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Building an AI Product Review Analyzer: Structured Outputs with Together AI and Maxim Observability

Building an AI Product Review Analyzer: Structured Outputs with Together AI and Maxim Observability

In today's data-driven world, businesses need to extract structured insights from unstructured text at scale. Whether it's analyzing customer reviews, processing support tickets, or extracting key information from documents, the ability to get consistent, structured outputs from Large Language Models (LLMs) has become crucial. In this

✨ Voice simulation, Flexi evals, Adaptive load balancing, and more

✨ Voice simulation, Flexi evals, Adaptive load balancing, and more

🎙️ Feature spotlight 🤖 Voice simulation and evals are live on Maxim! Teams can now simulate multi-turn conversations with their voice agents and monitor performance across hundreds of scenarios and user personas – at a fraction of the time and effort required for manual testing. You can simply bring your voice agents onto

Best LLMs for Legal AI Agents: A Deep Dive into LegalBench Performance

Best LLMs for Legal AI Agents: A Deep Dive into LegalBench Performance

From contract analysis to legal research, from compliance monitoring to case preparation, artificial intelligence is transforming how legal professionals work. However, the stakes in legal practice are uniquely high. A single error can result in malpractice claims, regulatory violations, or adverse case outcomes. This reality makes choosing the right AI

Building a Resume Checker with LlamaIndex and Maxim Observability

Building a Resume Checker with LlamaIndex and Maxim Observability

In this comprehensive tutorial, we'll build an intelligent Resume Checker agent using LlamaIndex that analyzes resumes and provides detailed feedback. We'll also integrate Maxim observability to monitor the agent's performance and gain insights into its decision-making process. What We'll Build Our Resume

SafeBench 2025’s top picks: The Benchmarks That Actually Matter for AI Safety

SafeBench 2025’s top picks: The Benchmarks That Actually Matter for AI Safety

You know that feeling when your AI model aces every benchmark but still somehow manages to fail spectacularly in the real world? Yeah, that's exactly why SafeBench exists. While everyone's been obsessing over MMLU scores and coding benchmarks, the real question isn't just "

MCPToolBench++: Raising the Bar for Realistic AI Agent Tool-Use Benchmarks

MCPToolBench++: Raising the Bar for Realistic AI Agent Tool-Use Benchmarks

Introduction At the heart of reliable AI agents lies one critical skill: effective tool calling. We can see this in action with systems like the new Kimi K2, which connects seamlessly to dozens of tools, including web search, map navigation, financial analysis, and automated workflows. This results in impressive versatility

✨ Prompt simulations, File attachments, Claude 4, and more

✨ Prompt simulations, File attachments, Claude 4, and more

🎙️ Feature spotlight 🤖 AI-powered simulations in Prompt Playground We’ve extended simulation capabilities in the Prompt Playground, allowing you to simulate multi-turn interactions/user follow-ups and evaluate your prompts' performance across real-world scenarios and custom user personas. Key highlights: * Seamlessly connect MCP tools or attach context sources to simulate tool-calling

When AI Snitches: Auditing Agents That Spill Your Model’s (Alignment) Tea

When AI Snitches: Auditing Agents That Spill Your Model’s (Alignment) Tea

Sure, your model aced every benchmark, but can you trust it when the stakes are real? Every frontier lab runs alignment post-training before shipping their chat models to the world. The problem? Actually auditing whether this alignment worked can be an absolute nightmare. You're basically trying to find

Shipping Exceptional AI Support: Inside Comm100's Workflow

Shipping Exceptional AI Support: Inside Comm100's Workflow

About Comm100 Comm100 is a leading omnichannel customer engagement platform that helps organizations enhance customer loyalty through faster, more effective service. It serves businesses across gaming, education, government, and commercial sectors with a comprehensive suite including Live Chat, Ticketing, and AI-powered chatbots. As one of the early pioneers in live

👀 Observing Tool Calls 🔨 and JSON Mode Responses from Fireworks AI

👀 Observing Tool Calls 🔨 and JSON Mode Responses from Fireworks AI

Modern AI applications require robust monitoring and observability to track model performance, understand usage patterns, and debug complex interactions. When working with advanced features like tool calls and structured JSON responses, having comprehensive logging becomes even more critical. In this guide, we'll explore how to integrate Maxim'

A Recipe for Privacy Preserving Autocorrect in GBoard: FL, DP, and Synthetic Data Sprinkles

A Recipe for Privacy Preserving Autocorrect in GBoard: FL, DP, and Synthetic Data Sprinkles

The Personalisation Paradox Training language models for tasks such as autocomplete or error correction isn’t just a matter of fixing typos. Sure, you can turn “pleaes” into “please”, that’s easy. But what about Dave, who always types “frmly” when he means “formally”? You don’t just need autocorrect,

Building High-Quality Document Processing Agents for Insurance Industry

Building High-Quality Document Processing Agents for Insurance Industry

Generative AI is reshaping how insurers operate and serve their customers. Across sectors like health, life, auto, and property & casualty, insurers are embracing GenAI to enhance customer experience, drive efficiency, and improve decision-making. This shift isn’t just theoretical; over two-thirds of insurers are already using GenAI regularly, and

When Your AI Can't Tell the Difference Between "Fine" and Frustration

When Your AI Can't Tell the Difference Between "Fine" and Frustration

Final Results of SER Accuracy of Gemini 2.5 Flash and GPT 4o across the two modalities.

When Your AI Transcription Turns "Tasty Burger" Into "Nasty Murder"

When Your AI Transcription Turns "Tasty Burger" Into "Nasty Murder"

WER vs SNR for Transcription Models