious how you feel and think about this whole LLM -> AI agent discussion.

討論​

評論 1:

Unfortunately it an overly broad term right now. To me it means an app that includes custom workflow (e.g. planning, chains, routing, sub-tasks), and/or tools.

評論 2:

The difference between automation and agent workflows:

https://claude.site/artifacts/c7b28f25-511d-4e67-8758-509668c8634e

評論 3:

I always thought of an agent as something I could give a project too, where as an LLM is something I'd give a task too. I'm pretty new to this tho...so.. I encourage a devils advocate lol.

評論 4:

That’s a fascinating perspective! I agree—just adding tools doesn’t necessarily create an agent; intent and autonomy matter. Some frameworks try to simulate this with planning and memory (like AutoGPT), but true agency feels like a missing piece. What insights from GPT-4o stood out to you the most?

評論 5:

Here’s the aforementioned link to full GPT-4o’s take:  https://www.toolongautomated.com/posts/2025/agent-continue.html

16. Anybody using the openai agents sdk?​

这篇文章的核心讨论主題是：
作者在開發基於某技術（推測為OpenAI相關產品）的系統時，遇到線上討論資源不足的問題，並尋求相關社群或使用者的交流渠道。

具體要點包括：

  1. 技術資源的匱乏：作者提到遇到問題時，網上缺乏相關討論，僅能依賴官方文檔（OpenAI Docs）。
  2. 尋求社群支持：希望找到使用相同技術的社群或個人，以進行經驗分享與問題討論。

隱含背景：

內容​

I've developed quite a few systems with it since it's launch, but when I hit a roadblock for somethings or another, I find that there is a huge lack of discussion online about it.

The only resource ends up being the openai docs lol.

Anyway, do you guys know of any communities or individuals using it? Would love to join and discuss

討論​

評論 1:

I like what they did with agents handoff but only allowing Openai models is such a turnoff

評論 2:

I used the agents SDK to build a computer use agent that can do competitor analysis for me, here is my blog describing a minimal version of how I built it - https://medium.com/@kuldeep.paul08/i-got-ai-agents-to-keep-tabs-on-my-competitors-so-i-can-focus-on-building-7b23500d1dd7

評論 3:

OpenAI is becoming more like "CloseAI," especially since it doesn’t seem to support self-hosted LLMs. When it comes to enterprise applications, it's hard to seriously consider OpenAI Agents. After all, the open-source community already has so many great multi-agent orchestration frameworks available.

評論 4:

I am using it almost exclusively. Response API is amazing, built-int evaluation too and very flexible handoff process. What is my favourite however is the fact it supports natively guardrails.

I guess I'm lucky that my clients are not haggling over price of tokens so I don't have to always aim for the shittiest LLM to use in my projects and I'm ok using Responses API.

評論 5:

Out of interest, what were the roadblocks you were hitting with it? Just OpenAI models is obviously a big one, but wondering if there were any others

17. Ever heard of Decagon AI?​

這篇文章的核心討論主題可以總結為以下幾點：

  1. AI Agent新創公司在客戶支持市場的發展：作者關注專注於客戶支持領域的AI Agent新創公司（如Sierra和Decagon AI），並探討在這些公司工作的可能性。

  2. 對這類新創公司的看法：作者詢問對這類新創公司的評價，包括其技術、市場定位以及發展潛力。

  3. 市場定位的可持續性：作者質疑這類專注於客戶支持的AI細分市場是否只是當前AI熱潮（泡沫）的一部分，還是具有長期存在的價值。

整體而言，文章圍繞著「AI Agent在客戶支持領域的應用是否為可持續的市場機會」展開討論，並尋求對相關新創公司前景的見解。

內容​

I'm look at working at various AI Agent startups targeting the Customer Support market, places like Sierra and now Decagon AI. Ever heard of them? What are your thoughts on working at a startup like this? Is this niche segment just part of the AI bubble or do you think it will have some level of permanence in your opinions?

討論​

評論 1:

haven't heard of it, heard of sierra and amazon connect

評論 2:

Have you been able to see actual demos of how their tech work?

