freeCodeCamp·1yLearn to Use GitHub Actions: a Step-by-Step Guide
GitHub Actions allows developers to automate, build, test, and deploy their applications directly from GitHub. This guide provides step-by-step instructions for creating GitHub Actions using either the GitHub UI or locally via an IDE. It covers key concepts like workflows, events, jobs, and runners, and includes examples for setting up Node.js, Deno environments, zipping files, and deploying static websites to GitHub Pages.
YouTube·1yBuild Everything with AI Agents: Here's How
David Andre demonstrates how to build AI agents even for beginners using n8n, a no-code automation tool. He details the process of setting up triggers, integrating Telegram, and handling both text and voice messages. By adding tools such as Gmail and Google Calendar, he shows how to create powerful AI agents capable of automating various tasks. He also highlights the value of continuous testing and the potential productivity boosts these agents can provide.
Docker·1yMastering Docker and Jenkins: Build Robust CI/CD Pipelines Efficiently
Learn how combining Docker and Jenkins can enhance CI/CD pipelines, ensuring consistent environments and faster deployments. The guide covers setup essentials, pipeline configuration, using plugins, optimizing pipelines, and troubleshooting. Key benefits include uniformity from development to production, scalability, and reduced errors.
Daily Dose of Data Science | Avi Chawla | Substack·1y5 Agentic AI Design Patterns
Explore five agentic AI design patterns that enhance the effectiveness of AI agents through reflection, tool use, reason and act, planning, and multi-agent approaches. Learn how Firecrawl Extract facilitates web scraping by using simple English prompts to extract clean, structured data. Discover additional resources on machine learning techniques and data science provided by Daily Dose of Data Science.
Medium·1y7 AI Apps That Are a Must-Have in 2025
In 2025, several AI apps can significantly boost productivity and efficiency across various tasks. These include HoldSpeak for fast voice-to-text, Vidnoz for creating AI videos quickly, StealthGPT for generating human-like content, Gumloop for workflow automation, Buildpad for product development guidance, Feyman AI for enhancing learning and memorization, and Another Wrapper for building custom AI apps effortlessly.
Pulumi·1yPython for DevOps
Python is highly versatile for both quick fixes and long-term infrastructure in DevOps. It excels at building custom solutions and automating tasks using libraries like Django, Flask, Prometheus, Schedule, and Airflow. Additionally, Python supports network analysis with Scapy, security scanning with Bandit, container management with Docker SDK, and infrastructure as code with Pulumi. Combining these tools allows the efficient creation of dashboards, automations, and complex deployments from a single language.
Hacker News·1yGuten: A tiny newspaper printer
Guten is a customized thermal printer designed to print a tiny newspaper every morning at 7:00 AM. This newspaper includes the user's daily schedule from Google Calendar, a poem, and current news from the New York Times. The author is gauging interest to see if others would consider buying and using it.
AI·1yOut of 120 AI automation workflow, these 4 were the most popular
Rantir.com shared the top four most popular AI automation workflows from 2024. These include an autonomous AI crawler, mass production of YouTube video summaries, Gmail/Outlook AI auto-responders, and an AI-based question and answer system for PDF documents. Rantir is progressing and offering self-hosting options.
Oxylabs·1yBuilding a Real Estate Monitoring System
Alex discusses building a real estate monitoring system, focusing on the types of data that can be extracted from real estate websites, the use cases for the extracted data including price comparisons and market trends, and the challenges faced such as getting fresh data, overcoming anti-bot measures, and scaling the system. He then advises using Oxylabs' Real Estate Scraper API to handle these challenges efficiently.
Daily Open Source Tools·1yOpenSource ai agent maker releases: out of 120 AI automation workflow, these 4 were the most popular
Rantir.com shared their most popular AI workflows of 2024. The top workflows include an autonomous AI crawler for web data extraction, automated YouTube video summary production, an AI auto-responder for emails, and an AI-driven tool to ask questions about a PDF. These workflows showcase diverse applications of AI in improving productivity and efficiency. Rantir also highlighted that their platform can be self-hosted.
Decube·1yIntroducing Decube's Public API
Decube has released its Public API to streamline data governance workflows. The API facilitates bulk management of glossaries, manual lineages, and user groups, enhancing efficiency and scalability. It also ensures full accountability through secure audit logging. Upcoming features include data quality scores and monitor configuration, furthering Decube's mission to empower data teams.
Medium·1yLangGraph AI agents : Building a Dynamic Order Management System : A Step-by-Step Tutorial
Learn how to use LangGraph, a library designed for orchestrating complex workflows with Large Language Models (LLMs), to create a dynamic order management system. This tutorial covers setting up the environment, defining workflow nodes, integrating tools and LLMs, and visualizing and testing the workflow. By following the detailed steps, you can build a system capable of placing or canceling orders based on user queries.
Community Picks·1yAn introduction to Continuous Integration (CI) and Continuous Delivery (CD) pipelines for software testers
Continuous Integration (CI) and Continuous Delivery (CD) pipelines automate the process of building, testing, and deploying code, resulting in faster releases, better code quality, and immediate feedback. Testers, regardless of their automation skills, can significantly contribute to these pipelines by focusing on exploratory and non-functional testing, enhancing test reviews, and improving overall system quality.
