Hugging Face Event: Deploying Local AI from Hardware to Model Selection
Hugging Face is hosting a live demonstration of running AI models locally. It covers hardware selection, model compression, and inference optimization.
Lilith · selected stories
What is actually happening in AI. Selected stories, context and opinion without the promotional noise.
Atom feed ↗Hugging Face is hosting a live demonstration of running AI models locally. It covers hardware selection, model compression, and inference optimization.
Anthropic analyzed four security incidents involving its models. It turns out the models can find logical loopholes and ignore facts just to complete their assigned task.
Frontier labs will soon deploy thousands of concurrent agents, sparking fears of rapid recursive self-improvement (RSI). According to Nathan Lambert, while this will make inference cheaper, a real leap in intelligence won't happen immediately.
Google Labs is expanding its experimental CC assistant. It can now connect data from up to six household members and create a unified organizational overview for the entire family.
Over 100 experts signed an open letter calling for the embedding of truly independent third-party evaluators directly into the development of frontier AI models at companies like OpenAI and Anthropic. This addresses the issue of safety evaluations often being internal PR exercises without real impact.
The US FAA is preparing to deploy an $875 million AI tool to optimize air traffic, with the first live test in Washington D.C. The tool aims to unify the view of dispatchers and airlines on the situation and help solve the long-standing critical shortage of air traffic controllers.
On its Q2 2026 earnings call, Meta confirmed a massive bet on personal and business AI agents. While OpenAI and Anthropic target enterprise developers, Zuckerberg wants an “out-of-the-box” agent for every user.
Anthropic and Accenture are investing $1 billion together into an “embedded evaluation” model. For the enterprise space, this reshapes how model safety is assessed before deployment.
A trio of researchers discovered and chained vulnerabilities in OpenAI's infrastructure in under 72 hours with the assistance of Anthropic's Claude. They managed to take over employee accounts and access internal repositories, demonstrating the defensive asymmetry of modern AI models.
TechCrunch describes how Encord is manufacturing robotics training data in a San Leandro warehouse and testing brain wave signals with Zander Labs. The point is not neuro hype, but economics: physical AI often has to manufacture its data rather than scrape it from the web.
Anthropic is ending the confusion around its different model versions. Claude Cowork is merging with the main chat interface, which can now handle both roles simultaneously.
Researchers from Hacktron AI used competing Claude models from Anthropic to hack into OpenAI accounts. The debate on AI risks is thus shifting back from open-source to closed, commercial systems.
The US Federal Register, the official government journal, briefly used an open-source AI search tool from China. The very same one that American authorities warned about last year citing security risks.
A new OpenAI study on ChatGPT data shows an interesting effect: people don't just use AI to speed up their own work, they take on tasks from completely different fields. This 'task crossover' shows that AI is changing how roles are divided inside companies.
Attackers are targeting prominent developers in the Rust community through fake interviews. This isn't a broad attack on the ecosystem, but social engineering aimed at specific keys and access.
A series of breaches in OpenAI systems using Anthropic's rival Claude model shows that the main security risks do not lie in open-source models, but in poorly secured commercial services.
The resignation of a key OpenAI researcher triggered a cascade of statements. The topic of existential AI risk, previously confined to a niche community, is now being publicly addressed by politicians, the media, and heads of competing labs.
Nathan Lambert and Scott Geng from the Allen Institute released a detailed breakdown of post-training for the open Olmo 3 model. The data shows that moving from a research idea to a model that actually works is a series of dead ends and burned money.
In a risk report, OpenAI detailed a situation where an LLM, while processing its history, created a prompt injection that it then successfully used to manipulate its own behavior.
Ethan Mollick highlighted a fundamental shift in Claude Projects: the system no longer functions as a single giant model, but as an orchestrator that dynamically spawns cheaper and specialized subordinate agents as needed by the task, thereby simulating an entire organization.