# graph Published articles for graph. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Microsoft прекратит поддержку модулей Graph для Windows PowerShell 5.x DevFeed: [Microsoft прекратит поддержку модулей Graph для Windows PowerShell 5.x]() Original publisher: [Read original article]() Author: maybe\_elf Published: 2026-09-17T07:07:54Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [PowerShell](), [Microsoft](), [API](), [REST API]() Tags: [api](), [graph](), [microsoft](), [microsoft-365](), [microsoft-graph](), [powershell](), [rest-api](), [tag-13acbb3718a7](), [tag-25f83bc28918](), [tag-82fb255df683](), [tag-c85b5c428c35](), [windows]() ### AI overview Microsoft is ending support for Microsoft Graph PowerShell modules on PowerShell 5.x through a planned 12-month transition. Version 2.x modules will continue to work during that period, but new features will target PowerShell 7.x and version 3.x modules will support only PowerShell 7.x. ### Source excerpt Microsoft объявила о прекращении поддержки модулей Graph для PowerShell 5.x и порекомендовала пользователям перейти на новую версию. В 2025 году компания сообщила о планах по выводу из эксплуатации некоторых компонентов Graph. Читать далее ## PostgreSQL 19 graph queries fail the 'would you ship this?' test DevFeed: [PostgreSQL 19 graph queries fail the 'would you ship this?' test]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-15T09:42:59Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [PostgreSQL](), [SQL](), [Graphs](), [Databases]() Tags: [bugs](), [concurrent](), [databases](), [graph](), [postgresql](), [sql]() ### AI overview The article reports that PostgreSQL 19's SQL/PGQ graph queries were rejected because of unresolved bugs. It also discusses concurrent REPACK as a way to reduce overnight maintenance calls for database administrators. ### Source excerpt SQL/PGQ gets bounced over unresolved bugs as concurrent REPACK promises fewer midnight calls for DBAs ## The Palindrome Announces a Graph Theory for Visual Learners Video Course DevFeed: [The Palindrome Announces a Graph Theory for Visual Learners Video Course]() Original publisher: [Read original article]() Author: Tivadar Danka Published: 2026-09-12T08:47:28Z Content type: opinion Language: en Sources: [The Palindrome]() Topics: [Graphs](), [math]() Tags: [graph](), [graph-theory](), [subscriber](), [video]() ### AI overview The Palindrome announces a planned comprehensive graph theory video course called "Graph Theory for Visual Learners" and launches a support campaign for paid subscribers and founding members. The project will use custom animations and include an upcoming video release. ### Source excerpt I'm creating The Palindrome's most ambitious video yet. Become a paid subscriber and be part of it. ## Vespa Newsletter, September 2026 DevFeed: [Vespa Newsletter, September 2026]() Original publisher: [Read original article]() Author: Bonnie Chase Published: 2026-09-08T00:00:00Z Content type: news Language: en Sources: [Vespa Blog]() Topics: [ann](), [Latency](), [Provisioning](), [Algorithm](), [telemetry](), [Graphs]() Tags: [2026](), [algorithm](), [cloud](), [features](), [graph](), [latency](), [newsletter](), [product](), [provisioning](), [ranking](), [retrieval](), [september-2026](), [telemetry](), [updates]() ### AI overview The September 2026 Vespa newsletter announces updates including time-constrained ANN search, sub-query ranking support, flexible provisioning, new rank features, and telemetry export. It also introduces Vespa.ai Live, an in-person community meetup focused on retrieval and ranking systems. ### Source excerpt Advances in Vespa include time-constrained ANN search, sub-query ranking support, flexible provisioning, new rank features and telemetry export ## Building Trust in AI DevOps: Validating the Harness Knowledge Graph DevFeed: [Building Trust in AI DevOps: Validating the Harness Knowledge Graph]() Original publisher: [Read original article]() Author: Vikram Sahu Published: 2026-08-31T18:37:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [Testing](), [Graphs](), [Artificial Intelligence](), [Software]() Tags: [ai](), [ai-evals](), [api](), [automated](), [data](), [devops](), [evals](), [graph](), [knowledge-graph](), [lifecycle](), [operational](), [other](), [pipeline](), [schema](), [sdlc](), [security](), [services](), [software](), [software-delivery](), [validation]() ### AI overview This article explains how Harness validates answers from its SDLC Knowledge Graph. Its multi-layered approach combines AI evaluations, schema traversal, API checks, direct product verification, production data, and shift-left testing to improve reliability. ### Source excerpt Discover our multi-layered validation approach combining AI evals to ensure reliable AI-powered software delivery insights. | Blog ## Pulumi Context API: query your infrastructure as a graph DevFeed: [Pulumi Context API: query your infrastructure as a graph]() Original publisher: [Read original article]() Author: Levi Blackstone Published: 2026-08-26T00:00:00Z Content type: release Language: en Sources: [Pulumi]() Topics: [API](), [Graphs](), [Cloud APIs](), [infrastructure as code (IAC)](), [Command-line interface](), [JSON](), [pulumi-neo]() Tags: [agent](), [ai-agents](), [api](), [cli](), [features](), [graph](), [iac](), [infrastructure-as-code-iac](), [json](), [product](), [pulumi](), [pulumi-cloud](), [pulumi-neo](), [rest-api]() ### AI overview Pulumi is launching the Context API, a read-only API that connects Pulumi-managed and discovered resources, stacks, and their relationships into a graph. Available in public preview for Enterprise and Business Critical organizations, it supports infrastructure-impact, coverage, and cleanup queries through the Pulumi CLI or REST API. Pulumi Neo uses it out of the box, and other authenticated agents can access its schema and query guidance. ### Source excerpt Every platform team fields the same questions: What is running? What breaks if we change this? What can we safely delete? The answers exist, but they're scattered across state files, cloud consoles, and the memories of whoever set things up. Today we're launching the Pulumi Context API, a read-only API that connects Pulumi-managed and discovered resources, stacks, and their relationships into a graph. It's designed agent-first: Pulumi Neo, our infrastructure agent, uses it out of the box, and other agents can fetch the current graph vocabulary and query guidance on demand. It's available in public preview for organizations on the Enterprise and Business Critical editions. Answers that follow infrastructure relationships Pulumi already records the resources your programs manage, their dependencies, how stacks consume each other's outputs, and the resources Pulumi Discovery finds outside infrastructure as code (IaC). The Context API connects this data so you can ask questions that depend on the relationships: Impact: Which stacks are affected if we upgrade this provider? If this stack changes, what consumes its outputs? Coverage: How much of our infrastructure lives outside IaC, and in which accounts? Cleanup: Which stacks have no dependents and are candidates for retirement? A query is a JSON document with a handful of clauses. anchor names the starting nodes, traverse follows relationships from there, and return chooses what comes back. You can run a query through the Pulumi CLI or REST API. Here's a selector that starts from AWS provider instances older than version 7.0.0 and follows incoming provided_by relationships back to the visible resources they manage: { "anchor": { "nodeType": "resource", "match": { "type": "pulumi:providers:aws", "fields": { "provider_version": { "op": "lt", "value": "7.0.0" } } } }, "traverse": [ { "edgeTypes": ["provided_by"], "direction": "in", "alias": "managed" } ], "return": { "select": ["anchor", "managed"] } } A response for one m ## Block frequency DevFeed: [Block frequency]() Original publisher: [Read original article]() Published: 2026-08-23T07:00:00Z Content type: article Language: en Sources: [MaskRay]() Topics: [LLVM]() Tags: [algorithm](), [graph](), [llvm]() ### AI overview This article explains how LLVM turns branch probabilities into per-block frequencies through linear-time propagation over loop-structured regions. It also notes that irreducible control flow reduces accuracy. ### Source excerpt Estimating branch probabilities says how one branch splits. BlockFrequencyInfo turns those local numbers into per-block frequencies, which