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“Your app isn’t production until the padlock turns green.”
This guide merges the one-liner speed of the quick-start and the step-by-step clarity of the original long-form post.
Follow along and you’ll mint fresh Let’s Encrypt certificates, wire them into Dify’s Nginx, and set-and-forget auto-renewal—all in ~15 minutes.
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“Facts are everywhere; the magic is fetching the right ones before your LLM starts talking.”
This deep‑dive turns that magic into engineering: a turn‑key benchmark + cookbook that lets you measure, compare, and future‑proof every similarity‑search trick you throw at Retrieval‑Augmented Generation (RAG).
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Lexical substitution is one of those NLP tasks that seems simple until you try to do it well. You want a tool that can replace a word in a sentence with a substitute that makes sense, stays grammatically correct, and doesn’t rely on a 60GB language model. 😅
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“If a fact falls in a database and nobody retrieves it, does it make a sound?”
Retrieval‑Augmented Generation (RAG) lives or dies on that first hop—can the system put the right snippets in front of the language model?
In this post we peel back the buzzwords (BM25, EM@5, F1) and show how to turn them into levers you can actually pull.
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“A goal without a plan is just a wish—unless a planner makes it real.”
This guide lifts the lid on the algorithms that tell robots what to do next, one step at a time.
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If you’ve ever written a to-do list—“get key → unlock door → leave house”—
you’ve already built a plan. Classical planning turns that intuition into algorithms that guarantee the sequence really works.
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If you’ve ever said “Everyone in the club must pay dues, therefore
Alice must pay,” you’ve already used first-order logic (FOL).
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If you’ve ever used clues in a murder-mystery board game—
“Mrs Peacock and the Revolver can’t both be innocent!”—
you already reason like a logical agent.
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If you’ve ever solved a Sudoku, arranged seats so rivals don’t sit together,
or packed a suitcase “Tetris-style,” you already think like a CSP solver.
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If you’ve ever played chess, Rock-Paper-Scissors, or even ‘‘Guess Which Hand?’’
you already grasp the soul of adversarial search:
make a move that’s good for you and bad for the other side.
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If you’ve ever tried every twist of a Rubik’s cube,
searched for Wi-Fi by inching your laptop around,
or navigated a dark room with arms outstretched—
congrats! You already know local search, nondeterminism, and partial observability.
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If you’ve ever followed Google Maps or played hide-and-seek,
you already know the heart of agents and search.
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If you can put toys into big and small groups,
or ask “Is it an animal?” when you play Guess Who?,
then you already think like a machine learning model!
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If you can stack toy blocks, you already have the right intuition for the math that powers today’s AI.
This post unpacks the four shapes of numbers—scalars, vectors, matrices, and tensors—and the handful of moves we do with them.
No whiteboard proofs, no scary symbols—just pictures, stories, and runnable code snippets.
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Imagine this: You send a message to a server. That message is completely encrypted—nobody can read it. But the server still knows whether your message is harmful or not. It didn’t decrypt it. It didn’t peek inside. It just… ran a program on the encrypted data. Sounds like magic? That’s Homomorphic Encryption (HE).