Medium·1y5 New AI Tools That Are Starting to Replace Humans
Artificial intelligence tools are stepping into roles traditionally filled by humans, automating tasks, and revolutionizing industries. LangChain 2.0 automates data processing and analysis, MidJourney 6 creates digital art and design, Bolt.New assists in coding, Synthesia PRO generates video content, and DoNotPay AI offers legal support. These innovations enhance efficiency but raise ethical and societal challenges, including workforce displacement and the need for reskilling.
Data Engineer Things·1yWhy I Love Python as Data Engineer
Python is favored by data engineers for its versatility, simplicity, and rich library ecosystem. It excels in both small and large-scale data tasks, making data manipulation and automation easier. Despite some limitations like slower execution speed and memory consumption, its readable code and efficient debugging make it a preferred choice for many. Python integrates well with tools like Apache Spark and libraries for data visualization, adding to its appeal.
The Coding Sloth·1yUsing "My" Programming Skills To Beat An Infinite Game
The author attempts to use their programming skills and AI to automate a game called Infinite Craft, aiming to disprove its claim of being an infinite game. Despite various challenges and setbacks, including issues with debugging and optimizing the automation process, the author persists. They eventually create a working program but ultimately conclude that the game may indeed be infinite.
LangChain·1yHow Minimal Built a Multi-Agent Customer Support System with LangGraph & LangSmith
Minimal is revolutionizing customer support for e-commerce businesses using a multi-agent system within the LangChain ecosystem. The AI agents handle up to 90% of support tickets autonomously by integrating with popular helpdesk tools and performing tasks like order management, significantly enhancing efficiency and customer satisfaction. Key components include the Planner Agent, Research Agents, and Tool-Calling Agent, each specializing in different aspects of support. This modular approach enables scalability, reduces errors, and facilitates customization, making it a robust solution for the evolving needs of e-commerce companies.
RalphNex·1yStupid-Simple AI Call Widget: Convert visitors to customers 🚀
Automaticall.io offers an AI-driven call widget that enhances customer engagement by providing instant call-backs, tailored interactions, and detailed analysis. It's easily embedded on websites, ensuring prompt communication and faster conversions. Additionally, it includes a lead generation engine that uses AI-powered calls to nurture prospects through the sales funnel.
NetBox Labs·1yHow to Automate Proxmox Virtual Machine Configuration with NetBox and Ansible AWX/Tower/Automation Platform (AAP)
NetBox is a popular Network Source of Truth (NSoT) that can be integrated with Proxmox to automate and manage virtual machine (VM) states using Ansible AWX/Tower/Automation Platform (AAP). This integration leverages event-driven automation with webhooks and event rules in NetBox to trigger automated actions in Proxmox using predefined Ansible playbooks. Version 1.1.0 of netbox-proxmox-automation facilitates this setup, requiring a running AWX instance, defined projects, credentials, and job templates. Users must configure necessary scripts and settings to ensure seamless automation.
Getting started with AI·1yChatGPT will now do your tasks for you
OpenAI has introduced a new feature called ChatGPT Tasks, which allows users to automate routine tasks and recurring prompts, making ChatGPT function as a personal AI assistant. It is currently in beta and available only on paid plans. Users can configure it to perform tasks like daily news roundups, market research, and content creation without needing technical skills. While the feature shows great potential, it has limitations such as a maximum of 10 simultaneous tasks and being restricted to web and non-Windows platforms for now. Future updates may include conditional tasks based on specific variables.
The Linux Cast·1yMy Favorite Scripts
Bash scripting can significantly enhance productivity by automating repetitive tasks. The author shares five favorite bash scripts, including those for backups, theme changes in qtile, Git operations, random wallpaper settings, and generating markdown files for podcast episodes. These scripts are tailored to the author's specific workflow but illustrate the broader utility and time-saving benefits of learning bash scripting.
DEV·1yHow to Get Started with Bash Scripting for Automation
Bash scripting is a powerful tool for automation, especially beneficial for cybersecurity tasks. It allows for efficient handling of repetitive tasks, offers flexibility by interacting with various tools, and provides low-level control over the operating system. Basic knowledge of Bash syntax, setting up a Linux environment, creating simple automation scripts, and best practices are essential for effective scripting. Real-world applications include automated network scans, log monitoring, and backup management. Resources like books, online courses, and practice platforms can further enhance your scripting skills.
DEV·1yThe Art of Over Engineering on the Power Platform
Using Power Automate and Power Apps can lead to over-engineering if not considered carefully. Focus on the simplest solutions like SharePoint forms for data gathering and Power BI for data visualization. Assess the entire lifecycle of an application including deployment, support, and scalability. Simpler alternatives like Microsoft Forms and Outlook rules might be more efficient in certain scenarios.
Hacker News·1yUsing uv as your shebang line
Scripts in the ~/bin/ directory can be automated using Python with uv. By using a specific shebang line (#/usr/bin/env -S uv run --script), the script can be run directly from the command line, automatically handling its dependencies when given executable permissions with chmod.
Planet Python·1yWe have been writing Ansible tasks wrong
The author discusses a preference for writing Ansible tasks in a way that resembles Python code due to its procedural clarity. They explore how Ansible’s declarative nature can still benefit from programming language constructs like if-else, loops, and exception handling. Examples are provided to show how tasks can be written more intuitively, although some limitations remain.