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Linus Torvalds | Changed the World Using Linux & Git

לפני שנה 8:33

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תוכן מסופק על ידי Kabir. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Kabir או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

A deep dive at Linus Torvalds biography of the Finnish-American software engineer, most notable for his creation of the Linux kernel. We cover Torvald’s early life and education, contributions to open-source software development, personal life and beliefs, and awards and accolades. We also explore the origin of "Linux", Torvalds' authority in the open-source community, and his other software projects, such as the version control system Git.

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Linus Torvalds | Changed the World Using Linux & Git

Kabir's Tech Dives

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תוכן מסופק על ידי Kabir. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Kabir או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

A deep dive at Linus Torvalds biography of the Finnish-American software engineer, most notable for his creation of the Linux kernel. We cover Torvald’s early life and education, contributions to open-source software development, personal life and beliefs, and awards and accolades. We also explore the origin of "Linux", Torvalds' authority in the open-source community, and his other software projects, such as the version control system Git.

Support the show

Podcast:
https://kabir.buzzsprout.com
YouTube:
https://www.youtube.com/@kabirtechdives
Please subscribe and share.

… continue reading

240 פרקים

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🎬 One-Minute Video Generation via Test-Time Transformer Training 14:55

לפני 24 hours14:55

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Researchers introduced Test-Time Training (TTT) layers to enhance the ability of pre-trained Diffusion Transformers to generate longer, more complex videos from text. These novel layers, inspired by meta-learning, allow the model's hidden states to adapt during the video generation process. To validate their approach, they created a dataset of annotated Tom and Jerry cartoons for training and evaluation. Their model, incorporating TTT layers, outperformed existing methods in generating coherent, minute-long videos with multi-scene stories and dynamic motion, as judged by human evaluators. While promising, the generated videos still exhibit some artifacts, and the method's efficiency could be improved. The study demonstrates a step forward in creating longer, story-driven videos from textual descriptions. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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The Next Token and Beyond: Unraveling the LLM Enigma 19:27

לפני יומיים19:27

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Yes, I can certainly provide a long and detailed elaboration on the topics covered in the sources, particularly focusing on LLM-generated text detection and the nature of LLMs themselves. The emergence of powerful Large Language Models (LLMs) has led to a significant increase in text generation capabilities, making it challenging to distinguish between human-written and machine-generated content. This has consequently created a pressing need for effective LLM-generated text detection . The necessity for this detection arises from several critical concerns, as outlined in the survey. These include the potential for misuse of LLMs in spreading disinformation, facilitating online fraudulent schemes, producing social media spam, and enabling academic dishonesty. Furthermore, LLMs can be susceptible to fabrications and reliance on outdated information, which can lead to the propagation of erroneous knowledge and the undermining of technical expertise. The increasing role of LLMs in data generation for AI research also raises concerns about the recursive use of LLM-generated text, potentially degrading the quality and diversity of future models. Therefore, the ability to discern LLM-generated text is crucial for maintaining trust in information, safeguarding various societal domains, and ensuring the integrity of AI research and development. To address this need, the survey provides clear definitions for human-written text and LLM-generated text . Human-written text is characterized as text crafted by individuals to express thoughts, emotions, and viewpoints, reflecting personal knowledge, cultural context, and emotional disposition. This includes a wide range of human expression, such as articles, poems, and reviews. In contrast, LLM-generated text is defined as cohesive, grammatically sound, and pertinent content produced by LLMs trained on extensive datasets using NLP techniques and machine learning methodologies. The quality and fidelity of this generated text are typically dependent on the model's scale and the diversity of its training data. Table 1 further illustrates the subtlety of distinguishing between these two types of text, noting that even when LLMs fabricate facts, the output often lacks intuitively discernible differences from human writing. The process by which LLMs generate text involves sequentially constructing the output, with the quality being intrinsically linked to the chosen decoding strategy . Given a prompt, the model calculates a probability distribution over its vocabulary, and the next word ($y_t$) is sampled from this distribution. The survey highlights several predominant decoding techniques: Greedy search selects the token with the highest probability at each step, which can lead to repetitive and less diverse text . Beam search considers multiple high-probability sequences (beams), potentially improving quality but still prone to repetition . Top-k sampling randomly samples from the top $k$ most probable tokens, increasing diversity but risking incoherence if less relevant tokens are included . Top-p sampling (nucleus sampling) dynamically selects a subset of tokens based on a cumulative probability threshold $p$, aiming for a balance between coherence and diversity . These decoding strategies demonstrate that LLM text generation is not a deterministic process but involves probabilistic selection and strategic c Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🤖 AI and Machine Learning: A Multi-Source Overview 17:05

