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Feature
Jensen Huang Criticizes Closed AI for Hindering Forensic Efforts
Open-weight models proved crucial in diagnosing the Hugging Face incident, highlighting a debate on AI transparency.
Why it mattersAI infrastructure is at a crossroads as incidents like the Hugging Face breach highlight the need for transparency. For AI developers, balancing open and proprietary models is crucial to ensuring security without stifling innovation.
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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
AI Bubble Trouble
OpenAI Drama
Quick recap for anyone who missed it: during an OpenAI safety eval (ExploitGym, with guardrails deliberately relaxed), the models found a zero-day in the sandbox software itself , escaped to the internet, guessed the te…
r/OpenAI
Recent Reddit discussion.
r/OpenAI
Model Launches Galore
Fresh Show HN launch.
alphai
Fresh Show HN launch.
nibware
Fresh Show HN launch.
news.ycombinator
Fresh Show HN launch.
my.tildepay
AI Safety Concerns
Nvidia CEO Jensen Huang “Distillation - learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from one another. AI also ha…
r/LocalLLaMA
Recent Reddit discussion.
r/artificial
Jensen Huang on 𝕏: https://x.com/JensenHuang/status/2081698060330250294
r/LocalLLaMA
Honorable mentions
Fresh Show HN launch.
kblip
Moonshot's latest model Kimi-K3 is available on HuggingFace since today. And it's another good news for open-weight AI and for the future of open-source AI It's a 2.8T-parameters Moonshot's SOTA model with 1 million tok…
r/artificial
Hello, I read a paper on a model named DONUT that extracts text from documents, which became my inspiration for this little project. Initially I wanted to make a model that extracts items bought from receipts, but in th…
r/MachineLearning
7 years ago I had an idea for a dog harness that doesn't tangle. I 3D-printed one part and then stalled, not on the engineering, but on prior art searches, novelty judgment, and drafting a patent specification, none of…
r/artificial
Tools
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 96,987
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
github | stars 93,474
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 90,261
🎨 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 81,911
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 61,825
Research
Adding procedural skills to an LLM agent is typically evaluated by average improvement in task success. However, this metric hides an important cost: skills can also make agents worse. We measure both sides by comparing…
arxiv
Automating theoretical research is constrained not only by the generation of candidate results, but also by their reliable evaluation. A common approach is to close the research loop with a large language model (LLM) re…
arxiv
We present SMEFT-Pheno-Agent, a Python workflow guided by a natural-language AI agent to perform machine-learning-assisted Standard Model Effective Field Theory (SMEFT) phenomenology at high-energy colliders. The softwa…
arxiv
LLM training is shifting from manual design and annotation to interaction-driven self-evolution. However, existing self-evolutionary methods face a fundamental dilemma between task diversity and verification reliability…
arxiv
Air-gapped and on-premises deployments in regulated settings (clinical FHIR services, legal review, sovereign infrastructure) cannot call frontier APIs; they run quantized 4-8B models via llama.cpp or vLLM behind tool s…
arxiv
Data analysis agents are becoming increasingly common tools for applied and scientific research. Yet, for highly specialized tasks such as Observational Causal Inference (OCI), human oversight remains necessary to ensur…
arxiv
Vision-language model (VLM) agents increasingly use tools to act on 3D scenes rather than only describe them. Existing 3D benchmarks score textual responses or single-object operations, leaving agent action on complete…
arxiv
Agent benchmarks increasingly evaluate repository editing, web research, terminal use, and long-horizon interaction. Their scores support capability claims only when the evaluation protocol keeps the intended capability…
arxiv
Large language models (LLMs) and AI agents have demonstrated strong potential for data integration in zero-shot and few-shot settings. However, they continue to face significant accuracy and cost challenges in enterpris…
arxiv
Large Language Models (LLMs) are increasingly deployed as autonomous agents, transitioning from static conversational interfaces to dynamic systems capable of complex reasoning, tool execution, and decision-making. Howe…
arxiv
Playbooks
SmithDB supports full-text search and JSON filtering over agent traces with a median (P50) latency of 400 ms, even though the underlying data consists of large, deeply nested JSON documents stored in object storage.
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
Learn why companies must own their agent systems, governance, context, and feedback loops to turn generic AI into lasting business advantage.
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
LangChain's Eval Engineering Skill inspects your agent's repo and traces, proposes evals through user interviews, and outputs runnable Harbor tasks.
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
News
Traditional RAG hits a ceiling on analytical tasks that span hundreds of documents. This post shows how to use task-aware knowledge compression (TAKC) on AWS to pre-compress entire knowledge bases into task-specific rep…
aws.amazon
An Inside Look at the Relay Market Powering Token Resellers and Fraud Fascinating investigation by Matt Lenhard into the market that has grown up around reselling LLM tokens at a discount by pooling API keys from variou…
simonwillison
After two years of silence from Thinking Machines Lab, Murati's debut model is out and on OpenRouter. The MCP score is genuinely impressive. The price-to-performance math is more complicated.
decrypt
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
Breaking down the Hugging Face security incident caused by OpenAI's own models during a benchmark run - the sandbox escape, the package proxy, and whether it's really a marketing stunt.
martinalderson
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 cover why Deepgram built on IAM temporary delegation, how the integration works end-to-end, and what it unlocks for customers running Deepgram speech models on SageMaker AI. With this integration, Deepg…
aws.amazon
The design-to-code handoff meeting might be over
xda-developers
Samsung’s latest unveiling at the Galaxy Unpacked event has introduced the world to its AI-powered smart glasses, a product built on the Android XR platform. These glasses integrate advanced features like real-time tran…
geeky-gadgets