nearly every profitability decision in LLVM ends up reading. The core is a linear-time propagation over loop-packaged regions. Where no such structure exists -- irreducible control flow -- the accuracy goes with it. ## Add the dependency graph to a Kotlin K2 migration checklist DevFeed: [Add the dependency graph to a Kotlin K2 migration checklist]() Original publisher: [Read original article]() Published: 2026-08-18T00:00:00Z Content type: tutorial Language: en Sources: [Koin - Cloud-Inject.io -Kotzilla]() Topics: [Kotlin](), [migration](), [Dependency injection](), [Graphs](), [Compiler](), [toolchain](), [Android Gradle Plugin](), [Compose]() Tags: [android-gradle-plugin](), [compiler](), [compose](), [dependency-injection](), [graph](), [kotlin](), [migration](), [rollback](), [toolchain](), [verification]() ### AI overview A practical checklist for Kotlin K2 migrations in projects that generate or validate dependency wiring. It recommends mapping the compiler and build toolchain, upgrading from a known compatibility matrix, cleaning and inspecting generated output, verifying dependency graphs across flavors and targets, testing runtime slices, and keeping rollback changes coherent. ### Source excerpt A K2 migration is usually planned around source compatibility and compiler diagnostics. Projects that generate or validate dependency wiring need another checklist: compiler plugin versions, generated sources, metadata compatibility, and graph verification on every target. Map the toolchain first Record the Kotlin plugin, Compose compiler plugin, KSP or annotation tooling, dependency-injection compiler plugin, Android Gradle plugin, and target libraries. Upgrade from a known matrix instead of selecting each latest version independently. ## Build a dependency-graph inventory before changing the container DevFeed: [Build a dependency-graph inventory before changing the container]() Original publisher: [Read original article]() Published: 2026-08-18T00:00:00Z Content type: tutorial Language: en Sources: [Koin - Cloud-Inject.io -Kotzilla]() Topics: [Dependency injection](), [Android](), [test](), [Gradle]() Tags: [android](), [build](), [configuration](), [dependency](), [graph](), [process](), [test]() ### AI overview This tutorial recommends creating a framework-independent dependency-graph inventory before migrating dependency injection. It explains how to document entry points, ownership, implementations, lifetimes, qualifiers, modules, replacement values for tests, and architectural constraints, then validate the design through smaller executable test slices. ### Source excerpt A dependency-injection migration often starts with framework syntax. That is too late. First describe the graph without using the framework's vocabulary. The inventory should show which objects exist, who owns them, and which runtime fact selects one implementation over another. Start from entry points List application entry points: the Android application, a worker, a navigation destination, a command-line process, and each test fixture. Trace the objects requested at each entry point. Stop the trace at explicit boundaries such as a database driver, HTTP transport, clock, file system, or platform service. ## What an Ontology for AI Agents Actually Needs DevFeed: [What an Ontology for AI Agents Actually Needs]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-14T12:38:18Z Content type: opinion Language: en Sources: [Data Engineering Weekly]() Topics: [Artificial Intelligence](), [Graphs](), [Structured-data](), [data]() Tags: [ai](), [ai-agents](), [data](), [graph](), [knowledge-graph](), [schema]() ### AI overview The article argues that an ontology for AI agents should be treated as a governed semantic system rather than a single file or graph. It distinguishes ontology meaning from knowledge-graph facts and explains how semantic capabilities support retrieval, planning, action, verification, and operational governance. ### Source excerpt How to think about the semantic system that makes an agent coherent, governable, and useful. ## Estimating branch probabilities DevFeed: [Estimating branch probabilities]() Original publisher: [Read original article]() Published: 2026-08-09T07:00:00Z Content type: article Language: en Sources: [MaskRay]() Topics: [LLVM]() Tags: [algorithm](), [analysis](), [functions](), [graph](), [llvm](), [structure]() ### AI overview This post explains how LLVM estimates branch probabilities when profile data is unavailable. It examines the heuristic that classifies control-flow blocks and successor paths using unreachable, cold, unwinding, and loop information, and presents a standalone reimplementation. ### Source excerpt LLVM's BranchProbabilityInfo assigns every multi-successor terminator a probability distribution over its successors. This post describes the estimation used when no profile is available and reimplements it as a standalone program. ## The day RBAC stops scaling: role explosion and what comes after DevFeed: [The day RBAC stops scaling: role explosion and what comes after]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-06T00:18:10Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Authorization](), [Access Control](), [Graphs]() Tags: [access-control](), [authorization](), [graph]() ### AI overview The article explains how per-resource permissions cause role explosion in growing B2B applications. It describes the operational and cognitive costs of increasingly complex RBAC models and presents Zanzibar's relationship-based graph model as an alternative. ### Source excerpt Role-based access control breaks when roles outnumber users. Why per-resource permissions cause role explosion, and how to move past it without a rewrite. ## Philip Rathle on why AI agents keep reaching for a knowledge graph DevFeed: [Philip Rathle on why AI agents keep reaching for a knowledge graph]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-05T23:21:28Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Neo4j](), [Graphs](), [graph-database](), [data]() Tags: [agents](), [ai](), [ai-agents](), [data](), [graph](), [graph-database](), [knowledge-graph]() ### AI overview WorkOS CEO Michael Grinich interviews Neo4j CTO Philip Rathle about using knowledge graphs as an AI knowledge layer. Rathle argues that agents need deterministic, explainable access to structured company data for questions where accuracy, sovereignty, and access controls are critical, while noting that graph retrieval can provide context for better model decisions. ### Source excerpt Neo4j CTO Philip Rathle on why over 70% of new business is now the AI knowledge layer, where agents need deterministic answers, and how error rates compound. ## Waiting for PostgreSQL 19 - SQL Property Graph Queries (SQL/PGQ) DevFeed: [Waiting for PostgreSQL 19 - SQL Property Graph Queries (SQL/PGQ)]() Original publisher: [Read original article]() Author: depesz Published: 2026-07-31T16:57:44Z Content type: opinion Language: en Sources: [select \* from depesz;]() Topics: [PostgreSQL](), [SQL](), [Graphs](), [function](), [pattern matching]() Tags: [function](), [graph](), [graphs](), [pattern-matching](), [pg19](), [pgq](), [postgresql](), [property](), [sql](), [uncategorized](), [waiting]() ### AI overview The article discusses PostgreSQL's SQL/PGQ implementation for property graph queries, including GRAPH_TABLE, graph pattern matching, and related DDL commands. It notes that the change was later rolled back and expresses the author's uncertainty about the practical benefits and usability of the syntax. ### Source excerpt Important update This change has been rolled back. Discussion can be found here. On 16th of March 2026, Peter Eisentraut committed patch: SQL Property Graph Queries (SQL/PGQ) Implementation of SQL property graph queries, according to SQL/PGQ standard (ISO/IEC 9075-16:2023). This adds: - GRAPH_TABLE table function for graph pattern matching - DDL commands ... Continue reading "Waiting for PostgreSQL 19 - SQL Property Graph Queries (SQL/PGQ)" ## Charting your team's knowledge & skills map DevFeed: [Charting your team's knowledge & skills map]() Original publisher: [Read original article]() Author: Anton Zaides Published: 2026-07-21T06:01:00Z Content type: opinion Language: en Sources: [Manager.dev]() Topics: [Graphs](), [GitHub](), [Pull Request](), [Claude](), [systems](), [Open Source]() Tags: [claude](), [collaboration](), [github](), [graph](), [process](), [scope](), [soft-skills]() ### AI overview An engineering