How to build complex AI workflows with Genkit's Flow system

Many RAG pipelines are slow. I was determined to build one that was both powerful and incredibly fast. Here's a look at the Telar AI engine's RAG pipeline in action, running against the @GroqAPI LPU Inference Engine. https://lnkd.in/dx5npeRQ This isn't an accident. It's the result of one key architectural decision: building a provider-agnostic service in Go from day one. This design allows the engine to be flexible and cost-effective, switching between: Ollama: For free, private, self-hosted development. Groq: For world-class, low-latency performance in production. I just published a deep-dive on the "why" behind this "what." It's a blueprint for designing AI systems that are built to evolve, not just to work for a single provider. You can read the full architectural breakdown here: https://lnkd.in/dpZ3ubHi #Go #SystemDesign #AI #RAG #SoftwareArchitecture #Groq #Ollama

Open-source AI just became more valuable than knowing React. And 90% of engineers haven't noticed yet. xAI dropped Grok 2.5 as open source this week, I realized something game-changing. Elon Musk just made xAI's Grok 2.5 model completely open source on Hugging Face, with Grok 3 following in about 6 months. This isn't just another model release - it's a 500GB powerhouse that was xAI's flagship in 2024. Here's what most engineers are doing: -Waiting for companies to build AI features for them -Relying solely on API calls to closed models -Missing the chance to understand how frontier AI actually works Meanwhile, the ones positioning for senior roles are: -Downloading and experimenting with open-source models like Grok 2.5 -Building custom AI solutions using accessible model weights -Learning system architecture by studying production-grade AI implementations. 📌 The reality is: The license allows commercial use with guardrails but prohibits training other foundation models meaning you can build real products, but you can't create competing AI companies. Here are 3 ways senior engineers can leverage this: -Study Production Architecture: The model requires 8 GPUs with 40GB+ memory each and uses SGLang for inference perfect for learning enterprise-scale AI deployment patterns -Build Custom AI Features: Use the model weights to create specialized tools for code review, documentation generation, or technical interviews - differentiating yourself as someone who builds, not just consumes AI -Master AI Integration: Understanding how to work with 500GB model weights and multi-GPU setups positions you for the inevitable AI infrastructure roles every company will need. The shift is happening now. While others debate whether AI will replace developers, smart engineers are learning to work with these systems at the foundational level. Ready to turn your AI knowledge into interview wins? Understanding systems like Grok 2.5 is exactly what sets you apart in technical interviews get my complete guide to crushing every round: https://lnkd.in/d64MhyMr

#Day30 It's a Wrap! 30 Days of AI Voice Agents - Final Submission! 🎉 I'm incredibly proud to announce the completion of the #30DaysOfAIVoiceAgents challenge by Murf AI! Code & Journey: Over the past month, I've gone from concept to a fully deployed, real-time conversational AI, "Vocalix." It has been an intense, challenging, and immensely rewarding journey. 🔗 GitHub Repo : https://lnkd.in/g_CB5C7D 🤖 Try Vocalix Live : https://lnkd.in/gfiT3b84 Core Features : 🗣️ End-to-End Voice Interaction 🧠 Context-Aware Memory 🎙 Single Smart Record Button 🤖 Sophisticated AI Persona 📚 Dynamic Tool Usage >> Live Web Search, Real-Time Weather, Current Time, Website Opener (more to come) 🔑 User-Provided API Keys 🎤 Real-time Voice Visualizer 🛡️ Robust Error Handling ✨ Sleek, Minimalist UI ☁️ Cloud-Deployed My Experience & Biggest Challenges: Building Vocalix was a deep dive into the entire lifecycle of an AI application. The architecture combines Python (FastAPI) for the backend, WebSockets for real-time communication, and a suite of powerful APIs: AssemblyAI for transcription, Google Gemini for intelligence, and Murf AI for lifelike speech. Some of the toughest bugs I encountered were: -- Real-time Audio Synchronization: Managing the stream of audio data from text-to-speech, ensuring all packets were received, buffered, and played back smoothly on the client-side without clicks or gaps, was a significant hurdle. The solution involved careful handling of WAV headers and buffering audio chunks before playback. -- Dynamic Function Calling: Making Vocalix do things—like perform a live web search or open a website—was complex. The key challenge was reliably parsing the LLM's intent and securely managing user-provided API keys on the backend for these external tools. -- The "It Works on My Machine" Problem: Deployment always brings new challenges! I worked through classic issues like "404 Not Found" errors for static files (the missing "voice.png"!) and debugging server logs on Render to understand the platform's lifecycle. It was a practical lesson in DevOps and cloud deployment. What's Next? This challenge has been a catalyst. I've gained hands-on experience in building and deploying end-to-end AI systems, working with real-time data streams, and integrating multiple complex services. I'm excited to apply these skills to future projects involving interactive AI, automation, and more complex agentic workflows. ▶️ Check out the final demo of Vocalix in action! A huge thank you to the organizers of this challenge for an incredible learning experience! #MurfAI #30DayChallenge #BuildwithMurf #30DaysofVoiceAgents #AIProjects #Python #VoiceAgent #FastAPI #WebDev #BackendDevelopment #BuildInPublic #WebSockets #AssemblyAI #GoogleGemini #CodingChallenge #SoftwareEngineering #CloudDeployment