18. Learning coding​

這篇文章的核心討論主題可以總結為以下幾點：

  1. 創建有價值的AI自動化代理機構：作者希望建立一個與眾不同的AI自動化代理機構，超越常見的聊天機器人或簡單解決方案，專注於提供更高價值的服務。

  2. 是否需要學習編程：作者詢問是否需要學習編程才能實現這一目標，並探討技術能力在構建高價值AI解決方案中的必要性。

  3. 推薦的編程語言：如果學習編程是必要的，作者希望了解應該從哪些編程語言開始學習，以支持其AI代理機構的發展。

整體而言，文章的核心圍繞著如何打造一個獨特且高價值的AI自動化業務，並探討技術能力（尤其是編程）在其中的角色與具體學習方向。

內容​

Currently im building my ai automation agency

But I don’t want to build it like other agencies ( chat bots and normal easy shit )

I want to actually build something valuable, so what do you think guys ? Do I need to learn coding for that? And if yes, what languages should I start learning?

討論​

評論 1:

While you can start an AI automation agency without deep coding skills, learning programming—especially Python—will empower you to create more sophisticated solutions. Focus on understanding the fundamentals and leveraging existing tools to maximize your agency's potential.

If you're new to coding or want to enhance your skills, consider the following resources:

  1. AI Python for Beginners by Andrew Ng: This free course focuses on Python basics while integrating AI applications. It combines practical coding with theoretical knowledge.
  2. Codecademy’s Python Course: Offers interactive lessons that guide you through Python fundamentals with hands-on projects.
  3. Khan Academy’s Intro to Computer Science: A structured course that covers core programming concepts through engaging projects.

評論 2:

Bro what lol

評論 3:

Hi there! This is a common question in our community. While you can create basic automation without coding, building truly valuable AI agent systems typically requires programming knowledge.

I recommend starting with Python (most common for AI/ML) and JavaScript/TypeScript (for web integrations). Frameworks like LangChain, AutoGen, and Semantic Kernel are also worth exploring.

For more insights, try searching our subreddit: AI Agents Coding Requirements

(I am a bot) Source

評論 4:

Imo you can learn as you go as long as you know how to talk to the language models and get out what you need. As your ideas require more complex programming you will need to be able to break down ideas into small manageable chunks of logic.

I don't think you NEED to know coding to do this. But you will need to be able to think like a programmer to break down problems and instruct the language model. They are great at small manageable tasks.. They will not be great if you just ask them to create a miracle. You have to break that down into manageable chunks. After that it doesn't matter if you write the code or a language model

19. Is AI making us unable to trust each other?​

这篇文章的核心討論主題是：在網絡交流中，AI生成內容與人類創作內容的界線模糊化，以及資訊的價值與來源之間的權衡。

具體探討以下問題：

  1. AI與人類內容的辨識困境：格式工整、邏輯清晰的內容容易被誤認為AI生成，反映兩者差異逐漸模糊。
  2. 資訊價值的優先性：對於尋求答案或討論的人而言，若內容本身有價值，其是否為AI生成是否仍重要？
  3. 驗證資訊的恆常性：無論來源為何，使用者始終需花時間驗證資訊，過程中可能耗費成本（如時間、精力）。
  4. 來源與內容的取捨：當AI生成內容的品質提升，人們是否應更重視內容本身，而非糾結於其生產者？

本質上，文章質疑傳統對「人類創作」的偏好，並呼籲重新思考「價值」與「真實性」的衡量標準。

內容​

I've noticed something lately - whenever you write a thoughtful, well-formatted response online, people often assume it's AI-generated content. And honestly, it's becoming harder to tell the difference ourselves.

But for those seeking answers or engaging in discussions, does it really matter if content is AI-generated as long as the information is valuable? Sometimes we get direct answers, sometimes just hints that require further verification and testing. We've always had to spend time verifying information, extracting value, and making attempts that occasionally waste our time.

So what's really different now? How do you see this issue? Is the source more important than the content itself?

討論​

評論 1:

I could give you a well reasoned AI generated response. How would you feel? You asked this on reddit where you expected someone to care enough to answer. Next time just talk to chatGPT if it makes no difference to you?

I’m not trying to be unfriendly, more point out that if I wanted an AI response I would not be here talking to all of you. I’d feel cheated.

評論 2:

The problem is that people think only because its formatted by AI the human had nothing to do with it. Which is just not true. Every single email by me is formatted by AI but the content is obviously by me, but because my grammar etc is dogshit(as you can see in this message) I always let the AI clean things up for me.

評論 3:

What's your ulterior motive?

評論 4:

People who point out that my writing is worse don't realize it.
It's easy enough to write as if I'm not an AI, but I don't want to put in that much effort.

