# automation Published articles for automation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## \[Aug 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[Aug 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-09-17T05:12:15Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [Google AI](), [Google](), [google-antigravity](), [Artificial Intelligence](), [MCP Server](), [computer-use](), [Fine-tuning]() Tags: [2026](), [agentic-ai](), [ai](), [ai-coding-agents](), [ai-studio](), [antigravity](), [api](), [automation](), [community](), [computer-use](), [gemini](), [google](), [google-ai](), [google-cloud](), [mcp](), [mcp-server](), [multi-agent](), [ocr](), [paper](), [pitfalls]() ### AI overview A monthly roundup of Google AI community activities and achievements, covering Antigravity prototyping and engineering, AI coding agents, MCP-based remote control, computer-use agent orchestration, earthquake research, and TPU fine-tuning and migration guidance. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravityPrototype App: OCR and Text Extraction by the author Prototyping and Bringing Ideas to Application Using Google AI Studio and Antigravity 2.0 by AI GDE Joan Santoso (Indonesia) shares a rapid prototyping workflow building an AI-powered Form Extractor using the Gemini API, featuring a lightweight OCR and text extraction workflow. Antigravity Engineering Series by GDE Amulya Bhatia (Germany) focuses on key features of Antigravity 2.0 across 10 articles covering topics such as multi-agent orchestration, safety architecture, and workflow automation, accompanied by source code examples. (image soruce) Remote Control for Google Antigravity: Drive Your AI Coding Agent From Telegram 🛰 by GDE Nicola Guglielmi (Italy) introduces an open-source MCP server that turns Telegram into a remote control surface for AI coding agents. Before the Quake: How Antigravity CLI's AI Agents & IoT Data Predict Earthquakes by GDE Kanshi Tanaike (Japan) introduces the paper establishing Unified LAIC-AGW Theory by integrating ultra-dense IoT weather data with seismic moment tensors. It demonstrates a pre-seismic early warning capability by capturing enthalpy anomalies and acoustic-gravity waves. ADKAI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) AI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) introduced UISurf: An Operator-Centric Multi-Agent Platform for Observable and Cross-Environment UI Automation at the Agentic AI Summit 2026. They highlighted how the model-agnostic framework leverages the Google Cloud and Gemini ecosystems, such as GEAP and ADK, to orchestrate and evaluate computer-use agents across web, desktop, and mobile environments. Frameworks and ResearchTPU Introduction to SFT on TPU with Tunix -- 10 pitfalls until 2 ## Rate limits on GitLab.com are changing DevFeed: [Rate limits on GitLab.com are changing]() Original publisher: [Read original article]() Author: Sam Wiskow Published: 2026-09-17T00:00:00Z Content type: release Language: en Sources: [GitLab]() Topics: [GitLab](), [Automation](), [Users](), [account]() Tags: [2026](), [automation](), [credentials](), [gitlab](), [limits](), [platform](), [product](), [user](), [users]() ### AI overview GitLab.com will align rate limits with subscription tiers beginning October 19, 2026. Free accounts and unauthenticated requests change first, while Premium and Ultimate limits change in January 2027. Unauthenticated requests are limited to 60 requests per hour per IP address, with preview windows scheduled for October 7 and 14. ### Source excerpt GitLab.com hosts millions of projects for teams of every size that need a platform they can rely on. Demand is climbing quickly, and we expect platform load to grow several times over this year. Predictable limits are what keep GitLab.com fast for everyone on it, including the automation and agent workloads teams are building on the platform. To hold that as we scale, we're updating how rate limits work. Starting October 19, 2026, rate limits on GitLab.com will align with your subscription tier. Free accounts and unauthenticated requests happen first, on October 19. Premium and Ultimate move in January 2027. What is changing Limits align with your subscription. Free, Premium, and Ultimate subscription plans get their own limits, applied per user and per top-level group. Free takes effect October 19; Premium and Ultimate in January 2027. Signing in gets you the full limit. An authenticated request is governed by your subscription plan below. A request that arrives with no credentials gets 60 requests per hour per IP address. The per-plan limits are