In this tutorial, we explore how an intelligent agent can gradually form procedural memory by learning reusable skills directly from its interactions with an environment. We design a minimal yet powerful framework in which skills behave like neural modules: they store action sequences, carry contextual embeddings, and are retrieved by similarity when a new situation …
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Empromptu raises $2M pre-seed to help enterprises build AI apps
Sheena Leven says she learned two important lessons when building her first company, CodeSee. The first lesson was knowing the difference between what businesses need versus what sounds visionary; the second was that the fundamentals always apply, even with new technologies such as AI. “Security, compliance, reliability, quality, those things don’t just go away for enterprise …
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Nvidia Announces ‘You Vote, We Build’ Campaign With a Custom Build Gaming PC Giveaway for Its Fans, and More – IGN India
Nvidia Announces ‘You Vote, We Build’ Campaign With a Custom Build Gaming PC Giveaway for Its Fans, and More IGN India Source link
Over 65? Do this personal trainer’s 5-move routine to build strength in your core and upper body — no gym required
We cover a lot of senior-friendly workouts here at Tom’s Guide, not because we assume older adults can’t do other routines, but because the body naturally changes with age and its needs evolve. Muscle mass typically reduces, joint mobility becomes more limited and core stability can require a little more attention. The good news is, …
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How to Build an Adaptive Meta-Reasoning Agent That Dynamically Chooses Between Fast, Deep, and Tool-Based Thinking Strategies
We begin this tutorial by building a meta-reasoning agent that decides how to think before it thinks. Instead of applying the same reasoning process for every query, we design a system that evaluates complexity, chooses between fast heuristics, deep chain-of-thought reasoning, or tool-based computation, and then adapts its behaviour in real time. By examining each …
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How to Build a Meta-Cognitive AI Agent That Dynamically Adjusts Its Own Reasoning Depth for Efficient Problem Solving
In this tutorial, we build an advanced meta-cognitive control agent that learns how to regulate its own depth of thinking. We treat reasoning as a spectrum, ranging from fast heuristics to deep chain-of-thought to precise tool-like solving, and we train a neural meta-controller to decide which mode to use for each task. By optimizing the …
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