Archive: 2026/01 - Page 2
Differential Privacy in Large Language Model Training: Benefits and Tradeoffs
Differential privacy adds mathematically provable privacy to LLM training by injecting noise into gradients. It prevents data memorization and meets GDPR/HIPAA standards, but slows training and reduces accuracy. Learn the tradeoffs and how to implement it.
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When to Transition from Vibe-Coded MVPs to Production Engineering
Vibe coding gets you to your first users fast, but it collapses under real traffic. Learn the three hard signals that tell you it’s time to stop coding by feel and start building for scale - before it’s too late.
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Large Language Models: Core Mechanisms and Capabilities Explained
Large language models power today’s AI assistants by using transformer architecture and attention mechanisms to process text. Learn how they work, what they can and can’t do, and why size isn’t everything.
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Multimodal Transformer Foundations: Aligning Text, Image, Audio, and Video Embeddings
Multimodal transformers align text, images, audio, and video into a shared embedding space, enabling cross-modal search, captioning, and reasoning. Learn how VATT and similar models work, their real-world performance, and why adoption is still limited.
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