# tasks Published articles for tasks. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## OpenAI research examines how workers incorporate AI tasks beyond their usual occupations DevFeed: [OpenAI research examines how workers incorporate AI tasks beyond their usual occupations]() Original publisher: [Read original article]() Published: 2026-09-16T09:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [ChatGPT]() Tags: [ai](), [chatgpt](), [global-affairs](), [openai](), [report](), [research](), [tasks](), [work](), [workflow]() ### AI overview OpenAI Economic Research analyzes more than 1.5 million work-related ChatGPT messages to examine whether workers repeatedly use AI for tasks outside their usual occupations. The findings suggest that some cross-occupation tasks become recurring parts of workers' AI use and that work design matters alongside access to AI tools. ### Source excerpt New OpenAI Economic Research shows how workers use AI beyond traditional roles and which new activities become recurring parts of their work. ## Build Your Own AI-Powered Slack Bot with the Laravel AI SDK DevFeed: [Build Your Own AI-Powered Slack Bot with the Laravel AI SDK]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-08-31T12:59:25Z Content type: tutorial Language: en Sources: [freek.dev - all blogposts]() Topics: [Laravel](), [Artificial Intelligence](), [SDK](), [Slack]() Tags: [ai](), [build](), [knowledge-base](), [laravel](), [php](), [providers](), [sdk](), [slack](), [spatie](), [tasks](), [tools]() ### AI overview A tutorial on building an AI-powered Slack bot with the Laravel AI SDK. The bot can switch AI providers, answer questions, execute tasks, and retrieve information from a controlled knowledge base. ### Source excerpt In this article, we'll build an AI-powered Slack bot with the Laravel AI SDK. Unlike tools such as Claude Tag, it can switch AI providers, answer questions, execute tasks, and retrieve information from a knowledge base you control. Read more ## How to Use Codex for Work: My Complete System at OpenAI | Jason Liu DevFeed: [How to Use Codex for Work: My Complete System at OpenAI | Jason Liu]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-07-26T13:05:16Z Content type: tutorial Language: en Sources: [Behind the Craft]() Topics: [codex](), [email](), [Slack](), [OpenAI](), [sessions](), [Learning]() Tags: [codex](), [email](), [how-to](), [openai](), [sessions](), [slack](), [tasks](), [work]() ### AI overview The article describes how Jason Liu uses Codex throughout his workday to manage Slack and email, learn from past sessions, and run long tasks with verifiable goals. ### Source excerpt How Jason uses Codex throughout his workday for managing Slack and email, learning from past sessions, and running long tasks with verifiable goals ## What is Ansible? DevFeed: [What is Ansible?]() Original publisher: [Read original article]() Author: Roger Perkin Published: 2026-07-23T18:25:09Z Content type: tutorial Language: en Sources: [Roger Perkin Network Automation Consultant]() Topics: [Ansible](), [Automation](), [configuration](), [Network](), [Open Source](), [ssh](), [YAML](), [Server]() Tags: [ansible](), [applications](), [automation](), [configuration](), [faq](), [network](), [open-source](), [servers](), [ssh](), [tasks](), [yaml]() ### AI overview Ansible is an open-source, agentless IT automation tool for configuring systems, deploying applications, and managing network devices. It uses human-readable YAML playbooks and connects through SSH to automate repetitive changes across routers, switches, and servers. ### Source excerpt Ansible is an open-source IT automation tool that configures systems, deploys applications, and manages network devices using simple, human-readable YAML files called playbooks. It's agentless, connecting via SSH, and widely used by network engineers to automate repetitive tasks like configuration changes across routers, switches, and servers at scale. ## Why AI Still Requires Human Delegation and Ownership DevFeed: [Why AI Still Requires Human Delegation and Ownership]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-05-09T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Artificial Intelligence](), [Job](), [context](), [round robin]() Tags: [agents](), [ai](), [delegation](), [job](), [ownership](), [tasks]() ### AI overview The article argues that AI can provide more hands to do work but does not eliminate the human responsibility of managing context, tracking tasks, delegating, and taking ownership of problems and domains. ### Source excerpt On AI, mental load, and delegation ## Operation "Date Bait": AI-enabled scam targeting loveseekers