# Workflow Published articles for Workflow. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## GitHub Actions: правила запуска стали общедоступными, для pull\_request\_target вводят ограничения DevFeed: [GitHub Actions: правила запуска стали общедоступными, для pull\_request\_target вводят ограничения]() Original publisher: [Read original article]() Author: MaxRokatansky (OTUS) Published: 2026-09-18T07:27:15Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [GitHub Actions](), [GitHub](), [API](), [ci]() Tags: [api](), [ci](), [cicd-2622a5c70597](), [github](), [github-actions](), [rest-api](), [tag-070ecaaf0eda](), [tag-431721c35e50](), [tag-7d2878fc3cc1](), [tag-9e079357c819](), [workflow]() ### AI overview GitHub has made workflow execution protections for GitHub Actions generally available. The rules control who can trigger workflows and under which events, support file-specific policies, provide evaluation results in Insights, and can be managed through the REST API. GitHub is also introducing default restrictions for pull_request_target in affected public repositories. ### Source excerpt GitHub вывел в общий доступ защиту запуска рабочих процессов Actions: правила позволяют контролировать, кто и при каких событиях может запускать пайплайны. Отдельное изменение касается pull_request_target, который в публичных репозиториях начнут блокировать по умолчанию. Разобраться в изменениях ## How to Decide How Much Authorship AI Should Have in Software Work DevFeed: [How to Decide How Much Authorship AI Should Have in Software Work]() Original publisher: [Read original article]() Author: Kyle Humphrey Published: 2026-09-17T12:00:32Z Content type: opinion Language: en Sources: [Atomic Object]() Topics: [agentic-engineering](), [Artificial Intelligence](), [implementation](), [Software]() Tags: [agentic](), [agentic-engineering](), [ai](), [artificial-intelligence](), [development-practices](), [engineering](), [implementation](), [ownership](), [software](), [workflow]() ### AI overview This commentary examines how AI-generated meeting summaries, backlog items, and implementation work can introduce vocabulary, decisions, scope, and structure that a team does not recognize or own. It argues that the artifact's purpose should guide how much authorship is delegated to AI, using Curiosity, Craft, and Care as principles for making that decision. ### Source excerpt Atomic's Agentic Engineering for Teams describes how work moves through a product backlog, definition, planning, implementation, and review when agents do much of the building. While working with a small team during a Research, Design, and Planning (RDP) engagement, I started paying attention to a smaller decision inside that process: how much authorship we give [...] The post Use Curiosity, Craft, and Care to Decide What AI Should Write appeared first on Atomic Spin. ## How Cooley is accelerating IPO work with ChatGPT DevFeed: [How Cooley is accelerating IPO work with ChatGPT]() Original publisher: [Read original article]() Published: 2026-09-17T12:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT](), [OpenAI]() Tags: [agentic](), [agents](), [ai](), [chatgpt](), [product](), [technology](), [with](), [workflow]() ### AI overview Cooley built GO Public with ChatGPT Work to support IPO preparation. Its agentic workflow brings together client information, public sources, and curated precedents while defining which tasks agents can perform automatically and where lawyers must review the work. ### Source excerpt Cooley built GO Public with ChatGPT Work to bring intelligence to the IPO process, helping lawyers surface issues earlier and focus judgment where it matters most. ## Anthropic merges Claude Chat and Cowork into one interface DevFeed: [Anthropic merges Claude Chat and Cowork into one interface]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T16:46:36Z Content type: news Language: en Sources: [The New Stack]() Topics: [anthropic](), [Claude]() Tags: [ai-agents](), [ai-infrastructure](), [anthropic](), [chat](), [claude](), [connectors](), [context](), [developer-tools](), [tools](), [workflow]() ### AI overview Anthropic is merging Claude Chat and Cowork into a single interface, allowing conversations to handle simple questions, multi-step projects, connected tools, and background execution. Claude Docs and Claude Slides are launching in beta on paid plans, while Claude Design is moving into conversations. ### Source excerpt Using Claude for anything beyond a quick question has always started with a routing decision to use Chat or Cowork? The post Anthropic bet users were choosing wrong. So it removed the choice. appeared first on The New Stack. ## How 8 AI Skills Automate