manager describes using Unblocked's Social Graph Builder to visualize collaboration and codebase expertise from GitHub pull request history, then explains a spreadsheet-based method for mapping a team's product knowledge, technology skills, soft skills, and non-engineering skills. The article argues that expert knowledge is important for evaluating AI-assisted work with Claude and avoiding harmful implementations in unfamiliar domains. ### Source excerpt If you rebuilt your team again today, knowing what you know now, would it look the same? ## How to Build Unified Agent Memory from Scratch DevFeed: [How to Build Unified Agent Memory from Scratch]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-07-14T05:01:54Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI Engineering](), [Claude Code](), [Graphs](), [LangChain](), [Large Language Model](), [Database](), [Model Context Protocol]() Tags: [agent](), [ai-engineering](), [claude-code](), [graph](), [graph-database](), [knowledge-graphs](), [langchain](), [llm](), [mcp]() ### AI overview A tutorial on building a unified agent memory layer from scratch using knowledge graphs, including ingestion, querying, and serving. It discusses trade-offs among vector databases, graph databases, temporality, versioning, MCP servers, CLIs, and skills, and explains why understanding the underlying memory layer matters. ### Source excerpt Ingest, query, and serve a unified memory from a single database. ## DoorDash's Personalization Stack Uses Semantic Memory, Embeddings, and Context Graphs DevFeed: [DoorDash's Personalization Stack Uses Semantic Memory, Embeddings, and Context Graphs]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-07-13T15:30:43Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [personalization](), [Embeddings](), [Language models](), [Large Language Model](), [data](), [Graphs]() Tags: [ai](), [data](), [embeddings](), [graph](), [llms](), [ml](), [personalization](), [software]() ### AI overview The article describes DoorDash's unified memory platform for personalization. It explains how behavioral signals are converted into semantic memory using layered context, LLM-synthesized memory blocks, versioned manifests, asymmetric dense embeddings, and a consumer context graph. ### Source excerpt PLUS: Uniqlo Decoded 🚨, Agentic patterns⚡, Be the idiot mindset 👨💻 ## How Razorpay runs network-isolated Hermes AI agents for employees DevFeed: [How Razorpay runs network-isolated Hermes AI agents for employees]() Original publisher: [Read original article]() Author: ashwath kumar Published: 2026-07-12T14:22:53Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [AI Agent](), [Kubernetes](), [Open Source](), [Cloud](), [data](), [Embeddings](), [Graphs]() Tags: [ai](), [ai-agent](), [architecture](), [autonomous](), [cloud](), [credentials](), [embeddings](), [github](), [google](), [graph](), [kubernetes](), [network](), [open-source](), [slack]() ### AI overview Razorpay describes its platform for running Hermes, an open-source AI agent, for employees. Each agent runs in an isolated Kubernetes namespace with encrypted storage, a separate cloud identity, and its own network policy. The article explains how the platform supports persistent autonomous sessions while limiting access to other employees' data and the public internet. ### Source excerpt How we run a personal AI agent for everyone at Razorpay: always on, multi-model, and safe by construction. Contributors: Siddharth Tripathi Today, more than 220 Razorpay employees each have their own always-on AI agent. On a typical day, about 84 of them are actively working. Every one runs in its own isolated Kubernetes namespace, with its own encrypted storage, its own cloud identity, and its own network policy. It learns new skills as its owner works, and it keeps running long after they close their laptop: one employee's agent has already logged more than 15,000 sessions, 90% of them while its owner was asleep or away. Provisioning a new one takes under two minutes. Running the agents was never the hard part. Running them safely was: 220 of them, each with shell access and live credentials, without any single agent becoming a path into another employee's data or out to the open internet. The answer came