לפני 3 ימים17:05

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This episode provides a comprehensive exploration into the realm of Artificial Intelligence (AI) and Machine Learning (ML), specifically within the context of educational environments. At its core, AI is defined as the simulation of human intelligence processes by machines, particularly computer systems [from prior conversation]. This encompasses various capabilities such as learning, reasoning, problem-solving, perception, and language understanding [from prior conversation]. Machine Learning, a significant subfield of AI, empowers computer systems with the ability to learn and improve their performance on a specific task over time without being explicitly programmed [from prior conversation]. This learning occurs through the analysis of data, allowing machines to identify patterns, make predictions, or make decisions [from prior conversation]. The discussion highlights the growing relevance and integration of AI technologies within K-12 and library education [from prior conversation]. Recognizing this shift, the Wisconsin Department of Public Instruction has developed guidance to support K-12 educators, librarians, students, and administrators in navigating and responsibly leveraging these powerful technologies [from prior conversation]. This guidance aims to foster a thoughtful and ethical approach to AI adoption in educational settings [from prior conversation]. Several key goals underpin this guidance. One crucial objective is the development of policies for the ethical use of AI [from prior conversation]. This involves considering the moral principles and systems that govern behavior in the context of AI applications. Ensuring the privacy of data is another paramount concern [from prior conversation]. As AI systems often rely on data, safeguarding personally identifiable information becomes critical. The guidance also strongly advocates for a human-centered approach to AI, encapsulated by the "H > AI > H" mnemonic [from prior conversation, 104]. This simple yet effective tool, borrowed from Washington State's AI guidance (2024), serves as a reminder that responsible AI use begins with a carefully crafted human prompt to elicit a relevant AI response. Subsequently, the information gathered from AI should be thoroughly examined by humans before being implemented in practice. This three-part concept emphasizes the crucial element of human oversight in all AI interactions. Furthermore, maintaining a human-centered approach is essential to ensure that AI serves as a complement to, rather than a replacement for, the interpersonal connections vital for social emotional learning (SEL) development. The episode further delves into practical applications of AI within education, showcasing projects designed to cultivate an understanding of AI concepts across diverse subject areas and grade levels [from prior conversation]. These hands-on AI projects aim to empower students to understand, use, and potentially even create AI technologies. Topics such as data bias, where AI systems can reflect and amplify biases present in the data they are trained on, are likely explored [from prior conversation]. Recommender systems, which utilize AI to suggest items or content based on user preferences, might also be examined [from prior conversation]. Beyond specific applications, the broader societal impact of AI is a significant area of focus, prompting students to consider the far-reaching consequences of these technologies [from prior conversation, 117]. Framewor Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🤖 AI Trends and Innovations for 2025 17:52

לפני 4 ימים17:52

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This episode explores the anticipated trajectory of artificial intelligence in 2025, highlighting key trends impacting various sectors. AI agents , capable of autonomous reasoning and action, are a prominent focus across multiple sources. Advancements in multimodal AI , processing diverse data types, and the growth of open-source and more minor, more efficient AI models are also emphasized. The increasing integration of AI into everyday applications and workplaces , alongside its potential to accelerate scientific research and transform industries like healthcare and supply chains, is frequently discussed. Furthermore, the significance of responsible AI development , including measurement, customization, and the need for broader regulation and ethical considerations , is underscored. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🥊 AI Giants Compete for College Students 9:06

