Fireworks AI CEO on Compound Systems

Business Catalyst Series – Week 2: Generative AI 🤖 Foldable phones are, to quote Will Ferrell from Zoolander’s Mugatu, 'so hot right now,' according to an article I read this morning. Just like foldable phones (however it’s something that won’t go out of fashion), this week’s post features Generative AI. In short, our teams aren’t peddling AI software as part of a one-size-fits all approach. We partner with our clients. Truly gaining an understanding of the current state and where they want to end up. This ranges from education and Gen AI use-case development -- to those clients currently incorporating Gen AI and Large Language Models (“LLM”) in their tech stack looking to unleash ROI and promise. Sub-catalysts you may encounter driving your decision around this topic include: +     Automating invoice and statement ingestion +     Reducing data gathering efforts and increasing accuracy +     Streamlining workflows and reducing manual tasks Embark client example: Our client in the Renewable Energy space struggled to manage and scale their monthly data entry for hundreds of pipeline statements. Traditional tools like OCR and regex were ineffective due to frequent, unpredictable format changes, and required extensive development time given the number of formats. Maintaining these exceptions would also prove to be burdensome to the client’s small IT team. How Embark helped: + Deployed a flexible and scalable tool to extract data from unstructured and semi-structured data across 120+ different formats ranging from 1 page to 80 pages in length + Set up OpenAI GPT-4 model inside the client's Azure tenant and prompt engineered for key statement formats + Built a user interface in PowerBI with read/write/edit abilities of the statement data to support a new QA/QC process of the automated readings Client results: + Development costs reduced by 75% + ROI of ~100% in year 2 based on replacement of manual tasks + Saving ~2,000+ hours/year in manual data gathering/entry tasks + Fine-tuned LLM prompts with a 98% accuracy rate for most formats Relevant Embark Service Offerings noted in this post: +     AI Use Case Development +     Process Automation & Efficiency +     AI Proof of Concepts for Innovation What catalysts are influencing your decision to consider AI? Let us know in a comment below!

AI isn’t about which single LLM wins. It’s about how to orchestrate many. Imagine a day ever having to guess which AI model to use. A day when the right LLM is selected automatically for each query: writing, analysis, coding, creative production, creative brainstorming. And imagine not having to waste tokens or switch tabs. That day is closer than you think. The GPT Router is coming, and it’s about to change everything. What is the GPT Router? It’s a feature that dynamically routes you to the best model for the job: GPT-4, GPT-5, Claude, Gemini, or even specialized small models. This isn’t just another AI feature. It’s a new infrastructure layer. What are the benefits? ✅ Greater accuracy delivering complex tasks ✅ Faster responses where speed matters ✅ Lower cost for simple lookups or workflows ✅ Automatic context management across work streams ✅ Managable “model overwhelm” (virtually none) while building AI workflows Why should you care? Our LLM landscape is fragmenting fast: open-source, closed, specialized, small, massive. For your outcomes to be most valuable, it's becoming critical to choose the right model just as much as choosing the right prompt. (Remember the early days of the internet when we had to figure out which search engine to use?) The GPT Router is transforming a chaotic landscape of models into a seamless, orchestrated system. Are you ready?

The reality of building generative AI systems is that you spend 60 to 80% of the time evaluating. It used to be the case that to build AI systems you also had to train a model. That isn't the case when building generative AI systems. You can use a foundation model like ChatGPT. In practice, this means that you go from 0 to 70% within hours (impressive demos!), but going from 70 to 100% takes weeks or months. This has to do with the open ended nature of generative AI. You can get ten different answers for the same input. So nowadays you spend more time on prompt engineering, crafting the right context, evaluating, evaluating, and then even more evaluating. You need to test edge cases, check for hallucinations and measure accuracy across different prompts. Over and over again. Many companies find themselves caught off guard, especially those diving into AI with Gen AI as their first use case. They didn't expect how labor intensive it would be. Getting reliable AI systems requires constant iteration with domain experts. This was already the case with predictive machine learning, but now applies even more with an off-the-shelf Gen AI model. As it turns out, these systems are less ‘autonomous’ than you expect from the headlines. What evaluation challenges have surprised you the most in your AI projects?

