Running a 28.9M Parameter LLM on an $8 Microcontroller

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 96,251

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 93,135

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 89,700

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 81,675

Trending AI model on Hugging Face — text-generation.

🤗huggingface

Open-source AI job search: scan job portals, evaluate listings with a structured A-F rubric into a 1.0-5.0 score, tailor your CV, track applications — runs locally in your AI codi…

github | stars 61,643

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

🤗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

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

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

Digital search is undergoing a fundamental transformation from a human-driven process of discovery to an agent-mediated system of delegated decision-making. In the traditional model of digital search, users translate in…

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

Learn why companies must own their agent systems, governance, context, and feedback loops to turn generic AI into lasting business advantage.

langchain

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

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

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

langchain

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

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

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

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

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

This post covers Opus 5’s improvements and practical guidance for AI engineers integrating the model into agentic systems and production inference workloads on Amazon Bedrock. See the documentation for Claude Platform o…

aws.amazon

Top 5 MCP Servers for High Performance Agentic Development • 10 Newsletters Keeping You Ahead in AI • Kaggle + Google’s Free 5-Day Agentic AI Course • Language Model Hallucination Evaluation with GraphEval

kdnuggets

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

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

Anthropic launched Claude Cowork, an autonomous AI agent for office tasks on macOS. Here's what it does and why crypto investors should care. The post Anthropic launches Claude Cowork, an AI agent for everyday office ta…

cryptobriefing

Sam Altman told the US Senate that America must lead in both open-source and closed-source AI, with OpenAI planning an open model release this The post Sam Altman urges US to lead in open-source and closed-source AI mod…

cryptobriefing

Coinbase launched AI agent payments for businesses, expanded automated crypto trading tools and introduced an x402 SDK for developers. The post Coinbase enables AI agents to pay businesses and execute crypto trades appe…

cryptobriefing