🤖 LaunchDarkly AI Configs now support agents! In this tutorial, Scarlett Attensil shows you how to built a multi-agent system that: - Uses RAG to answer questions based on your data - Redacts PII for sensitive queries - Is configurable at runtime so you can change your models, prompts, and parameters without needing to deploy - Automatically collects metrics such as latency, costs, and response quality https://lnkd.in/eGyW8fzR

Debugging AI is a pain. What if your observability platform could let an AI fix bugs for you? This is what @samuel_colvin demoed with Pydantic Logfire. ✅ Full-stack traces (FastAPI + LLM) ✅ Auto-retries on validation errors ✅ An AI agent that queries logs & fixes your code This is the future of building AI apps. See how it works in the recording or talk notes below Oh and Pydantic is sponsoring our AI coding course that starts soon and will provide $200 of credit to each student https://lnkd.in/e7Xwzcrd Check out the talk for full details 👇 https://lnkd.in/eqgBkP3k

6 Agent SDKs that will put you ahead of 99% of AI developers. 🤖 Building simple LLM wrappers is yesterday's news. The real frontier is autonomous AI agents that can reason, plan, and execute complex tasks across multiple tools. But where do you even start? Mastering the right Software Development Kit (SDK) or framework is your unfair advantage. Here are the go-to agentic frameworks I'm seeing everywhere: 1) LangChain 🦜🔗 - https://www.langchain.com/ The original Swiss Army knife for building with LLMs. LangChain provides a comprehensive set of tools for chaining prompts, managing memory, and, most importantly, building agents. If you're serious about building in this space, you have to know LangChain. It's the foundational layer for many other tools on this list. 2) OpenAI Assistants API v2 🤖 - https://lnkd.in/gtNSngnx Why build from scratch when you can use the official toolkit from the creators of GPT? The Assistants API gives you persistent threads, built-in retrieval (RAG), and a powerful code interpreter out of the box. It’s the most direct way to build powerful, stateful agents on the OpenAI stack. 3) Google's Genkit (Firebase) 🚀 - https://genkit.dev/ Google's answer to building production-ready AI applications. Integrated with Firebase, Genkit is a TypeScript-first framework designed for building, deploying, and monitoring reliable agentic workflows powered by Gemini. Its focus on observability and structured flows makes it a serious contender for real-world applications. 4) CrewAI 🧑🤝🧑 - https://lnkd.in/gJ3XmQQi One of the hottest new frameworks, CrewAI is designed for orchestrating role-playing, autonomous AI agents. It helps you create a "crew" of specialized agents (e.g., a "Researcher" and a "Writer") that collaborate to solve complex tasks. It's a fascinating look into the future of multi-agent systems. 5) Microsoft Autogen 🔄 - https://lnkd.in/g_btCErn A powerful framework from Microsoft Research for creating conversational applications with multiple agents. Autogen excels at simulating complex workflows and conversations between different LLM agents, human users, and tools. It's more research-oriented but incredibly powerful for complex multi-agent simulations. 6) LlamaIndex 📚 - https://www.llamaindex.ai/ While famous for being the premier data framework for RAG, LlamaIndex has robust agent capabilities. Its true power lies in creating agents that can intelligently reason over vast amounts of private data. If your agent needs to be an expert on your documents, LlamaIndex is your best friend. The agentic AI space is exploding, and I've definitely missed some great ones. 👉 What frameworks or SDKs are on your radar? #AI #ArtificialIntelligence #Developers #SDK #AIAgents #LLM #OpenAI #Google #LangChain #SoftwareDevelopment #GenAI #Genkit #crewai #langchain #python