published in the rate limits documentation. What happens on October 19 There will be two preview windows for Free and unauthenticated traffic, on October 7 and October 14 from 15:00 to 19:00 UTC. Signed-in Premium and Ultimate requests are not affected, since those limits do not change until January. Unauthenticated requests are capped no matter where they come from, including automation running against a paid account without credentials. A preview window (engineers call these brownouts) is a short, planned window where we switch the new limits on and then switch them back off. Nothing else about the service changes while it runs. The point is to give you a real look at how your own workloads behave under the new limits, weeks before they apply for good. On October 19 the new limits take effect. We set these limits by looking at how GitLab.com is actually used. Almost all users are already inside the new limits and won't n ## n8n Patches 16 Security Vulnerabilities, 12 Rated High Severity DevFeed: [n8n Patches 16 Security Vulnerabilities, 12 Rated High Severity]() Original publisher: [Read original article]() Author: Christian Rakoot Published: 2026-09-16T18:31:20Z Content type: news Language: en Sources: [Self Host Lab]() Topics: [n8n](), [Vulnerabilities](), [patches](), [Security](), [Self-hosted]() Tags: [automation](), [n8n](), [news](), [patches](), [security](), [security-vulnerabilities](), [self-hosted](), [update](), [vulnerabilities]() ### AI overview n8n published a bi-weekly security update disclosing 16 fixed advisories: 12 rated High severity and 4 rated Medium. The article highlights an unauthenticated NoSQL injection in the MongoDB Chat Memory node that can disclose chat history across sessions, along with five High-severity credential-handling advisories. ### Source excerpt n8n, the self-hosted workflow automation platform covered regularly on this site, published its bi-weekly security update on September 16, 2026. The bulletin, posted on the official n8n Community forum by a member of the n8n security team, discloses 16 advisories fixed since the previous update on September 2: 12 rated High severity and 4 rated [...] ## Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review DevFeed: [Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review]() Original publisher: [Read original article]() Author: Sarah Deitke Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [Code review](), [Pull Request](), [Tooling](), [engineering-culture](), [observability]() Tags: [adoption](), [agile](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [artificial-intelligence](), [automated](), [automation](), [autonomous](), [case-study](), [code-review](), [code-reviews](), [culture](), [culture-methods](), [developer](), [developer-experience](), [development](), [duolingo-ai-literacy-code-review](), [infoq](), [metrics](), [ml-data-engineering](), [observability](), [pairing](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [transcripts]() ### AI overview Sarah Deitke presents Duolingo's approach to cultural AI adoption through internal AI literacy workshops, observability dashboards, and safe AI guardrails. The presentation includes a case study on redesigning code review with an automated PR risk-assessment bot and reports faster delivery without increased defect rates. ### Source excerpt Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates. By Sarah Deitke ## Cisco UCS Manager: Celebrating the legacy, shaping the future with Cisco Intersight DevFeed: [Cisco UCS Manager: Celebrating the legacy, shaping the future with Cisco Intersight]() Original publisher: [Read original article]() Author: Jacob Van Ewyk Published: 2026-09-15T21:00:37Z Content type: opinion Language: en Sources: [Cisco Blogs]() Topics: [Cisco](), [Server](), [Automation](), [configuration](), [API](), [legacy]() Tags: [api](), [automation](), [cisco](), [data-center](), [end-of-life](), [eol](), [infrastructure](), [legacy](), [lifecycle](), [management](), [server]() ### AI overview Cisco describes the legacy of Cisco UCS Manager and positions Cisco Intersight as the strategic platform for future Cisco UCS software management. The article also outlines announced end-of-sale and end-of-life milestones, including a planned Last Date of Support of December 31, 2030, subject to applicable contracts and service terms. ### Source excerpt Cisco UCS Manager shaped 17 years of infrastructure management. Now, with its end-of-life announced, Cisco Intersight carries that foundation forward with centralized visibility, automation, and scale. Learn the key dates for the UCS Manager