🤝 Think Tank in Action: How Multi-Agent LLMs Are Reimagining Speed, Accuracy, and Trust in AI ⚡️🤖 Ever wonder how today’s top AI systems deliver blazing fast yet highly accurate answers? It’s not magic—it’s multi-agent collaboration behind the scenes. 👥 Multi-Agent LLMs are the invisible think tanks of the AI world. Instead of relying on a single model, they use multiple specialized agents working together in real time to: ✅ Split and route tasks to the best-suited model ✅ Process in parallel to reduce latency ✅ Cross-verify each other’s outputs ✅ Deliver precise, context-aware responses—fast The result? 🎯 Fewer hallucinations ⚡ Lightning-fast answers 🔒 Rock-solid reliability 🙌 Seamless user experience 📈 From customer support to finance and healthcare, these systems are revolutionizing how we interact with AI. And the best part? Users don’t see any of this complexity. They just get smart, fast, and accurate responses—every time. This is the future of AI: collaborative, invisible, and deeply reliable. 💬 Curious how multi-agent systems are reshaping prompt engineering? Let’s connect. https://lnkd.in/eXW-D_vc #AI #LLM #PromptEngineering #MultiAgentSystems #AIThinkTank #GPT #TechInnovation #AIAutomation #FutureOfAI #CustomerExperience #TrustInAI

🤝 Think Tank in Action: How Multi-Agent LLMs Are Reimagining Speed, Accuracy, and Trust in AI ⚡️🤖 Ever wonder how today’s top AI systems deliver blazing fast yet highly accurate answers? It’s not magic—it’s multi-agent collaboration behind the scenes. 👥 Multi-Agent LLMs are the invisible think tanks of the AI world. Instead of relying on a single model, they use multiple specialized agents working together in real time to: ✅ Split and route tasks to the best-suited model ✅ Process in parallel to reduce latency ✅ Cross-verify each other’s outputs ✅ Deliver precise, context-aware responses—fast The result? 🎯 Fewer hallucinations ⚡ Lightning-fast answers 🔒 Rock-solid reliability 🙌 Seamless user experience 📈 From customer support to finance and healthcare, these systems are revolutionizing how we interact with AI. And the best part? Users don’t see any of this complexity. They just get smart, fast, and accurate responses—every time. This is the future of AI: collaborative, invisible, and deeply reliable. 💬 Curious how multi-agent systems are reshaping prompt engineering? Let’s connect. https://lnkd.in/ecH9tZ6c #AI #LLM #PromptEngineering #MultiAgentSystems #AIThinkTank #GPT #TechInnovation #AIAutomation #FutureOfAI #CustomerExperience #TrustInAI

Vibe Coding and Context Engineering are essential components in shaping the future of AI-generated interactions. Vibe Coding focuses on infusing AI responses with specific emotional nuances and communication styles, going beyond mere factual correctness. By incorporating intentional tone and personality, AI systems can engage in more natural, human-like interactions that resonate with the audience. On the other hand, Context Engineering plays a crucial role in providing AI systems with the necessary information to interpret inputs, generate coherent outputs, and perform tasks effectively. It ensures that AI workflows consider surrounding context, leading to responses that are personalized, relevant, and seamless. In the realm of modern conversational AI and agentic systems, context is key for maintaining continuity, adapting to user needs, and delivering personalized experiences across multiple interactions. Context Engineering transforms AI models from reactive question-answering machines into intelligent agents capable of understanding user identity, task history, and ongoing dialogues. To delve deeper into the intricacies of Vibe Coding and Context Engineering, check out the detailed insights at https://lnkd.in/d_5UhuiG. Let's embrace these advancements shaping the future of AI interactions.

Revolutionizing Enterprise Workflows: The Promise of Lightning-Fast Agentic AI with Arch-Function LLMs https://lnkd.in/gWkw-num Unlocking the Future with Agentic AI: Insights from Arch Function and LLMs Discover how Arch Function is revolutionizing enterprise workflows through advanced agentic AI technology. Their insights into large language models (LLMs) promise unparalleled efficiency, making complex processes not just manageable but lightning-fast. Key Highlights: Enhanced Workflow Automation: Streamlines operations by integrating AI into everyday tasks. Data-Driven Decision-Making: Supports better choices with real-time analytical insights. User-Centric Design: Aims to simplify interactions and enhance user experience. As AI continues to evolve, understanding its implications is crucial for professionals in tech and beyond. The article emphasizes the growing role of AI in driving productivity and innovation across enterprises. Are you ready to embrace the AI revolution? Dive into the full article for deeper insights and strategies that could transform your business landscape. 📢 Share your thoughts or experiences with AI in the comments below! Let's spark a conversation! Source link https://lnkd.in/gWkw-num

