Latest breakthroughs in large language models and generative AI shaping the future of artificial intelligence and machine learning.
When Nvidia wrapped up its latest earnings call, two things caught my eye: a sudden jump in chip orders linked to generative-AI workloads, and the off-the-cuff reveal of Gemini 3, the next version of the rival large-language model.
We can already hear the holiday buzz, and tech companies are scrambling to turn AI into a sort of checkout helper.
When you look at big language models, most people just check whether the final answer is right or wrong. Lately, though, a few of us have started to wonder about the steps that get there.
I asked a language model to fetch this quarter's revenue from our SQL warehouse, hoping it would just spit out the right number. A lot of firms are playing with “AI analysts” that let anyone type a question and get a chart or a short summary.
When OpenAI launched the GPT-4o A/B test in April 2025, the change felt more like a personality tweak than a code update. One branch of ChatGPT leaned into friendly banter, while the other stuck to the straight-talk style we’d gotten used to.
If you’re a developer looking to play with Gemini 3 Pro’s language features, the first thing you’ll probably do is grab the right client library for the environment you already have - say a Python notebook or a Node.js service.
When I watched Jensen Huang’s recent interview, one line stuck with me: China probably houses about half of the world’s AI researchers.
OpenAI just rolled out a new twist on ChatGPT: it now tries to act like a personal shopper, digging through the bits of data it’s stored about you, your browsing history, old searches, even hints that you were thinking about buying something.
When I first opened Abacus AI I expected another code-generator that spits out snippets. Instead, the pitch says it can take you from idea to a live product - everything from Stripe payments to user auth and a database, all wired together.
Google’s AI team just dropped a new benchmark, and it’s already buzzing around the LLM crowd.
I spent an afternoon trying to get a new LLM running, only to hit a CUDA version mismatch, a missing wheel, and a dependency clash that kept my notebook stuck for hours. Those little hiccups turn a quick test into a full-on debugging session.
When the researcher who built most of ChatGPT’s mental-health tweaks quit, people started talking about a larger effort inside OpenAI.
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