評論 5:

there's a lot of formatting that looks ai generated, sometimes you'll see it in this sub too

it's the too many emojis, a bunch of randomly placed titles, all that random bullshit

20. Original painting to video​

這篇文章的核心討論主題是：

「現有AI工具無法將用戶的原創壓克力畫作（自製圖像）成功轉換為影片」。

作者嘗試了多種推薦的AI工具（如Kling、Vidfly等），發現這些工具雖然能處理現有圖庫中的圖片，卻無法根據其自訂的繪畫圖像生成影片。可能的問題包括：

  1. 技術限制：當前AI是否尚未支援此類自訂圖像的影片轉換？
  2. 圖像複雜度：畫作的細節或格式是否影響AI的解析能力？

作者質疑這是否為現階段AI技術的瓶頸，並尋求潛在的解決方案或解釋。

內容​

Hi. I've explored many recommendations--Kling, Vidfly, and several others via their free and trial options. None of them have been able to convert an "image" of my original acrylic painting ( my own work) into video. Any thoughts on what the issues are? All options seem to work if one plugs into existing libraries of images but none has generated a video from my desired image--and that's when a given option accepts it. Maybe this is a bridge too far for the AI options at the moment? Is it a question of the detail in the painting itself and the AI not being able to read the image? Thanks for any ideas.

討論​

評論 1:

How could we possibly diagnose your problem without any details? Exactly what image did you try and what error message or behavior did you see and with which provider and which tool and config?

Yes, image to video works and has created tens of millions of short videos already.

You should also consider paying for a few of the tools. If this is important to you. Its so infuriating that .. why am I wasting my time talking to you

21. New and looking for help​

这篇文章的核心討論主題是：

如何為初學者建立一個自動化行銷代理（agent），具體功能需求包括：

  1. 處理從Meta廣告生成的潛在客戶（leads），這些資料儲存在Google Sheets中。
  2. 透過WhatsApp自動回覆客戶關於治療服務的諮詢。
  3. 預約安排（整合日曆系統）。
  4. 處理付款流程。

作者尋求適合新手的工具或平台建議（如Lindy），並希望獲得相關指引以實現此自動化流程。

關鍵字：Meta廣告潛在客戶、WhatsApp自動回覆、預約系統、支付整合、初學者解決方案。

內容​

Hey guys,

I am looking to build an agent that will respond to leads generated from meta ads.

They populate on a google sheet. I need them to respond via WhatsApp answer any questions about the treatment, book them into a calendar and then take payment.

I have been looking into Lindy but if anyone can share the best place to begin for a total beginner I would be super grateful.

討論​

評論 1:

I would suggest you try building a MVP with n8n, it has whatsapp integration and would enable you to take the project off the ground without much coding, let me know if you need more help on this!

22. I need a career/business advice. Since we are more or less selling the same product. Should I start finding a market position (niche) in order to stay competitive?​

这篇文章的核心討論主題是：

在競爭激烈的商業環境中，如何透過市場分析（如競爭對手分析）和利基市場定位來建立差異化優勢，特別是在AI相關領域（如AI工作流程、人機協作、數據庫對話、自主AI代理等）。

具體探討的要點包括：

  1. 競爭的雙面性：競爭證明市場存在需求，但也可能導致產品或服務同質化（僅品牌和價格不同）。
  2. 市場分析的重要性：是否應進行競爭對手分析以評估現有市場狀況？
  3. 利基市場策略：是否應尋找未被競爭對手滲透的細分領域，以避開紅海競爭？
  4. AI領域的應用：如何將上述策略應用於新興的AI技術場景（如自主AI代理等）。

整體而言，文章聚焦於在飽和市場中透過差異化策略（分析競爭或開拓利基）來突圍，並結合AI趨勢探討可行性方向。

內容​

In business, competition is good because it shows that there is an existing market and there is demand for it. But to a certain point, we are all selling the same product/service but the brand and the price is different. AI Workflows, human in the loop work flows, Chatting to DBs, Agentic AI. Should I be doing a competitors analysis in order to assess the existing market? Should I be finding a niche that is so specific that my competitors didn’t penetrate?

討論​

評論 1:

you should be talking to customers

評論 2:

just look for a job a human can do and try doing it with AI, take over people jobs... (i am being sarcastic & probably downvoted saying the truth about this AI shit)

評論 3:

You are overthinking it. Create a product that you know your customers are asking for and don't worry about competition. Your brand will stand out only if you have a product that your customers truly want

評論 4:

Create something that is more then an LLM api wrapper. Create solutions that scale. Don't worry about others, just do something you enjoy. Ideas are a dime a dozen, implementing an idea is where the goal should be. /end ted talk.

