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@DrIbrarAhmedAI
How modern AI works when you strip away the demos: next-token prediction, attention, embeddings, vector search, RAG, KV cache, speculative decoding, and agents with MCP. Each article pairs with the full lecture.
This AI lecture walks through "GPT-6 Astra Explained | The Biggest Change Isn’t Intelligence" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
This AI lecture walks through "Kimi K3: 2.8T Parameters, But Only 104B Active Per Token" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
This AI lecture walks through "How ChatGPT Actually Writes: Next Token Prediction Explained" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
This AI lecture walks through "Why AI Models Think Longer Before Answering | Test-Time Compute Explained" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
This AI lecture walks through "Speculative Decoding: Faster LLM Token Generation" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
This AI lecture walks through "Why LLM Prediction Is Compression (Entropy Explained)" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
This AI lecture walks through "How AI Agents Work: Tools, Memory, MCP, Guardrails" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
One query. A million stored vectors. Do we compare against every one? That is exact nearest neighbor, and it is correct, and it is linear. At that scale, latency is the product. A point in space is not a search. This lecture is the store those numbers live in, and the hop that finds the nearest neighbor without reading every row.
This AI lecture walks through "RAG Explained: Why LLM Guesses Without Retrieval" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
This AI lecture walks through "AI Embeddings Explained: Vectors Behind RAG Search" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
A model reads context. But how? Down by the river bank. By the time you reach bank, river has already settled which meaning you intended, and you never had to think about it. Same problem, new sentence. Apple raised its price target. Which Apple? You knew instantly, because the words beside it did the work. The model gets no such help: it starts with exactly the same vector either way.
This AI lecture walks through "How LLMs Work: Next-Token Prediction Explained" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
This AI lecture walks through "RAG + MCP: 5 Production AI Agent Failures" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.
This AI lecture walks through "AI Agent Architecture: What Happens After You Prompt" with the same production focus I use on the channel: what breaks, what the metrics say, and what to change.