EOL. ## Using the Komodo MCP Server to Manage Docker Infrastructure with AI Assistants DevFeed: [Using the Komodo MCP Server to Manage Docker Infrastructure with AI Assistants]() Original publisher: [Read original article]() Author: Brandon Lee Published: 2026-09-15T12:38:26Z Content type: tutorial Language: en Sources: [Virtualization Howto]() Topics: [Homelab](), [Model Context Protocol (MCP)](), [Docker](), [Docker Compose](), [GitOps](), [Artificial Intelligence]() Tags: [ai](), [automation](), [containers](), [docker](), [gitops](), [home-lab](), [home-server](), [infrastructure](), [mcp]() ### AI overview This tutorial introduces the Komodo MCP Server, which connects AI assistants such as Claude and GitHub Copilot to Komodo for managing Docker environments. It describes natural-language control of containers, stacks, deployments, servers, builds, repositories, and related infrastructure through the Model Context Protocol. ### Source excerpt There are so many cool projects now that we have access to in the the realm of home lab, especially when it comes to automation and AI Ops for our... The post I Gave AI Control of My Docker Home Lab With Komodo MCP appeared first on Virtualization Howto. ## Fanora Control Terminal: A Fan-Shaped Smart-Home Control Panel with Jade and Aluminum DevFeed: [Fanora Control Terminal: A Fan-Shaped Smart-Home Control Panel with Jade and Aluminum]() Original publisher: [Read original article]() Author: JC Torres Published: 2026-09-15T09:15:23Z Content type: article Language: en Sources: [Yanko Design]() Topics: [Automation](), [Terminal]() Tags: [a-design-award-and-competition](), [automation](), [gadgets](), [gadgets-homes-interiors-technology-a-design-award-and-competition-smart-home](), [home-automation](), [homes](), [interface](), [interiors](), [lighting](), [motion-detection](), [screen](), [sensors](), [smart-home](), [technology]() ### AI overview The article presents Fanora, a wall-mounted smart-home control panel shaped like a partially opened folding fan. It describes eight mechanical buttons, an aluminum body, a natural Hetian jade centerpiece, motion and temperature sensors, and customizable button labels for lighting and other home-automation scenes. ### Source excerpt Smart Home Panels Look Like Tech, Fanora Looks Like Fine Craft A folding fan used to say something about the person holding it, signaling taste and refinement long before anyone cared about voice assistants or smart... ## Mintlify rebuilds automations around pre-built templates for common use cases DevFeed: [Mintlify rebuilds automations around pre-built templates for common use cases]() Original publisher: [Read original article]() Author: Patrick Foster Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Automation](), [Documentation](), [changelog](), [Pull Request](), [Markdown](), [YAML]() Tags: [announcements](), [automation](), [changelog](), [docs](), [documentation](), [execution](), [markdown](), [prompt](), [quality](), [rest]() ### AI overview Mintlify rebuilt its automations around pre-built templates for common use cases, including syncing content with code changes, generating changelogs, and improving documentation from user feedback and support signals. Custom prompts remain available for specialized tasks. ### Source excerpt We redesigned automations for quicker setup with pre-built automations optimized for common use cases. Choose the right automations for your project and you're set. No context engineering required. ## 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. ## Automate replenishment with MMF, Databricks Genie, and Amazon Quick DevFeed: [Automate replenishment with MMF, Databricks Genie, and Amazon Quick]() Original publisher: [Read original article]() Author: Venkatavaradhan Viswanathan Published: 2026-09-14T15:42:06Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [databricks](), [Amazon S3 Tables](), [Amazon S3](), [Automation](), [foundation-models](), [API]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-s3-tables](), [api](), [automation](), [databricks](), [foundation-models](), [machine-learning](), [retail](), [technical-how-to]() ### AI overview This technical walkthrough presents an unattended replenishment workflow for retail. Databricks Many Model Forecasting uses Chronos-2 to predict seven-day demand for each SKU, Databricks Genie detects demand surges, and Amazon Quick reconciles those surges with supplier availability in Amazon S3 Tables. The workflow places routine purchase orders through a Supplier Order API and escalates cases without a suitable single supplier for human review. ### Source