Anthropic says ‘evil’ portrayals of AI were responsible for Claude’s blackmail attempts
Fictional portrayals of artificial intelligence can have a real effect on AI models, according to Anthropic.
Source: TechCrunch AI
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OpenAI just released its answer to Claude Mythos
OpenAI is launching Daybreak, an AI initiative focused on detecting and patching vulnerabilities before attackers find them. Daybreak uses the Codex Security AI agent that launched in March to create a threat model based on an organization's code and focus on possible attack paths, validate likely vulnerabilities, and then automate the detection of the higher risk ones. Its launch comes just over a month after rival Anthropic announced Claude Mythos, a security-focused AI model it claimed was too dangerous to publicly release and only shared privately as a part of its own initiative, dubbed Project Glasswing. Still, that didn't stop at leas … Read the full story at The Verge.
OpenAI launches DeployCo to help businesses build around intelligence
OpenAI launches DeployCo, a new enterprise deployment company built to help organizations bring frontier AI into production and turn it into measurable business impact.
GM just laid off hundreds of IT workers to hire those with stronger AI skills
Some of the positions focus on AI-native development, data engineering and analytics, cloud-based engineering, and agent and model development as well as prompt engineering and new AI workflows.
Taming Unpredictable User Input: Building a RAG Triage Agent in Node.js
The Problem with Raw User Data Passing this raw text directly into a database requires a human administrator to manually read and route every single ticket. To automate this, we need to extract structured JSON from unstructured panic. The RAG Solution Here is a simplified version of the extraction logic using the OpenAI API: async function categorizeIssue(userReport, cityRulesContext) { const systemPrompt = ` You are a strict city infrastructure triage agent. Analyze the user report against the provided City Rules context. You must return ONLY a JSON object with the following keys: - category (string: must be one of the approved categories in context) - severity (integer: 1-5) - department (string: target routing department) `; const response = await openai.chat.completions.create({ model: "gpt-4-turbo", response_format: { type: "json_object" }, messages: [ { role: "system", content: systemPrompt }, { role: "user", content: `Context: ${cityRulesContext}\n\nReport: ${userReport}` } ] }); return JSON.parse(response.choices[0].message.content); } By utilizing...
Geometry-free prediction of inertial lift forces in microfluidic devices using deep learning
arXiv:2605.08109v1 Announce Type: new Abstract: Inertial microfluidic devices (IMDs) offer low-cost, high-throughput alternative techniques for many traditional particle- (or cell-) manipulation tasks, but simulating them requires being able to predict particle migration, and thus particle lift forces, under a variety of possible channel geometries. Recent work has demonstrated that machine learning models can be used to drastically speed up these numerical simulations, but doing so required training individual models for every unique channel cross-section type (e.g., rectangular, triangular) -- shifting the burden from the simulation step to the training step. In this paper, we develop a novel approach for predicting particle lift forces that contains no explicit geometric parameters. We train a neural network model using a new parameter set and show that while it performs comparably to existing models on channel geometries in the training set, it is able to generalize to unseen channel geometries far more effectively. We...
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