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Feature
Why Inference Costs Are Dropping: The Role of Hetzner's Experiment
Hetzner's move into AI inference highlights a shift in infrastructure costs.
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Each morning: models, tools, research, and conversations distilled from across the web — with why they matter for builders.
Around the Web
Open Weights Debate
Top Hacker News discussion.
politico
The Open Letter was initiated by Microsoft and published today: “ Open Weights and American AI Leadership ”. It argues against broad or premature restrictions on open-weight models and explicitly says policymakers shoul…
r/LocalLLaMA
The earlier post on this subreddit by 20+ companies signing the petition including Microsoft, Meta, Nvidia, YC ( https://www.microsoft.com/en-us/corporate-responsibility/topics/open-weight/ ) etc plus this https://xcanc…
r/LocalLLaMA
LLM Inference Buzz
Built an open-source AI coding agent that was 7%–75% cheaper than a cold "claude -p" run on 6/6 well-localized tasks across repositories up to ~82k LOC. The biggest difference: Cold agent: $6.83, 207 turns AutoDev Studi…
r/MachineLearning
Recent Reddit discussion.
r/artificial
AI Job Cuts & Spending
Skepticism on AI Narratives
New Tools
Fresh Show HN launch.
github
Honorable mentions
Moonshot AI’s Kimi K3 shows how opening a model to outsiders can turn other companies’ computing power into a competitive advantage
r/artificial
Are they secretly testing new models? Pro subscriber here.
r/OpenAI
I'm downloading it again now. So far, the model hasn't performed well with reasoning tasks, but I really appreciate the work being done to fix this.
r/LocalLLaMA
The interesting finding from a new [arXiv paper]( https://arxiv.org/abs/2607.16165 ) isn't that a frontier vision model failed a new benchmark, that happens weekly, but the specific shape of the failure and the fact tha…
r/MachineLearning
Tools
An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.
github | stars 194,884
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 95,089
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 92,680
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 88,860
🎨 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 81,273
Research
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
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
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
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
Building socially calibrated large language models, which can learn from others without simply yielding to them, requires more than reducing sycophancy as a one-dimensional failure mode. Models must distinguish when to…
arxiv
The spread of generative AI (GenAI) in higher education has raised concerns that students offload cognitive effort to AI, earning high grades without learning. If this "GenAI substitution hypothesis" is true, grades sho…
arxiv
Playbooks
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
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
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
NemoClaw Deep Agents blueprint, LangSmith Sandboxes free trial, Fleet Slack integration, voice tracing, OpenWiki Brains, and RLMs in Deep Agents. See what's new at LangChain
langchain
News
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
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
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
DevSecOps for AI agents requires more than code review -- Harness beefs up behavioral and security controls and hints at a possible observability expansion.
techtarget
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
A secured agent that can't reach anything is just expensive autocomplete with a badge. In "Why prompt-level guardrails aren't enough," I walked through how Red Hat AI allows you to give each agent a cryptographic identi…
redhat
Copilot now bills usage at listed API rates. Compare direct model access with the coding workflow, policy, and harness work around it. The post Copilot vs. raw API access: What are you actually paying for? appeared firs…
github
Attackers have shifted from hiding from AI tools to running inside them. By poisoning the config files that govern AI coding assistants, a new worm class achieves silent persistence, evades AI-based scanners, and spread…
tenable
Amazon Bedrock AgentCore optimization surfaces silent behavioral failures in production AI agents: the ones that pass every health check but still deliver wrong outcomes. Learn how insights discovers, explains, and rank…
aws.amazon
I genuinely believe that if you took an open weights model from 2025 and built a pentest harness for it, it could do this kind of sandbox escape and scan/hack in most networks. This is only surprising because you assume…
simonwillison