excerpt Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended, escalating to a human only when no supplier can cover a surge. ## Article: Implementing Durable Workflows on Postgres Without an External Orchestrator DevFeed: [Article: Implementing Durable Workflows on Postgres Without an External Orchestrator]() Original publisher: [Read original article]() Author: Raman Varma Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Databases](), [SQL](), [Provisioning](), [incident](), [CI/CD](), [Kubernetes](), [AWS Step Functions](), [Artificial Intelligence]() Tags: [ai](), [ai-ml-data-engineering](), [architecture-design](), [article](), [automation](), [aws-step-functions](), [database](), [durable-workflows-postgres](), [incident-response](), [infrastructure](), [kubernetes](), [ml-data-engineering](), [orchestration](), [postgres](), [queue](), [relational-databases](), [sql](), [workflows]() ### AI overview This article explains how to implement durable workflows on Postgres without an external orchestrator. It describes using row-level locking as a concurrent work queue, primary-key checkpoints for idempotency, and leases with a sweeper for crash recovery. Workflow state, sleeps, and human approvals can persist in the database and survive process restarts. ### Source excerpt Postgres can serve as the durable state store and coordination layer for workflows, eliminating the need for an external orchestrator. SKIP LOCKED enables concurrent work processing, primary-key checkpoints enforce idempotency, and leases support crash recovery. Workflow sleeps and human approvals can also be persisted as database state and survive restarts. By Raman Varma ## shot-scraper 1.12 DevFeed: [shot-scraper 1.12]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-13T23:58:14Z Content type: release Language: en Sources: [Simon Willison's Weblog]() Topics: [Automation](), [Playwright](), [Web]() Tags: [automation](), [playwright](), [playwright-15](), [release](), [screenshot](), [screenshots](), [shot-scraper](), [shot-scraper-71]() ### AI overview shot-scraper 1.12 adds WebP screenshot support, including a quality option and lossless output when quality is not specified. The article says WebP screenshots are usually significantly smaller than JPEG or PNG equivalents. ### Source excerpt Release: shot-scraper 1.12 I've added WebP support to my shot-scraper screenshot automation tool. You can now take a WebP screenshot of a web page like this: shot-scraper https://simonwillison.net -o screenshot.webp --quality 80 The --quality option sets the quality - without that option the WebP file will be lossless. In my experience WebP screenshots are almost always significantly smaller in file size than their JPEG or PNG equivalents. See the PR for some examples. I shipped this feature so I could use it to generate the screenshot for my new commit-rewriter tool. Tags: playwright, shot-scraper ## Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack DevFeed: [Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-12T18:24:22Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AWS Outposts](), [Amazon Web Services](), [cloud-infrastructure](), [Network](), [Latency](), [data-processing](), [Security]() Tags: [5g](), [automation](), [aws](), [aws-outposts](), [cloud](), [compute](), [connectx](), [data-processing](), [enterprise](), [governance](), [latency](), [low-latency](), [networking](), [security]() ### AI overview AWS has generally released a second-generation single-rack AWS Outposts configuration: a self-contained 42U rack combining compute, storage, and networking with up to 2,688 vCPUs and 100 TB of Amazon EBS. The article describes its on-premises cloud compatibility, compact footprint, supported instance families, and accelerated networking options for trading floors and 5G cores. ### Source excerpt AWS has made second-generation single-rack AWS Outposts generally available, a self-contained 42U rack that puts compute, storage, and networking together with up to 2,688 vCPUs and 100 TB of Amazon EBS. It runs the same APIs, console, automation, governance policies, and security controls as the multi-rack second-generation Outposts and the parent AWS Region, so an The post Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack appeared first on StorageReview.com. ## Android's Agentic APIs, App Integration, and Agent Distribution DevFeed: [Android's Agentic APIs, App Integration, and Agent Distribution]() Original publisher: [Read original article]() Author: Thomas Künneth Published: 2026-09-12T12:42:56Z Content type: opinion Language: en Sources: [Thomas