Mark Newton’s Post

📢 Today, I’m excited to share that I’ve launched KitchenAI on Product Hunt! KitchenAI is an open-source LLMOps tool I've built to solve a frustrating problem for AI dev teams. Over the past year of building AI-enabled SaaS applications, I kept hitting the same wall. Going from a Jupyter notebook full of AI RAG techniques to something usable in my app was a nightmare. Here's the problem: - Notebooks are great for testing ideas, but they’re not meant for building applications around them. - I had to manually dissect notebooks, build a proof-of-concept API server, integrate it into my app, and pray it worked. - The feedback loop was *painfully* long—and most of the time, I canned the project because it didn’t quite fit. This frustration comes from a gap in roles: 1. Data Scientists/AI Devs want notebooks to experiment with methods and techniques—but it's not their main focus to also create an API for other applications to use. 2. App Developers just want simple APIs to test and integrate quickly to see if it actually enhances their app. This is where KitchenAI comes in. KitchenAI bridges this gap by transforming your AI Jupyter notebooks into production-ready API server in minutes. But why?? - Shorter Development Cycles Test, iterate, and deploy AI techniques faster and cut the feedback loop in half. - Vendor and Framework Agnostic Use the libraries you’re comfortable with, no lock-ins. - Plugin Architecture Extend functionality with plugins for evaluation frameworks, observability, prompt management, and more. - Open Source and Local-First Built on trusted technologies like Django, so you stay in control—no 3rd-party dependencies required. - Docker-Ready Share your API server as a lightweight container for easy collaboration. We’ve released KitchenAI as an Apache-licensed open-source tool, so anyone can use it. ❗ Up next: a managed cloud version with deeper integrations, metrics, analytics, and workflows for teams with more complex needs. One short term goal is to go straight from Colab to a KitchenAI cloud hosted API so development can be absolutely seamless. I’d love your feedback, and if you find it interesting, your support with a like or comment on Product Hunt would mean a lot! Check it out here: https://lnkd.in/ePjnXw4U Thanks for your support and for helping spread the word! 🙏

I wanted to give you a heads-up on an exciting new tutorial I’ve got in the works. This video is all about building a full-stack SaaS application that pulls in YouTube comments, processes them with CrewAI enterprise, and generates actionable content ideas. If you’re interested in building full-stack AI applications, this is for you! 🛠️ Here’s What You’ll Learn In this tutorial, I’ll guide you through each part of the process of creating this full-stack application. Here’s a breakdown: 1️⃣ Building the Frontend and Backend: We’ll use Next.js to set up the app and deploy it on Vercel. Plus, you’ll get hands-on experience connecting to a Neon Postgres database to manage data. 2️⃣ Integrating with CrewAI Enterprise: Learn how to harness CrewAI’s enterprise features to analyze YouTube comments, filter out casual messages, and focus on meaningful feedback. You’ll see how to create a system that automates data analysis and transforms raw comments into structured, actionable insights. 3️⃣ Generating Video Titles and Descriptions: We’ll configure CrewAI to turn filtered comments into potential video titles and descriptions. You’ll build a workflow that streamlines idea generation and content planning, using CrewAI’s advanced capabilities. 💡 Why This Tutorial is Worth Your Time This tutorial doesn’t just cover building one app—it teaches you how to apply the synthesize pattern for data processing, a core skill in AI development. Here’s why this matters: ✅ Real-World Adaptability: The synthesize pattern goes beyond YouTube. After learning it, you can apply it to dozens of other applications where large datasets need to be turned into insights. Imagine using this pattern for customer feedback, product analysis, or trend monitoring—there are endless opportunities to build your own AI-powered apps! ✅ Hands-On Full-Stack Skills: Get practical experience with tools like Next.js, Vercel, and Neon, and learn how to bring everything together into a seamless app. 📅 How to Catch the Release I’ll be releasing this video on my YouTube channel. Head over there, subscribe, and turn on notifications to be the first to know when it drops! I’ll also have links to my YouTube, my free Skool community, and the Pro community down in the comments below. Drop any questions you have, and see you soon! 👋 CrewAI Neon Vercel

PlanAI v0.2 is here! I've just released a major update with new features and important fixes. This release brings: - Enhanced Logging & Monitoring: Gain better insight into OpenAI prompt usage, e.g. such as cached tokens - Expanded Model Support: Now includes o1-mini and o1-preview models which don't support JSON mode or structured outputs yet. - Interactive User Input: Tasks can now request input from users when needed. It has become increasingly challenging to fetch content from the web. - New Social Media Example App: Automatically suggests post topics based on profile interest queries. - Serper Search Integration: Simplifies frequent search scenarios. Explore the full update at GitHub: https://lnkd.in/g7PxtEVj Let me know what you think. #PlanAI #AI #OpenSource #TaskAutomation #Innovation

