Treating AI prompts as software artifacts: A new approach to AI development.

I got featured in International Business Times 🚀 They wrote about how we're building the next generation of intelligent software at Hyperbound. AI is driving a revolution, one where software is finally intelligent enough to understand concepts, not just commands. We're no longer teaching machines to follow step-by-step instructions; we're teaching them to grasp the meaning behind entire conversations. That shift changes everything, from how products are built to how humans interact with technology. We talked about what it really means to build AI that collaborates with humans, not competes with them. Real call scoring that adapts to each team's culture and psychology Using AI to understand tone, flow, and intent, not just words. Building narrow, purposeful systems instead of chasing general intelligence What started as an experiment now evaluates hundreds of thousands of real sales calls for Fortune 100s and. Full article here: https://lnkd.in/evJ4kTME

The AI Productivity Confusion 😅 Lately, many companies are asking teams to use tools like Cursor, Copilot or ChatGPT to “increase productivity” and “get more done in less time.” Makes sense. Everyone wants efficiency. But then the client says, “If you’re using these tools, why are deliveries still taking the same time?” And that’s where things get tricky. AI helps. It really does. It makes coding, writing, and some decisions quicker. But project delivery isn’t just about how fast someone types. It depends on clarity, reviews, dependencies, testing, and all the coordination that happens around the actual work. Most of the time, the hours AI saves get used for things we usually wish we had more time for: - cleaning up code - writing better tests, - improving design, - mentoring juniors, - or just thinking through long-term maintainability. So yes, work gets faster, but also deeper. Maybe it’s time both sides (teams and clients) stop seeing AI as a shortcut to speed and start seeing it as a way to build better software. Faster isn’t always progress. Smarter usually is. What are your thoughts or opinions?

🔥 Why MCP (Model Context Protocol) is a Game-Changer for AI Development I've been exploring Anthropic's Model Context Protocol (MCP), and it's genuinely revolutionary. Here's why every AI developer should pay attention: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗖𝗣? MCP is an open protocol that standardizes how AI assistants connect to data sources and tools. Think of it as USB-C for AI—one universal standard instead of countless custom integrations. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗶𝘁 𝗦𝗼𝗹𝘃𝗲𝘀: Before MCP, every AI tool needed custom integrations for each data source: - Claude needed custom code to read Google Drive - ChatGPT needed separate plugins for Notion - Each tool, each database, each API = new integration This created fragmentation, maintenance nightmares, and wasted developer time. 𝗛𝗼𝘄 𝗠𝗖𝗣 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴: 1. 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱: One protocol works with any MCP-compatible AI 2. 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Build once, use everywhere 3. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀: Controlled access to sensitive data 4. 𝗘𝗮𝘀𝘆 𝗘𝘅𝘁𝗲𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Add new tools without rebuilding everything 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁: Imagine connecting your AI assistant to: ✅ Company databases ✅ Internal wikis ✅ Project management tools ✅ Custom APIs ...all through one standardized interface. No more vendor lock-in. No more integration hell. Just plug and play. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗡𝗢𝗪: We're moving from "AI as a chatbot" to "AI as infrastructure." MCP is the foundation that makes this scalable, maintainable, and secure. If you're building with AI, MCP isn't just another protocol—it's the future of how AI systems interact with the world. What's your take on MCP? Are you already implementing it? #AI #MachineLearning #MCP #Anthropic #AIInfrastructure #TechInnovation #DeveloperTools

🎥 I just released a new YouTube video: 👉 “Top 10 AI Tools every developer must know in 2025” Discover the essential AI tools that are revolutionizing software development in 2025. This video breaks down the #top10 AI technologies every developer should master to stay competitive and efficient. https://lnkd.in/eB6z8eJ9 1. ChatGPT (GPT-5) - Your ultimate development assistant for debugging, API generation, and documentation 2. GitHub Copilot - AI pair programmer that accelerates coding with smart suggestions 3. Tabnine - Code completion tool that can be trained on private codebases 4. Cody by Sourcegraph - AI coding agent that helps understand, write, and fix code across large projects 5. Replit Ghostwriter - Built-in AI assistant for rapid prototyping in the Replit IDE 6. Hugging Face - Repository of open-source AI models for NLP and computer vision 7. LangChain - Framework for building LLM-powered applications and connecting them to your data 8. Weaviate/Pinecone - Vector databases essential for AI memory and semantic search 9. OpenAI/Anthropic Claude APIs - Powerful interfaces for integrating AI directly into your applications 10. Cursor IDE - AI-centered code editor combining refactoring, explanation, and testing capabilities These tools aren't replacing developers—they're empowering us to build faster, smarter, and with fewer bugs. Like, subscribe, and comment with your favorite AI development tool! #AIforDevelopers #DevTools #CodingAI #SoftwareDevelopment #AITech

My AI Assistant Does More: It Reads Your Docs, Crawls Websites, and Manages Your Inbox 🚀 Building an AI Assistant That Does More Than Just Chat 🤖 I'm excited to share a powerful, multi-modal AI Knowledge Assistant I've developed using n8n, LangChain, and Google Gemini. This isn't just a simple chatbot; it's a complete data interaction and automation engine. Here’s what it can do: 🌐 Understands Any Input: Users can interact by uploading files (PDFs, DOCX), providing a website URL, or just chatting. The workflow automatically routes the input for the correct processing. 📄 Deep Document & Web Analysis: For URLs, it doesn't just scrape one page—it can crawl the entire website to build a comprehensive knowledge base. For files, it extracts the text and prepares it for analysis. 🧠 RAG-Powered Intelligence: Using Retrieval-Augmented Generation (RAG), the assistant creates a vector store from the provided content. This means it can answer complex questions with high accuracy, grounding its responses directly in the source material. No more generic answers! 🛠️ Equipped with Powerful Tools: The AI agent can do more than talk. It's equipped with tools to: Send Emails directly via the Gmail API. Perform Calculations. Provide Real-Time Progress Updates to the UI, so the user knows what's happening behind the scenes (crawling, indexing, etc.). This workflow is a perfect example of how low-code platforms like n8n can be used to orchestrate sophisticated AI agents that solve real-world problems—from customer support to sales assistance and data analysis. What other tools or capabilities would you add to an AI assistant like this? Let me know in the comments! #AI #Automation #n8n #GoogleGemini #LangChain #GenerativeAI #LowCode #RAG #AIassistant #WorkflowAutomation