DevFeed: [Operation "Date Bait": AI-enabled scam targeting loveseekers]() Original publisher: [Read original article]() Published: 2026-02-01T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [ChatGPT](), [Automation](), [API]() Tags: [ads](), [ai](), [api](), [automation](), [chatgpt](), [engagement](), [openai](), [safety](), [scam](), [social-media](), [structured](), [tasks](), [telegram](), [translation](), [trust]() ### AI overview OpenAI describes Operation "Date Bait," in which accounts likely originating in Cambodia used ChatGPT and API access for a semi-automated romance and task scam targeting Indonesian men. The operation used translated and emotionally manipulative outreach across advertisements, an AI chatbot, Telegram, and dating platforms. ### Source excerpt OpenAI banned accounts that very likely originated in Cambodia and used AI for scam outreach to Indonesian loveseekers, including translation and engagement. ## A human-in-the-loop workflow for OpenCode agents, subagents, and Beads DevFeed: [A human-in-the-loop workflow for OpenCode agents, subagents, and Beads]() Original publisher: [Read original article]() Author: Leonidas Partsas Published: 2026-01-06T14:51:35Z Content type: tutorial Language: en Sources: [le0nidas]() Topics: [configuration](), [context window]() Tags: [agent](), [agentic-coding](), [agents](), [ai](), [beads](), [configuration](), [context-window](), [execution](), [know-your-tools](), [opencode](), [productivity](), [review](), [tasks]() ### AI overview The article describes a personal development workflow using one primary OpenCode agent, one subagent, and Beads to manage planned tasks. The primary agent selects tasks, delegates implementation, requests human review, and commits approved changes, with an inner loop for requested revisions. ### Source excerpt OpenCode's agents configuration and Beads. A match made in heaven. My flow until now Working on a task starts with the Plan agent. I provide an initial prompt of what I want to achieve and the agent responds with a plan. If the plan needs adjustments I ask the agent to update it. When I'm ... Continue reading Pinky and the Brain: my agent/subagent duo -> ## PayPal Releases Agentic Toolkit to Accelerate Commerce DevFeed: [PayPal Releases Agentic Toolkit to Accelerate Commerce]() Original publisher: [Read original article]() Author: PayPal Tech Blog Team Published: 2025-04-14T20:27:50Z Content type: release Language: en Sources: [PayPal Technology]() Topics: [releases](), [SDKs](), [API](), [AI Agent](), [MCP](), [TypeScript](), [vercel ai sdk]() Tags: [agent](), [agentic-ai](), [ai](), [ai-agents](), [api](), [artificial-intelligence](), [model-context-protocol](), [paypal](), [releases](), [sdk](), [tasks](), [typescript](), [workflows]() ### AI overview PayPal introduces the PayPal Agentic Toolkit, a library that helps developers integrate PayPal commerce APIs into AI agent workflows. The toolkit provides pre-built tools for orders, invoices, disputes, shipment tracking, transaction search, and subscriptions, with support for TypeScript and compatibility with MCP servers and Vercel's AI SDK. Python support is described as coming soon. ### Source excerpt The following is a repost from the PayPal Developer Blog. Building on the release of PayPal's MCP servers, PayPal is excited to introduce the PayPal Agentic Toolkit*. This toolkit empowers developers to seamlessly integrate PayPal's comprehensive suite of APIs -- including those for managing orders, invoices, disputes, shipment tracking, transaction search and subscriptions -- into various AI frameworks. With PayPal Agentic Toolkit, developers can now build sophisticated agentic workflows that handle financial operations with intelligence and efficiency. Overview of PayPal Agent Toolkit The PayPal Agentic Toolkit is a library designed to simplify the integration of PayPal's core commerce functionalities into AI agent workflows. By providing a structured and intuitive interface, the toolkit bridges the gap between PayPal's powerful APIs and modern AI agent frameworks, allowing agents to perform tasks such as creating and managing orders, generating and sending invoices, and handling subscription lifecycles. It eliminates the need for developers to manually integrate with API calls and data formatting by offering pre-built tools and abstractions to streamline these interactions. Key Features of the Toolkit Easy integration with PayPal services, including functions that correspond to common actions within the orders, invoices, disputes, shipment tracking, transaction search and subscriptions APIs, eliminating the need to delve deep into the specifics of each API