You're Paying for Tokens, Not Thinking
Most teams treat LLM context windows like RAM and wonder why costs explode. Here's what's actually happening and how to fix it.
Inside the algorithms, tools, and systems powering the AI revolution and modern software.
Most teams treat LLM context windows like RAM and wonder why costs explode. Here's what's actually happening and how to fix it.
When AI models give conflicting answers to the same question, something real is happening under the hood. Here's what it actually means.
AI writing tools are getting better at finishing your sentences. That's exactly the problem.
Vector databases find 'nearest neighbors' using distance math, but distance and similarity are not the same thing. Here's where that gap causes real problems.
Prompts influence LLM outputs, but the real controls are baked in long before you type a word. Here's what actually shapes what you get.
A large context window sounds like a simple upgrade. The reality involves quadratic costs, attention decay, and some genuinely surprising tradeoffs.
Heisenbugs disappear when you try to observe them. Here's why they happen and how to actually catch them.
You probably think of embeddings as an AI feature. They're actually becoming foundational infrastructure, quietly running under search, recommendations, caching, and more.
Deleting your account doesn't mean your data disappears. Here's what actually happens to the conversations, fine-tuning data, and model weights you've contributed.
The failure modes are predictable. Here's what actually breaks distributed systems, and what you can do about each one.
Vector similarity feels intuitive until you realize it's not measuring what concepts mean, but how they tend to appear together. That distinction matters more than most engineers admit.
Static analysis, dead code elimination, loop unrolling — your compiler has been making intelligent decisions about your code for decades. Here's what that history tells you about AI.
Shipping a machine learning model isn't like shipping software. The failure modes are different, subtler, and far more expensive to debug.
Prompt engineering feels like a permanent new skill. It isn't. Here's why that's actually the point, and what comes after it.
Every technique AI boosters claim is revolutionary, your compiler has been doing since the Reagan administration. Here's what that actually means.
Deleting your account doesn't delete your data from the model. Here's what actually happens, and what it means for you.
Your carefully engineered prompts are dependencies on a moving target. Treat them like any other brittle infrastructure.
Variable names are free at runtime but expensive in practice. Here's why naming is one of the highest-leverage decisions in software.
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