# Graphs Published articles for Graphs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Python 3.15 Preview: Sampling Profiler DevFeed: [Python 3.15 Preview: Sampling Profiler]() Original publisher: [Read original article]() Author: Bartosz Zaczyński Published: 2026-08-26T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [cpu](), [Processes](), [dashboards]() Tags: [concurrency](), [cpu](), [graphs](), [processes](), [profile](), [python](), [tracing](), [tutorial]() ### AI overview A tutorial previewing Python 3.15's new low-overhead sampling profiler. It covers profiling scripts, modules, threads, async tasks, and live processes, with CPU/I/O/GIL modes and outputs such as flame graphs, heatmaps, and a terminal dashboard. ### Source excerpt Explore Python 3.15's new sampling profiler and learn low-overhead profiling of scripts, threads, and live production processes. ## Why AI coding agents need context graphs DevFeed: [Why AI coding agents need context graphs]() Original publisher: [Read original article]() Author: Talia Kohan Published: 2026-08-25T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [AI-assisted coding](), [Graphs](), [coding](), [API](), [internal developer platform](), [context window](), [long-context]() Tags: [ai-coding-agents](), [api-governance](), [apis](), [context-window](), [developer](), [developer-platform](), [engineering](), [general](), [graphs](), [knowledge-graphs]() ### AI overview The article argues that AI coding agents struggle in real codebases primarily because they lack access to a knowledge graph connecting code, APIs, services, ownership, dependencies, policies, and related organizational context. It describes service catalogs, API registries, ownership maps, dependency graphs, and internal developer platforms as different forms of the same underlying structure. It also argues that simply increasing the context window does not solve retrieval and context-quality problems. ### Source excerpt AI coding agents don't fail from small context windows. They fail without a knowledge graph of your code, APIs, and vendors. The post Why AI coding agents need context graphs appeared first on Postman Blog. ## The Single Most Undervalued Fact of Linear Algebra DevFeed: [The Single Most Undervalued Fact of Linear Algebra]() Original publisher: [Read original article]() Author: Tivadar Danka Published: 2026-08-24T09:47:15Z Content type: opinion Language: en Sources: [The Palindrome]() Topics: [Graphs]() Tags: [animation](), [arts](), [audio](), [graphs](), [linear](), [linear-algebra](), [matrices](), [video]() ### AI overview The author presents matrices and graphs as related representations and describes remastering an earlier piece into a video with improved animation and audio recording. ### Source excerpt Matrices are graphs and graphs are matrices ## GraphRAG: How AI Answers Questions Hidden Across Many Documents DevFeed: [GraphRAG: How AI Answers Questions Hidden Across Many Documents]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-19T15:31:18Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Graphs](), [Retrieval-Augmented Generation](), [Artificial Intelligence]() Tags: [graphs](), [knowledge-graphs](), [rag](), [retrieval-augmented-generation](), [search]() ### AI overview This tutorial explains how GraphRAG addresses questions whose answers emerge from patterns across an entire document collection, contrasting it with standard RAG, which is designed to retrieve information from specific documents. ### Source excerpt GraphRAG was designed to handle the second kind of questions, and we are going to learn more about it in this article. ## Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy DevFeed: [Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-18T16:48:08Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Data analysis](), [GPU](), [Graphs](), [datasets](), [RAPIDS](), [Nvidia]() Tags: [accelerate](), [analysis](), [cuda-x](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [datasets](), [feature](), [featured](), [gpu](), [graphs](), [multi-gpu](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [training](), [vector](), [visualization]() ### AI overview This article explains how multi-GPU UMAP scales dimensionality reduction to datasets containing tens to hundreds of millions of vectors. A feature in NVIDIA cuML and cuVS 25.06 distributes all-neighbors kNN graph construction across multiple GPUs, enabling workloads of several hundred gigabytes to run in minutes while preserving nearest-neighbor relationships and accuracy. ### Source excerpt Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications... ## powa-archivist 5.3.0 is out! DevFeed: [powa-archivist 5.3.0 is out!]