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Integrating Safety Measures into GPT-5.2-Codex: A Workflow Perspective

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GPT-5.2-Codex is positioned as an agentic coding model for professional software engineering and defensive cybersecurity. In that context, “safety” isn’t one feature—it’s a stack. The official system card addendum for GPT-5.2-Codex describes safeguards at two levels: model-level mitigations (how the model is trained and tuned) and product-level mitigations (how the agent is contained and what it is allowed to do). This matters because agentic coding workflows can touch sensitive surfaces: repositories with secrets, build systems, dependency installers, CI/CD pipelines, and (when enabled) external network access. The right question is not “Is the model safe?” but “How do model behavior and product controls combine to reduce risk during real work?” TL;DR Model-level safety focuses on reducing harmful outputs and improving resistance to prompt injection patterns during normal interaction. Product-level safety focuses on containment: agent sandboxing plus ...
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OpenAI's New Under-18 Principles Enhance AI Ethics and Teen Safety in ChatGPT

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On December 18, 2025, OpenAI updated its Model Spec —the written set of behavioral expectations that guides how ChatGPT should respond—by adding a new section: Under-18 (U18) Principles . The goal is straightforward: teens (ages 13–17) have different developmental needs than adults, and a “one-size-fits-all” safety posture can create gaps in higher-risk situations. At a high level, the update clarifies how existing safety rules apply in teen conversations and adds age-appropriate guidance where needed. The principles emphasize prevention, clearer boundaries, and stronger encouragement toward real-world support when risks show up. This article explains what the U18 Principles are, why they matter, and what “safe, age-appropriate behavior” looks like in practice—without turning teen safety into vague slogans. If you’re interested in related context on teen safety work, you may also want to read: OpenAI’s Teen Safety Blueprint . TL;DR What changed: OpenAI added ...
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New Tools in Gemini App Enhance Verification of Google AI-Generated Videos for Productivity

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AI-generated video is getting good enough that “just trust your eyes” is no longer a reliable strategy. That creates a very practical workplace problem: teams waste time debating whether a clip is real, edited, or partially synthetic—especially when the video is used in marketing, internal comms, training, customer support, or public-facing updates. The Gemini app addresses part of this problem with a targeted verification feature: you can upload a video and ask whether it was created or edited using Google AI . Gemini then scans for SynthID , Google’s imperceptible watermark, and returns a result that can include where (which segments) the watermark appears across the audio and visual tracks. TL;DR What Gemini can verify: whether a video contains Google’s SynthID watermark (i.e., created/edited with Google AI tools that embed SynthID). What it cannot verify: it doesn’t prove a video is “real,” and it won’t reliably detect content made with non-Google ...
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Harness Gemini Prompts to Secure Your New Year’s Resolutions with Data Privacy in Mind

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New Year’s resolutions usually fail for a boring reason: the goal is too big and the plan is too vague. AI tools like Gemini can help by turning “I want to improve” into a structure you can actually follow—weekly steps, daily habits, and a realistic review loop. But goal-setting can also make people overshare. Resolutions often involve health, finances, relationships, work stress, or personal routines—exactly the kinds of information you may not want to paste into any tool casually. This guide gives you 10 Gemini prompts designed to protect privacy while still producing useful plans, plus a quick template for “safe prompting” you can reuse all year. TL;DR Gemini prompts can break resolutions into actionable steps, habits, and weekly reviews. Privacy-first prompting means using general placeholders and avoiding personal identifiers and sensitive specifics. This page includes 10 prompts + a reusable safe-prompt template + a short privacy checklist. ...
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Understanding Machine Learning Interatomic Potentials in Chemistry and Materials Science

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Machine learning interatomic potentials (MLIPs) sit in a sweet spot between classical force fields and expensive quantum chemistry. They learn an approximation of the potential energy surface from reference calculations (often density functional theory or higher-level methods), then use that learned mapping to run molecular dynamics and materials simulations far faster than direct quantum calculations—while keeping much more chemical realism than many traditional empirical potentials. That speed-up changes what scientists can attempt: longer time scales, larger systems, broader screening campaigns, and faster iteration between hypothesis and simulation. But MLIPs also introduce new failure modes: silent extrapolation, dataset bias, uncertain reproducibility, and “it looks right” results that may not hold outside the training domain. This page explains MLIPs in a practical way—how they work, which families exist, how to build them responsibly, and how to trust (or distrust...
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Maximizing Productivity with December 2025 Gemini App Updates

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December 2025 is a useful checkpoint for the Gemini app. Instead of “one big redesign,” the month’s updates are best understood as a set of practical capabilities that make Gemini more helpful in everyday work: faster responses, more grounded research, better visual editing, and more context-rich local results. This page breaks down what’s new in the Gemini app in December 2025 and, more importantly, how to turn those updates into repeatable productivity workflows you can use daily—planning, research, writing, and decision-making—without getting overwhelmed by options. TL;DR Faster core model: Gemini 3 Flash (a major model upgrade) is now available globally, improving speed and everyday responsiveness. Sharper research workflows: NotebookLM can be used as a source in Gemini, and Deep Research reports now include visuals for Ultra users to digest dense information faster. More practical “do” features: Image edits are more precise (Nano Banana), and l...
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Strengthening ChatGPT Atlas Against Prompt Injection: A New Approach in AI Security

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As AI systems become more agentic—opening webpages, clicking buttons, reading emails, and taking actions on a user’s behalf—security risks shift in a very specific direction. Traditional web threats often target humans (phishing) or software vulnerabilities (exploits). But browser-based AI agents introduce a different and growing risk: prompt injection , where malicious instructions are embedded inside content the agent reads, with the goal of steering the agent away from the user’s intent. This matters for systems like ChatGPT Atlas because an agent operating in a browser must constantly interact with untrusted content—webpages, documents, emails, forms, and search results. If an attacker can influence what the agent “sees,” they can attempt to manipulate what the agent does. The core challenge is that the open web is designed to be expressive and untrusted; agents are designed to interpret and act. That intersection is where prompt injection thrives. TL;DR ...
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