🚀 OpenAI Delays Launch of Its Open-Source Reasoning AI Model Indefinitely 🤖 OpenAI has announced a significant delay in the release of its highly anticipated open-source reasoning AI model, originally slated for launch this year. This unexpected postponement marks a notable shift in OpenAI's timeline and raises questions about the challenges in delivering advanced capabilities to the public domain. 🧠 The model was designed to push boundaries in logical reasoning and complex problem-solving by leveraging novel architectures and training paradigms beyond traditional large language models. Its open-source nature promised greater transparency and broader collaboration opportunities. However, the delay indicates unresolved technical hurdles, likely in model robustness, safety constraints, or scalability, which OpenAI is prioritizing before public deployment. 🔬 For industry professionals and enterprises, this delay slows the democratization of cutting-edge reasoning AI tools that could empower more sophisticated automation, decision support, and development workflows. Organizations anticipating integration of this model into their AI stacks will need to recalibrate timelines and explore alternative solutions. It also underscores the complexity and responsibility involved in releasing powerful AI technologies openly. 🎯 Looking ahead, this development spotlights the growing pains of balancing innovation, ethical considerations, and technical maturity in generative AI. It invites reflection on how companies can maintain transparency while ensuring safety and reliability in AI systems. Will OpenAI’s eventual release redefine open-source AI collaboration, or will this signal a more cautious approach industry-wide? 💡 How do you view this delay in the context of open-source AI progress? What strategies should businesses adopt to adapt to such uncertainties in AI innovation roadmaps? 🔥 🔗 Read full details: https://lnkd.in/dtmT7c3Z #OpenAI #ReasoningAI #OpenSourceAI #AIModelDelay #GenerativeAI #AIEthics

GPT-5 Launch: Intelligence Revolution This August OpenAI confirms GPT-5 release in early August 2025—the biggest leap in AI reasoning yet. This isn't just another model update; it's unified intelligence. What's Changing GPT-5 consolidates OpenAI's advanced reasoning (o3, o4-mini elements) into one system delivering enhanced logic, expanded context, and seamless performance across chat, coding, and multimodal tasks. Internal testing exceeded leadership expectations with impressive speed and reasoning gains. This moves us from model-switching to "intelligence in continuity." Business Impact Advanced Multi-Step Reasoning: Complex legal review, scheduling logic, and intricate workflow automation become significantly more reliable. Democratized Access: Mini and Nano variants make enterprise-grade reasoning available to teams of all sizes. Enhanced Context: Opens new possibilities for voice agents and sophisticated MCP-based ecosystems. Beyond the Model: At NicheFinders AI, we know successful AI deployment extends beyond model selection. True transformation requires adaptable frameworks, agent orchestration, and memory-aware protocols that evolve with your business. The winners won't just upgrade models—they'll build resilient, scalable AI architectures. Preparing for GPT-5: If you're on GPT-4 or earlier, August presents both opportunity and urgency. Smart teams are mapping workflows to identify where GPT-5's reasoning delivers the highest impact, enabling strategic upgrades without complete overhauls. Ready to evaluate how GPT-5 can transform your operations? We analyze workflows and develop modular upgrade strategies that maximize ROI while maintaining continuity. The future isn't about having the latest model—it's about building systems that adapt, scale, and deliver sustained advantage. Let's Chat...

The Open-Source AI Revolution Just Got Real Kimi AI's new K2 model is making headlines for good reason - this 1 trillion parameter coding model is delivering results that rival the best proprietary AI systems, and it's completely open source. What's impressive: Creates complex 3D applications and production-ready websites in a single prompt Runs locally on consumer hardware despite its massive size Consistently outperforms other open-source models across coding benchmarks Uses breakthrough "Muon clip optimizer" technology for unprecedented training stability Why this matters for business: The gap between open-source and proprietary AI is closing fast. When free models can match or exceed paid services, it fundamentally changes the competitive landscape. Companies building AI-powered products now have access to frontier-level capabilities without the associated costs. The bigger picture: This continues a pattern of Chinese AI labs introducing more efficient training methods and open-sourcing their innovations. We're seeing democratization of cutting-edge AI capabilities that could accelerate development across every industry. For developers and businesses evaluating AI strategies, the message is clear: open-source options are becoming increasingly viable alternatives to expensive proprietary solutions. The AI industry just got more competitive - and more accessible.