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Daniel Rothmann provides evidence-based analysis of technical decisions, focusing on software R&D, structured pilots, and building complex systems at scale.

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Technical decisions Software R&D R&D methodology Structured validation pilots Building complex systems

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This article concludes a series on AI-driven audio processing, proposing a listener-processor model inspired by human hearing to improve sound classification accuracy and reduce latency.
The post examines the challenges of applying CNN-based image processing techniques to audio spectrograms, highlighting fundamental differences between visual and auditory data.
The article examines how integrating memory into AI audio processing can enhance sound understanding by utilizing temporal context and neural network architectures.
Learning and building in software development are interconnected processes that should not be separated, as embracing this relationship leads to better project outcomes.
Confidential computing protects sensitive data during processing by creating secure environments, addressing significant security gaps in traditional data protection methods.
The post argues for valuing software investments through adaptability and strategic flexibility, using Real Options Analysis to better capture their true potential.
Navigating uncertainty in software engineering requires a balance of exploration and exploitation, akin to Elsa's journey in 'Frozen 2,' to make informed decisions.
The post advocates for a 'Company as Code' approach to create a dynamic, programmatic representation of organizational structures and policies for improved compliance and efficiency.
Modeling human auditory systems through AI can significantly enhance machine hearing capabilities, leading to more meaningful audio signal processing.
AI advancements in audio processing, particularly through technologies like WaveNet, present both opportunities and challenges, necessitating new approaches tailored to auditory understanding.
The author describes the development and practical application of 'Firm', a tool that enables structured business management through a code-like approach, enhancing productivity and automation.
Measuring prediction uncertainty in machine learning is crucial for critical applications, with Monte Carlo dropout being a practical technique to approximate Bayesian uncertainty.
The AWS outage underscores the risks of EU reliance on US cloud providers, prompting a reevaluation of hosting strategies and the exploration of EU-sovereign alternatives.
Identifying and avoiding anti-patterns in Unity software design can enhance maintainability and streamline development processes.
Seven strategies are outlined for successfully prototyping machine learning models using small datasets, focusing on realistic expectations and innovative data handling techniques.
The post argues against Corporate Agile's inefficiencies and advocates for a return to Basic Agile principles that prioritize adaptability and team autonomy.
The post argues that a generalist approach, as advocated in David Epstein's 'Range,' is more beneficial in complex fields like software engineering than narrow specialization.
Adopting an 'upgrader mindset' helps developers focus on improving existing code and systems while fostering a collaborative team environment.
Vibe coding simplifies software development for novices using AI, but the author finds it frustrating and inadequate for production-level applications.