Protected: The model guesses. The grammar proves.
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Category: Blog
NLP++ in VS Code: Everything New in Version 3
Version 3 has been the biggest chapter yet for the NLP++ extension for Visual Studio Code. What started as a way to compile and run analyzers has grown into a complete development environment for the NLP++ language — with the kind of code intelligence you’d expect from a first-class programming language. […]
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Using Claude with NLP++
Yes, you can have Claude write NLP++ for you — and no, that doesn’t make it another black box. This step-by-step guide takes you from a blank machine to a working analyzer: install VS Code, add the NLP++ extension, run Claude Code, and use the built-in prompts to generate real, glass-box code. What you keep is deterministic, auditable, and runs on its own — no model required.
8 Weeks with Claude and NLP++
I spent the last 8 weeks using Claude to eventually replace Claude. Ironic…
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Every Way to Run an NLP++ Analyzer
One of the strengths of NLP++ is that once you’ve written an analyzer in the VisualText VS Code extension, you are not locked into a single way of running it. The same glass-box, 100% rule-based analyzer can be driven from Python, from Node.js, from TypeScript, or straight from the command […]
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Inside Version 3 of the VisualText NLP Engine: Compiled, Cloud-Built, on npm — and AI-Assisted
For most of its life, the NLP++ engine has been a powerful tool for writing rule-based analyzers — explainable, glass-box, and deterministic natural-language processing you can actually read, debug, and reproduce exactly. It has run on Windows, Linux, and macOS for years. What Version 3 changes is how you build, […]
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NLP++ Version 3: Compiled Analyzers, Faster Execution, and Protected Code
Compiling NLP++ analyzers is now a single click. Version 3 brings native-code speed and lets you deploy analyzers without exposing your source.
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Why NLP++ Is the Only Technology That Can Ultimately Replace LLMs
LLMs guess. NLP++ understands. And that difference is exactly why NLP++ is the only technology positioned to eventually replace large language models in real‑world text processing. LLMs are probabilistic black boxes. They don’t know anything; they predict. They require teaming — layers of prompts, validators, guardrails, and secondary models — […]
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