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On "Forbes Newsroom," Emerson College Polling Senior Director Matt Taglia discussed a new poll on the standings of possible 2028 presidential contenders.
We want to hear from you. Has T-Mobile's plan turmoil pushed you towards an unplanned carrier switch?
Yemen has proven oil reserves estimated at three billion barrels, but the security environment hinders its export.
MMA legend Cris Cyborg reflects on the changes in women's MMA as she prepares for her retirement fight against Ketlen Vieira.
I’ve been building Echo ( https://echo.tracerml.ai/ ), an experiment in making one AI system out of a pool of open-weight models rather than choosing a single model and using it for every task. It started with a simple experiment. I took a group of models, including GLM-5.2, Kimi K2.7 and others, and ran them on the same evaluations. Then I measured what would happen if, for each problem, you somehow knew in advance which models would be useful and how their outputs should be combined. That hypothetical system performed substantially better than any individual model in the pool. Of course, it is not something you can actually deploy because it relies on knowing which decisions were good after seeing the result. Echo is my attempt to recover some of that advantage without having that information in advance. For each request, Echo decides how much computation to allocate, which models should participate, and how their work should be combined. Some prompts may only need a relatively small amount of inference, while others benefit from multiple models working on different parts of the problem. One thing that surprised me while building it was how complementary the models are. A model that is clearly weaker overall can still be extremely useful on particular problems or as part of a combination. On my first evaluation mix, Echo consistently performed better than the best individual model in its pool. It also reached roughly the same aggregate result as Fable, which I used as one of the stronger comparison systems, at around one third of the inference cost. There are still some cases where Echo makes the wrong allocation or combination decision. I’m currently spending a lot of time understanding those failures, as well as testing whether the same approach holds up on coding and agentic tasks where measuring the quality of each decision becomes much harder. I built a chat interface (echo.tracerml.ai) and an OpenAI-compatible API ( https://echo.tracerml.ai/docs/api ) so the system can be tested outside the evaluation setup. Here is a short/high level video on how it works: https://www.youtube.com/watch?v=lJFJSvOdXhg I wrote up the evaluation methodology, individual model results, costs and current limitations here: https://echo.tracerml.ai/eval I would love for you to try it! Especially if you hit any weird failure cases or places where the allocation looks unintuitive.
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The FDA hasn't said where the cases are or how they're linked.
Most of those evacuated were tourists from campsites around Arcachon Bay in France's Gironde region.
Google's selfie videos can be used for account access, AI Avatars, and age verification.
Protesters refuse to back down until Education Minister Dharmendra Pradhan resigns.
Continuing agency efforts to bring space closer to home, NASA+ is heading to more streaming platforms. On Thursday, NASA announced its programming is on Fire TV Channels. Fire TV customers can easily access this content by asking Alexa+ on compatible devices. Future programming on Fire TV may include science mission launches, a test flight for […]
Bill would let Homeland Security chief decide when an AI should be shut down.
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“GPT-5.5's API pricing is reshaping how startups build AI products”
