The AI industry has become adept at measuring itself. Benchmarks improve, model scores rise, and every new release arrives with a list of metrics meant to signal progress. And yet, somewhere between the lab and real life, something keeps slipping. Which model actually feels better to use? Which answers would a human trust? Which system would you put in front of customers, employees, or citizens and feel comfortable standing behind it? That gap is where LMArena has quietly built its business, and why investors just put $150 million behind it at a $1.7 billion valuation, in a Series A round.…This story continues at The Next Web [...]
Perplexity has announced an orchestrator that combines AI models running on your own computer with powerful cloud models and automatically decides which task gets processed where.<br /> The arti [...]
OpenAI has acquired tech talk show TBPN. The show will supposedly remain editorially independent but report to OpenAI's communications department. That's as contradictory as it sounds. So wh [...]
Imagine you do two things on a Monday morning.First, you ask a chatbot to summarize your new emails. Next, you ask an AI tool to figure out why your top competitor grew so fast last quarter. The AI si [...]
Unlike GPT-4o or Qwen3.5-Omni, Audio Interaction doesn't wait for a recording to end: it translates, transcribes, chats, and picks up everyday noises like coughing in a single stream. Code, model [...]
A code migration agent finishes its run, and the pipeline looks green. But several pieces were never compiled — and it took days to catch. That's not a model failure; that's an agent decid [...]
Meta has spent the last few years remaking Facebook’s main feed into a “discovery engine” that primarily serves up recommended content from pages, groups and accounts users don’t already follo [...]
Artificial Intelligence is entering the late stage of its hype cycle. Not a collapse. A correction. For the past two years, AI has dominated venture capital flows, with capital pouring into the secto [...]