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I am doing some personal research at MIT on how companies handle the growing chaos of multiple AI agents and copilots working together. I have been seeing the same problem myself- tools that don’t talk to each other, unpredictable outputs, and zero visibility into what’s really happening. Who feels this pain most — engineers, compliance teams, or execs? If your org uses several AI tools or agents, what’s the hardest part: coordination, compliance, or trust? (Not selling anything- just exploring the real-world pain points.) submitted by /u/AppointmentJust7518 [link] [comments]

submitted by /u/BousWakebo [link] [comments]

submitted by /u/SystematicApproach [link] [comments]

It looks like the guy who was in charge of Builder.ai's finances for two years before its collapse also pocketed 200k from a loan he pushed for as the company collapsed. Great moves. He should sell courses on this. submitted by /u/NaisB8M8 [link] [comments]

https://www.wsj.com/tech/ai/ai-apocalypse-no-problem-6b691772?mod=e2tw submitted by /u/MetaKnowing [link] [comments]

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submitted by /u/Possible_Cheek_4114 [link] [comments]

This is in regards of the current discord quest regarding comet Do not install it it worms it's way inside your pc and scraps data to feed its ai to help it develop if you have already completed the quest and uninstalled it, it's not actually gone since some files still remain in the case you have already installed it install revo uninstaller and do one of the two things if the application itself is still installed then use revo to scan your system for traces of comet and once done ALWAYS check the file route as it may go overboard and uninstall something vital to the system but once checked and nothing vital is being used then delete the program through revo if you have already done a regular uninstall on comet then you have to reinstall it so revo can trace the wormed files. Then conti...

https://arxiv.org/abs/2509.14139 submitted by /u/MetaKnowing [link] [comments]

Some quotes from the author that I found insightful about the paper: Most prior hallucination detection work has focused on simple factual questions with short answers, but real-world LLM usage increasingly involves long and complex responses where hallucinations are much harder to detect. Trained on a large-scale dataset with 40k+ annotated long-form samples across 5 different open-source models, focusing on entity-level hallucinations (names, dates, citations) which naturally map to token-level labels. They were able to automate generation of the dataset with Closed Source models, which circumvented the data problems in previous work. Arxiv Paper Title: Real-Time Detection of Hallucinated Entities in Long-Form Generation submitted by /u/Envoy-Insc [link] [comments]