評論 5:

Figuring out a niche is not like sitting in a room and writing a pitch deck, you have to go knee deep in the mud and churn through customers with a MVP and figuring out what your customer needs/ is willing to pay for. Spend the early days listening to your customers more than anything else, try to walk in their shoes and find a sizeable cohort of customers willing to pay for a particular niche, build it, get more feedback and iterate continuously

23. IMO: AI Agents won’t win on tech alone – UX & Business logic will define the best ones​

這篇文章的核心討論主題可以總結為以下幾點：

  1. AI代理的普及與技術門檻降低：
    隨著AI代理（agentic AI）技術的發展，其成本將降低、更容易構建且更普及，導致核心技術本身不再是差異化的關鍵。

  2. AI代理成功的關鍵因素：

    • 業務邏輯（Business Logic）：AI代理是否能準確理解問題並在正確時機做出決策。
    • 用戶體驗（User Experience）：產品設計是否能以最直觀的方式幫助用戶高效完成目標，而非增加認知負擔。

  3. 實用性AI的挑戰：
    AI代理不僅需要具備技術能力，還需真正「解決問題」（如自動處理郵件、追蹤待辦事項等），而非僅提供表面功能。文章以DearFlow為例，強調主動執行、直觀的任務卡片設計及人性化直覺的重要性。

  4. DearFlow的設計理念：
    主打「主動執行」（proactive execution）與無縫整合用戶工作流程，減少用戶干預，同時透過直觀的界面（如Task Card UI）和智能優先級排序，提升實際效用。

  5. 未來方向：
    AI代理需深度理解用戶需求，才能真正融入工作流程並成為「不可或缺的工具」，而非僅是技術展示。

總結來說，文章聚焦於「如何打造真正實用且能長期留存用戶的AI代理」，並以DearFlow為案例，探討其設計哲學與解決方案。

內容​

(Featuring our case with DearFlow - proactive AI assistant)

Over time, agentic AI will become cheaper, easier to build, and more widespread? As more tools integrate LLM-powered agents, the core technology itself will become less of a differentiator.

So what makes an AI agent stick? Business logic and user experience.

  • Business logic: How well does the agent understand the problem it solves? Does it make the right decisions at the right time?

  • User experience: Depending on the problems you're trying to solve, how are you gonna present your solution in an interface that helps users achieve their goals the fastest? For example, in our project, we try to solver the admin task overload - improving users' productivity. So we will ask ourselves - Is our interface/product actually seamless to use, or does it require users to “manage” it like another tool - which costs them more time and cognitive load?

This is something our have been laser-focused on while building DearFlow, a proactive AI assistant that actually takes over admin work.

The Challenge: AI That Actually Feels Useful

AI agents sound amazing in theory, but in reality, most users don’t want to “talk to an agent” all day. They want things done.

For example, an agent that can technically “draft an email” isn’t enough. What actually matters is:

✅ Does it understand which emails require a response?

✅ Can it write in your tone and context?

✅ Will it remind you at the right time, so nothing falls through the cracks?

These nuances are what separate AI that just “exists” from AI that actually eliminates work.

Our Approach with DearFlow

Instead of just giving users another chatbot-style assistant, we focused on:

  • Proactive execution: Users don’t need to prompt it, it clears inbox clutter, drafts responses, tracks follow-ups, and suggest unsubscribes automatically, with human reviewing the work and making final decisions.

  • Task Card UI design: Instead of overwhelming users with notifications, emails are presented as task cards with prepared suggestions, making it easy to just get things done.

  • Human-like intuition: Prioritizes tasks based on actual urgency, not just keyword matching.

It takes time to prove success, but we believe AI agents will only become truly useful when they blend into users’ workflows effortlessly, which only can be done if we understand our users enough.

Open to more discussion on this viewpoint and also your feedback on the product approach!

討論​

評論 1:

Your website is fire, 🔥 I love it

評論 2:

Here is our website featuring our product demo video to illustrate the product design approach I mentioned in the post: https://www.dearflow.ai

評論 3:

Absolutely agree, tech alone won't carry agents far anymore. The magic happens when the agent deeply understands the domain and delivers value without becoming another thing users need to manage. The future belongs to tools that disappear into the workflow, not demand attention.

