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The Discipline Layer: Harnesses as the Missing Piece in Autonomous Coding

The Discipline Layer: Harnesses as the Missing Piece in Autonomous Coding

Introduction If you've been working with AI agents on longer tasks, you've probably developed your own tricks for dealing with context window limits. Maybe you hit /summarize in Cursor when things get bloated or you ask the agent to write a summary.md file at the

Breaking the Context Window: How Recursive Language Models Handle Infinite Input

Breaking the Context Window: How Recursive Language Models Handle Infinite Input

Long-context understanding has been a persistent challenge in language model research. Despite architectural innovations (ALiBi, YaRN, RoPE variants) and massive context window expansions (Claude 3.5 at 200k tokens, GPT-5 at 256k+), models still exhibit performance degradation on long inputs, a phenomenon known as "context rot." The community

Scaling Personalized Sleep Coaching: Rise Science's Journey with Maxim AI

Scaling Personalized Sleep Coaching: Rise Science's Journey with Maxim AI

About Rise Science Rise Science is a sleep management platform that helps people understand and address the root causes of their energy issues. The platform focuses on two key principles: sleep debt (how much sleep a person owes their body) and circadian rhythm (their body’s natural energy schedule). This

Beyond the SDK: Why AI Teams Love HTTP Endpoint-Based Evals

Beyond the SDK: Why AI Teams Love HTTP Endpoint-Based Evals

Since the beginning, HTTP Endpoint-Based Offline Evals have been a core feature of the Maxim platform and a favorite among our users. While our SDKs allow engineers to integrate evaluations directly into their codebase, a purely code-based approach introduces friction, often limiting who can run them and how they are

November 2025 Updates - Maxim AI

✨ Flexible data curation, Cost charts, Reasoning column, and more

🎙️ Feature spotlight 🧩 Fully flexible data curation flows While curating and refining test datasets from logs and test runs, you can now reference and modify any data point from a trace or test run entry; without being limited to predefined fields like input or output. Use Maxim’s DSL in the

Streaming Speech Synthesis Without the Trade-offs: Meet StreamFlow

Streaming Speech Synthesis Without the Trade-offs: Meet StreamFlow

The last few years of neural speech synthesis have been wild. Flow matching models, diffusion transformers, and insanely natural TTS systems keep raising the bar. The catch? Most of these models expect to see the whole audio sequence at once . While this is great for offline generation, but if you’

Building a Customer Support AI Agent with AWS Bedrock and Testing It at Scale

Building a Customer Support AI Agent with AWS Bedrock and Testing It at Scale

Introduction Customer support is one of the most impactful use cases for AI agents. A well-designed support agent can handle thousands of inquiries simultaneously, provide instant responses, and maintain context across complex conversations. But how do you ensure your agent actually works before unleashing it on real customers? In this

What are Offline Evaluations and How to Set Them Up for Your AI System Using Maxim AI

What are Offline Evaluations and How to Set Them Up for Your AI System Using Maxim AI

Introduction Before deploying your AI system to production, you need confidence that it performs well across various scenarios, maintains quality standards, and produces consistent results. This is where offline evaluations become essential. Offline evaluations use curated datasets, scenario simulations, and evaluators to benchmark prompts, workflows, and agents before deployment. They

Basics of AI Observability: Sessions, Traces, and Spans

Basics of AI Observability: Sessions, Traces, and Spans

Observability in AI applications differs fundamentally from traditional application monitoring. While conventional systems deal with deterministic request-response cycles, AI applications involve multi-turn conversations, complex reasoning chains, multiple model invocations, and retrieval operations - all of which need visibility for debugging, optimization, and understanding system behavior. Maxim's observability platform

VITA-Audio: Making AI Voice Assistants Actually Feel Instant

VITA-Audio: Making AI Voice Assistants Actually Feel Instant

Have you ever noticed that frustrating pause when you ask your voice assistant a question? You speak, there's a beat of silence, and then it finally starts responding. That delay might seem minor, but it breaks the natural flow of conversation and reminds you that you're

Kimi K2 Thinking: Engineering Deep Reasoning at Scale

Kimi K2 Thinking: Engineering Deep Reasoning at Scale

Introduction Moonshot AI recently open-sourced Kimi K2 and its reasoning-optimized variant, K2 Thinking. As someone who works with large language models, I wanted to break down what makes this release interesting and where it pushes forward the state of open-source AI. K2 Thinking is a 1-trillion parameter model that can

AgentFold : What If AI Agents Managed Memory Like Humans Do?

AgentFold : What If AI Agents Managed Memory Like Humans Do?

Introduction If you've spent time working with LLM agents for web research, coding assistance in cursor or even extended conversations in ChatGPT, you've probably noticed something: as tasks or multi turn conversations grow longer and more complex, the quality of responses deteriorates - essentially because of

FastLongSpeech: 30x Compression That Doesn't Murder Your Context

FastLongSpeech: 30x Compression That Doesn't Murder Your Context

Speech models are having a moment and seems like they’re here to stay. They can transcribe your rambling, understand your questions, and even tell when you're being sarcastic. But ask them to process anything longer than a TikTok video and they straight-up collapse. The problem? Speech can

What are Online Evaluations and How to Set Them Up for Your AI System Using Maxim AI

What are Online Evaluations and How to Set Them Up for Your AI System Using Maxim AI

Introduction Building an LLM-powered application is one thing; ensuring it performs optimally in production is another challenge entirely. In this blog we will go deeper into Online Evaluations & setting them up for production usecases. Let's start by understanding the difference between Online & Offline Evals. Online vs.