() Original publisher: [Read original article]() Published: 2026-08-17T00:00:00Z Content type: release Language: en Sources: [PostgreSQL news]() Topics: [PostgreSQL](), [releases](), [benchmarking](), [monitor](), [Graphs](), [real-time](), [GitHub]() Tags: [bugs](), [changelog](), [contributors](), [documentation](), [github](), [graphs](), [metrics](), [monitor](), [performance](), [postgresql](), [real-time](), [release](), [releases]() ### AI overview The PoWA team announces powa-archivist 5.3.0, a PostgreSQL Workload Analyzer extension that collects and aggregates metrics from multiple PostgreSQL instances, provides real-time charts and graphs for monitoring and tuning servers, and suggests index optimizations. The release also includes a fix for PostgreSQL 19 compatibility. ### Source excerpt Tainan, Taiwan - Sun 16 Aug 2026 powa-archivist 5.3.0 The PoWA team is pleased to announce the release of the version 5.3.0 of powa-archivist, the core extension of the PoWA project. PoWA (PostgreSQL Workload Analyzer) is a performance tool, compatible with all supported PostgreSQL versions. It allows to collect and aggregate metrics gathered from multiple PostgreSQL instances using various extensions covering all parts of PostgreSQL and provides real-time charts and graphs to help monitor and tune your servers. It also suggest optimizations, like global or per-query index suggestions, to easily improve performances. Misc Fix pg19 compatibility with pg19beta2 pg_stat_lock.wait_time datatype change (Julien Rouhaud) Links & Credits Thank to the users who reported bugs or submitted patches, they are all cited in the CHANGELOG file and the CONTRIBUTORS file. powa-archivist is an open project. Any contribution to build a better tool is welcome. You just have to send your ideas, features requests or patches using the github repository at github.com/powa-team/powa-archivist. Links Documentation: powa.readthedocs.io/ Download: github.com/powa-team/powa-archivist/releases Bug tracker: github.com/powa-team/powa-archivist/issues ## How Product Managers Can Use Graphs DevFeed: [How Product Managers Can Use Graphs]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-08-12T04:30:42Z Content type: tutorial Language: en Sources: [Product Growth]() Topics: [Graphs]() Tags: [graphs]() ### AI overview The article explains how product managers can use graphs. ### Source excerpt Peter Steinberg said Graphs are the new loops. And he's right. Here's how to use them as a PM. ## 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)" ## Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions DevFeed: [Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions]() Original publisher: [Read original article]() Author: Junpeng Niu Published: 2026-07-30T00:00:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [AI search](), [AI, ML & Data Engineering](), [Language models]() Tags: [algorithms](), [data](), [engineering](), [graphs](), [knowledge-graph](), [large-language-models-llms](), [llm](), [machine-learning](), [search](), [validation]() ### AI overview The article presents a feedback-driven system for verifying knowledge-graph relationships through live search interactions. It tests unverified parent-child or sibling edges in search suggestions, measures user engagement, and uses greedy exploration to balance data collection with result quality. ### Source excerpt Introduction The efficacy of semantic search relies on the accuracy of the underlying Knowledge Graph (KG). In high-velocity domains like on-demand food delivery or e-commerce, the catalog of entities like dishes, products, and merchants changes rapidly. Current methods for KG construction and maintenance face three critical challenges: Inaccuracy and hallucination from Large Language Models (LLMs): Automated models often infer relationships based on statistical text co-occurrence rather than semantic reality. For instance, an LLM might incorrectly classify "Pho" as a child of "Italian Noodle Soup" due to linguistic similarity, leading to irrelevant search results. Scalability limits of manual verification: Traditional verification relies on human annotators or domain experts. This approach is slow, expensive, and unable to keep pace with dynamic catalogs containing millions of entities. For example, daily changes in restaurant menus or grocery stock keeping units (SKUs). Error propagation in ranking: Inaccurate graph edges propagate errors downstream. If a parent-child relationship is wrong, query expansion algorithms will retrieve irrelevant items, directly degrading Click-Through Rate (CTR) and user trust. We introduce a feedback-driven verification engine that operationalizes the search interface as a validation environment. Key contributions include: User feedback-driven verification: The system treats unverified graph edges as hypotheses. Instead of accepting them as truth, it tests them against live traffic by injecting them into search suggestions and measuring user engagement. Hierarchical relationship refinement: Unlike systems that only validate entities (nodes), this framework validates structural links (edges). It confirms whether entity A is truly a parent, child, or sibling of entity B, ensuring structural integrity. Adaptive exploration: The system