a Content Workflow and Save 5+ Hours per Week DevFeed: [How 8 AI Skills Automate a Content Workflow and Save 5+ Hours per Week]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-09-16T14:36:57Z Content type: tutorial Language: en Sources: [Behind the Craft]() Topics: [Artificial Intelligence]() Tags: [ai](), [ai-skills](), [automate](), [skills](), [workflow]() ### AI overview The article describes a three-step system that turns 15 manual content-workflow steps into 8 AI skills, reportedly saving more than 5 hours per week. ### Source excerpt How I turned 15 manual steps into 8 AI skills that save me 5+ hours a week, and how you can do the same for your own work ## Test GitHub Actions Locally with Act DevFeed: [Test GitHub Actions Locally with Act]() Original publisher: [Read original article]() Author: Valerie Nielson Published: 2026-09-16T12:00:44Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [GitHub Actions](), [CI/CD](), [Docker](), [Testing](), [unit test]() Tags: [ci-cd](), [developer-tools](), [docker](), [github](), [github-actions](), [pull-request](), [testing](), [unit-test](), [workflow]() ### AI overview This tutorial explains how to test GitHub Actions workflows locally with act. It describes how act uses Docker to reproduce the workflow environment, including environment variables and the file system, and demonstrates testing pull-request workflows and unit tests before merging. ### Source excerpt GitHub Actions are a great way to implement CI/CD into your projects, whether personal or professional. GitHub has a lot of great tools to help with CI/CD, but GitHub Actions in particular is very helpful. However, writing actions can be tedious and a little annoying, especially if you don't know what you're doing. On top [...] The post Test GitHub Actions Locally with Act appeared first on Atomic Spin. ## Babel in Khan Academy's i18n Toolchain DevFeed: [Babel in Khan Academy's i18n Toolchain]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-06-22T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Babel](), [Internationalization (i18n)](), [toolchain](), [es6](), [JavaScript](), [Parser](), [Universal Syntax Tree.](), [Open Source]() Tags: [api](), [architecture](), [babel](), [build](), [build-system](), [code](), [engineering](), [es6](), [eslint](), [frontend](), [javascript](), [news](), [plugin](), [source](), [structure](), [syntax](), [tests](), [toolchain](), [types](), [web-frontend](), [workflow]() ### AI overview Khan Academy describes switching its ES6 and JSX frontend compilation from jstransform to Babel. The article explains a custom Babel plugin for preserving the project's internationalization behavior and discusses matching generated output, line numbers, whitespace, and the existing build chain. ### Source excerpt By Kevin Barabash We've been using ES6 (along with JSX) for sometime at Khan Academy. Right now, we're ... Read more ## 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. ## On-Demand Masked Sessions with Twilio Proxy, Voice and Serverless DevFeed: [On-Demand Masked Sessions with Twilio Proxy, Voice and Serverless]() Original publisher: [Read original article]() Author: Leroy Chan Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [proxy](), [sessions](), [communications](), [Serverless](), [Back end](), [functions](), [callback]() Tags: [backend](), [callback](), [communications](), [developer-insights](), [functions](), [proxy](), [ridesharing](), [serverless](), [sessions](), [state](), [voice](), [workflow]() ### AI overview This tutorial explains how to build an on-demand masked calling system with Twilio Proxy, Twilio Voice, and Twilio Serverless Functions. It uses a reserved Twilio number, IVR-based tracking-code collection, a backend lookup, and a two-bounce workflow with Twilio Sync to create private sessions dynamically when phone numbers cannot be pre-associated. ### Source excerpt Manage marketplace communications without pre-associating numbers. Use Twilio Proxy, Voice, and Serverless to build dynamic, Just-in-Time masked session calls. ## Who gets to define an AI agent's intent? DevFeed: [Who gets to define an AI agent's intent?]