Building AI demos is easy now, but deployment is still the hard part. Most platforms haven't solved this gap yet. #AIEngineering #BuildingAI

𝐀 𝐲𝐞𝐚𝐫 𝐚𝐠𝐨, 𝐦𝐨𝐬𝐭 𝐀𝐈 𝐝𝐞𝐦𝐨𝐬 𝐞𝐧𝐝𝐞𝐝 𝐚𝐟𝐭𝐞𝐫 𝐨𝐧𝐞 𝐢𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐚𝐧𝐬𝐰𝐞𝐫. Today, users expect AI to: • Research • Plan • Use tools • Remember context • Complete real work That shift is exactly why traditional RAG is no longer enough. AI systems are now evolving through three major stages: 𝐑𝐀𝐆 → 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆 → 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐑𝐀𝐆 Here is the difference 👇🏻 📚 𝐑𝐀𝐆 • Retrieves relevant knowledge before generation • Grounds responses using documents and external data • Best for enterprise search, document chat, and grounded Q&A ➨ Useful when the goal is better answers. 🤖 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆 • Goes beyond answering questions • Uses planning, memory, tools, and reasoning • Dynamically decides the next best action • Best for copilots, research workflows, and automation systems ➨ Useful when the goal is execution. 🏢 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐑𝐀𝐆 • Multiple specialized agents collaborate together • Separates retrieval, reasoning, validation, and execution • Coordinates workflows across tools and systems • Best for enterprise-scale autonomous operations ➨ Useful when the goal is coordination at scale. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐬𝐡𝐢𝐟𝐭: • RAG improves answers • Agentic RAG completes tasks • Multi-Agent RAG scales intelligence After all, The future of AI products will not be defined only by smarter models. It will be defined by smarter systems built around them. Which stage do you think most companies still underestimate today: RAG, Agentic RAG, or Multi-Agent RAG? ♻️ Repost to help someone stay ahead in AI. ➕ Follow @coder_surya for daily hands-on AI learnings. - Join AI Community: tinyurl.com/mvsz79eu - Save the post. - Repost to your network. #AI #RAG #AgenticAI #MultiAgentSystems #LLM #GenerativeAI #AIAgents #AIEngineering #AIArchitecture #BuildingAI

I’m a prompt engineer who’s been quietly buildingAI workflows, client systems, content that’s starting to pull attention. I’m not here to try it out. I’m here to go all the way in. Pick me and you won’t regret it.

Literally me right now 12:47am 🤣 can't remember the last time I slept yesterday, I always sleep today morning 🤧 #tech #buildingai #Artificialintelligence #digital

很多 AI 项目做到 Agent、RAG、知识库就停住了，但真要上线给用户用，还要补注册、会员、计费、支付、模型管理和后台运营。 BuildingAI 是企业级开源智能体平台，把 AI Conversations、AI Agents、Knowledge Base、MCP Integration、Model Management 和 Billing & Payments 放在同一套系统里。 核心就一句话：把“AI 能力能跑”往后推一步，补上 AI 产品上线和收费需要的基础设施。 · AI Agents：创建带记忆、目标和工具使用能力的智能体 · Knowledge Base：从文档构建知识库，并用 RAG 增强生成 · MCP Integration：通过 SSE 和 Streamable HTTP 调用 MCP 工具 · Model Management：统一规格接入主流大模型 · Billing & Payments：内置会员、计费和支付能力 适合做 AI SaaS、企业内部平台、或想把 Agent 变成可运营产品的人。不适合只想快速试一个单点 demo。

Agent 和 RAG 能跑只是前半段，真想做成 AI 产品，还要用户、会员、计费、支付、模型管理和后台运营。BuildingAI 的看点是把 AI Agents、Knowledge Base、MCP Integration 和 Billing & Payments 放到同一平台里。我把部署命令和适用边界放在评论里。链接放评论区。

Building AI is easy. Making it work reliably at scale is where things get interesting. That’s where we spend most of our time. #BuildingAI #AI #GammaEdge

The unresponsive vendor angle here is the real problem—SSRF in file upload functions is trivial to exploit at scale, so if you're running BuildingAI ≤26.0.1, you need compensating controls (network segmentation, request signing) while waiting for a patch, since the exploit is already public.

🚨 HIGH Severity: CVE-2026-7065 (CVSS 7.3) BidingCC BuildingAI ≤26.0.1 vulnerable to SSRF via uploadRemoteFile function. Exploit public, vendor unresponsive. Patch immediately if using affected versions. #CVE #Vulnerability #PatchNow #ThreatIntel

CVE-2026-7065 Server-Side Request Forgery in BidingCC BuildingAI Remote Upload API Up to 26.0.1 vulmon.com/vulnerabilitydeta…

CVE-2026-7065 A vulnerability has been found in BidingCC BuildingAI up to 26.0.1. Impacted is the function uploadRemoteFile of the file packages/core/src/modules/upload/services/file… cve.org/CVERecord?id=CVE-202…

Excellent tutorial on building AI systems. #AI #BuildingAI

Most teams are bolting AI onto existing products. The winners are rebuilding their entire architecture around AI. There's a massive difference. One scales. The other breaks. #AIProducts #BuildingAI #ProductStrategy

Two powerful ecosystem buildingAI intelligence layer. @permacastapp & @dgrid_ai

AI builders: what's the most counterintuitive thing you've learned about getting enterprise users to actually adopt what you ship? Not the model problem. The human problem. 👇 #AILeadership #BuildingAI

AI that sits next to a workflow gets evaluated. AI that lives inside a workflow gets used. Where does your team's AI live relative to where decisions actually happen? #AIEngineering #EnterpriseAI #BuildingAI

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5 ai tools that simplify dev workflows: 1. langchain — streamlines llm integration 2. transformers — pre-trained models for nlp tasks 3. gradio — easy ui creation for ai demos Bookmark this #buildingai #GenerativeAI #BuildInPublic Follow along if you're building with AI too

Success kills most AI products: token costs spike, models drift, profits vanish. Design for worst-case first. Structural, not hype. Get the book: a.co/d/021ZeDbA #BuildingAI #IndieHackers