評論 4:

This is quite an interesting take, truly AI agents that actually eliminate work rather than just adding another layer of complexity will be the real winners. The shift isn’t just about automation it’s about seamless execution and intuitive decision-making.

That’s why I’m keeping an eye on Agenda 47, it redefines news telling and gives you access to trending news in a fun and interesting way.

評論 5:

99% of AI agent companies aren't exactly building their own models from scratch, so I think in most ways "tech" and IS the business logic and UX.

24. Can a System msg be Cached?​

这篇文章的核心討論主題是：如何優化基於LLM（大型語言模型）的代理系統（agentic systems）的提示（prompt）傳輸效率，特別是針對緩存靜態系統消息（static system message）以減少重複傳輸和節省token使用量的技術方法。

具體問題聚焦於：

  1. 避免重複傳輸：在每次用戶觸發代理（如電子郵件起草代理）時，系統會將「冗長的靜態系統消息」（如產品描述、範例）與用戶輸入（如產品名稱）一起發送給LLM，導致冗余的token消耗和延遲。
  2. 可能的解決方案：探討是否能在技術層面（無論是通過No-code工具如n8n/Flowise，或代碼框架如PydanticAI + Langchain）實現：
    • 緩存靜態部分的系統提示（system prompt），僅在初始化時傳送一次。
    • 後續請求僅傳遞動態的用戶輸入（如產品名稱），並與緩存的靜態內容結合處理。

目標是提升效能（減少延遲）、降低成本（節省token）並維持功能一致性。討論範圍涵蓋技術可行性及現有工具的支持程度。

內容​

I've been building agentic systems for a few months, and I usually find most of the answers and guides that I need here on reddit or by asking an AI model.

However there this questions that I haven't been able to find a definitive answer to. I'm hoping someone here may have insights into these topics.

In the case of building a single CAG agent using no-code(e.g. n8n/Flowise) or code (PydanticAI + Langchain), is there a way to cache the static part of the system msg with the LLM to avoid sending that system message to the that LLM everytime a new user/session triggers the agent?

Any info is much appreciated.

Edit (added an example from my reply below):

Let's say I have a simple email drafting agent on n8n with a long and detailed system message, that includes multiple product descriptions and a lot of examples (CAG example):

Input: Product Name

Output: Email with product specs

When a user triggers the agent with a product name, n8n will send this large system message along with the name of product to the LLM in order to return the correct email body

This happens every time a user triggers the flow. The full system msg + user msg are sent to the LLM.

So what I'm trying to find out is whether there's a way to cache the static part of the prompt being sent to the LLM, and then each time a user triggers the flow, only the user msg (in this case the product name) is sent to the LLM.

This would save a lot of tokens, improve the speed of inference, and eliminate redundancy.

討論​

評論 1:

like you want the llm to always use the same system instructions without specifying them each time? or without having to re-send them each time?

評論 2:

Some LLM providers can support this feature. As far as I know, DeepSeek supports caching the system_prompt, and the cached part has a much cheaper token cost.

25. Best Open-Source AI agent? Help! Switching from Manus & OpenAI​

這篇文章的核心討論主題是：尋找適合本地運行的開源AI替代方案，並探討相關的硬體需求、推薦工具、資源成本及最新資訊來源。

具體要點包括：

  1. 動機：作者希望擺脫商業AI（如ManusAI、OpenAI）的付費限制，尋找能在本地硬體上運行的開源AI解決方案，即使速度較慢但能保證輸出品質。
  2. 關鍵問題：
    • 硬體需求：本地運行強大AI所需的設備規格、預算建議（如是否需要專用PC）。
    • 開源AI推薦：適合新手的開源AI工具（如Anus、openManus等）。
    • 附加資源與成本：是否需要依賴第三方API或額外資源（如數據集、雲端服務）。
    • 資訊追蹤：獲取LLM（大型語言模型）和開源AI最新動態的管道（如論壇、社群）。
  3. 目標：在預算內打造最佳的本地AI體驗，並尋求社群的建議與經驗分享。

整體而言，這是一個關於「自主部署本地AI的實用指南請求」，聚焦於開源生態、硬體門檻及資源整合。

內容​

Hey everyone,

I've been using ChatGPT since its launch, and recently I got a taste of what ManusAI can do. Honestly, it's been mind-blowing. But with their new pricing model, whether it's $39 or $200, it feels a bit too limiting.