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T20:25:55Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [AI Agent](), [Artificial Intelligence]() Tags: [ai](), [ai-agent](), [identity](), [policy](), [scopes](), [tool](), [workflow]() ### AI overview The article explains how to define an AI agent's intent while preserving identity, resource-access controls, and company policy. It argues that the requester should specify the agent's purpose and intended effects, while the organization determines which actions are permitted. Using Airlock examples, it distinguishes task intent from policy decisions such as approval or denial of specific operations. ### Source excerpt An agent's task describes the work to do. Learn how to define useful intent while keeping identity, resource access, and company policy in force. ## Announcing Meta Business Messaging MCP: Set up and manage WhatsApp Business from your AI agent DevFeed: [Announcing Meta Business Messaging MCP: Set up and manage WhatsApp Business from your AI agent]() Original publisher: [Read original article]() Published: 2026-09-15T14:00:49Z Content type: release Language: en Sources: [Facebook Developers]() Topics: [Model Context Protocol](), [MCP Server](), [AI Agent](), [Messaging](), [Meta](), [API]() Tags: [agent](), [ai-agent](), [api](), [mcp](), [mcp-server](), [messaging](), [meta](), [testing](), [whatsapp](), [workflow]() ### AI overview Meta announces Business Messaging MCP, an MCP server that connects AI coding agents to the WhatsApp Business Platform. It supports account and phone-number setup, message templates, webhooks, and test messages within the developer workflow. ### Source excerpt Business Messaging MCP connects your AI coding agent to the WhatsApp Business Platform, so you can create accounts, add numbers, build templates, and send test messages without leaving your workflow. ## How Delphi ships 100 times a day with its Python backend on Vercel DevFeed: [How Delphi ships 100 times a day with its Python backend on Vercel]() Original publisher: [Read original article]() Author: Kevin Sundstrom Published: 2026-09-15T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Python](), [Vercel](), [Back end](), [feature flags](), [Amazon Web Services](), [Docker](), [airflow]() Tags: [aws](), [backend](), [docker](), [feature-flags](), [python](), [vercel](), [workflow](), [workflows]() ### AI overview A Vercel case study describes how Delphi rebuilt its Python backend on Vercel after previously using AWS, ECS, Docker Desktop, and local databases. The move gave the team a shared deployment workflow, faster onboarding, and more than 100 production deploys per day, while Vercel Workflows and Queues support long-running work and background jobs. ### Source excerpt Delphi on Vercel 10 engineers with no dedicated infrastructure role Everyone ships code, including product and design 100+ production deploys a day behind feature flags Delphi builds digital minds. They capture what someone has written, recorded, and taught, so anyone can tap that expertise on demand. Delphi is "a destination where you can find and interact with interesting minds," says founding engineer Spencer Schoeben. Nobody has built a digital mind network before, so Delphi figures out what to build by shipping features and watching how people use them. That only works if the product is fast to change. "If we are going to win, we need to have an excellent developer and agentic experience," Spencer says. "Otherwise, we could not adapt to how fast our user's demands change." Delphi's frontend had run on Vercel since day one. "The frontend has always been easy," Spencer says, "but the backend was a nightmare." Six months ago, the team rebuilt its Python backend on Vercel as well. Today the whole team ships to production 100+ times a day. Building a Python backend that's easy to work onThe backend slowed every change Delphi's backend previously ran on AWS, with ECS, Docker Desktop, and local databases. In the earliest days, the setup worked because the team was small and much of the context lived in people's heads. As Delphi grew, that made onboarding slower than the team wanted: getting a new engineer to their first deploy took a full day of environment setup. Delphi could have fixed that without leaving AWS. But fixing it properly would have meant designing and owning the infrastructure themselves, and the team didn't want to own infrastructure at all. "That is not our current goal as a company," Spencer says. After moving the backend to Vercel, new engineers could get to production much faster, with the same deploy workflow the rest of the team used every day. Workflows and Queues made the move possible When Delphi was founded, Vercel Workflows and Vercel Queues ## Design in KiCad, Quote with AIVON: A Faster Path to PCB Prototyping (Sponsored) DevFeed: [Design in KiCad, Quote with AIVON: A Faster Path to PCB Prototyping (Sponsored)]() Original publisher: [Read original article]() Author: Sponsored Post Published: 2026-09-15T02:00:57Z Content type: article Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Hardware](), [systems]() Tags: [aivon](), [engineering](), [hardware](), [kicad](), [manufacturing](), [pcb](), [plugin](), [prototyping](), [sponsored](), [verification](), [workflow]() ### AI