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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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AIAS Logistica - Un servizzio orribile

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Tradotto dall'articolo in inglese a https://www.alanbonnici.com/2025/04/aias-logistica-horrendous-delivery-story.html Sommario AIAS Logistica è un'azienda di trasporti con sede in Sicilia, Italia, che è stata incaricata di consegnare una cisterna d'acqua da 5000 litri acquistata da Tecnomat a Ragusa. Nonostante accordi chiari per la consegna in date specifiche (16-17 aprile), molteplici conferme e il pagamento anticipato completo, l'azienda ha ripetutamente mancato di rispettare le consegne programmate senza fornire preavviso. Quando finalmente è stato tentato il trasporto, l'autista si è rifiutato di posizionare correttamente l'oggetto nonostante avesse l'attrezzatura adeguata, lasciando la pesante cisterna in mezzo alla strada. Questo articolo descrive l'esperienza frustrante di un cliente con il servizio inaffidabile, la scarsa comunicazione e il comportamento poco professionale di AIAS Logistica, che hanno causato notevoli disagi e difficoltà prati...
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AIAS Logistica - The horrendous delivery story

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Summary AIAS Logistica is a delivery company based in Sicily, Italy that was contracted to deliver a 5000L water cistern purchased from Tecnomat in Ragusa. Despite clear arrangements for delivery on specific dates (April 16-17), multiple confirmations, and full advance payment, the company repeatedly failed to honor scheduled deliveries without providing notice. When delivery was finally attempted, the driver refused to complete proper placement of the item despite having appropriate equipment, leaving the heavy cistern in the middle of the road. This article details a customer’s frustrating experience with AIAS Logistica’s unreliable service, poor communication, and unprofessional conduct that resulted in significant inconvenience and physical hardship.   The Order I ordered a 5000L water cistern from Tecnomat (Ragusa) after confirming I would only be available to take delivery on April 16th or 17th. My contact at Tecnomat assured me this wouldn’t be an issue. On March 15th, I ...
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Comparing Audacity's OpenVINO Whisper Transcription LLMs

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Introduction Several months ago, I posed a question in the r/audacity subreddit regarding the differences between various OpenVINO Whisper Transcription models and their impact on transcription quality. Having received no response, I conducted this comparative study independently. Evaluating the performance of these models across diverse audio content is essential for a comprehensive assessment. This report compares four audio processing models: base , small , medium , and large-v3 . The analysis encompasses scores achieved by each model on ten different audio tracks (labelled Track 1 through Track 10), along with their respective processing durations. The objective of this analysis is to provide a data-driven foundation for assessing each model’s effectiveness, examining their processing efficiency, and identifying the strengths and weaknesses of each model.   Analysis Data All tracks, outputs from the different Audacity models, source code used to generate the scores, and in...
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HowTo Install OpenVINO AI Plug-in in Audacity

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In this How-To, we’re going to explore how to install the OpenVINO AI plug-in for Audacity. The OpenVINO AI plug-in adds additional functionality to Audacity. One feature is music separation, allowing you to split a mono or stereo audio track into individual stems, such as vocals, drums, and other instruments. This AI plug-in also includes noise suppression capabilities, enabling you to remove background noise from audio recordings. Another feature is music generation and continuation. It generates music based on a prompt and settings that you can adjust. The final feature is audio transcription. You can highlight the section of audio you want to transcribe, select OpenVINO's Whisper Transcription option, and Audacity will add a timestamped text track with the transcribed content. The OpenVINO LLM is installed locally on your computer, ensuring that no data is transmitted to third-party companies during processing.   The installation process. You need to have the latest version of ...
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AI Got It Wrong - Comprehension

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We evaluate the ability of different AI systems to comprehend complex narratives. We use a modified version of the nursery rhyme "Jack and Jill," where they ascend a hill and return on their own two feet, unlike the original version where they tumble "head over heels." The story begins with Jack and Jill having 10 apples. However, it introduces additional details about the distribution of apples between them. Notably, Jill also has oranges, and at some point, Jack is said to pick up two shrivelled apples, which ultimately leads to the correct answer. Some AI systems incorrectly interpret the scenario. Most provide insight into their reasoning processes, which can help readers arrive at the correct conclusion by analysing these explanations. An intriguing observation from this exercise is that semantically confusing information can lead AI systems to produce incorrect outputs. For content creators, particularly in corporate environments where AI may be used to summar...
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