endpoint. Compatibility with popular frameworks and languages, including compatibility with leading AI agent frameworks, such as Model Context Protocol (MCP) servers and Vercel's AI SDK, ensuring broad applicability. The toolkit currently supports the Typescript programming language with Python support coming soon. Extensibility, allowing developers to build upon core PayPal functionalities, and enable use with 3rd party toolkits that allows for complex, multi-step operations tailored to their specific agentic us ## Automating Web Apps with Cron-Triggered Tasks Using Spin and WebAssembly DevFeed: [Automating Web Apps with Cron-Triggered Tasks Using Spin and WebAssembly]() Original publisher: [Read original article]() Author: Tim McCallum Published: 2024-05-22T12:00:00Z Content type: tutorial Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [WebAssembly](), [Web]() Tags: [automate](), [tasks](), [web](), [web-apps](), [webassembly]() ### AI overview This article explains how to automate web apps with time-based events using Spin and WebAssembly, including cron-triggered tasks. ### Source excerpt Automate web apps with time-based events using Spin and WebAssembly, featuring cron-triggered tasks. ## Introducing Setup and Teardown tasks DevFeed: [Introducing Setup and Teardown tasks]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2023-08-18T00:00:00Z Content type: article Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [Data pipelines](), [GPU]() Tags: [airflow](), [blog-post](), [cleanup](), [data-pipelines](), [dependencies](), [setup](), [tasks]() ### AI overview This article introduces setup and teardown tasks in Airflow 2.7 for managing infrastructure around work in data pipelines. It explains their dependency semantics, cleanup behavior, DAG run state handling, and behavior within task groups. ### Source excerpt In data pipelines, commonly we need to create infrastructure resources, like a cluster or GPU nodes in an existing cluster, before doing the actual "work" and delete them after the work is done. Airflow 2.7 adds "setup" and "teardown" tasks to better support this type of pipeline. This blog post aims to highlight the key features so you know what's possible. For full documentation on how to use setup and teardown tasks, see the setup and teardown docs. Why setup and teardown? Before we dig into examples, let me state at high level what setup and teardown bring to the table. More expressive dependencies Before setup and teardown, upstream and downstream relationships could only mean one thing: "this comes before that". With setup and teardown, in effect we can say "this requires that". And what it means in practice is, if you clear your task, and it requires a setup, that setup will be cleared too. And if that setup has a teardown, that will run again as well. Separating the work from the infra Sometimes the part of the dag you care about is not, say, the cleanup task. For example, suppose you have a dag that loads some data and then deletes temp files. As long as the data loads, you want your dag to be marked successful. By default, this is how teardown tasks work; that is, they are ignored when determining dag run state. Simple case A simple example is one setup / teardown pair, and one normal or "work" task. Setups and teardowns are indicated by the up and down arrows, respectively. From that we can see that .create_cluster is a setup task and delete_cluster is a teardown. The link between a setup and a teardown is always dotted to highlight the special relationship. Some things to observe: If create_cluster fails, neither run_query nor delete_cluster will run. If create_cluster succeeds and run_query fails, then delete_cluster will still run. If create_cluster is skipped, run_query and delete_cluster will be skipped By default, if run_query succeeds, and delete_c ## Apache Airflow For Newcomers DevFeed: [Apache Airflow For Newcomers]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2020-08-17T00:00:00Z Content type: tutorial Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [Development]() Tags: [airflow](), [apache-airflow](), [community](), [dependencies](), [python](), [scheduler](), [scheduling](), [tasks](), [workflows]() ### AI overview This tutorial introduces Apache Airflow for newcomers, explaining how to author workflows with Python scripts, represent them as DAGs, define task dependencies, and use the scheduler to execute tasks when dependencies are met. ### Source excerpt Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. A workflow is a sequence of tasks that processes a set of data. You can think of workflow as the path that describes how tasks go from being