# Actor Published articles for Actor. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Enterprise Architecture Quietly Depended on Humans in Far More Places Than We Realised DevFeed: [Enterprise Architecture Quietly Depended on Humans in Far More Places Than We Realised]() Original publisher: [Read original article]() Author: flashdba Published: 2026-06-08T14:19:07Z Content type: opinion Language: en Sources: [flashdba]() Topics: [Enterprise Architecture](), [Architecture & Design](), [Artificial Intelligence](), [Actor](), [properties]() Tags: [actor](), [agentic](), [agentic-ai](), [ai](), [architecture](), [cloud](), [databases](), [databases-and-agentic-ai](), [enterprise-architecture](), [inferencing](), [performance](), [properties]() ### AI overview This article argues that enterprise architecture depended on implicit properties supplied by human participants, including predictable workload, intent, accountability, feedback, and limited capacity. It concludes that agentic AI exposes pressure across multiple technical and organisational domains by removing those assumptions simultaneously. ### Source excerpt Enterprise architecture was built around a human actor whose presence provided properties nobody designed and nobody priced. Agentic AI is the first technology to remove those properties simultaneously - and the absence is now visible across every domain. ## Player sessions that survive anything DevFeed: [Player sessions that survive anything]() Original publisher: [Read original article]() Author: Cecil Phillip Published: 2026-05-28T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [sessions](), [Actor](), [backends](), [Processes](), [Server](), [Python]() Tags: [actor](), [backends](), [build](), [crashes](), [database](), [distributed](), [how-to](), [python](), [server](), [sessions](), [temporal-concepts](), [workflow]() ### AI overview This article presents a Temporal Python pattern for modeling each multiplayer player session as a durable, uniquely addressable Actor Workflow. The workflow keeps player state, resumes after server failures, coordinates game actions, and uses Continue-As-New to prevent unbounded history. ### Source excerpt Build multiplayer player sessions that survive server crashes. See how to model each player as a durable Actor Workflow in Temporal Python. ## Operation "Trolling Stone": Russia-linked influence activity DevFeed: [Operation "Trolling Stone": Russia-linked influence activity]() Original publisher: [Read original article]() Published: 2026-02-01T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [Artificial Intelligence](), [ChatGPT](), [Actor]() Tags: [actor](), [ai](), [chatgpt](), [news](), [openai](), [safety]() ### AI overview OpenAI describes banning ChatGPT accounts linked to "Trolling Stone," a Russia-linked influence operation that used AI to generate social media comments and articles about the arrest of an alleged Russian cult leader in Argentina. The activity involved coordinated users and content published across Facebook, YouTube, Medium, and some Argentine news websites. ### Source excerpt OpenAI banned accounts linked to a previously unreported operation we dubbed "Trolling Stone", using AI to generate comments about an alleged Russian cult leader's arrest in Argentina. ## How to Prevent Race Conditions in Coroutines DevFeed: [How to Prevent Race Conditions in Coroutines]() Original publisher: [Read original article]() Author: author@typealias.com (Dave Leeds) Published: 2025-11-24T00:00:00Z Content type: tutorial Language: en Sources: [Dave Leeds on Kotlin - typealias.com]() Topics: [Coroutines](), [kotlin-coroutines](), [Kotlin](), [Concurrency]() Tags: [actor](), [atomicint](), [atomics](), [concurrency](), [coroutine](), [coroutines](), [how-to](), [kotlin](), [kotlin-coroutines](), [limitedparallelism](), [mutex](), [programming](), [race-condition](), [shared-mutable-state](), [state](), [stateflow](), [structured-concurrency]() ### AI overview This tutorial explains that Kotlin coroutines and structured concurrency do not automatically protect shared mutable state from race conditions. It introduces the issue using an example that creates 100,000 random bakery orders. ### Source excerpt Thanks to structured concurrency, there are a lot of concerns that we don't have to handle manually when working with Kotlin coroutines. For example, parent coroutines automatically wait for their children to complete before completing themselves. And when a parent coroutine is cancelled, its children are automatically cancelled, too. But when it comes to shared mutable state - mutable data that multiple coroutines could update at the same time - there's nothing magical in the design of coroutines to ensure that the state is updated properly. ## Durable Digest: October 2025 DevFeed: [Durable Digest: October 2025]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2025-10-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [SDKs](), [Ruby](), [Logging](), [audit](), [MCP](), [Actor](), [time tracking](), [clients]() Tags: [actor](), [ai](), [announcements](), [audit](), [events](), [logging](), [mcp](), [ruby](), [sdk](), [workflow]() ### AI overview Temporal's October 2025 newsletter covers Ruby SDK general availability, Server v1.29.0, Eager Workflow Start in public preview, a Langfuse integration, Cloud Audit Logging in public preview, Saved Views general availability, field guidance, an adoption spotlight, AI guides, and upcoming events. ### Source excerpt Temporal's latest updates: Ruby SDK GA, Server v1.29.0, Eager Workflow Start, Langfuse integration, Cloud Audit Logging, and Saved Views. Plus AI guides, and live events! ## Designing a hierarchical Authorisation system DevFeed: [Designing a hierarchical Authorisation system]() Original publisher: [Read original article]() Author: Authorisation Published: 2025-09-20T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Actor](), [object](), [structure](), [service](), [properties]() Tags: [actor](), [apis](), [billing](), [finance](), [object](), [recursion](), [service](), [structure]() ### AI overview This article explains how to design a hierarchical authorization system for Goiter. It contrasts a flat mapping of users, objects, and actions with a hierarchical model in which groups can have multiple parents and inherit rules. The article notes that recursive parent lookup can reduce rule bloat, while assuming a maximum recursion depth of 10. ### Source excerpt . [Designing a Hierarchical Authorisation System](designing-a-hierarchical-authorisation-system-cover... ## @isolated(any) DevFeed: [@isolated(any)]() Original publisher: [Read original article]() Author: Matt Massicotte Published: 2025-08-04T07:00:00Z Content type: article Language: en Sources: [NSHipster]() Topics: [async]() Tags: [actor](), [async](), [suspend](), [swift]() ### AI overview This article explains why @isolated(any) was introduced, using async functions to show how isolation can change when a task suspends. It also describes the tradeoff between flexible function types and the loss of statically inspectable isolation information. ### Source excerpt There are cases where just a little more visibility and control over how to schedule asynchronous work can make all the difference. ## Chain-of-table: Evolving tables in the reasoning chain for table understanding DevFeed: [Chain-of-table: Evolving tables in the reasoning chain for table understanding]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-11T19:08:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Chain-of-thought](), [Natural language processing](), [Large Language Model](), [data](), [LLM evaluation / benchmarking]() Tags: [actor](), [ai](), [awards](), [benchmarks](), [chain-of-thought](), [data](), [information](), [language-models](), [machine-intelligence](), [models](), [natural-language-processing](), [performance](), [reasoning](), [research](), [table](), [verify]() ### AI overview The article presents Chain-of-Table, a framework for table understanding that trains large language models to reason step by step by iteratively updating a table. The approach uses in-context learning to generate operations dynamically and reports improved results on the WikiTQ, TabFact, and FeTaQA benchmarks. ### Source excerpt Posted by Zilong Wang, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team People use tables every day to organize and interpret complex information in a structured, easily accessible format. Due to the ubiquity of such tables, reasoning over tabular data has long been a central topic in natural language processing (NLP). Researchers in this field have aimed to leverage language models to help users answer questions, verify statements, and analyze data based on tables. However, language models are trained over large amounts of plain text, so the inherently structured nature of tabular data can be difficult for language models to fully comprehend and utilize. Recently, large language models (LLMs) have achieved outstanding performance across diverse natural language understanding (NLU) tasks by generating reliable reasoning chains, as shown in works like Chain-of-Thought and Least-to-Most. However, the most suitable way for LLMs to reason over tabular data remains an open question. In "Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding", we propose a framework to tackle table understanding tasks, where we train LLMs to outline their reasoning step by step, updating a given table iteratively to reflect each part of a thought process, akin to how people solve the table-based problems. This enables the LLM to transform the table into simpler and more manageable segments so that it can understand and analyze each part of the table in depth. This approach has yielded significant improvements and achieved new state-of-the-art results on the WikiTQ, TabFact, and FeTaQA benchmarks. The figure below shows the high-level overview of the proposed Chain-of-Table and other methods. Given a complex table where a cyclist's nationality and name are in the same cell, (a) generic, multi-step reasoning is unable to provide the correct answer (b) program-aided reasoning generates and executes programs (e.g., SQL queries) to deliver the answ ## How the Swift compiler knows that DispatchQueue.main implies @MainActor DevFeed: [How the Swift compiler knows that DispatchQueue.main implies @MainActor]() Original publisher: [Read original article]() Author: Ole Begemann Published: 2024-02-29T18:54:47Z Content type: article Language: en Sources: [Ole Begemann]() Topics: [Swift](), [Compiler](), [Concurrency](), [Xcode]() Tags: [actor](), [compiler](), [concurrency](), [dispatchqueue](), [swift](), [xcode]() ### AI overview The article explains that Swift treats closures passed to exactly spelled DispatchQueue.main.async calls as @MainActor-isolated. It reports that this behavior comes from a coarse syntax-based check in the compiler's semantic analysis rather than an annotation on the method, and notes that equivalent expressions using aliases or variables do not receive the same inference. ### Source excerpt You may have noticed that the Swift compiler automatically treats the closure of a DispatchQueue.main.async call as @MainActor. In other words, we can call a main-actor-isolated function in the closure: import Dispatch @MainActor func mainActorFunc() { } DispatchQueue.main.async { // The compiler lets us call this because // it knows we're on the main actor. mainActorFunc() } This behavior is welcome and very convenient, but it bugs me that it's so hidden. As far as I know it isn't documented, and neither Xcode nor any other editor/IDE I've used do a good job of showing me the actor context a function or closure will run in, even though the compiler has this information. I've written about a similar case before in Where View.task gets its main-actor isolation from, where Swift/Xcode hide essential information from the programmer by not showing certain attributes in declarations or the documentation. It's a syntax check