Künneth]() Topics: [Android](), [Artificial Intelligence](), [Operating system](), [Automation](), [API]() Tags: [ai](), [android](), [apis](), [architecture](), [automation](), [code](), [coding](), [community](), [development](), [engineering](), [google](), [inclusive](), [mobiledev](), [os](), [software]() ### AI overview This commentary examines Android's shift toward an intelligent system through three layers: on-device model runtimes and API stability, app integration with agents, and agent distribution. It argues that Google's platform messaging combines distinct engineering claims and that some agent capabilities remain less mature than the marketing suggests. ### Source excerpt I have spent the last year and a half building against Android's agentic APIs, not reading about them from a keynote seat. Be nice got an AppFunction so Gemini could invoke createAppPair directly; a few months later, bumping a single alpha broke it, because alpha10 quietly stopped shipping the service component alpha08 depended on. That scar tissue is a decent filter for keynote language. So when Android's president, Sameer Samat, describes the platform moving from an operating system to an intelligent system, I try not to start with whether that's true. I start with which layer of the stack that sentence is describing, because "Intelligence System" covers at least three very different things at once, and only one of them is finished enough to build on. Three Claims Wearing One Name Layer one is the model runtime: what executes on the device, which APIs call it, how stable those APIs are. Layer two is app integration: how an app exposes itself to an agent, and how much of that exposure the app developer controls versus how much Android does for them without asking. Layer three is distribution: who gets to be the agent standing in front of the user, and whether that's a genuinely open contest or a foregone one. Google's own framing blurs all three into a single narrative arc. The February 2026 "Intelligent OS" post redefined what success means for an app: not opens, but completed tasks, with or without a human touching the UI. Android's May I/O recap made the platform claim explicit: agents like Gemini can already "complete task automation" on Android, navigating an app on the user's behalf without anyone tapping through it themselves. One sentence, three separate engineering claims bundled together. I want to pull them apart in the order I trust them least to most, which also happens to be the order in which the marketing runs furthest ahead of the shipping code. Layer Three First, Because It's the Weakest: Who Gets to Be the Agent Start with distribution, since tha ## CrowdStrike Delivers the Next Evolution of the Agentic SOC DevFeed: [CrowdStrike Delivers the Next Evolution of the Agentic SOC]() Original publisher: [Read original article]() Author: Brandon Benke Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Agentic SOC](), [SOC](), [Security](), [Artificial Intelligence](), [Automation](), [data](), [real-time](), [Reconnaissance](), [Cloud](), [Network]() Tags: [2026](), [agentic](), [agentic-soc](), [agents](), [ai](), [automation](), [cloud](), [data](), [identity](), [network](), [operations](), [platform](), [real-time](), [saas](), [security](), [soc]() ### AI overview CrowdStrike describes the next evolution of its agentic SOC, where analysts and AI agents work together in a unified system to investigate and respond to threats in real time. The Falcon platform combines data generation, enrichment, investigation, orchestration, and governance, with capabilities for detection-ready third-party data and coordinated specialist agents. ### Source excerpt Expert agents that reason together, learn your environment, and run on data CrowdStrike owns. See how we deliver the agentic SOC. Learn more! ## Teaching AI to Reason Through Detection Triage DevFeed: [Teaching AI to Reason Through Detection Triage]() Original publisher: [Read original article]() Author: Amol Khanna - Manu Nandan - Cristian Viorel Popa - Joan Pujol-Roig - Diana Bolocan - Laura Vasilie - Alexandru Apostu - Chase Helwig - Mihaela Gaman - Mickey Brautbar - Edward Raff - Chase Midler - Sv Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [AI Chat]() Tags: [agentic](), [agentic-soc](), [ai](), [automation](), [chain-of-thought](), [classification](), [cybersecurity](), [language-models](), [llm](), [model](), [nemotron](), [nvidia](), [open](), [reasoning](), [research](), [security](), [soc](), [windows]() ### AI overview CrowdStrike describes research on a reasoning-enabled language-model classifier