I'm a total newbie in this space and I’m on the lookout for a powerful alternative that I can run locally on my own hardware. It doesn't need to be as lightning-fast as Manus or OpenAI, but as long as it produces quality output given enough time, I’m happy.

I’ve come across a few names like Anus or openManus, but I’m sure there’s a lot more out there. So I have a few questions for you all:

  • Hardware Requirements: What kind of hardware do I need to run a powerful AI locally? Would a dedicated PC be enough? What would you recommend, and what budget are we talking about?

  • Open-Source AI Agents: Which open-source AI agent do you recommend diving into?

  • Third-Party Resources: What additional resources might I need, and what are their typical costs? I assume some agents rely on APIs like OpenAI's.

  • Staying Updated: Where do you keep up with the latest developments in LLMs, AI agents, and open-source projects?

I’m really eager to dive into this community and get the best local AI experience possible without breaking the bank. Any advice, tips, or recommendations would be greatly, greatly appreciated!

Thank you!!

討論​

評論 1:

Our startup is actually developing something similar to Manus. I would love to know what do you wish to accomplish using Manus or Operator? Or what features do you expect to have?

評論 2:

Gemini

評論 3:

Its not open source but rtrvr.ai is much more economical than Manus.

We are an AI Web Agent Chrome Extension, since it runs on your browser our agent has access to your browser and is much cheaper to operate. We are at 10$/mo, what chich should give you 10000 webpage actions

評論 4:

check out npcsh  https://github.com/cagostino/npcsh it can run open models or API based ones and lets you use and develop agents with a simplistic data layer. its free and open source and all your data from the shell will be stored locally so you can use the results and conversations later

評論 5:

Anthropic's MCP stuff is fire rn

總體討論重點

以下是25篇文章的討論重點總結，以條列方式呈現並附上對應的錨點連結：

1. 企業對AI代理的需求與痛點​

  • 目標客戶：新創、中小企業、大型企業對ROI、整合性、安全性的差異需求。
  • 應用場景：內部自動化（會議助理、程式審查）、客戶服務（客服、潛在客戶開發）。
  • 採購動機：降低成本、解決人力短缺、提升效率。
  • 痛點：討厭按人頭計費，偏好整合性與快速部署。

2. 客戶支援AI代理的開發指南​

  • 開發流程：需求釐清、技術架構選擇（RAG vs. 多代理）、監控工具（LangSmith）。
  • 關鍵挑戰：避免LLM幻覺、設計容錯機制。

3. Cloudflare Agents SDK的遷移效益​

  • 效能提升：延遲降低70%，簡化架構（刪減5,000行代碼）。
  • 開發體驗：直觀SQL結構取代複雜DynamoDB。

4. 低代碼網頁監控代理​

  • 需求：偵測網頁內容變化（如價格）並觸發動作（點擊按鈕）。
  • 工具建議：Zapier、Power Automate。

5. AI智商評估工具的潛力​

  • 核心概念：訓練AI判斷文件作者智商（人類或AI），應用於專業服務選擇。

6. 大型JSON編輯的AI工具困境​

  • 問題：現有工具（ChatGPT、DeepSeek）因記憶限制無法穩定處理大型法律案件JSON。

7. MCP規範的授權功能​

  • 重點：新增auth機制優化代理與API整合，需更靈活的互動設計。

8. B2B客戶開發工具需求​

  • 需求：查找潛在客戶郵件與公司技術堆棢的自動化工具。

9. 後端開發者轉型AI代理開發​

  • 學習需求：短期掌握LLM與聊天機器人開發，長期轉型機器學習。

10. 自由工作者協助開發AI代理​

  • 功能需求：多語言文件檢索、翻譯、摘要生成，尋求報價與建議。

12. AI代理自動化運維任務​

  • 應用場景：診斷遺留系統故障，需動態決策能力（非規則驅動）。

13. 大規模AI代理配置管理​

  • 挑戰：管理千種專用代理，需平衡後端複雜度與前端易用性。

14. 社交媒體AI代理​

  • 目標：透過具個性的代理自動發文，提升追蹤數。

15. AI代理的定義爭議​

  • 核心辯論：LLM具工具存取能力是否等同代理？需目標導向行為。

16. OpenAI Agents SDK資源匱乏​

  • 問題：開發者缺乏社群支援，僅能依賴官方文檔。

17. 客戶支援AI新創前景​

  • 討論：專注客服的AI代理公司（如Sierra）是否為泡沫或可持續市場？

18. AI代理機構與編程需求​

  • 關鍵問題：高