Gemini 3 Pro beats frontier models with long‑horizon planning and higher returns
When I skimmed the newest benchmark headlines, it was hard not to notice how the race for smarter language models seems to be heating up.
Latest breakthroughs in large language models and generative AI shaping the future of artificial intelligence and machine learning.
When I skimmed the newest benchmark headlines, it was hard not to notice how the race for smarter language models seems to be heating up.
When I first opened the “5 Fun NLP Projects for Absolute Beginners” series, the fifth tutorial caught my eye because it tackles text classification - the backbone of everything from sentiment analysis to content moderation.
Right now the internet looks a lot shakier than usual. On X, people are posting about missing timelines; a few of us trying to chat with ChatGPT have seen sessions time out, and Downdetector’s own page is flashing red.
When I first saw a model that could read a paragraph, glance at a picture, hear a snippet of audio and even watch a short video, it felt a bit like watching a Swiss-army knife in action.
When I tried Google’s newest AI Mode update, the assistant stopped acting like a chat bot and turned into a sort of visual planner.
Fidji Simo, OpenAI’s product chief, is nudging the company toward a wider, revenue-focused model for ChatGPT.
When you hand a LLM a string, each extra character is a tiny hit to its compute budget and can blur what it’s supposed to do. That’s why many of us still reach for CSV when we need to trim down data - it’s just rows and commas, nothing fancy.
When the Wall Street Journal broke the story, it turned out the breach of Anthropic’s Claude wasn’t a software bug at all, it was a ruse.
When OpenAI rolled out the Reddit AMA to show off GPT-5.1, the thread turned into what many called a “karma massacre.” Within minutes the model’s answers started racking up down-votes, and users began piling on criticism.
When you try to launch a startup with barely any cash, it feels a bit like trying to keep a few plates spinning at once. You have a concept, maybe a month or two of runway, and a to-do list that seems to grow every day.
I was flipping through a paper on Transformers and the first thing that jumps out is the obvious split between encoder and decoder stacks.
When I skimmed Google’s newest paper, the first thing that stuck out was how they finally address a gripe that’s been floating around the open-source world for a while: tiny language models just can’t seem to work through multi-step puzzles without...
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