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What adversarial prompt generation means Adversarial prompt generation is the practice of designing inputs that intentionally try to make an AI system misbehave—for example, bypass a policy, leak data, or produce unsafe guidance. It’s the “crash test” mindset applied to language interfaces. A Simple Analogy (that sticks) Think of an LLM like a highly capable […]
Adversarial Prompt Generation: Safer LLMs with HITL Read More »
On Thursday, the Wikimedia Foundation announced licensing deals with Microsoft, Meta, Amazon, Perplexity, and Mistral AI, expanding its effort to charge major tech companies for using Wikipedia content to train the AI models that power AI assistants like Microsoft Copilot and OpenAI’s ChatGPT. While these same companies previously scraped Wikipedia without permission, the deals mean
Wikipedia signs AI training deals with Microsoft, Meta, and Amazon Read More »
OpenAI, the company that developed the models and products associated with ChatGPT, plans to announce a new audio language model in the first quarter of 2026, and that model will be an intentional step along the way to an audio-based physical hardware device, according to a report in The Information. Citing a variety of sources
OpenAI reorganizes some teams to build audio-based AI hardware products Read More »
Following two years of immense hype in 2023 and 2024, this year felt more like a settling-in period for the LLM-based token prediction industry. After more than two years of public fretting over AI models as future threats to human civilization or the seedlings of future gods, it’s starting to look like hype is giving way to pragmatism:
From prophet to product: How AI came back down to earth in 2025 Read More »
AI coding agents from OpenAI, Anthropic, and Google can now work on software projects for hours at a time, writing complete apps, running tests, and fixing bugs with human supervision. But these tools are not magic and can complicate rather than simplify a software project. Understanding how they work under the hood can help developers
How do AI coding agents work? We look under the hood. Read More »
When I first started as a data scientist, there was a gap. I met with dozens of organizations who would invest time and resources into building accurate and tuned models and then ask, “What now?” They had a fantastic model in hand but couldn’t get it into a place and […] The post There is
If you have ever trained a model, fine-tuned an LLM, or even experimented with AI on a weekend, chances are you have landed on Hugging Face. It has quietly become the GitHub of datasets – a place where developers, researchers, and data professionals go to build models and accelerate ideas. From code benchmarks and web-scale
10 Most Downloaded Hugging Face Datasets and Their Use-cases Read More »
With the popularity of AI coding tools rising among some software developers, their adoption has begun to touch every aspect of the process, including human developers using the tools to improve existing AI coding tools. We’re not talking about runaway self-improvement here; just people using tools to improve the tools themselves. In interviews with Ars
OpenAI built an AI coding agent and uses it to improve the agent itself Read More »
On Thursday, OpenAI released GPT-5.2, its newest family of AI models for ChatGPT, in three versions called Instant, Thinking, and Pro. The release follows CEO Sam Altman’s internal “code red” memo earlier this month, which directed company resources toward improving ChatGPT in response to competitive pressure from Google’s Gemini 3 AI model. “We designed 5.2
OpenAI releases GPT-5.2 after “code red” Google threat alert Read More »
When a person admits they made a mistake, something surprising happens. The confession often restores trust rather than breaking it. People feel safer around someone who owns their errors than someone who hides them. Accountability builds confidence. What if AI models can do the same? Most AI systems give confident answers, even when they are
How Confessions Can Keep Language Models Honest? Read More »