down to one design choice, and everything in this post is a consequence of it: Isolation is a property of the infrastructure, not the application. Hermes itself is an open-source agent by Nous Research; what we built is the platform that runs it safely & isolated, for the whole company. Proof It's Real: One Instance, Eight Weeks In Before any of the architecture, here's the proof that people actually use this. The following is one real instance from our cluster, over its first eight weeks (numbers pulled live, the person anonymised). In eight weeks, this one user's Hermes ran 15,039 sessions. Only 391 of those were the person sitting down to chat with it; the other 13,570 were autonomous runs the agent kicked off on its own schedule while its owner was asleep or in meetings. That ratio is the whole idea in one statistic: the assistant does most of its work when you're not there. What is it doing in those runs? They'd wired up 21 always-on scheduled jobs that turn Hermes into a personal intelligence service: Ingest. Every hour it pulls from 23 Slack channels (plus ## Irreducible loops DevFeed: [Irreducible loops]() Original publisher: [Read original article]() Published: 2026-07-12T07:00:00Z Content type: tutorial Language: en Sources: [MaskRay]() Topics: [Graphs](), [Algorithms](), [Code](), [LLVM]() Tags: [algorithm](), [analysis](), [code](), [entries](), [flow](), [graph](), [graphs](), [llvm](), [loops](), [static](), [structure]() ### AI overview This technical post explains why dominator-based natural-loop detection fails for irreducible control-flow graphs, which can have multiple entries. It describes reducibility, the irreducible three-node pattern, and a DFS-based loop-nesting forest using Havlak's convention. ### Source excerpt The dominator tree lets us identify natural loops: a back edge T->H whose head H dominates its tail T defines a loop with the single entry H. This works only for reducible control flow graphs. Optimized machine code and decompiler output routinely contain irreducible loops, which have more than one entry and thus no dominating header, so the dominator-based method cannot see them. This post builds a loop-nesting forest for an arbitrary CFG with the single-pass depth-first search of 韦韬、毛剑、邹维、陈宇(Tao Wei, Jian Mao, Wei Zou & Yu Chen) A New Algorithm for Identifying Loops in Decompilation, SAS 2007 (The 14th International Static Analysis Symposium). ## Graph-Shaped Shared Memory for AI Agents DevFeed: [Graph-Shaped Shared Memory for AI Agents]() Original publisher: [Read original article]() Author: Neo Kim Published: 2026-07-07T11:32:10Z Content type: tutorial Language: en Sources: [System Design Newsletter]() Topics: [AI Agent](), [Graphs](), [graph-database](), [Amazon S3](), [Retrieval Augmented Generation (RAG)](), [Open Source]() Tags: [ai-agent](), [ai-agents](), [amazon-s3](), [graph](), [graph-database](), [open-source](), [rag]() ### AI overview A guide to shared memory for multiple AI agents, using Omnigraph as a case study. It explains why shared folders and vector databases can fail, how graph-shaped memory represents knowledge, and how transactions, versioning, and combined retrieval methods can help agents share context. ### Source excerpt #160: A full guide to graph shaped memory for AI agents ## The Aboulafia Graph Has Dihedral Symmetry DevFeed: [The Aboulafia Graph Has Dihedral Symmetry]() Original publisher: [Read original article]() Author: Yehonathan Sharvit Published: 2026-07-06T10:00:00Z Content type: article Language: en Sources: [Klipse]() Topics: [Graphs]() Tags: [aboulafia](), [graph](), [math](), [mirror](), [rotation](), [series](), [symmetry]() ### AI overview The final article in a four-part series explains the dihedral symmetry of the Aboulafia graph. It connects the graph's recursive construction to a rotation and a reflection, and describes a proof that these are its only symmetries. ### Source excerpt Aboulafia's Tserouf - Part 4 of 4 <- Previous: Too big to draw, but yet drawable ## Как проект из ШАДа попал в Spotlight статей на конференции ICML 2026 DevFeed: [Как проект из ШАДа попал в Spotlight статей на конференции ICML 2026]() Original publisher: [Read original article]() Author: mightyneighbor (Яндекс) Published: 