לפני 5 ימים9:06

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OpenAI and Anthropic are actively competing to become the primary AI tool for college students. Both companies have recently unveiled initiatives aimed at higher education, with Anthropic introducing Claude for Education and OpenAI making ChatGPT Plus temporarily free for university students in the U.S. and Canada. Anthropic's approach includes "Learning mode" with Socratic questioning to foster critical thinking, while OpenAI highlights advanced features for academic tasks. This simultaneous push underscores the high value placed on college students as the next generation of AI users. Ultimately, this competition seeks to establish each company's AI as the default choice within academia. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🤖 Therabot: AI Chatbot Shows Mental Health Therapy Benefits 13:34

לפני 6 ימים13:34

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Dartmouth researchers conducted a clinical trial of their AI-powered therapy chatbot, Therabot, and found significant mental health improvements in participants with depression, anxiety, and eating disorder risks. The study showed symptom reductions comparable to traditional therapy, with participants reporting trust and connection with the AI. These findings suggest that AI therapy could increase access to mental health support, especially for those lacking regular care. Researchers emphasize that while promising, AI therapy requires clinician oversight to ensure safety and efficacy. The Therabot trial indicates the potential for AI to offer scalable and personalized mental health assistance. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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📉 Microsoft Adjusts AI Data Center Growth Amid New Trends 12:52

לפני 7 ימים12:52

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12:52

Microsoft is reportedly scaling back its ambitious AI data center expansion plans. This decision follows the emergence of new, more cost-effective AI model development methods, particularly from Chinese companies. These methods demonstrate that advanced AI can be achieved without the massive computing infrastructure initially anticipated. Consequently, Microsoft has paused or delayed several planned data center projects across multiple countries and U.S. states. This adjustment suggests a potential shift in the AI landscape, where expensive, large-scale data centers might not be the inevitable future. Microsoft's spokesperson acknowledged these changes as a demonstration of their strategy's flexibility in response to evolving AI demands. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🚀 Efficient and Portable Mixture-of-Experts Communication 16:59

לפני 8 ימים16:59

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16:59

A team of AI researchers has developed a new open-source library to enhance the communication efficiency of Mixture-of-Experts (MoE) models in distributed GPU environments. This library focuses on improving performance and portability compared to existing methods by utilizing GPU-initiated communication and overlapping computation with network transfers. Their implementation achieves significantly faster communication speeds on both single and multi-node configurations while maintaining broad compatibility across different network hardware through the use of minimal NVSHMEM primitives. While not the absolute fastest in specialized scenarios, it presents a robust and flexible solution for deploying large-scale MoE models. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🤝 Vana: User-Owned AI Models from Decentralized Data 11:09

לפני 8 ימים11:09

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11:09

Vana, a decentralized platform originating from an MIT project, aims to shift control of data used for AI training back to individual users. Frustrated by the current model where tech companies profit from user data, Vana allows individuals to upload their information and collectively decide how it's used to develop AI. Users who contribute data gain ownership stakes in the resulting AI models, receiving proportional rewards when those models are utilized. This approach fosters a user-owned network where individuals can pool their data, even across different platforms, to create more powerful and personalized AI applications while maintaining privacy. By enabling users to benefit from the AI they help create, Vana seeks to democratize AI development and break down the data silos of large tech companies. This innovative system has already attracted over a million users and facilitated the creation of numerous user-governed data pools for AI model training. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🤖 AI and Copyright: US Copyright Office Report 20:47