employs a greedy exploration policy. It intelligently balances exploitation by showing known good resul ## Knowledge Management Trends in 2026: What's Actually Changing DevFeed: [Knowledge Management Trends in 2026: What's Actually Changing]() Original publisher: [Read original article]() Author: Janeera Published: 2026-07-28T15:44:41Z Content type: article Language: en Sources: [Knowledge Management Tips, Best Practices and More]() Topics: [knowledge-management](), [Artificial Intelligence](), [Generative AI](), [Graphs]() Tags: [2026](), [agentic-ai](), [ai](), [consistency](), [data](), [documentation](), [generative-ai](), [governance](), [graphs](), [knowledge-graphs](), [knowledge-management](), [knowledge-management-software](), [knowledge-management-system](), [maintenance](), [metrics]() ### AI overview This article examines 2026 knowledge management trends, arguing that structured, governed knowledge is becoming foundational infrastructure for AI systems. It discusses semantic layers, knowledge graphs, agentic AI, conversational discovery, governance, and self-healing maintenance, while noting a shift toward metrics such as answer quality, search success, freshness, and consistency. ### Source excerpt According to Mordor Intelligence, the knowledge management software market is on track to ... The post Knowledge Management Trends in 2026: What's Actually Changing appeared first on Document360. ## \[no-LLM\] \`nix-weather-hs\` - analyzing un-built derivation graphs for cache availablility DevFeed: [\[no-LLM\] \`nix-weather-hs\` - analyzing un-built derivation graphs for cache availablility]() Original publisher: [Read original article]() Author: MagicRB Published: 2026-07-22T18:52:23Z Content type: article Language: en Sources: [Announcements - NixOS Discourse]() Topics: [Nix](), [Caching](), [Graphs](), [configuration]() Tags: [announcements](), [build](), [cache](), [configuration](), [graphs]() ### AI overview A forum post announces the first functional version of nix-weather-hs, a Nix project that checks cache availability across a derivation closure, builds an in-memory graph, and exports it in multiple formats. It is designed to help debug derivations that cannot be built because of resource limits or other reasons. ### Source excerpt no artificial intelligence, only human stupidity (specifically mine) Hey everyone, I am hereby announcing the first functional version of nix-weather-hs. This project is inspired and based on nix-weather by @cafkafk. For details see the README.org, but the brief version is that nix-weather-hs will check cache availability for a whole closure, build a graph in memory and then it allows you to export said graph into multiple formats. Before export there are a few filters available, which make the resulting graphs more readable. As an example you can use the following command to check the availability of a NixOS configuration, reduce the graph to the 2nd level leaves of the uncached portion. nix-weather-hs graph --filter 'uncached' --filter 'leaves@2' --output dot $(nix eval --raw .#nixosConfigurations.nixos.config.system.build.toplevel.drvPath) As you can see, nix-weather-hs currently works with derivations, which is a defining feature compared to nix-weather. My version was specifically built to debug cases, where you are unable to even built the derivation, due to resource limits or other reasons. If you want to contribute, open issues, you should be able to log into my Forgejo with GitHub (codeberg planned). If any issues arise, please ping me. If you want to reach out privately, to talk about, well anything, please don't hesitate to contact me here, on matrix @magic_rb:matrix.redalder.org or Signal magic_rb.01. If you're considering asking an LLM a question about this project, please ask me instead. Thank you for your time and hopefully nix-weather-hs is useful to you. 27 posts - 4 participants Read full topic ## How We Refresh Razorpay's Data Warehouse 10x Faster with Graphs and Indexes DevFeed: [How We Refresh Razorpay's Data Warehouse 10x Faster with Graphs and Indexes]() Original publisher: [Read original article]() Author: Amit Prabhu Published: 2026-07-14T14:06:16Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [data](), [airflow](), [Apache Spark](), [Amazon S3](), [Apache Iceberg](), [microservices architecture](), [parquet](), [Architecture & Design]() Tags: [airflow](), [analytics](), [apache-iceberg](), [architecture](), [batch](), [data](), [graphs](), [infrastructure](), [parquet](), [pipelines](), [razorpay](), [spark](), [trino](), [warehouse]() ### AI overview Razorpay describes its data warehouse refresh pipeline, which builds wide denormalized Facts by joining data from multiple microservices. The article covers the original Airflow- and Spark-based full-refresh process, the underlying lake formats and query layer, and the scaling challenges that led the team to reconsider refresh strategy, data layout, and high-cardinality dimensions. ### Source excerpt