for security detection triage. The model produces a verdict and an auditable rationale, with the stated goals of improving accuracy, transparency, and safe alert automation. ### Source excerpt New CrowdStrike research shows how step-by-step reasoning can improve detection triage accuracy, transparency, and safe automation. ## How to call external APIs from Webflow without leaking API keys DevFeed: [How to call external APIs from Webflow without leaking API keys]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [API](), [API keys](), [Authentication](), [Back end](), [Automation](), [Caching](), [Next.js](), [Node.js](), [GitHub]() Tags: [api-keys](), [apis](), [authentication](), [automation](), [backend](), [cache](), [github](), [guides](), [how-to](), [next-js](), [node-js]() ### AI overview A practical guide to integrating external APIs with Webflow through a Webflow Cloud server-side proxy, client-side fetch for public APIs, or no-code automation. It emphasizes keeping API credentials out of the browser and explains why the proxy is preferred for authenticated production integrations. ### Source excerpt Learn three ways to connect external APIs to Webflow (server-side proxy, client-side fetch, and no-code automation). ## Marketing ops as code: Automating events from planning to follow-up on GitHub DevFeed: [Marketing ops as code: Automating events from planning to follow-up on GitHub]() Original publisher: [Read original article]() Author: Tomoko Tanaka Published: 2026-09-11T18:26:10Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [AI-assisted coding]() Tags: [ai-ml](), [api](), [automation](), [cli](), [events](), [github](), [github-actions](), [github-copilot](), [github-issues]() ### AI overview A GitHub marketing lead describes using GitHub Copilot and GitHub Actions to automate event operations from a GitHub Issue, including setup, daily registrant processing, and post-event cleanup. ### Source excerpt If you can write down how you do your work, you can automate it. Here's what I did to support GitHub's APAC marketing team. The post Marketing ops as code: Automating events from planning to follow-up on GitHub appeared first on The GitHub Blog. ## Unify your marketing data with Lakeflow Connect DevFeed: [Unify your marketing data with Lakeflow Connect]() Original publisher: [Read original article]() Author: Sonia Bendre; Giselle Goicochea Published: 2026-09-11T18:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Software as a service](), [Databases](), [API](), [Automation](), [dashboards](), [hubspot](), [CI/CD]() Tags: [analytics](), [api](), [automation](), [churn](), [ci-cd](), [connectors](), [crm](), [customer](), [customers](), [dashboards](), [data](), [databases](), [databricks](), [engineering](), [marketing](), [product](), [retention](), [saas](), [salesforce](), [series]() ### AI overview This first post in a series presents Lakeflow Connect as a fully managed data-ingestion service for unifying fragmented marketing and Customer 360 data. It describes native connectors for SaaS applications, databases, and files, configured through a point-and-click UI or API, with governed tables in Unity Catalog and integrations such as HubSpot and Salesforce. The article highlights reduced maintenance compared with custom pipelines and support for downstream reporting, analytics, retention, and churn analysis. ### Source excerpt This is the first post in a new series exploring how Lakeflow Connect brings fully... ## From zero-shot forecast to purchase order with Amazon Bedrock AgentCore DevFeed: [From zero-shot forecast to purchase order with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Hyunsoo Kim, Ph.D. Published: 2026-09-11T14:08:01Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [Training AI Models](), [SDKs]() Tags: [advanced-300](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [automation](), [best-practices](), [orchestration](), [strands-agents](), [time-series](), [training](), [zero-shot]() ### AI overview An architecture article on using Amazon Chronos2 zero-shot forecasting and Bedrock AgentCore multi-agent orchestration to turn demand forecasts into validated purchase orders without per-product model training. ### Source excerpt Combine zero-shot forecasting with Amazon Chronos2 and multi-agent orchestration on Amazon Bedrock AgentCore to turn demand forecasts into validated purchase orders. No per-product model training, with business rules, auditability, and cost that scales to zero. ## Introducing automatic remediation policies with Cloudflare CASB DevFeed: [Introducing automatic remediation policies with