I’ve stumbled across Bee Agent Framework, and it’s been an impressive experience. If you’re exploring agentic AI workflows, this framework deserves your attention. What struck me the most is how intuitive and modular it is. Perfect for building intelligent agents that can handle complex tasks, and its almost production ready. Workflows felt natural, and has a clean architecture. Best of all it’s open-source, so there’s plenty of room to explore, adapt, and contribute to its community. Its OpenAI compatible or BYO(Model). Highly recommend giving it a try. npm install bee-agent-framework to get started Would love to hear your thoughts if you’ve used it! Let’s compare notes. 🐝 https://lnkd.in/eTQH7Zir #AI #AgentFramework #BeeAgentFramework #AIWorkflows #OpenSource #SmartAgents #AgenticAI

With MindsDB you can connect your data from a database, a vector store, or an application, to various AI/ML models, including LLMs and AutoML models. By doing so, MindsDB brings data and AI together, enabling the intuitive implementation of customized AI systems. MindsDB enables you to easily create and automate AI-powered applications. You can deploy, serve, and fine-tune models in real-time, utilizing data from databases, vector stores, or applications, to build AI-powered apps — using universal tools developers already know. Docker, Inc

I’ve been watching the explosion of AI in developer tools lately, and I’m convinced it’s not _all_ hype. Something genuinely new is happening. Companies are rushing to add AI capabilities everywhere—some of it’s messy experimentation, some of it’s already delivering value, and the rest is still shaking out. But the direction is clear: AI is becoming part of the fabric of how folks build software. Take code generation - GitHub Copilot, Vercel’s V0, Cursor, Codeium’s Windsurfer, AWS Q, Sourcegraph Cody—these tools all help write code for you. They’re getting so good, it’s starting to feel commoditized. If one tool doesn’t impress you this month, another one will pop up soon that’s even smarter. It’s wild how fast things are moving. Beyond codegen, AI is creeping into other parts of the dev stack. Observability tools like Datadog are using AI to pinpoint issues. Documentation tools like Swimm and Docify are auto-generating and maintaining docs. Security and analysis platforms - Snyk, Checkmarx, Semgrep - are trying to detect vulnerabilities and risky patterns with fewer false alarms. Even feature flags and data analytics tools (LaunchDarkly, PopSQL, Hex) are dabbling in AI-powered insights. But what’s truly interesting is what hasn’t happened yet. We haven’t seen as much AI in the heavy ops side: builds, deployments, hosting. These are the gritty, performance-critical workflows that devs rely on every day. They’re deterministic, stable, and predictable—introducing “intelligence” feels risky. Yet I suspect that over time, AI will break into these areas too. Imagine a system that predicts test failures before running them, reorders build tasks for faster feedback, or orchestrates safer, smarter deployments. That’s where things get really exciting. https://lnkd.in/eQCDW4Fu

💡 Exploring New Horizons in AI with the IBM Bee Agent Framework 🐝🤖 I'm thrilled to share my first steps contributing to the IBM Bee Agent framework! https://lnkd.in/degvcHaH This journey is an incredible opportunity to refine my understanding of agent-based systems, enhance interaction patterns, and explore new ways to build more effective AI tools. What I'm Working On: I’ve been experimenting with HumanTool and improving decision-making processes in agent interactions. My current draft already works for simple cases, and here's a sneak peek of a working example: Example Interaction: 👉 User: "Can you write the formula to calculate the area of a triangle?" 🤖 Agent: Provides a clear and accurate response. 👉 User: "I need help to calculate an area of a shape." 🤖 Agent: Realizes more details are needed, calls HumanTool to gather additional input, and then provides the formula for a circle based on user clarification. Why This Matters: 1️⃣ Learning: I'm diving deep into the Bee Agent framework to expand my understanding of agent orchestration and interaction. 2️⃣ Exploration: These experiments inspire me to innovate in creating seamless, intuitive interactions between users and AI agents. 3️⃣ Application: I'm beginning to migrate parts of Project Copilot, my AI Project Manager, to this framework to leverage its strengths for planning and backlog generation. Next Steps: I’m actively refining the prompts, enhancing modularity, and aligning my work with community best practices. Early feedback from this draft will help me iterate toward a cleaner, more robust implementation. 📢 I’d love to hear your thoughts or suggestions as I continue this exciting journey! If you’ve worked with the IBM Bee Agent framework or similar systems, I’d be delighted to connect and exchange ideas. Let’s build something great together! 🌟 #AI #OpenSource #BeeAgentFramework #GenerativeAI #ProjectCopilot

