Introducing GPT-5: The Smartest Coding Model for Businesses

For chatbot based systems, users rarely provide every detail in a single turn and in order to gather all details we ask relevant questions iteratively in a conversational manner. This process involves two fundamental steps: Intent Parsing: Determining the user's ultimate goal or requirement. Slot Filling : Fill relevant details as provided by the user in conversations While powerful, proprietary platforms like Google Dialogflow and Azure Conversational Language Understanding (CLU) are designed specifically for this task, developers often need more control, specialized logic, or lower costs. In the following medium article I am exploring some of the custom approaches that can serve the same purpose for deterministic and domain specific use cases. The two primary custom approaches I have outlined are: 1. Keyword/Regex-Based Approach (Rule-Based NLU) 2. Large Language Model (LLM)-Based Approach (Generative NLU) Medium : https://lnkd.in/g_BGpNcc

🚨 Quick AI Showdown: Best Models for AL Development 🧩 Cursor – The top pick for AL development. It understands .al structures, keeps full project context, and provides clean refactoring suggestions. ⚡ GitHub Copilot – Handy for quick snippets but often struggles with Business Central logic and object syntax. 💡 ChatGPT (GPT-5) – Great for explaining, debugging, and generating complete AL extensions with docs and tests. 🔬 Claude 3.5 – Strong reasoning skills, but limited AL/BC knowledge, sometimes invents fields or properties. 💥 For real-world AL dev, Cursor + GPT-5 is the winning combo💥 #Dynamics365 #Dynamics #MSDyn365 #MicrosoftDYN365 #MSDyn365BC #businesscentral #AI

Last year, building AI agents was hard. Today, you can do it in minutes thanks to MCP. Here are 5 AI agents you can spin up this weekend using MCP 1) Claude Desktop + Local Filesystem Give Claude access to your local files with the pre-built Filesystem MCP Server. This lets Claude search for relevant context, read files, and create new ones. 2) ChatGPT + G-Suite Connect ChatGPT to your Google Drive, Calendar, and GMail using native MCP (Settings > Connectors). This requires no fancy setup. Just authenticate your account, and everything works out of the box 3) Cursor Agent + Context7 Don't suffer from outdated LLM-generated code. Use the Context7 MCP server to give Cursor updated docs whenever it writes code. 4) Custom Notion Agent Connect Notion's official MCP server to your favorite LLM to create a Notion agent. As a bonus, give it instructions and context about your work and the tasks you want help with. 5) Custom YouTube Agent Build a custom MCP server that can extract YouTube video transcripts and feed them to any LLM. You can then similarly create a custom agent to idea number 4! 👉 What are some other agents you've created with MCP? -- P.S. Tomorrow (Oct 3) at 10AM CST, I'm hosting a free workshop on MCP where I'll walk through how to build idea number 5! Register here: https://lnkd.in/gmsRMwZd

𝗟𝗼𝗰𝗮𝗹 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿 Just spent the last few days diving into MCP (Model Context Protocol) and it's been such a fun learning experience! 🚀 𝗦𝗼 𝘄𝗵𝗮𝘁'𝘀 𝗠𝗖𝗣 ?      • It's basically how AI (like ChatGPT) can talk to external tools and data. The lifecycle is pretty straightforward - Initialization → Operation → Shut Down. Simple concept, but the implementation is where things get interesting! Started with LOCAL servers using STDIO (it's basically input/output streams). The way it works:   • Host launches the server as a subprocess on your own machine   • Client writes 𝗝𝗦𝗢𝗡-𝗥𝗣𝗖 messages into STDIN   • Server reads, processes, and writes back responses I used Claude AI to implement this! In Claude's interface, there's a connectors section where local MCP servers are already available to use. Tried out:     • 𝗙𝗶𝗹𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘀𝗲𝗿𝘃𝗲𝗿 - reading, writing, moving files around     • 𝗡𝗼𝘁𝗲𝘀 𝘀𝗲𝗿𝘃𝗲𝗿 - managing notes programmatically     • 𝗦𝗲𝗮𝗿𝗰𝗵 𝘀𝗲𝗿𝘃𝗲𝗿 - querying the web and getting real-time info The coolest part? Understanding MCP Primitives:     • 𝗧𝗼𝗼𝗹𝘀 (actions the AI can perform)     • 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 (data sources it can read)     • 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 (predefined templates to guide behavior) Drew so many diagrams trying to understand the architecture 😅 But it clicked when I saw how the transport layer (JSON-RPC 2.0) moves messages between client and server. --Learning by doing is honestly the best way. #𝗠𝗖𝗣 #𝗟𝗼𝗰𝗮𝗹𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 #𝗔𝗜 #𝗖𝗹𝗮𝘂𝗱𝗲𝗔𝗜

