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The Practical AI Digest

Mo Bhuiyan via NotebookLM
Machine Learning
Artificial Intelligence
Graph Neural Networks
Generative Adversarial Networks
AI and Machine Learning
Agentic AI
Impact-E Framework
Image Generation
Large Language Models
Data Structures
Data Preparation
Loss Functions
Navigation Systems
Marketing Mix Modeling
Reinforcement Learning
AI Applications
Tool Use
Hyperparameters
Chip Design
Gpt-3

Distilling AI/ML theory into practical insights. One concept at a time. No jargon.

PublishesTwice monthlyEpisodes24Foundeda year ago
Number of ListenersCategory
Technology

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Artwork for The Practical AI Digest

Latest Episodes

A 30B parameter model runs on a MacBook because only 3B parameters fire per token. Mixture of Experts splits memory cost from compute cost, and that changes everything about where AI can run.

90% of enterprises deploy AI agents. Only 23% scale them. The gap is interoperability. Three protocols, MCP, A2A, and ACP, are racing to build the connective tissue before the ecosystem fragments.

Your GPU is not compute-bound. It is memory-bound. The KV cache is eating half your inference budget, and two ICLR 2026 breakthroughs KVTC and TurboQuant are about to change the math entirely.

Models advertise million-token windows but accuracy degrades well before the limit. Three recent studies, the mechanisms behind the rot, and a practitioner playbook for what to do Monday.

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Kula Rao
author associated with the successor paper to Laura
Episode: Efficient Fine-Tuning: Adapting Large Models on a Budget

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How Embeddings and Vector Databases Power Generative AI
Q: How do we extract information from video content effectively?
The extraction process starts with automatic speech recognition to transcribe audio and then key visual frames are identified for further processing.
How Embeddings and Vector Databases Power Generative AI
Q: Let's talk about Retrieval Augmented Generation, or RAG.
RAG is crucial for allowing AI models to pull in current specific information rather than just relying on prior training.

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Frequently Asked Questions About The Practical AI Digest

What is The Practical AI Digest about and what kind of topics does it cover?

Across recent episodes, practical AI topics drive the conversation, with a strong emphasis on production realities for large language models and AI systems. Listeners get actionable guidance on memory economics, deployment pipelines, and observability, including techniques to optimize memory use, per-request KV cache management, and the trade-offs between memory and compute bottlenecks. The show frequently covers LLM operations, retrieval-augmented workflows, prompt engineering, and governance, paired with real-world anecdotes and pragmatic playbooks for teams building or operating AI in production. A notable strength is the clear emphasis on measurable outcomes—reliability, efficiency, and safety—over hype, with discussions ranging from Ti... more

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1. The Daily

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The Practical AI Digest launched a year ago and published 24 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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1. Kula Rao

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