Compass Labs’ Post

🚅 Optimizing Dynamic AMM Fees with Machine Learning ⚡️ The OpenGradient team is excited to publish its latest research on leveraging ML models for optimizing AMM trading fees. Led by Nathan Burton, the OpenGradient research team delves into fascinating research on how risk forecasts from machine learning models can be used to dynamically adjust trading fees for traditional Automated Market Maker (AMM) protocols to ultimately improve profitability for liquidity providers (+18% in our simulations). This research explores how shifting from static, fixed fee systems to dynamic fee systems can help better align incentives of liquidity providers with market volatility and perceived market risk. By adjusting fees in real-time with ML models powered by OpenGradient’s infrastructure, AMMs could offer a smarter, more sustainable approach to decentralized finance protocols. This research piece is a step forward in demonstrating how secure AI inference can redefine DeFi’s potential through systematic optimization. We’re excited to share these insights as part of our mission to make decentralized finance more optimized and efficient. Check out the full insights below! 🔗 Read here: https://lnkd.in/gbjkyn_z 🤖 Check out the model here: https://lnkd.in/gq_7Msg6 #MachineLearning #Inference #AI #Trading #DeFi #AMM #OpenGradient #BlockchainInnovation #Fintech

💸 Deep Pricing - Powered by Singularity AI 🤖 The multi-trillion dollar derivatives market demands precision. Derivatives pricing, one of the most complex operations for financial institutions, plays a critical role in risk management. At Multiverse Computing , we're revolutionizing this process. Using deep learning models, enhanced by quantum-inspired techniques, our parametric tool delivers reliable fair value, sensitivities, and metrics for even the most exotic derivatives. Why settle for traditional methods that are slow, compute-heavy and unstable? 📈 Enhanced Pricing: Improve Bermudan option prices by up to 10%. 🚀 Lightning-Fast Speed: Experience up to 1000x faster basket pricing. 🏛️ Efficient Greeks: As easy as getting prices 2000x faster (single price) 🌱 Sustainable: Reduced GPU workloads lower your climate impact. Learn more about transforming your derivatives pricing with Deep Pricing: https://lnkd.in/dwsmxrpi #MultiverseComputing #QuantumAI #Singularity #SingularityAI #Derivatives #Finance

The multi-trillion dollar derivatives market demands precision, but pricing these complex instruments presents several challenges: 🧠 Model Complexity 📉 Market Volatility 📊 Data Gaps 💻 Computational Intensity ⚖️ Regulatory Pressure 💧 Liquidity & Counterparty Risk ⚙️ Operational Risk Check how at Multiverse Computing , we're revolutionizing this process. Using deep learning models, enhanced by quantum-inspired techniques, offering a parametric tool which delivers reliable fair value, sensitivities, and metrics for even the most exotic derivatives. The multi-trillion dollar derivatives market demands precision. Derivatives pricing, one of the most complex operations for financial institutions, plays a critical role in risk management. #Derivatives #RiskManagement #FinancialMarkets #MarketVolatility #DataAnalytics #FinTech #QuantitativeFinance #FinancialRisk #RegulatoryCompliance #CapitalMarkets #FinancialServices #ComputationalFinance #LiquidityRisk #OperationalRisk

About the scale of AI integration into our lives. If you think AI is far away and your business can somehow manage without it, I suggest reading this statement from OpenAI: “Dedicated instances can make economic sense for developers working with more than ~450 million tokens per day. Additionally, this allows for direct optimization of the developer’s workload based on hardware performance, which can significantly reduce costs compared to shared infrastructure. For special requests, [contact us](https://lnkd.in/d3FCF2MQ Carl! Think about it, Carl! There are already companies that need more than 450 million tokens per day. How many tokens does your business consume per day? I can suggest a simple formula to see how far behind you are and how close you are to bankruptcy: subtract your token consumption from 450 million. The closer the result is to 450 million, the closer you are to shutting down. I think banks will soon introduce such a metric to evaluate your assets.

📈Get deeper market insights, faster financial fraud detection, algorithmic trading and more with the @NVIDIA DGX BasePOD for #AI development in financial services. Learn about it in this brief.

📢 Integrate the power of real-time news into your fintech solutions with NewsEdge. Enhance your AI and machine learning models 💡 with enriched metadata and sentiment analysis from over 1 million stories daily 📰 ! Stay ahead of emerging risks and opportunities by integrating real-time news insights into your technology-driven workflows 🚀 . #FintechInnovation #AIEnhancement #RealTimeInsights

🚀 Unlocking New Data with LLM Routing and Multimodal Agents! 🚀 Excited to share my latest project: “Structured Output with Multimodal Agents.” built with LangChain's LangGraph framework. This notebook shows how combining structured data extraction using vision and LLM routing has the ability to revolutionize automation and AI integration. Why Structured Output? Structured output transforms raw data into organized, easily interpretable formats, reducing errors and boosting efficiency. It’s essential for reliable automation, advanced analytics, and data pipelines. Why Multiple LLMs? Only use what you need, when you need it, to maximize speed and cost. In this notebook I use 2 different LLMs: - Anthropic's Claude 3 Haiku: Extracts key data points from images, like colors, text, and objects using vision. - Groq's Function Calling with Llama 70B: Generates haikus based on the extracted data, providing detailed reasoning with extremely fast inference. This approach optimizes performance, cost, and accuracy, pushing the boundaries of what AI can achieve. The result? An LLM powered pipeline that can extract structured data from images, reason over it, and provide an output derived from data. Notebook Repo --> https://lnkd.in/gk5U2FSx #ai #langchain #automation

An In-depth Introduction to W3AI Powered by AIOZ NETWORK AIOZ W3AI is a cutting-edge Web3 AI computing infrastructure that revolutionizes the execution of AI computational tasks such as "Model Training & Inference" for the better. It leverages the combined computing resources (Processing Power, Storage, & Bandwidth) of 170,000+ AIOZ DePIN nodes to execute AI computational tasks in a completely decentralized fashion. W3AI empowers AI developers, businesses, and individuals to harness the power of decentralized AI computing while offering seamless AI-as-a-service integration. https://lnkd.in/g2Bik7GR Please find more infomations about W3AI Whitelist Program Overview as follows: https://lnkd.in/gP8NTghF

Generative AI and competitive advantage. This post reveals the cost per token is coming down. I wonder how tech firms will make money from generative AI. I suspect it will be the ecosystem providers that will prosper. Think co-pilot, easy RAG, workflow management.