overview AIVON's official KiCad Plugin connects a completed PCB layout to an online quotation form. After design-rule checking, it prepares manufacturing data and reduces manual exporting, zipping, uploading, and form entry during PCB prototyping. ### Source excerpt Finished your PCB layout in KiCad? You don't necessarily need to leave the PCB Editor, export a separate set of manufacturing files, and manually upload them to a PCB manufacturer. AIVON's KiCad Plugin creates a direct path from your PCB design to an online PCB quote. AIVON has integrated an official KiCad plugin that connects a finished PCB layout to the company's online quotation page. After DRC, users can click the plugin in the PCB Editor, automatically prepare manufacturing data, and land on a pre-filled quote form instead of exporting, zipping, and uploading files by hand. Rather than introducing a new plugin framework, AIVON's integration leverages KiCad's existing Plugin and Content Manager ecosystem. For developers who already use KiCad for schematic capture and PCB layout, the integration provides another way to connect the design stage with PCB fabrication without manually repeating every manufacturing-file preparation step. Connecting KiCad PCB Design with [...] The post Design in KiCad, Quote with AIVON: A Faster Path to PCB Prototyping (Sponsored) appeared first on CNX Software - Embedded Systems News. ## Validating multi-agent decisions with Step Functions and Bedrock AgentCore DevFeed: [Validating multi-agent decisions with Step Functions and Bedrock AgentCore]() Original publisher: [Read original article]() Author: Ben Freiberg Published: 2026-09-14T16:47:23Z Content type: article Language: en Sources: [AWS Compute Blog]() Topics: [Amazon Bedrock AgentCore](), [AWS Step Functions](), [Artificial Intelligence](), [Generative AI](), [Automation]() Tags: [ai-agents](), [amazon-bedrock-agentcore](), [audit-trail](), [automation](), [aws-step-functions](), [generative-ai](), [regulatory](), [technical-how-to](), [workflow]() ### AI overview The article presents a pattern for validating multi-agent airline rebooking decisions. Amazon Bedrock AgentCore agents propose options, while AWS Step Functions applies deterministic validation, supports large-scale parallel processing, enables human review, and maintains an execution history for auditing. ### Source excerpt Orchestrating specialized Amazon Bedrock AgentCore agents with AWS Step Functions gives you the reasoning power of generative AI with the guardrails of deterministic validation. Agents propose options, and deterministic code validates them before any action is taken, demonstrated here with an airline rebooking workflow. ## Building Depth: Designing and Developing a 3D Renderer Inside Figma DevFeed: [Building Depth: Designing and Developing a 3D Renderer Inside Figma]() Original publisher: [Read original article]() Author: Aleksei Kipin Published: 2026-09-13T11:34:39Z Content type: article Language: en Sources: [Codrops]() Topics: [Figma 3D renderer](), [Figma]() Tags: [3d](), [3d-design](), [3d-modeling](), [3d-renderer-for-figma](), [3d-rendering](), [3d-visualization](), [3d-workflow](), [articles](), [case-study](), [creative-development](), [depth-3d-renderer](), [depth-figma](), [depth-figma-plugin](), [design-engineering](), [design-tools](), [figma](), [figma-3d](), [figma-3d-renderer](), [figma-plugin](), [figma-plugin-design](), [interactive-3d](), [plugin-development](), [product-design](), [product-design-case-study](), [real-time-rendering](), [three-js](), [ui-design](), [ux-design](), [visualization](), [webgl](), [workflow]() ### AI overview An article about building Depth, a Figma plugin for quickly rendering 3D models during design work. It describes the workflow, plugin architecture, rendering behavior, and export approach. ### Source excerpt A look at how Depth came together, from building a 3D renderer inside Figma to designing a website that explores what it can do. ## Perplexity trusts GPT-6 Astra with end-to-end systems DevFeed: [Perplexity trusts GPT-6 Astra with end-to-end systems]() Original publisher: [Read original article]() Published: 2026-09-12T11:14:21.124044Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [Language models]() Tags: [api](), [code](), [gpt](), [monitor](), [production](), [search](), [systems](), [testing](), [workflow]() ### AI overview Perplexity describes using GPT-6 Astra to write code and communications, modify and monitor production software, and create end-to-end tests with simulated service responses. ### Source excerpt Perplexity uses Astra to write communications, change software, and monitor production systems, and checks in much less frequently than with earlier models. ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## AI x QA Leadership Summit 2026: Building the AI-Native QA Engine DevFeed: [AI x QA Leadership Summit 2026: Building the AI-Native QA Engine]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-09-11T06:34:59Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [AI Strategy](), [Artificial Intelligence]() Tags: [2026](), [ai](), [events](), [free](), [governance](), [leadership](), [practitioner](), [qa](), [quality-engineering](), [september-2026](), [summit](), [virtual-event](), [workflow](), [workshops]() ### AI overview BrowserStack's 11th QA Leadership Summit is a free virtual event on 23 September 2026. It offers separate tracks on AI strategy and governance for QA leaders and applied AI testing skills for practitioners, with the theme "Building the AI-Native QA Engine." ### Source excerpt BrowserStack's 11th QA Leadership Summit runs 23 September 2026, free and virtual. Two tracks: AI strategy and governance for QA leaders, applied AI testing for practitioners. Speakers from AWS, Amazon, Atlassian, KPMG and S&P Global. ## How Featured's users make 100K media pitches per month on Vercel DevFeed: [How Featured's users make 100K media pitches per month on Vercel]() Original publisher: [Read original article]() Author: Susan Aziz Published: 2026-09-11T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [migration](), [vercel ai sdk](), [AI Chat](), [SDKs]() Tags: [ai](), [ai-gateway](), [aws](), [featured](), [infrastructure](), [migration](), [models](), [vercel](), [vercel-ai-sdk](), [workflow]() ### AI overview Featured describes migrating 374 Sanity sites from AWS Elastic Beanstalk to Vercel while using Vercel's AI SDK, AI Gateway, and Workflow SDK to reduce infrastructure work for a three-engineer team. ### Source excerpt Featured on Vercel 3 engineers supporting 3 brands and 100,000+ users on Vercel Migrated 374 Sanity sites from AWS Elastic Beanstalk to Vercel AI SDK and AI Gateway power Featured's chat bot across 17 models Workflow SDK replaced custom long-running job infrastructure Featured is a co-pilot for public relations (PR) that subject matter experts and PR teams use to find media opportunities. Tell Featured's agents what you know, and it surfaces opportunities across journalist requests, podcasts, awards, and GEO, with no PR background required. Founder Brett Farmiloe knows from experience how hard and time consuming getting press is. He spent 10 years running Markitors, a digital marketing agency with 500 small business clients. Every client, from an eyelash extension supplier to an equipment financing company, had real expertise to share, but no way to get it in front of journalists. PR, as Farmiloe puts it, "has always been about who has access to what." He founded Featured to change the question from who has access to who has knowledge. Featured connects one of their users with a journalist or publisher every 6 seconds. Their agents deliver more than 100,000 media pitches per month, and have sent over 100 million Help A Reporter Out (HARO) emails in the past year. Behind it all is an engineering team of just three people. With a team that lean, there's no time to manage servers or piece together custom integrations. Every hour spent on infrastructure is an hour taken away from building features what will help their customers land more media placements. The cost of managing infrastructure by hand Before Vercel, Featured's infrastructure work pulled the team away from product development. Hosting lived on AWS Elastic Beanstalk, AI features depended on custom provider integrations, and long-running, multi-step jobs ran on separate orchestration infrastructure. Each layer worked, but each one added operational overhead for a three-person team supporting multiple brands. ## Set Up Cloud OIDC From the Pulumi CLI DevFeed: [Set Up Cloud OIDC From the Pulumi CLI]() Original publisher: [Read original article]() Author: Sean Yeh Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [OpenID connect (OIDC)](), [Command-line interface](), [AWS IAM](), [Cloud](), [AWS Identity and Access Management (IAM)](), [Azure](), [Google Cloud Platform (GCP)]() Tags: [automated](), [aws](), [azure](), [cli](), [cloud](), [configuration](), [docs](), [esc](), [google-cloud](), [how-to](), [iam](), [identity](), [oidc](), [openid-connect](), [product](), [pulumi](), [pulumi-cli](), [security](), [terminal](), [workflow]() ### AI overview This tutorial explains how to use the Pulumi CLI command pulumi env setup to configure Pulumi ESC as an OIDC provider for AWS, Azure, and Google Cloud. It covers interactive onboarding, non-interactive