Kimi K3 is the best performing model on nextjs.org/evals, ahead of Fable, reaching a comparable success rate in less time. This is the first time that an open model is ahead of all proprietary ones for this comprehensive web engineering benchmark. Notes: ▪️ Benchmarks don’t always tell the full story, although this is important signal, adding to mounting evidence that this could be a breakthrough moment for open models ▪️ No model as of yet has reached 100% completion on this set of evals. The top performer peaks at 92% and 96% “with help”

Grok 4.5 is definitely a big leap not only a strong jump in terms of intelligence, but also in coding and agentic work, where it matches GPT-5.5 in the Grok build while using far fewer tokens and costing much less the costs is $2/m input and $6/m output token

// What MCP, A2A, and ACP cannot express // MCP and A2A solve capability discovery and message passing, then stop right where enterprise deployment begins. New research runs a systematic gap analysis of the agent interoperability protocols (MCP, A2A, ACP, ANP, ERC-8004) against a six-dimension governance taxonomy drawn from organizational theory. The dimensions are membership, deliberation, voting, dissent preservation, human escalation, and audit or replay. These protocols support task-oriented coordination but cannot express a governed agent community. You cannot state who gets a vote, how dissent is preserved, or when a human must be escalated to. Paper: arxiv.org/abs/2606.31498 Learn to build effective AI agents in our academy: academy.dair.ai/

3 years ago I gave a talk at the first @aiDotEngineer conference on "Advanced RAG" techniques in order to work around the limitations of naive RAG. It's insane how much the world has changed since then, and the world has evolved into standardized, higher-level abstractions around agent harnesses and context. Some general patterns: 1. Retrieval complexity can be encoded at the agent layer. This means that you can give relatively simple but performant search tools to an agent (e.g. really fast bm25, vector search), and let the agent reasoning enter the right queries to find the right results. 2. To some extent this is still evolving, but I do think we will increasingly care less about "hacking" the context window and more about deciding what business context is relevant in the first place. 3. The way we build agents has fundamentally changed from defining code, to defining runbooks, to defining goals. Big congrats to @swyx and the entire AI Engineer team for continuing to put out awesome conferences every year.

Claude Sonnet 5 is ready for you in the Agent Arena! In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks from a global community of users. Models can access web search, filesystem, and terminal tools to complete complex workflows. The leaderboard measures model performance on outcomes relative to the average model using a causal tracing methodology. In Addition to Agent Arena, Sonnet 5 is also available for: Text, Vision, Document and Code Arena: Frontend. Head over and check it out (link in thread)