🔰 The 2025 Prompt Rulebook — Microsoft Study Reveals 300% Accuracy Gap 🚀 🎯 Concept: Format Sensitivity as the New Accuracy Booster Two developers, same model, same question — yet one gets a brilliant response, the other a confusing one. The reason? Prompt format sensitivity. According to a Microsoft study, changing your format alone can shift accuracy by over 300%. 🧠 Analogy: Think of AI like a musician. Same lyrics, but different sheet formats — the way you write the song changes how it’s played. 💡 Real-Time Example: QA automation testers, developers, and AI creators have started standardizing prompt formats using XML or Markdown to make results stable, repeatable, and logical. 🪄 Tips: Be consistent in structure. Avoid mixing symbols, JSON, or random delimiters. Separate reasoning from the final output with clear XML tags or Markdown dividers. 🧩 Conclusion: Prompt design isn’t creativity alone — it’s structure, science, and syntax. Master it, and you unlock the model’s full potential. 🔖 Hashtags: #AIPrompting #PromptEngineering #MicrosoftResearch #AIAccuracy #OpenAI #ClaudeAI #Hack

What Is MCP — and Why It Matters 🧠 Large Language Models (LLMs) are smart — but they’re not useful on their own. They need context, tools, and access to your digital world. That’s where Model Context Protocol (MCP) comes in. 🔍 What is MCP? MCP is an open protocol that lets AI agents discover and use external tools — like APIs, apps, and services — in a standardized way. It’s how agents learn what tools are available, what they do, and how to use them safely and effectively. 🛠️ How It Works: 1. Agent → “Schedule a meeting with Lisa and summarize our last call.” 2. MCP → Lists available tools: Google Calendar, Zoom API, transcription service, summarizer. 3. Agent → Uses those tools autonomously to complete the task — no hardcoded logic required. ✅ Why It’s Powerful: - Agents become tool-aware and actionable, not just chatty - Enables multi-step workflows across apps and services - Promotes interoperability across vendors and ecosystems - Reduces the need for custom integrations or retraining 📦 Real-World Examples: - 🗓️ A sales agent books meetings using your calendar and CRM - 🧾 A finance bot pulls invoices from your ERP and sends payment reminders - 🎨 A design assistant turns Figma mockups into working code using dev tools - 🧠 A research agent queries your internal knowledge base and drafts reports 🔧 Tools You Can Use: - LangGraph, OpenAgents → for agent orchestration - Tool schemas → exposed via MCP-compatible metadata - Any API → wrapped with MCP to make it discoverable and usable by agents 💡 TL;DR: MCP is the bridge between smart models and real-world action. It’s how we move from isolated LLMs to intelligent, integrated AI teammates. #AI #MCP #ModelContextProtocol #AgentFrameworks #LLM #FutureOfWork #AIIntegration #OpenAgents #LangGraph #ToolUse #AIWorkflows

I became DATA Engineer overnight! 👩💻 Okay, not really. But the AI powered workflow that I have built this time made me feel like one. Meet my innocent looking n8n workflow diagram that is actually doing some powerful heavy lifting behind the scenes. An automated AI pipeline that performs media ETL (Extract, Transform, Load) and inference, all stitched together with FREE tools. It fetches video data, preprocesses it into audio, and uses a speech-to-text large language model to generate transcriptions, storing structured outputs back into a Google Sheet for downstream analysis. Under the hood, this workflow: • Ingests structured input data (video URLs) • Downloads and preprocess and transforms video data (MIME type from MP4 to M4A) • Calls the OpenAI Whisper API for speech-to-text inference • Updates and organizes results in a structured format • Uses controlled execution loops and throttling The best part? 💡 While there are plenty of paid tools that can achieve this with polished UIs, the real win was designing, orchestrating, and optimizing this entire media-to-text AI pipeline using only free tools and APIs, proving what’s possible with creativity, problem-solving, and persistence. My Tech Stack: • n8n (self-hosted) – Robotic Processing Automation & orchestration tool • Google Sheets – Data ingestion & output management • OpenAI Whisper API – AI inference (speech-to-text LLM) • HTTP Requests + Custom Code Nodes – Data acquisition & preprocessing • Free Scraper API for data scraping Each node may look simple, but together they form a robust end-to-end system capable of automated audio extraction, transformation, and transcription, all running seamlessly with zero-cost infrastructure. Real Life Use case: Extract audios from your Lectures(online or offline) in bulk (as you can see mine is processing 300 files) and transform the data however you like. For example: Create your own notes! #AITools #MachineLearning #Automation #n8n #OpenAI #Whisper #LowCode #AIEngineering #ETL #DataTransformation #LLM #SpeechToText #AIWorkflow #MediaProcessing #TechInnovation #PersonalDevelopment #AICommunity #DataPipeline #MLOps #FreeTools

🚀 The 𝗔𝗜 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 is evolving fast, and it’s key to understand the differences between 𝗟𝗟𝗠 ��𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗥𝗔𝗚, 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀, and 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜. 🔹 𝗟𝗟𝗠 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 → Fast, simple, great for text generation & summarization. 🔹 𝗥𝗔𝗚 → Boosts accuracy with external knowledge. 🔹 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 → Brings reasoning + tool usage for automation. 🔹 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 → Multi-agent collaboration for complex, autonomous tasks. This framework highlights when to use each approach depending on your use case: from 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀 to 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗮𝗴𝗲𝗻𝘁𝘀. 📌 Knowing when to use each can help build scalable and impactful 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀. #AI #RAG #AIagents #AgenticAI #LLM