🚀Stepping into the world of MCP (Model Context Protocol) Lately, everyone’s been talking about Agentic AI, AI Automations, and Agents that act. But the real magic that makes this possible? MCP — the Model Context Protocol. So, what is it really? 🤔 Think of MCP as a bridge that lets AI models like ChatGPT or Claude talk directly to real-world tools and data sources — safely and in a standard way. It’s like giving AI access to your APIs, databases, Notion pages, or Slack… but under your control. 💡 Why it’s powerful: AI can fetch live data, not just what it was trained on. It can take action — create GitHub issues, trigger workflows, send reports, etc. It removes the need for hard-coded plugins or manual integrations. Imagine a future where instead of building workflows manually (like we do in n8n or Zapier)… you simply tell an AI: “Summarize this week’s GitHub commits and email the report to my team.” And it does — by using your connected tools through MCP. 🤯 I’ve just started exploring how MCP works under the hood — building small connectors, experimenting with local servers, and understanding how it can power truly autonomous AI agents. This feels like the next layer of automation — where AI doesn’t just think, but also does. More learnings soon! ⚙️ If you’re curious about AI + automation + real-world integration — follow along 👇 #AI #MCP #AgenticAI #Automation #Developers #Innovation #LearningJourney

Three principles. One question. Stephen Covey: "Begin with the end in mind" Simon Sinek: "Start with why" Steve Jobs: "Start with the customer and work backward to the technology" The question: What are we actually trying to solve with AI? Scrolling LinkedIn lately feels like pharmaceutical commercials - features looking for problems, technology searching for use cases. "I didn't know I needed that... wait, maybe I do?" I started delta : kitsune with a why: helping someone I love manage a chronic illness that's elusive, subjective, and mysterious. That purpose drives every decision - privacy-first architecture, local deployment, accessibility over scale. The end in mind isn't "build impressive AI." It's "help people who need tools that don't exist yet." Jobs was right. The hardest part isn't the technology. It's fitting it into a vision that actually serves people. https://lnkd.in/gFvG7jmw

Everyone’s talking about OpenAI’s new Agent Builder. I get the excitement, but let’s be honest about what it really means. It’s a smart move. OpenAI is moving beyond chat into automation, where real productivity gains happen. Agent Builder lets you create AI agents that can search, analyse data, summarise information, send emails and connect with external tools. I’ve seen people build task managers, ad campaign checkers and compliance bots in minutes. This looks a lot like what platforms such as n8n, Zapier or Make offer - a visual canvas, logic nodes, integrations, routing and data flows. That’s not a bad thing. It just means OpenAI is entering a space many of us already live in. Useful? Definitely. Transformational? Not yet. Right now, it still suits people who already understand how automation fits together. Most professionals aren’t there. I still hear from big firms, especially law firms, that are only just working out what an LLM actually is and whether it can provide value (it clearly can). For them, Agent Builder won’t suddenly unlock anything new. It’s another tool for those already building with AI, not an entry point for those still catching up. The real shift will come when you can build and debug automations entirely through conversation - when plain English replaces logic maps and code. That’s when the skill set of today’s automation builders starts to lose its edge. Until then, Agent Builder is a solid upgrade for people already in the game, not the revolution some are calling it. What do you think: a step toward true no-code automation, or just another tool for the usual crowd? #AI #Automation #OpenAI #ChatGPT #AgentBuilder

AI is a great assistant but a terrible scientist. I find myself telling people (and computers) "one thing at a time" a lot these days. If there is anything left in science, it is the method: hypothesis, test, repeat. This may not come as a surprise to anyone, but I've seen AI break this method a lot and it is therefore going into my copilot instructions*. Copilot (using any of the main models) often changes several things at once. This is likely coming from trying to be efficient, but in research and development it is terrible practice (unless all of the fixes are very obvious and isolated). The scattershot approach generally makes for very frustrating work sessions. My latest additions to "copilot-instructions.md": 1) First change the thing I asked you to change, then offer if you see other fixes that may help. 2) If you are finding fixes on your own, try only one thing at a time and test along the way. 3) Always assume there is more to do. Don't summarize the whole project until we are done with the whole project. I had to add (2) because I had a few sessions in a row where copilot took off on a tangent "fixing" several things and broke stuff all over the place. I added (3) because I find copilot "summarizing" it's work after every prompt. Don't do that, just do the thing, and expect that there is more to do. I'll decide when I'm done, not you. *I'm referring to github copilot, but similar settings are available for other AI tools https://lnkd.in/gsxyrjvX

Ever spend hours reviewing AI-generated code… only to bin most of it? It happens more often than anyone admits. The promise is productivity, the reality is half-baked code, weird bugs, and endless re-explanations just to “train” the tool again. Sometimes it feels like we’re working for the #AI instead of the other way around. It raises a bigger question: Should engineering teams stick with these tools, even when they’re inconsistent? Or pause and rethink how (and where) AI fits into the workflow? #SoftwareDevelopment #AIEngineering #Developer