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Understand what AI is doing, where it fails, and which common myths waste time.
24 stories (11 articles · 13 videos)
A few good first pieces before you browse the full feed.
6 min readExplain what LLMs do, where they are useful, and when to verify their output before acting on it.
6 min readUse the next-token mental model to write better prompts without implying that models think like people.
7 min readSeparate realistic AI capability from common myths so adoption decisions are calmer and more accurate.
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10 min readBuild a production AI failure-mode register with controls for hallucination, stale context, prompt injection, unsafe tool use, and weak fallbacks.
11 min readEvaluate the implementation pattern, failure modes, and guardrails before building.
10 min readEvaluate the implementation pattern, failure modes, and guardrails before building.
10 min readEvaluate the implementation pattern, failure modes, and guardrails before building.
7 min readTurn the workflow into a small practical experiment with a clear quality check.
8 min readBuild prompts with role, context, task, constraints, examples, and output format instead of relying on one-off wording tricks.
6 min readUnderstand the idea well enough to try it safely in a low-risk setting.
6 min readRecognize hallucination-prone tasks and use verification, search, or source-grounding before relying on specifics.
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