Knowledge Base
Explore articles on deep learning, functional programming and AI agents.
Soft Actor-Critic (SAC)
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Proximal Policy Optimization (PPO)
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PPO vs. SAC: When to Use Which?
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RL Fundamentals: MDPs, Reward, Policy
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Actor-Critic Architecture
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From Q-Learning to Policy Gradients
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Can AI Take Our Jobs?
A personal reflection on why artificial intelligence changes work — but does not replace the people who do it.
Spec First Komponenten
Eine Plattform, auf der Komponenten als framework-unabhängige Spezifikationen geteilt werden – der AI-Agent wird zum Compiler ins Ziel-Framework. (Vormals als `flowcation` geführt; das Projekt wurde …
Reinforcement Learning for Code Agents
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What Does "Self-Learning" Mean for AI Agents?
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Memory & Experience Replay for Agents
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Automated Tests as Reward Signal
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kb: The Knowledge Pipeline for Agents
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himi-ai: Reading Medical Aid Contracts Automatically
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Beuys: Every Agent is an Artist
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Sentiment & Content Insights with Small LLMs
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Engagement Prediction with Classical Models
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Click-Through-Rate (CTR) Prediction
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Predictive Marketing – What Can Actually Be Predicted?
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A/B Testing & Uplift Modelling
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Functional Programming with Python – The Fundamentals
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Functor, Applicative, Monad – Category Theory for Python Developers
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Why FP and AI Agents Are a Dream Team
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Shifting Complexity – From if/else Chains to Functional Composition
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Algebraic Data Types & Pattern Matching in Python
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Category Theory Basics for AI Agents
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Monads as Agent Protocol
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Why Functional Programming Is the Ideal Foundation for AI Agents
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Structured Instead of Vibe-Coding: Deterministic Practice in Nondeterministic Systems
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FP Patterns for Agent Systems
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