Allgemein

In this episode, Joerg sits down with Kevin Today to get alook at AgenC „the CEO command center for your fleet of Claudes“ and unpack the philosophy and mechanics behind agentic AI, from building personal “work factories” to designing systems that scale your output without losing your edge.

We are launching our very own and very first podcast: on.agent.ops It will be all about making agentic AI in small to large scale org setups work. So, we are delighted to have Sohaib Anwaar as our first guest, who as extensive experience implementing multi-agent setups. In this episode, Sohaib Anwar reveals the hidden mechanics

Dreaming is still a little understood phenomenon of the human psyche, although there is a steady but slow uptake in releated research [1,2]. Especially its purpose remains somewhat a mystery and many suggetions have been put forth [3,4].In this short article I do not want to sketch a general answer to all these questions nor speculate on the nature and function of dreams. I rather want to spend some time exploring the a certain aspect of dreams, that is their ‘detail-specificity’.

With crass.ai getting ready for public beta, we thought it’s time to take it for a spin and have a dance with some of the recent LLMs (large language models).As a starting point, we chose facebook’s BlenderBot (for convenience followingly written without the second capital “B”). The Blenderbot is a multipurpose Q&A open-source chat bot, so it is an ideal fit for the question-styled counterfactual conditionals employed by crassi.ai inaugural test set. (btw. counterfactual conditionals do not have to be framed as questions, as you could say: “John would have picked the red team, if he had known they would win.”, but construing them as questions makes it easier to have a starting point for judging the strength of an LLM, as the potential answer is easier to classify.)