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Meta-Harness: What if we let an agent optimize the code around an LLM?

There's a pattern that anyone who's shipped an LLM-powered product has run into: you pick a model, you wire it up, and then you spend the next three months discovering that the thing around the model matters as much as the model itself. What you retrieve,

The Receipts Are Real, but So Is the Playbook: Making Sense of Anthropic's Mythos Moment

What holds up, what doesn't, and why the framing matters more than the findings. Three years ago, a former SpaceX engineer ran an experiment. He pointed GPT-3, then one of the most advanced AI models available, at a collection of 129 files containing known security flaws, and asked

From Drowning in Logs to Conversing with Your Data: Introducing Maxmallow

Picture this: It's 3 AM, your AI agent is behaving erratically in production, and users are complaining. You open your observability dashboard and you're greeted with thousands of traces, sessions, and log entries. You know the answer is somewhere in there: but where do you even

Computation Beyond Tool Use: Executing Programs Inside a Transformer

When LLMs need to compute something reliably, we've settled into a familiar pattern: the model writes code, an external interpreter runs it, the result gets injected back into the context, and the model carries on. It works well enough that we rarely stop to question the architecture. But

Attention Residuals: What If Your Network Could Choose Which Layer to Listen To?

Residual connections are one of those ML ideas that feels obvious in hindsight. Instead of each layer completely overwriting the previous representation, you just add the transformation on top: h_l = h_{l-1} + f(h_{l-1}) This single + sign is what made training 100-layer networks possible. It gives gradients a

Speculative Speculative Decoding: How Researchers Are Teaching LLMs to Think Ahead of Themselves

The Problem Nobody Talks About Enough You've probably noticed that ChatGPT, Claude, or any large language model streams text to you token by token, one word (or part of a word) at a time. This is a fundamental constraint of how these models work. Every time a transformer

PostTrainBench: How Far Can AI Agents Go in Automating LLM Post-Training?

Introduction Post-training is where the real cost of LLM development lives. Taking a pretrained base model and turning it into something actually useful - an assistant that follows instructions, reasons carefully, and behaves safely - requires months of supervised fine-tuning, reward modeling, and alignment work from teams of skilled ML

Is AI Distillation Theft or Just How Knowledge Evolves?

Last month Anthropic published something unusual: a detailed accusation. Three Chinese AI labs, DeepSeek, Moonshot AI, and MiniMax, had collectively generated over 16 million exchanges with Claude through ~24,000 fraudulent accounts. They bypassed regional access controls using commercial proxy networks. They targeted specific, high-value capabilities. Anthropic called it a

Post-Training Doesn't Create Your Model's Character. It Inherits One

Introduction Every team building on top of LLMs has a version of the same mental model: pretraining teaches the model what it knows, and post-training teaches it how to behave. Don't want it to say harmful things? Train that out. Want it to be more helpful? Push that

The Attention Arms Race: How Modern Open-Source LLMs Are Reinventing the Transformer's Core

Introduction If you follow the LLM space, you've probably heard a lot about parameter counts, context windows, and benchmark scores. What gets discussed far less often is the mechanism that makes all of it possible: attention. Every major language model (GPT, Llama, Gemini, Qwen, DeepSeek) is built on

Your Base Model Is Smarter Than You Think: And Here's How to Prove It

There's a quiet assumption baked into most of the recent excitement around reasoning models: that the impressive gains you see from systems like DeepSeek-R1 or similar RL-trained models come from something genuinely new: novel capabilities that the base model simply didn't have before training. A new

PersonaPlex: Full-Duplex Voice Without the Fixed Persona

Introduction Voice AI hit a genuine inflection point when full-duplex models arrived. Systems like Moshi finally cracked the core problem with conversational speech: the awkward cascade of listen, then transcribe, then think, then speak. Full-duplex models [models that listen and speak simultaneously over a continuous audio stream, the same way

Can LLMs Actually Judge Web Development Quality? Spoiler: Not Really

I recently came across a fascinating paper at ICLR’26 that tackles a question many of us AI developers have been wrestling with: can we trust LLMs to evaluate complex, interactive task? The authors focus on the domain of web development, and the short answer: we've got a

Beyond Autoregression: LLaDA2.1 and the Case for Self-Editing Language Models

Introduction Every mainstream large language model today generates text the same way: one token at a time, left to right, no looking back. It works remarkably well, but it has a structural flaw that's easy to overlook until you care about speed at scale. The model can never

Building the Future of Music Education: Yousician’s Journey with Maxim AI

About Yousician Yousician is the world's leading music education platform, helping over 20 million people learn to play instruments. The company has built one of the world’s largest interactive music learning ecosystems, combining structured lessons, real-time feedback, and practice-driven progression. As Yousician looks ahead, the team is

Beyond Autoregression: LLaDA2.1 and the Case for Self-Editing Language Models

Introduction Every mainstream large language model today generates text the same way: one token at a time, left to right, no looking back. It works remarkably well, but it has a structural flaw that's easy to overlook until you care about speed at scale. The model can never

Building the Future of Music Education: Yousician’s Journey with Maxim AI

About Yousician Yousician is the world's leading music education platform, helping over 20 million people learn to play instruments. The company has built one of the world’s largest interactive music learning ecosystems, combining structured lessons, real-time feedback, and practice-driven progression. As Yousician looks ahead, the team is

xMemory: Why Top-k Retrieval Breaks for Agent Memory

Introduction LLM agents no longer begin and end in a single context window. We’re now in the era of cross-session, long-running agents. Products like Claude Code, OpenClaw, and other agentic workflows are built to carry context across days of work, not minutes. The bottleneck is not context length anymore.

Beyond the Benchmark: Why TruthTensor Might Be the Eval Framework We've Been Missing

When was the last time you confidently trusted a benchmark to tell you how an LLM would actually perform in production? The gap between benchmark performance and real-world reliability is significant, and it's a problem that deserves more attention. I recently read through this paper by Inference Labs,

The Skills vs MCP Debate: Understanding Two Layers of the Same Stack

How coding agents reshaped the tool integration landscape and what actually survived We're in an interesting moment for AI's application layer. Agents can now write code (better than most programmers), call APIs, query databases, and orchestrate complex workflows. But the infrastructure underneath - how agents actually

Making Voice Assistants Faster Without Losing Accuracy

Have you ever noticed how some voice assistants seem to understand you instantly, while others leave you waiting? That delay isn't random. It's the result of a fundamental trade-off that has plagued speech recognition for years. Systems could either be fast or accurate, but rarely both.

Semantic Highlighting: Making RAG Cheaper Without Compromises

Recent research from the Zilliz team tackles a problem that shows up constantly in production RAG systems: how do you actually show users why a document is relevant to their query? Consider a typical scenario. A user asks: "How can I speed up my Python code?" The vector

Voice Simulation: Testing Voice Agents the Way Users Experience Them

Introduction Voice is rapidly becoming the next frontier of AI interaction (along with physical AI). As more companies deploy voice agents for customer support, sales, and service operations, the stakes have never been higher. A poorly tested voice agent doesn't just frustrate users - it can damage your

Logging and observability overhaul, MCP gateway, Evals on file attachments, and more

🎙️ Feature spotlight 🔀 Collaborative conflict resolution for Prompt changes To help teams collaborate on prompts without accidentally overwriting each other’s work, we’ve introduced session conflict resolution in the prompt playground. Here’s what’s new: * You’ll now land on your last active session instead of the prompt’s

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

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

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

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

✨ 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

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’