undone to done. Scheduling, on the other hand, is the process of planning, controlling, and optimizing when a particular task should be done. Authoring Workflow in Apache Airflow. Airflow makes it easy to author workflows using python scripts. A Directed Acyclic Graph (DAG) represents a workflow in Airflow. It is a collection of tasks in a way that shows each task's relationships and dependencies. You can have as many DAGs as you want, and Airflow will execute them according to the task's relationships and dependencies. If task B depends on the successful execution of another task A, it means Airflow will run task A and only run task B after task A. This dependency is very easy to express in Airflow. For example, the above scenario is expressed as task_A >> task_B Also equivalent to task_A.set_downstream(task_B) That helps Airflow to know that it needs to execute task A before task B. Tasks can have far more complex relationships to each other than expressed above and Airflow figures out how and when to execute the tasks following their relationships and dependencies. Before we discuss the architecture of Airflow that makes scheduling, executing, and monitoring of workflow an easy thing, let us discuss the Breeze environment. Breeze Environment The breeze environment is the development environment for Airflow where you can run tests, build images, build documentations and so many other things. There are excellent documentation and video on Breeze environment. Please check them out. You enter the Breeze environment by running the ./breeze script. You can run all the commands mentioned here in the Breeze environment. Scheduler The scheduler is the component that monitors DAGs and triggers those tasks whose dependencies have been met. It watc ## The Art of Automation DevFeed: [The Art of Automation]() Original publisher: [Read original article]() Published: 2020-04-18T13:09:26Z Content type: opinion Language: en Sources: [Jessie Frazelle]() Topics: [Automation](), [Script](), [API]() Tags: [automate](), [automation](), [tasks]() ### AI overview An essay about automating repetitive tasks to save time and improve productivity. It discusses personal automation practices, common patterns in tools such as Apple's Shortcuts, home automation, and IFTTT, and the tradeoff between automation effort and future manual work and maintenance. ### Source excerpt I am unsure if my love of automation comes from a dislike of doing the same thing twice or an overall desire to be more productive and make everything more efficient. Like a lot of programmers, I often ask myself "can this be scripted" when I find myself doing a manual task. I was inspired recently by reading Wolfram's writing on his personal infrastructure for productivity1. I, too, have written about my personal infrastructure2, but not at the level of depth or with the same focus on productivity as Wolfram. There is no time like the present to take another swing at it! Not only do I want to touch on some of the ways I have automated tasks in my life, I also want to spend some time unpacking how common automation patterns are starting to appear in a lot of things people use day to day. Apple's Shortcuts, home automation, and IFTTT make automation patterns available to the masses in a way that is unprecedented. Before diving into the details, let's first answer the question of why. Why automate? Time is one of the most valuable resources in the world. If there was something you could do to free more time for yourself, why wouldn't you? When I automate myself out of a task I transfer the burden of doing said task to some other script, service, API, or a combination of all of these. I, personally, feel at my best and most productive when I am building something, solving a problem, or learning something new. In none of those situations does that include "doing something manual that could otherwise be scripted/automated away." Or when it does... I automate it away. In any circumstance when I find myself in a position to automate something, I will automate it. Especially if I consider the time spent automating the task to be less than any time spent in the future doing the task manually. This is the ultimate pay off: time. I like to think of automation as the following equation. $$ \begin{equation} time \ gained = (time \ doing \ task \ manually) - (time \ to \ automate \ ## Running a P1, P2, P3 Team Prioritization Meeting DevFeed: [Running a P1, P2, P3 Team Prioritization Meeting]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2019-10-31T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Process](), [To-Do]() Tags: [management](), [meetings](), [offsite](), [process](), [startup](), [tasks](), [team](), [time]() ### AI overview This article describes a team prioritization meeting that classifies work as P1, P2, or P3. The team lists its tasks, assigns priority levels, and limits the number of top-level P1 priorities to roughly one per person or fewer. ### Source excerpt Really is anything more scary than prioritization???; Picture taken at Pete's Dueling Piano Bar, Austin, TX; Recommended I am a member of an 8 person team that is: part of a non profit distributed all over the country trying to get started on a new initiative and find our footing; we are about 1 year old (I've only been here for 2 months) just had our first offsite juggling way too much work hence the need to prioritize At the offsite I witnessed a fascinating exercises in setting priorities that my boss, Dave Sifry, orchestrated. I haven't seen prioritization done quite this way in the past so I wanted to write down how this was done. I also made one important contribution during the process Step 1 - Terms There are three types of priorities: P1 - something that you have to do the instant it comes up; drop everything and worry about it P2 - something that you can do on the second day P3 - something that can wait 3 days Step 2 - List What You Do This was a simple process: Put all people in the same room with a shared note space (white board or easel size post it ) Designate a person to write all the things that you do Time bound the exercise to say 20 minutes and try and make sure that each person's tasks are listed Pro Tip 1: Make sure that the person listing the priorities on the white board / easel size post it has decent hand writing. Our initial writer was close to illegible so we had to rewrite them all. Pro Tip 2: Use a different color pen for each person's tasks Step 3 - Apply the Priorities You then go through the tasks and write down P1, P2, P3 next to each task. Inherently you are going to err on the side of P1 which brings us to My Contribution. My Contribution My contribution to this process was actually pretty simple. I asked this question: How many top level, P1, priorities can a team our size support? My boss had a brilliant answer - "maybe one per person, possibly less". I really wasn't trying to be a jerk; just trying to apply my normal engineering ## Threat Modeling Building Blocks DevFeed: [Threat Modeling Building Blocks]() Original publisher: [Read original article]() Author: Adam Published: 2019-09-04T00:00:00Z Content type: article Language: en Sources: [Shostack & Friends Blog]() Topics: [data](), [Requirements]() Tags: [building](), [comparison](), [data-protection](), [models](), [requirements](), [results](), [reuse](), [tasks](), [techniques](), [things]() ### AI overview The article presents threat modeling as a collection of interconnected tasks and discusses research on breaking threat modeling into smaller, assessable building blocks. It highlights comparisons involving privacy and data protection, requirements, STRIDE, CAPEC, CWE, OWASP, data-flow-diagram variants, and CARiSMA extended. ### Source excerpt Threat modeling isn't one task -- its a collection of tasks that build on each other to produce more valuable insights. ## Robust queue job handling in Craft CMS DevFeed: [Robust queue job handling in Craft CMS]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2019-08-03T15:37:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [Content Management System](), [Job](), [async]() Tags: [async](), [avoid](), [cms](), [craft](), [failed](), [here-s](), [how-to](), [independent](), [insights](), [job](), [jobs](), [long-running](), [queue](), [runner](), [stalled](), [tasks](), [uses](), [using]() ### AI overview This tutorial explains how Craft CMS queues long-running tasks and why its default web-request-based queue execution can leave jobs stalled or failed. It discusses using an independent async queue runner. ### Source excerpt Craft CMS uses queue jobs for long-running tasks. Here's how to avoid stalled or failed queue jobs by using an independent async queue runner ## Choosing Checklist Tools for Different Tasks DevFeed: [Choosing Checklist Tools for Different Tasks]() Original publisher: [Read original article]() Author: Eddie Smith Published: 2017-07-25T00:00:00Z Content type: opinion Language: en Sources: [Eddie Smith]() Topics: [App](), [Tool](), [file](), [LaTeX]() Tags: [apple](), [application](), [apps](), [file](), [files](), [latex](), [mac](), [tasks](), [templates](), [tools]() ### AI overview The article argues that no single checklist tool is best for everyone. The appropriate format depends on the task and whether users need to track both unfinished