Search Results for "bash scripts book"
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Showing 3 open source projects for "bash scripts book"
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AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Outgrown Windows Task Scheduler?Windows Task Scheduler wasn't built for complex, cross-platform automation. Get a free diagnostic that shows exactly where things are failing and provides remediation recommendations. Interactive HTML report delivered in minutes.
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uweb browser: unlimited power
minimal suckless android web browser with unlimited power
... - Customizable: user-defined menus, (new) buttons and gestures for user agents, bookmarklets, url services, shell commands, internal functionality links and text processing etc. - Convenient: book/dictionary/txt/command line/app can be search engine. - Tiny: less than 200k - Fast: run fast, even with thousands of user provided css/scripts - Efficient: less touches, one click to reach any number of search engines without repeated input; automate online services. - URL bar command line support ("!" and .js files as commands)... -
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llm-chain
Rust crate for building chains in large language models
...Incorporates support for Stanford's Alpaca models, expanding the range of available language models for advanced AI applications. Enhance your AI agents' capabilities by giving them access to various tools, such as running Bash commands, executing Python scripts, or performing web searches. -
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Python Machine Learning
The "Python Machine Learning (2nd edition)" book code repository
This repository accompanies the well-known textbook “Python Machine Learning, 2nd Edition” by Sebastian Raschka and Vahid Mirjalili, serving as a complete codebase of examples, notebooks, scripts and supporting materials for the book. It covers a wide range of topics including supervised learning, unsupervised learning, dimensionality reduction, model evaluation, deep learning with TensorFlow, and embedding models into web apps. Each chapter has Jupyter notebooks and Python scripts that replicate the examples in the book, allowing readers to run, inspect, and tweak code directly as they follow material. ...
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