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Explore our thoughts on AI, development, and technology
Building Multi-Model AI Agents: Combining GPT, Claude, and RAG
As AI development matures, we're moving beyond single-model solutions. The most powerful AI agents today combine multiple models, each handling what they do best. But building these multi-model agents traditionally required complex infrastructure, careful API management, and significant cost overhead. In this post, we'll build a practical research assistant agent that combines GPT-4o, Claude, and Gemini Flash, demonstrating how to leverage each model's strengths while optimizing for cost and pe
From Text to Workflow: How Natural Language is Revolutionizing AI Development
"Create an AI workflow that takes a YouTube URL, transcribes it, summarizes the content, and emails me the result." Five years ago, this request would have required hundreds of lines of code across multiple services, careful API integration, and complex infrastructure management. Today, it's a single sentence that can generate a fully functional workflow. This isn't science fiction – it's the reality of modern AI development, where natural language is becoming a powerful interface for creating
The Hidden Costs of AI Development: Why Infrastructure Matters
As AI development becomes mainstream, many teams focus on selecting the right models and fine-tuning prompts. However, the hidden infrastructure costs of building AI applications often catch developers by surprise. Let's explore these often-overlooked aspects that can make or break your AI project's success. The True Cost of AI Infrastructure 1. Integration Overhead Every AI service you add to your stack brings its own integration challenges: * Multiple API authentication systems to manag
Function Calling vs Workflow Nodes: When to Use Each in Your AI Architecture
When building AI applications, developers often face a choice: should they use AI model tools (like GPT's function calling) or dedicated workflow nodes? While both can accomplish similar tasks, they serve different purposes and have distinct advantages. Let's explore when to use each approach. Understanding the Difference AI Tools (Function Calling) Tools are capabilities that an AI model can optionally use during execution. Think of them as a Swiss Army knife that the AI can reach for when
Building Real-World AI Applications: 6 Practical Use Cases with Waveloom
As AI development becomes more accessible, developers are looking for efficient ways to build practical applications. Here's how teams are using workflow automation to build real AI solutions, and how you can implement similar patterns in your projects. Why Waveloom for These Use Cases? Before diving into specific examples, it's important to understand what makes these implementations powerful. With Waveloom, you don't just get workflow automation - you get complete infrastructure: * No Inf
5 Essential Patterns for Production-Ready AI Workflows
As AI becomes a core component of modern applications, developers face new challenges in building reliable, scalable workflows. Through our work with AI developers, we've identified five essential patterns that can make the difference between a prototype and a production-ready AI application. 1. The Chain of Responsibility Pattern One of the most powerful patterns in AI workflows is the chain of responsibility, where each step in the process handles a specific task and passes results to the n
Why We're Building Waveloom: The Future of AI Workflows
The AI landscape is evolving at a breakneck pace. Every week brings new models, services, and capabilities that push the boundaries of what's possible. While this rapid evolution creates exciting opportunities, it also presents a significant challenge for developers and companies building AI-powered applications: how do you efficiently manage and orchestrate multiple AI services in production without drowning in complexity? The Growing Complexity of AI Development As developers ourselves, we'v