Remember my last post about diving into the Vercel AI SDK? Well, I've got a much deeper dive for you! I've written a detailed blog post titled: "Building Delight: A Multi-Provider AI Chrome Extension with Vercel AI SDK." This article unpacks the journey of solving AI fragmentation in the browser, detailing how I leveraged the #VercelAISDK to integrate 6 major AI providers (OpenAI, Anthropic, Google Gemini, Groq, SambaNova) into a seamless, high-performance Google Chrome extension. You'll get a deep dive into: - The architecture behind abstracting diverse AI APIs. - Intelligent context management for long conversations. - Robust fallback mechanisms (circuit breaker pattern). - Performance optimizations for real-time streaming and large chat histories. - How we're bringing powerful AI tools directly to your browser with features like a specialized built-in toolset and the Chrome sidepanel. Delight is coming soon to the Google Chrome Web Store, offering 5 trial AI requests without needing an API key! I can't wait to hear your feedback about using Delight Read the full technical breakdown and discover how I'm building this delightful browsing companion: https://lnkd.in/dApvMHfK #AI #ChromeExtension #WebDevelopment #Vercel #AISDK #Developer #Productivity #Tech #BrowserAutomation #Naviware #Delight

Lean tooling is the real growth hack for product teams. Paweł Huryn maps 7 free or cheap AI swaps to trim tooling costs (n8n, free LLMs, more) without stalling momentum. If you’re building scalable AI-enabled products, explore the original piece: https://buff.ly/51Iqid3 #ProductManagement #AI

