Curated Claude Code: A New Agent Harness with Intake Gate

An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.

github | stars 194,899

Trending AI model on Hugging Face — image-text-to-text.

🤗huggingface

Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local determi…

github | stars 95,660

Trending AI model on Hugging Face — image-text-to-text.

🤗huggingface

🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman

github | stars 92,909

Trending AI model on Hugging Face — image-text-to-text.

🤗huggingface

Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.

github | stars 89,266

Trending AI model on Hugging Face — image-text-to-text.

🤗huggingface

🎨 The open-source Claude Design alternative. 🖥️ Local-first desktop app. 🖼️ Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images &…

github | stars 81,474

Trending AI model on Hugging Face — text-generation.

🤗huggingface

Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make…

arxiv

Even a current high-capability LLM can appear safer when shown a dangerous objective directly than when other agents transform and relay its direction. Using OpenAI's gpt-5.6-sol model alias, we test 25 pre-specified mi…

arxiv

Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines…

arxiv

Radio Frequency Fingerprint Identification (RFFI) enables transmitter identification at the physical layer by learning device-specific impairments from received signals, yet the literature is inconsistent about where in…

arxiv

Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, large tool definition…

arxiv

AI agents are increasingly created inside organizations by non-engineering users through low-code, no-code, and conversational development environments. This democratization enables rapid local innovation, but it also c…

arxiv

Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging. Traditional computer graphics methods rely on manual creation, requiring extensive human effort…

arxiv

Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on p…

arxiv

As AI governance moves from benchmark scores toward auditable oversight, a central question is how reviewers can tell whether an LLM-generated audit report is actually supported by evidence. This paper studies that ques…

arxiv

Large language model (LLM)-empowered recommender systems have emerged as a promising paradigm for generative recommendation, leveraging their strong semantic reasoning and generative capacity to model complex, diverse u…

arxiv

Most multi-agent apps wire every flow in application code: the sequence of steps, branching, and handoffs between agents all live inside the program, making the orchestration harder to review, version, and change. Decla…

devblogs.microsoft

LangChain's Eval Engineering Skill inspects your agent's repo and traces, proposes evals through user interviews, and outputs runnable Harbor tasks.

langchain

Most AI teams focus on the wrong things. Here’s a common scene from my consulting work: AI TEAM Here’s our agent architecture – we’ve got RAG here, a router there, and we’re using this new framework for… ME [Holding up…

hamel

The capabilities that make agents useful also make them difficult to evaluate. The strategies that work across deployments combine techniques to match the complexity of the systems they measure. \n

anthropic

Your agents can now be built on a stable, batteries-included harness – the loop, planning, memory, context management, approvals, and telemetry that turn a model into an agent that actually does things – in both Python…

devblogs.microsoft

The gateway is the runtime control plane for enterprise AI, turning policy into enforceable decisions across every model call, tool call, and agent hop.

langchain

Today, I’m publishing evals-skills , a set of skills for AI product evals 1 . They guard against common mistakes I’ve seen helping 50+ companies and teaching 4,000+ students in our course . Why Skills for Evals Coding a…

hamel

Harnesses encode assumptions that go stale as models improve. Managed Agents—our hosted service for long-horizon agent work—is built around interfaces that stay stable as harnesses change.

anthropic

Part 1 of Build your own claw and agent harness with Microsoft Agent Framework. In the overview we said a “claw” is really just an agent harness: a loop around a model, wired up with tools, planning, memory, and more. I…

devblogs.microsoft

We revamped how we benchmark Deep Agents. Here's the eval setup we run in Harbor across coding, conversation, and retrieval, and how we use it to ship changes.

langchain

More than any of these eval scores, what is most exciting to me is something else: Opus 5 is our least prompt injectable model yet. It is a bit buried in the system card, but across PI evals and red teaming, Opus 5 is v…

simonwillison

In this post, we explore how Jefferies overcame these challenges with a solution built on Strands Agents, an agent harness SDK for building AI agents that can reason, plan, and act by orchestrating calls to foundation m…

aws.amazon

OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock. Learn how to select a model, run inference through the Responses API on the bedrock-mantle endpoint, reduce cost with prompt caching, co…

aws.amazon

Hetzner has launched an experimental LLM inference API. I tested its Qwen model—and have a few guesses about where the product could go next.

sliplane

The autonomous business of the future is being built. Certain skills are in high demand - and can help you stand out.

zdnet

AI code generation tools are fantastic at writing isolated snippets of code, but they quickly fall short when they need to understand a running application's state. When a compiled class fails, or a local database conta…

dzone

The article discusses an experiment where a code assistant had to design an agentic system using LangChain4j documentation. The assistant created a coding framework capable of writing, testing, and debugging code autono…

infoq

The bottleneck in a mature SOC is rarely analyst triage; rather, it is the detection-engineering team's ability to keep the rule base aligned with a threat landscape that evolves faster than rules can be written. Learn…

infoq

The first known runaway AI agent - or a very bad marketing stunt? Martin Alderson's commentary on the OpenAI accidental cyberattack against Hugging Face includes a couple of details I hadn't considered. First, Hugging F…

simonwillison

Secure your .NET MCP implementations with best practices for authentication, authorization, and tool design to protect AI integrations and enterprise data.

c-sharpcorner