What You'll Learn
- Why hash collisions are mathematically inevitable even with strong algorithms
- Why a possible collision is very different from a meaningful security threat
- How MD5 and SHA-1 went from trusted standards to broken algorithms
- Why modern password storage needs more than a fast hash and a salt
- When hash collisions matter for security and when they are harmless
- How crypto-agility helps systems survive when today’s algorithms eventually fail
What You'll Learn
- Why language models break text into tokens instead of reading whole words
- How subword tokenization balances vocabulary size with the amount of text a model must process
- How Byte Pair Encoding (BPE) learns useful token boundaries from patterns in training data
- Why the same sentence can use very different numbers of tokens across languages, code, and rare words
- How tokenization can cause surprising failures in spelling, letter counting, and unusual inputs
- Why token counts affect context limits, processing efficiency, and the cost of using an LLM
What You'll Learn
- Why an LLM’s context window is not the same thing as memory
- How chat applications create continuity even though the underlying model is stateless
- How attention lets earlier parts of a conversation influence the next token
- Why longer conversations become increasingly expensive for a transformer to process
- Why information can become harder to use even while it remains inside the context window
- How truncation, summarization, retrieval, and KV caching help manage long conversations
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