Cloudflare CASB]() Original publisher: [Read original article]() Author: Abe Carryl Published: 2026-09-11T13:00:00Z Content type: release Language: en Sources: [Cloudflare Blog]() Topics: [Software as a service](), [Cloudflare One](), [Orchestration](), [Authorization]() Tags: [automation](), [casb](), [cloudflare](), [cloudflare-one](), [event-driven](), [oauth](), [orchestration](), [policy](), [reactive](), [saas](), [security](), [security-operations-center](), [zero-trust]() ### AI overview Cloudflare introduces CASB policies that automatically remediate SaaS security findings, including revoking risky file shares and sending webhooks after a finding is detected. ### Source excerpt Cloudflare CASB policies introduce a native automation engine built directly on the Cloudflare developer platform to remediate SaaS risks automatically. Security teams can now design event-driven logic to revoke risky file shares and send webhooks without manual intervention. ## User Impersonation Can Trigger Analytics, Emails, Webhooks, and Background Jobs as the Wrong User DevFeed: [User Impersonation Can Trigger Analytics, Emails, Webhooks, and Background Jobs as the Wrong User]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Authorization](), [systems]() Tags: [analytics](), [authorization](), [automation](), [email](), [feature](), [feature-flags](), [notifications](), [support](), [webhooks]() ### AI overview The article explains that user impersonation is a session-level concern whose downstream effects can inherit the impersonated identity. It inventories risks including analytics, notifications, lifecycle email, chat activity, webhooks, feature flags, caches, and queued jobs, with examples of misleading user communications and unintended email automation. ### Source excerpt The side effects nobody documents: analytics, lifecycle email, feature flags, webhooks, and the background job that runs as the wrong person an hour later. ## Building an Agentic SOC on a Stream DevFeed: [Building an Agentic SOC on a Stream]() Original publisher: [Read original article]() Author: Pavel Lineitsev Published: 2026-09-10T19:19:05Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Agentic SOC](), [SOC](), [Security](), [Streaming](), [Architecture & Design](), [Artificial Intelligence](), [Automation](), [data loss prevention]() Tags: [agent](), [agentic](), [agentic-soc](), [agents](), [ai](), [ai-agents](), [architecture](), [automation](), [confluent-cloud](), [data-loss-prevention](), [security](), [security-tools](), [soc](), [streaming]() ### AI overview This article describes an Agentic SOC that uses AI agents and a continuous streaming architecture to investigate security alerts. Its pipeline combines central triage, specialized evidence agents, adversarial evaluation, and a self-learning knowledge base to analyze every alert, escalate higher-value cases, and surface true positives for analyst review. The approach is intended to address the backlog of low-priority alerts, including Data Loss Prevention alerts, whose volume makes manual investigation impractical. ### Source excerpt Discover how our security team built an automated multi-agent investigation pipeline that scaled alert triage throughput using a continuous streaming architecture. ## Cypress Ambassador Spotlight: Maksym Donets DevFeed: [Cypress Ambassador Spotlight: Maksym Donets]() Original publisher: [Read original article]() Published: 2026-09-10T16:12:22Z Content type: article Language: en Sources: [Cypress Blog]() Topics: [Cypress](), [Testing](), [Cloud](), [Automation]() Tags: [ambassador](), [automation](), [bugs](), [cloud](), [community](), [cypress](), [debugging](), [learning](), [qa](), [testing]() ### AI overview An ambassador spotlight featuring Maksym Donets, an Automation QA Engineer who discusses learning, sharing, helping developers debug failed pipelines with Cypress Cloud, and using cy.session() with cy.request() to simplify session handling and speed up tests. He also shares advice for getting started with Cypress and mentions catching bugs with end-to-end testing. ### Source excerpt Welcome back to another edition of our Ambassador Spotlight series, where we shine a light on the Cypress community members who make testing better for everyone through the blogs they write, the talks they give, and the day-to-day help they offer to fellow developers. Curious about the program? You can learn more about it here. This time, we're catching up with Maksym Donets, an Automation QA Engineer with a straightforward philosophy on testing and an even more straightforward philosophy on li [Next page]()