Browserbase provides a programmable browser platform that allows developers to automate complex online tasks using code. It offers features like advanced debugging, session recording, a proxy supernetwork, and bot detection avoidance to streamline web automation for compatibility with popular tools like Puppeteer, Playwright, and Selenium. The startup aims to serve as a key building block in an emerging AI software stack.
“It’s not going to be just developers writing this code, it’s going to be large language models generating this code longer term,” Klein explains. “So it’s both developers and LLMs controlling these web browsers to go out and automate the daily tasks we do online.
Rundeck is an open source automation service that allows users to easily run automation tasks across a set of nodes. This repository contains the source code for Rundeck, with features such as a web console, command line tools, and a WebAPI. It is built with Gradle and requires Java 1.8 and NodeJs 16. Documentation, development guides, and issue tracker are available.
OliveTin is a web-based interface that provides safe and simple access to predefined shell commands. It offers a responsive, touch-friendly UI, dark mode, accessibility, container integration, and more. It allows you to run complex commands, give access to commands to less technical people, and simplify command execution on various devices.
This article discusses the author's experience using ChatGPT to improve their understanding and proficiency in using Linux. Topics covered include navigating complex Linux concepts, understanding Linux commands, troubleshooting errors, automating tasks, and more.
Hallux.ai provides open-source solutions leveraging Large Language Models (LLMs) to streamline operations and enhance productivity for Production Engineers, SRE, and DevOps. Offering cutting-edge CLI tools for Linux and MacOS, they automate workflows, accelerate root cause analysis, empower self-sufficiency, and optimize daily tasks.
• Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
The paper proposes a two-phase framework called TnT-LLM to automate the process of end-to-end label generation and assignment for text mining using large language models, where LLMs produce and refine a label taxonomy iteratively using a zero-shot, multi-stage reasoning approach, and are used as data labelers to yield training samples for lightweight supervised classifiers. The framework is applied to the analysis of user intent and conversational domain for Bing Copilot, achieving accurate and relevant label taxonomies and a favorable balance between accuracy and efficiency for classification at scale.
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