לפני 10 ימים20:47

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20:47

In a January 2025 report, the U.S. Copyright Office addresses the copyrightability of works created using artificial intelligence. This second part of a broader study examines the level of human contribution necessary for AI-generated outputs to receive copyright protection in the United States. The report analyzes public feedback, legal precedents, and international approaches to conclude that current copyright law, requiring human authorship and original expression, can address AI-related issues without legislative changes. It clarifies that while AI can be a tool assisting human creativity, purely AI-generated content lacking sufficient human control or input is not copyrightable. However, humans can obtain copyright for their original contributions within AI-generated works, such as creative prompts, expressive inputs that are retained, and significant modifications or arrangements of AI outputs. The Copyright Office emphasizes that copyright aims to protect human creativity and will continue monitoring technological advancements. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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Unleashing Local AI on Your Mac Studio - From Ollama to DeepSeek 14:11

לפני 11 ימים14:11

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Are you intrigued by the power of AI but concerned about privacy or cloud costs? In this episode, dive into the exciting world of running Large Language Models (LLMs) directly on your Mac, iPhone, and iPad! We'll explore how tools like Ollama , an open-source platform, make local LLM deployment more effortless than ever, allowing you to run models like Llama 3 on your machine. But that's not all! We'll also delve into Private LLM and its support for the high-performance DeepSeek R1 Distill models, optimized for local use with a focus on reasoning, coding, and mathematical capabilities. Learn how to run these cutting-edge AI models offline on your Apple devices, ensuring complete privacy. We’ll cover the hardware requirements for different DeepSeek R1 Distill models on iOS and macOS devices, from the lightweight 8B models to the mighty 70B parameter giant. Understand the unique reasoning capabilities of DeepSeek R1 and potential applications in scientific research, education, and software development Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🤖 Agentic AI Courses and Learning Resources 15:05

לפני 13 ימים15:05

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This episode provides information about agentic AI and AI agent courses available in 2025. The courses cover topics like AI fundamentals, building AI agents, prompt engineering, and strategic implementation, catering to diverse skill levels and goals, with some being free and others requiring payment or subscription. Additionally, one source includes a report on the financial implications of generative AI for a specific company. At the same time, another offers a guide to resources and ethical considerations related to agentic AI. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🎭 DreamActor-M1: Hybrid Guided Holistic Human Image Animation 19:21

לפני 14 ימים19:21

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19:21

This episode is also about a research paper introducing DreamActor-M1, a new realistic human image animation framework. This DiT-based method utilizes hybrid guidance combining facial representations, 3D head spheres, and body skeletons for fine-grained control and expressive motions. It employs a progressive training strategy to handle diverse scales and poses and introduces complementary appearance guidance for long-term temporal consistency, outperforming existing animation techniques. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🤖 DreamActor-M1: Human Image Animation 14:53

לפני 14 ימים14:53

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DreamActor-M1 is a new framework for animating human images based on a diffusion transformer, utilizing a hybrid guidance system. This approach enables more precise control over the entire body, adapts to different image scales, and maintains consistent movement over time. The system uses a combination of facial representations, 3D head models, and body skeletons to guide motion, and it learns from diverse data featuring various resolutions and body poses. By integrating motion patterns with visual references, DreamActor-M1 generates expressive and realistic human animations, outperforming existing methods in areas like fine-grained motion, identity preservation, and temporal coherence across portrait, upper-body, and full-body scenarios. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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🔬 On the Biology of a Large Language Model 22:31

לפני 20 ימים22:31

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22:31

Researchers used a novel "circuit tracing" method to explore how Claude 3.5 Haiku works internally. They mapped out how the model handles tasks like reasoning, poetry, translation, and math, identifying key features and how they interact. The study reveals complex strategies like planning and explores behaviors like hallucinations and refusals. Their findings offer new insights into how large models compute, aiming to make AI more interpretable and safer. Send us a text Support the show Podcast: https://kabir.buzzsprout.com YouTube: https://www.youtube.com/@kabirtechdives Please subscribe and share.…

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