and completed items. It discusses spreadsheets, OmniOutliner, Apple Notes, Drafts, and file-organization methods for different uses. ### Source excerpt Gabe whipped up a great list of checklist tools. My favorite aspect of his post is that there's no clear winner. There shouldn't be. Checklists can come in all forms, and the ideal format depends entirely on the application. For me, checklists make sense when I need to see not only what needs to be done, but also what I've already done. Apps that automatically "vanish" completed tasks fail to do the latter. For me, sometimes there's just no substitute for a spreadsheet for large checklists, especially if each item can have multiple statuses or dimensions. Sometimes adding more columns is way more efficient than adding more tasks (rows). For packing lists, I've tried so many apps, but OmniOutliner is the best for me. It's simple checkbox feature is perfect, and I have several templates I use for different types of trips. Sometimes an Apple Note will suffice, and sometimes I just "x" lines in Drafts for a quick and dirty grocery list. When I'm working with large numbers of LaTeX files on my Mac, I use file colors, prefix schemes, and even moving files from one folder to another to keep what I've processed and what I haven't. Checklists are as old as civilization and are one of the most fundamental ways to augment the human mind, which needs help seeing where it's been and where it needs to go. Everyone can benefit from checklists. Just check out The Checklist Manifesto. ## Python Thread Pool DevFeed: [Python Thread Pool]() Original publisher: [Read original article]() Author: Chris Hager Published: 2016-04-11T00:00:00Z Content type: tutorial Language: en Sources: [Chris Hager]() Topics: [Python](), [Concurrency](), [Concurrent Programming](), [Code]() Tags: [batch](), [code](), [python](), [queue](), [tasks](), [thread](), [threading](), [threads]() ### AI overview This tutorial explains Python thread pools, including how a fixed group of worker threads processes queued tasks concurrently. It discusses ThreadPoolExecutor and an interruptible thread queue implementation for Python 2.x and 3.x. ### Source excerpt A thread pool is a group of pre-instantiated, idle threads which stand ready to be given work. These are often preferred over instantiating new threads for each task when there is a large number of (short) tasks to be done rather than a small number of long ones. Suppose you want do download 1000s of documents from the internet, but only have resources for downloading 50 at a time. The solution is to utilize is a thread pool, spawning a fixed number of threads to download all the URLs from a queue, 50 at a time. ## A Lesson from the Wal-Mart Model DevFeed: [A Lesson from the Wal-Mart Model]() Original publisher: [Read original article]() Author: Map Published: 2008-08-19T10:43:42Z Content type: opinion Language: en Sources: [Craig Kerstiens]() Topics: [service](), [Support]() Tags: [cost](), [leverage](), [service](), [support](), [tasks](), [time](), [work]() ### AI overview The article uses Wal-Mart's low-price business model as an analogy for outsourcing monotonous, tedious tasks to a low-cost service. It argues that managing the overhead effectively can free time and create value, while warning against over-managing the work. ### Source excerpt Many people criticize Wal-Mart for the way they run their business. I personally find no problems with it, as their goal is simply to make prices competitive. If you care about the other details then either A. shop else where or B. donate to those causes you feel should be supported with the money you save. While sure some of these qualms may be justified I'd like to hint at another thought, of why people don't take advantage of the same approaches. You see I recently started using a service, which I'd prefer not to disclose yet that gives me access to completing very monotonous and tedious tasks for very low price. Indeed there is some overhead involved but once you learn to manage it effectively, and that is the key to do it effectively. Because in reality anyone can manage, but the vast majority over manage things, rather than giving them just the right amount of attention. But back to the primary point, the idea of taking tasks that you normally wouldn't do because of their tedious low value nature and getting those completed for a very low cost can become extremely valuable for you. When you start to think out if you had more time to do those tasks hundreds of things probably come to mind, so I've encourage everyone to explore the low cost options for work/support and try to leverage them to your advantage