𝗙𝗿𝗼𝗺 𝗖𝗼𝗱𝗲 𝘁𝗼 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻: 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗔𝗣𝗜𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗔𝗜 𝗘𝗿𝗮 The paradigm shift toward Artificial Intelligence is here. As enterprises race to integrate AI agents and Large Language Models (LLMs), the most critical question isn't "Which model should we use?" but rather, "𝗜𝘀 𝗼𝘂𝗿 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝗮𝗱𝘆?" The answer lies in the often-overlooked backbone of modern enterprise: the Application Programming Interface (API). A robust, well-designed API ecosystem is no longer a technical nice-to-have; it is the bedrock of a successful AI strategy, determining whether your AI initiatives will fly or fail. 𝗬𝗼𝘂𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 The AI revolution isn't just about building intelligent models; it's about connecting that intelligence to meaningful data and actions. The bridge for that connection is the API. Building powerful, autonomous AI agents for your enterprise begins today with a disciplined commitment to your API architecture. By treating APIs as products, adopting a layered design, standardizing on machine-readable contracts, and adhering to clean design principles, you aren't just improving your IT infrastructure—you are defining the intelligence of your enterprise for tomorrow. 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝟭. 𝗔𝗣𝗜 𝗟𝗲𝗱 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 : API is the new business model, where their scope is not limited to internal use but rather expose them for external users. 2. ALWAYS 𝗱𝗲𝘀𝗶𝗴𝗻 APIs from an 𝗢𝘂𝘁𝘀𝗶𝗱𝗲-𝗶𝗻 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 - you look at what your customers need, and then work inwards to build that "experience" for them. 3. 𝗧𝗼𝘁𝗮𝗹 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝗻𝗱 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗨𝗫: Focus on 𝗰𝗼𝗺𝗽𝗼𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 by “write-once, share-everywhere” approach - Effort goes towards orchestration instead building new. 4. 𝗞𝗲𝘆 𝗙𝗼𝗰𝘂𝘀 𝗮𝗿𝗲𝗮𝘀 are a. API Consumer kits - SDKs, Documentation,Developer Programs b. API Architecture guidelines c. Develper experience d. API Discovery e. Tools for linting and automated Testing. 5. Understand and Apply : 𝗔𝗣𝗜 𝗗𝗲𝘀𝗶𝗴𝗻,𝗔𝗣𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗔𝗣𝗜 𝗙𝗶𝗿𝘀𝘁 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵,Modular APIs,Backend for Front End(BFF) & Micro frontEnds patterns ,API Security, 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗿𝗲𝗮𝗱𝘆 𝗔𝗣𝗜𝘀 - Machine-Readable API Specs, Semantic Annotations & Statelessness, 6. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗔𝗣𝗜𝘀 - Built for future, scalable for any business,Safe & Secure,𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 & 𝗔𝗴𝗲𝗻𝘁 𝗳𝗿𝗶𝗲𝗻𝗱𝗹𝘆,Organized & Documented,Plug & Play - Any integration, 𝗦𝗲𝗹𝗳 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 should be our focus point 7. Consider applying Domain-driven Design (DDD) principles and Improve API maturity per Richardson Maturity Model(https://lnkd.in/gAD3FySJ) #AgenticReadyAPI #APIDesign #APIStrategy #ComposableArchitecture #APIEconomy #ShunsTechTrails

7 Rules I Follow When Building MVPs with AI AI makes building 10x faster. But if you don’t have rules, you’ll waste weeks fixing bugs instead of shipping. These 7 rules are what I use daily to keep builds clean and ship MVPs fast ↓ 1/ Commit like crazy Cursor + Claude will break your code. It’s normal. That’s why I: → Create a new branch for every feature → Commit after every working step → Never let AI touch main This way when things break, I can roll back in seconds. 2/ Train your AI with memory AI forgets. If you don’t guide it, it’ll keep repeating the same mistakes. I keep memory docs inside every project (Cursor Project Rules + Notion files) with: → Auth patterns → Common queries → Security rules (RLS, validation, etc.) It’s like giving AI a mini playbook every time I build. 3/ Don’t let Cursor run on autopilot AI agents aren’t senior devs. If you just accept everything, you’ll ship broken apps. Instead: → Read what it’s changing → Stop patterns early before they spread → Use planning prompts (Taskmaster) to scope first You’re still the architect. Treat AI like an assistant, not the boss. 4/ Document features as they’re built Cursor/ CC loves leaving things half-done. So I document every feature in real time: → Files changed → How they connect → What still needs manual work Later I can just feed this back into Cursor to continue cleanly. 5/ Review your code with CodeRabbit Cursor writes fast, but it won’t always catch performance or security issues. So I run @coderabbitai checks at every stage: → Private vibe check inside the editor → Fix with AI button for instant improvements → PR review that feels like a conversation It’s caught bugs I would’ve never spotted myself. 6/ Reset when things feel “off” Context bloats. Once Cursor starts hallucinating, it rarely recovers. → Start a fresh chat → Revert to your last good commit → Feed it your project rules again A clean restart is faster than fighting broken context for hours. 7/ Plan in layers Before I code, I scope in 3 steps: → Product (features, users, must-haves) → UX (flows, screens, interactions) → Tech (endpoints, DB schema, Supabase setup) This layered planning means AI builds with structure, not random code dumps. Final word Anyone can throw prompts at Cursor. But if you want secure, production-ready MVPs, you need discipline. These 7 rules are what we use at @ignytlabs and inside @aimvpbuilders Bookmark this for your next project, it’ll save you weeks.