Contributors: Utkarsh Koppikar Rohan Background Razorpay provides the payment infrastructure for millions of merchants globally. Behind every payment, settlement, and refund is a microservices architecture where each service owns its own database. While this keeps services independent and scalable, it creates a challenge for stakeholders who need to see across those boundaries. The Data Platform team manages the infrastructure that bridges this gap. Transactional data flows into the lake via CDC pipelines, ingested onto S3 in Delta Lake, Apache Iceberg, or plain Parquet formats. On top of the lake, we build domain-specific warehouse tables -- wide, pre-joined tables that co-locate all the data a consumer needs, queryable via Trino. These power two use cases: Analytics (internal dashboards on Tableau and Superset) and Reporting (merchants and regulated entities who download structured data exports; Razorpay generates nearly a million such reports per month). The warehouse tables that power both use cases are called Facts. A Fact is a flat denormalised table on S3, produced by joining 10 to 30 microservice tables and materialising the result once. A settlement Fact, for example, merges payments, refunds, adjustments, and card details into a single wide row so that a dashboard or report reads from a single table instead of joining across services in real time. It is closer to a domain-specific materialised view than a classical data warehouse fact table. We maintain over 50 such Facts, and approximately 40% of all merchant reports are served directly from them. As data volumes and the number of entities per fact grew, the batch generation pipeline began to show its limits, prompting us to rethink the refresh strategy, the data layout, and how to handle high-cardinality dimensions. The rest of this post covers that journey. The Full Refresh Pipeline: Our Baseline and the Pain The original full-refresh pipeline was straightforward. Schedule: Airflow schedules Spark jobs o ## 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). ## Building a Stateful IT Service Desk Agent with LangGraph on Amazon EKS DevFeed: [Building a Stateful IT Service Desk Agent with LangGraph on Amazon EKS]() Original publisher: [Read original article]() Author: Sunil Ramachandra Published: 2026-06-26T14:16:08Z Content type: tutorial Language: en Sources: [AWS Open Source Blog]() Topics: [AI Agent](), [Amazon EKS](), [Kubernetes](), [Amazon DynamoDB](), [Persistence](), [FastAPI](), [OpenTelemetry](), [Orchestration](), [autoscaling](), [Graphs](), [tracing](), [Open Source]() Tags: [ai-agent](), [amazon-dynamodb](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [autoscaling](), [backend](), [graphs](), [kubernetes](), [open-source](), [opentelemetry](), [orchestration](), [persistence](), [tracing]() ### AI overview This article presents a stateful IT service desk AI agent built with LangGraph. It autonomously handles routine Level 1 support requests, escalates complex issues with context, persists state in Amazon DynamoDB, and uses Amazon EKS, FastAPI, OpenTelemetry, and Kubernetes autoscaling. ### Source excerpt IT support teams face a persistent challenge: employees expect instant answers to common questions (VPN setup, single sign-on troubleshooting, new-hire onboarding), but novel or complex issues still require human expertise. An AI agent that confidently answers, "How do I reset my VPN?" but hallucinates a response to "My IAM Identity Center session keeps expiring after [...] ## Knowledge graph retrieval-augmented generation (RAG): structured retrieval for AI agents DevFeed: [Knowledge graph retrieval-augmented generation (RAG): structured retrieval for AI agents]() Original publisher: [Read original article]() Author: Jim Allen Wallace Published: 2026-06-24T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [Graphs](), [AI search](), [Large Language Model]() Tags: [ai](), [embedding](), [graphs](), [knowledge-graph](), [rag](), [retrieval](), [tech-de]() ### AI overview This guide explains knowledge graph RAG as a structured retrieval approach for AI agents. It contrasts graph-based retrieval of connected entities and relationships with vector RAG's nearest-neighbor retrieval of isolated text chunks, and outlines how LLMs can build graphs from unstructured data to support multi-hop retrieval. ### Source excerpt A user asks your support agent: "is the slow-sync bug from my last ticket fixed in the version you told me to upgrade to?" Answering means connecting three documents: the customer's earlier ticket, the new release notes, and the engineering issue the ... ## The Depth-First Search Pattern: Exploring Trees and Graphs DevFeed: [The Depth-First Search Pattern: Exploring Trees and Graphs]() Original publisher: [Read original article]() Author: Franco Fernando Published: 2026-06-13T10:01:23Z Content type: tutorial Language: en Sources: [The Polymathic Engineer]() Topics: [Graphs]() Tags: [graphs](), [how-to]() ### AI overview This