2026-07-06T07:04:35Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [GPU](), [Graphs](), [Machine learning]() Tags: [2026](), [ai](), [cuda](), [gpu](), [graph](), [icml](), [icml-2026](), [ml](), [research](), [spotlight](), [tag-1e7f0701b819](), [tag-4004cf5948d3]() ### AI overview The article explains why graph neural networks underutilize modern GPUs: their irregular, sparse memory access patterns leave the hardware waiting for data. It describes a project that investigated the problem and produced three families of specialized GPU kernels. The resulting paper, "On Efficient Scaling of GNNs via IO-Aware Layer Implementations," was accepted to ICML 2026 as a Spotlight paper. ### Source excerpt Граф из миллионов вершин не загружает современную GPU на все 100%: видеокарта почти всё время не вычисляет, а ждёт загрузки данных из памяти. Графовые нейросети, или GNN, упираются в это давно: сами операции достаточно простые, но доступ к памяти нерегулярный и разреженный. И чем мощнее GPU, тем заметнее недостаточная её утилизация. Идея выросла из проектного курса в ШАДе. Толчком стало то, что один из самых популярных фреймворков для работы с графами, Deep Graph Library, на момент начала работы не обновлялся уже около года -- это знак того, что в области что-то застряло. Меня зовут Федя Великонивцев, я старший исследователь Yandex Research, руковожу группой, которая занимается эффективными вычислениями на GPU. На том курсе мы с коллегами -- Дарьей Фоминой из команды ML-инфраструктуры Яндекса, Вячеславом Ждановским из команды разработки инференса -- и студентами Даниилом Красильниковым, Алексеем Бойковым и Андреем Долговязовым взялись выяснить, почему графовые нейросети тормозят на современных GPU. Так появился проект, который мы оформили в отдельную статью -- On Efficient Scaling of GNNs via IO-Aware Layer Implementations. Её приняли на ICML-2026 со статусом Spotlight. Для контекста: из 23 918 поданных работ приняли 6 352 (26,6%), а Spotlight достался только 536 работам -- это 2,2% заявок с самыми высокими оценками программного комитета. Дальше расскажу, как мы прошли путь от этого вопроса до трёх семейств специализированных GPU-кернелов -- с парой неожиданных находок по дороге. Читать далее ## Introducing Autonomous Worker Agents DevFeed: [Introducing Autonomous Worker Agents]() Original publisher: [Read original article]() Author: Jyoti Bansal Rohan Gupta Published: 2026-06-30T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [Deployment](), [Security](), [Architecture & Design](), [Model Context Protocol]() Tags: [agents](), [ai-agent](), [governance](), [graph](), [mcp](), [release](), [safety](), [security](), [vpc]() ### AI overview Harness introduces Autonomous Worker Agents for software delivery. The agents run as pipeline steps or independently, use organizational context from the Harness Knowledge Graph and Harness MCP, and produce auditable outputs under controls such as scoped credentials, OPA policies, approval gates, and audit trails. ### Source excerpt Harness launches Autonomous Worker Agents: AI that runs as pipeline steps, with the governance enterprises need to trust agents in production | Blog ## How Airbnb Built an Internal Identity Graph with JanusGraph and DynamoDB DevFeed: [How Airbnb Built an Internal Identity Graph with JanusGraph and DynamoDB]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-05-25T15:30:54Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [graph-database](), [Latency](), [Scalability](), [Software Engineering](), [Amazon Web Services](), [Artificial Intelligence]() Tags: [database](), [graph](), [graph-database](), [infrastructure](), [latency](), [low-latency](), [scalability](), [software-engineering]() ### AI overview The article describes Airbnb's internal identity graph infrastructure for trust and safety use cases such as fraud detection, linked-account discovery, and suspicious-activity flagging. It reports that Airbnb replaced a third-party graph database with an internal system using JanusGraph for traversal and DynamoDB for persistence, alongside custom transaction handling, parallel fetches, query rewrites, and tenant isolation. ### Source excerpt PLUS: Vector database deep dive 👨💻, Avoiding AI code slop 🤖, DoorDash clusterless ML feature store ⚡ [Next page]()