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BNF Bank's Gone Wrong System Update

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Introduction In March 2025, Maltese BNF Bank plc (€1.28 billion in assets, 40,000 customers) initiated a critical IT modernization project involving its core banking systems, digital channels, a transition from VISA to Mastercard, and an account number change that also modified customers’ IBANs. However, the execution of this digital transformation took a decidedly calamitous turn. What was intended to be a seamless transition devolved into a textbook example of how a major IT upgrade can go spectacularly wrong. It checked all possible boxes in the worst ways: C-Suite handling of a disastrous outcome, Customer Relations, GDPR compliance, Security, Operations and, ultimately, failure to provide an improved solution for customers. The Plan It began with multiple notifications to customers that the Bank’s ATM network would be unavailable between 29th March 2025 and 1st April 2025. The downtime was planned to minimize customer impact since 30/03 was a Saturday and 31/03 was a Malt...
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AI Prompt Engineering - Use Code not Words

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AI language models don’t actually reason in a human sense. For those interested in how these systems are trained, I recommend checking out Demystifying LLMs with Andrej Karpathy .   The Token Challenge When processing text, language models work with “tokens” rather than complete words. The relationship between words and tokens isn’t always one-to-one. For instance, the term “LLM” gets split into two separate tokens in the paragraph below. Similarly, longer or unusual strings can be divided into numerous tokens. The word “ SuperCaliFragilisticExpialiDociouc ” is broken down by GPT-4o into 11 distinct tokens. It’s important to understand that AI responses are generated probabilistically, one token at a time, with deliberate randomness incorporated. This explains why asking the same question multiple times often yields different answers. These fundamental characteristics create significant constraints when AI attempts text analysis tasks. For example, until recently, many langu...
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Demystifying LLMs with Andrej Karpathy

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The emergence of Large Language Models (LLMs) represents a pivotal advancement in artificial intelligence, transforming multiple industries. Andrej Karpathy’s presentation, “ Deep Dive into LLMs like ChatGPT ”, offers an accessible yet comprehensive exploration of these models. As former Director of AI at Tesla and a founding member of OpenAI, Karpathy breaks down complex concepts for audiences regardless of technical background.  While most generative AI training focuses on prompt engineering to generate specific content, this only scratches the surface of how LLMs truly function.  Core LLM Development Process  LLMs are developed through several critical stages:  Data Acquisition and Preparation : Models are trained on massive datasets collected from internet sources. This extensive collection enables the LLM to learn statistical patterns in human language.  Data Cleaning : Internet-sourced data contains significant noise—duplicates, spam, and low-qual...
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