flags for scripts and agents, cloud credentials, account selection, access policies, and automatic creation of identity providers, IAM roles, policy attachments, and ESC environments. ### Source excerpt Pulumi ESC can act as an OpenID Connect (OIDC) provider for AWS, Azure, and Google Cloud, issuing short-lived, signed tokens that these clouds exchange for temporary credentials. This eliminates hard-coded credentials and improves your security posture. Last year, we introduced an onboarding flow in the Pulumi Cloud console that makes it super easy to configure OIDC for your cloud provider in a few guided steps. We're bringing Pulumi Cloud into the CLI so agents can use its capabilities directly from the terminal, without requiring a human to complete steps in the console. The new pulumi env setup command brings OIDC onboarding to that workflow, with interactive prompts for guided setup and non-interactive flags for scripts and agents. pulumi env setup - how it works Run the command with your desired cloud provider (aws, azure, gcp). For example: pulumi env setup aws The command then asks what it needs to configure your cloud, including your credentials, the accounts to configure, and the level of access. The questions differ per cloud. For AWS, it asks: How to authenticate to AWS. It uses the credentials you already have, or it signs you in with AWS SSO. Which accounts to configure. Which policy to attach to the OIDC role. Choose AdministratorAccess for Pulumi Deployments, ReadOnlyAccess for Pulumi Insights, or any other policy ARN. Then, it will print out the plan: About to configure OIDC for organization my-org: account 111111111111: create role pulumi-esc-oidc-622e86ea-319ba4c675bb3c00-role attach arn:aws:iam::aws:policy/AdministratorAccess create ESC environment my-org/aws-login/sandbox-account-env Proceed? [yes/no] After you confirm, the command creates the identity provider, the IAM role, and the policy attachment in each account. It then creates one ESC Environment per account, with the aws-login provider already configured. Non-interactive setup You can also run the command without interactive prompts by passing in the necessary flags. Each cloud has its ow ## Small Teams, Ambitious Projects DevFeed: [Small Teams, Ambitious Projects]() Original publisher: [Read original article]() Author: Dalai Felinto Published: 2026-09-10T15:56:53Z Content type: opinion Language: en Sources: [Blender]() Topics: [Development](), [Open Source](), [Software]() Tags: [animation](), [collaboration](), [development](), [general-development](), [open-source](), [productivity](), [testing](), [workflow]() ### AI overview Blender outlines strategic development targets for the next two years, focused on improving productivity and collaboration for individuals and small teams. Priorities include dynamic overrides, animation workflow improvements, scalable rigging, and smoother transitions from storyboards to previs. ### Source excerpt A first-look overview of Blender development's strategic targets for the next two years. ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## Introducing Neki DevFeed: [Introducing Neki]() Original publisher: [Read original article]() Author: Nick Van Wiggeren Published: 2026-09-10T12:00:00Z Content type: release Language: en Sources: [Blog -- PlanetScale]() Topics: [Database](), [MySQL]() Tags: [database](), [mysql](), [neki](), [platform](), [postgres](), [product](), [routing](), [scale](), [workflow]() ### AI overview PlanetScale announces Neki in platform preview, a sharded Postgres offering designed to scale a database across multiple machines while retaining Postgres compatibility. ### Source excerpt Neki, sharded Postgres by PlanetScale, is now available in platform preview. ## Rebuilding AUTOMATIC1111 with Gradio Workflow DevFeed: [Rebuilding AUTOMATIC1111 with Gradio Workflow]() Original publisher: [Read original article]() Author: yuvraj sharma; Abubakar Abid Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI Development](), [Graphs](), [stable-diffusion](), [Inference](), [Large Language Model](), [hugging face](), [vlm]() Tags: [automatic1111](), [comfyui](), [diffusion](), [flux](), [gradio](), [hugging-face](), [image-to-image](), [image-to-video](), [inference](), [inference-providers](), [llm](), [models](), [python](), [space](), [spaces](), [text-to-image](), [vlm](), [workflow](), [workflows]() ### AI overview A walkthrough of Workflow1111, a Gradio graph that recreates AUTOMATIC1111-style media pipelines with connected operator nodes for image generation, editing, prompting, and related tasks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. 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