So how is the magic behavior for DispatchQueue.main.async implemented? It can't be an attribute or other annotation on the closure parameter of the DispatchQueue.async method because the actual queue instance isn't known at that point. A bit of experimentation reveals that it is in fact a relatively coarse source-code-based check that singles out invocations on DispatchQueue.main, in exactly that spelling. For example, the following variations do produce warnings/errors (in Swift 5.10/6.0, respectively), even though they are just as safe as the previous code snippet. This is because we aren't using the "correct" DispatchQueue.main.async spelling: let queue = DispatchQueue.main queue.async { // Error: Call to main actor-isolated global function // 'mainActorFunc()' in a synchronous nonisolated context mainActorFunc() // ❌ } typealias DP = DispatchQueue DP.main.async { // Error: Call to main actor-isolated global function // 'mainActorFunc()' in a synchronous nonisolated context mainActorFunc() // ❌ } I found the place in the Swift compiler source code ## Customer Workflows: Building a loyalty program with Temporal DevFeed: [Customer Workflows: Building a loyalty program with Temporal]() Original publisher: [Read original article]() Author: Fitz Published: 2023-08-03T17:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Actor](), [fly](), [Code](), [Framework](), [GitHub](), [Go](), [Java](), [Python]() Tags: [actor](), [customers](), [github](), [go](), [how-to](), [java](), [loyalty-account](), [python](), [workflows]() ### AI overview This tutorial explains how Temporal Workflows can represent Actors through a customer loyalty account example. It reviews the Actor Model's requirements--message handling, creating Actors, and maintaining state--and describes how a loyalty account tracks points and rewards. The article includes Go, Java, and Python implementations. ### Source excerpt Discover how Temporal optimizes customer workflows in loyalty programs, managing actors and tasks efficiently to enhance customer engagement and retention. ## Workflows as Actors: Is it really possible? DevFeed: [Workflows as Actors: Is it really possible?]() Original publisher: [Read original article]() Author: Fitz Published: 2023-06-27T22:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Actor](), [systems](), [Object-oriented programming (OOP)](), [Complex Systems]() Tags: [actor](), [blog-post](), [concurrent](), [design-patterns](), [object-oriented](), [parallel](), [temporal-concepts](), [workflows]() ### AI overview This article introduces the Actor Model and compares actors with objects, focusing on message passing, state, actor creation, and encapsulation. It then explains how Temporal Workflows can be built to behave like actors. ### Source excerpt An overview on how to use Temporal Workflows in the form of an Actor Model ## Audit Logs now available in Apollo Studio Enterprise DevFeed: [Audit Logs now available in Apollo Studio Enterprise]() Original publisher: [Read original article]() Author: David Isquick Published: 2021-08-05T10:00:00Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [audit](), [Security](), [Graphs](), [data](), [API](), [Command-line interface]() Tags: [actor](), [announcement](), [api](), [audit](), [billing](), [cli](), [compliance](), [enterprise](), [graph](), [incident](), [logs](), [organization](), [provisioning](), [schema](), [security]() ### AI overview Apollo introduces audit logs in Apollo Studio Enterprise, allowing organization administrators to export records of individual and automated actions affecting their graphs. Logs can be filtered by actor, action, graph, and time period, with data retained for up to 180 days. ### Source excerpt Today, we're introducing audit logs in Apollo Studio Enterprise. With audit logs, you can export a data file with key actions taken within your organization. As your graph scales to more teams in your organization, you need visibility into all of the potential change points on your graph that could affect its stability or performance. We built audit logs to give you more confidence in your graph governance and help you meet any compliance or security requirements. ## Why the author stopped using the term "actors" when explaining the Actor Model DevFeed: [Why the author stopped using the term "actors" when explaining the Actor Model]() Original publisher: [Read original article]() Author: Sergey Bykov Published: 2021-05-05T07:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [Actor](), [Concurrency](), [Scalability](), [.NET](), [Java]() Tags: [actor](), [c-sharp](), [concurrency](), [distributed-system](), [java](), [net](), [temporal-voices]() ### AI overview The author explains why he stopped using the term "actors" when discussing the Actor Model, focusing on conflation between local and distributed actors and their differing roles in concurrency. ### Source excerpt While it may have taken me quite some time to get around to answering it, 2020 wasn't an ordinary year by any measure. ## Proofpoint's TA410 Report Shows How Attack Details Can Inform Better Defenses DevFeed: [Proofpoint's TA410 Report Shows How Attack Details Can Inform Better Defenses]() Original publisher: [Read original article]() Author: Adam Published: 2020-06-14T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Threat Research](), [proofpoint](), [Malware](), [Actor](), [Code](), [execution](), [file]() Tags: [actor](), [attacks](), [code](), [commands](), [defender](), [delivery](), [execution](), [file](), [malware](), [proofpoint](), [report](), [research](), [threat-research]() ### AI overview The article praises Proofpoint's report on TA410 attacks against U.S. utilities for its factual analysis and detailed explanation of the attack chain. It argues that details such as malicious macros, file renaming, cmd use, certutil execution, and delivery infrastructure can help defenders design better protections. ### Source excerpt I want to call out some impressive aspects of a report by Proofpoint.