In a somewhat muted reveal, Meta has released Llama 3.2, which, in my opinion, represents a significant leap forward for industries that demand high-performance AI with edge capabilities. Alongside this, the newly launched Llama Stack released a versatile, open-source foundation that drives efficiency, reduces costs, and ensures compliance in highly regulated sectors such as finance, healthcare, and retail. Here’s how Llama 3.2 I think it stands to make an impact: - Optimized for Edge Devices: Llama 3.2's lightweight models are designed for on-device deployment, enabling rapid processing while keeping sensitive data within local environments. This is a game-changer for privacy-focused industries, particularly those bound by stringent regulations like financial services. - Enhanced Multimodal Capabilities: Llama 3.2's vision models bring powerful applications to life, such as automating document analysis and streamlining compliance workflows. This makes it an ideal solution for managing large-scale document reviews. - Open-Source Advantage: With its fully open-source nature, Llama 3.2 provides unparalleled flexibility without hefty licensing fees. This allows organizations to customize the models according to their specific needs, offering a cost-effective yet high-performance solution. - Scalability and Seamless Integration: The introduction of the Llama Stack simplifies AI development and deployment. With features like Retrieval-Augmented Generation (RAG) and integrations with platforms like AWS, Llama 3.2 allows businesses to scale operations efficiently. To dive deeper into Llama 3.2 and the Llama Stack, visit: https://lnkd.in/g-cr_33F

It's amazing to see how a simple idea conversation with Jorge Torres, CEO of MindsDB, turned into a Docker Extension. This journey from a casual conversation to a full-fledged Docker extension is a testament to the power of collaboration and seeing ideas through. Huge thanks to Jorge and the entire MindsDB team for their support in making this happen! Building an AI-powered application requires significant resources, including qualified professionals, cost, and time. Prominent obstacles include: - Bringing (real-time) data to AI models through data pipelines is complex and requires constant maintenance. - Testing different AI/ML frameworks requires dedicated setups. Customizing AI with dynamic data and making the AI system improve itself automatically sounds like a major undertaking. These difficulties make AI systems scarcely attainable for small and large enterprises alike. The MindsDB platform, however, helps solve these challenges, and it’s now available in the Extensions Marketplace of Docker Desktop. Thanks to MindsDB team for their collaborative effort in authoring this blog post. https://lnkd.in/dU9SdzbC

Earning Agentic (and LangChain) Complexity by Michael Lanzetta via Microsoft Developer Blogs ([Global] Data Breach) URL: https://ift.tt/GX9WCUS Introduction Here at ISE we’ve had the privilege to work with some of Microsoft’s largest customers developing production Large Language Model (LLM) solutions. In any software project, the distance between Proof of Concept (POC) and production is significant, but with LLM solutions we’ve found that it’s often even more massive than traditional software (and even Machine Learning) projects, and yet even more underestimated by both crews and customers. One common anti-pattern that we’re seeing is what we like to call the adoption of “unearned complexity”. Customers, often before we’ve even arrived, have decided on technology like LangChain or multi-agentic solutions without experimenting enough to understand if they actually need that complexity for their solution. In this post we’ll go through some of the reasons why you might want to reconsider reaching for a complex answer before you truly understand your own question. We do make some LangChain-specific points in here, but this post isn’t (entirely) just to criticize that library. Rather, we want solution developers to consider the points we raise regardless of what technology they are evaluating, and avoid adding complexity to their solutions unless it’s actually earned a place. Agents of Chaos Agents have proven to be a popular starting point for Retrieval Augmented Generation (RAG) and chatbot projects because they seemingly provide a dynamic template for an LLM to ‘think/observe/act’. The agent pattern seems to promote a simple and powerful pattern to handle a range of scenarios like choosing to use external tools and knowledge bases, transforming inputs to better use external tools, combining information, checking for errors and looping through its decisions if the quality is not deemed good for some goal. When it works it’s amazing, but under the pressure of real-world data distributions we have observed that agent patterns can be incredibly brittle, hard to debug, and provide a lack of maintenance and enhancement options due to its general nature mixing several capabilities. Due to the stochastic nature of the models and the dynamic nature of agentic solutions, we have observed wide swings in accuracy and latency, as one call may result in many calls to the underlying model or models, or different orderings of tool evocation. If you have a fixed explicit set of components for a solution (e.g. routing, query rewriting, generation, guardrails) that are invoked in a predictable order, it makes it much easier to debug performance issues. This can definitely result in a perceived loss of flexibility, but in our experience most customers would prefer reliable and performant solutions to ones that can “flexibly fail”. In fact, even with the advent of agentic frameworks from Microsoft Research and others, recent customer...