tutorial explains depth-first search, a systematic method for exploring trees and graphs by going as deep as possible before backtracking. ### Source excerpt How to systematically search through all paths by going as deep as possible before backtracking ## Trailmark turns code into graphs DevFeed: [Trailmark turns code into graphs]() Original publisher: [Read original article]() Author: "Scott Arciszewski" Published: 2026-04-23T12:00:00Z Content type: article Language: en Sources: [The Trail of Bits Blog](), [The Trail of Bits Blog]() Topics: [Graphs](), [mutation-testing](), [Claude](), [Code](), [Claude Code](), [test-coverage](), [Tree-sitter](), [Python]() Tags: [ai](), [claude](), [claude-code](), [code](), [graphs](), [mutation-testing](), [open-source](), [python](), [research-practice](), [software](), [testing](), [tool](), [tool-release]() ### AI overview Trailmark is an open-source library that parses source code into queryable call graphs and exposes them through a Python API that Claude skills can call. The article explains how graph-level reasoning can help analyze codebases, triage surviving mutation-testing mutants by security relevance, and support Claude Code skills for tasks such as test vector generation and protocol diagramming. ### Source excerpt We're open-sourcing Trailmark, a library that parses source code into a queryable call graph of functions, classes, call relationships, and semantic metadata, then exposes that graph through a Python API that Claude skills can call directly. Install it now: uv pip install trailmark "Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win." John Lambert's widely cited observation about network security applies just as well to AI-assisted software analysis. When Claude reasons about a codebase, it reasons about lists: findings from static analyzers, surviving mutants from mutation testing, and line-by-line coverage reports. But the question that actually matters is a graph question: can untrusted input reach this code, and what breaks if it's wrong? We built Trailmark to answer that question. It gives Claude a graph to think with instead of a list. We're also releasing eight Claude Code skills we've built on top of it, designed for mutation triage, test vector generation, protocol diagramming, and more. When lists fall short Mutation testing is a great example of a method that benefits from graph-level reasoning. It's one of the best ways to measure test quality. It makes small changes to your source code (e.g., swapping a < for <=, replacing + with -) and checks whether your tests catch the difference. Mutants that survive reveal gaps in your test suite that code coverage metrics might miss. The downside is that a mutation testing run on a real codebase can produce hundreds of surviving mutants of varying significance. This is very much a list. Some surviving mutants are equivalent: the mutation doesn't change the program's behavior because of structural or mathematical constraints that the mutation testing tool can't see. Some are in dead code; some are in error message formatting; some are in the finite field arithmetic that underpins every cryptographic operation in your library. A flat list of surviving mutants doesn't tell you ## netlab Graphs with Multi-Access Links DevFeed: [netlab Graphs with Multi-Access Links]() Original publisher: [Read original article]() Published: 2026-03-16T07:03:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [Graphs]() Tags: [devices](), [graph](), [graphs](), [interfaces](), [netlab](), [node](), [ordering](), [router]() ### AI overview This tutorial explains how to improve GraphViz topology graphs generated from netlab descriptions of multi-access links. It covers graph.rank and graph.linkorder, including a current limitation that leaves setting graph.linkorder on host interfaces as the practical solution. ### Source excerpt A netlab user wanted to create a nice-looking topology graph from a simple topology connecting a few devices to a broadcast (multi-access) link. I don't have his exact topology, so we'll use this one (skipping the details like setting device types) nodes: [ r1, r2, h1, h2 ] links: - r1-r2 - interfaces: [ r1, r2, h1, h2 ] This is what GraphViz generates based on netlab's description of the lab topology: Read more ... ## How Whatnot Built Atlas to Represent and Automate Support SOPs as Code DevFeed: [How Whatnot Built Atlas to Represent and Automate Support SOPs as Code]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-03-10T15:01:01Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [Code](), [Automation](), [Graphs](), [Python]() Tags: [ai](), [ai-agent](), [automation](), [code](), [customer-experience](), [graph](), [graphs](), [python](), [trust]() ### AI overview Whatnot describes Atlas, a workflow execution and automation engine that represents support procedures as directed graphs in code using a small Python DSL. The team first formalized workflows as manual agent-guided steps before adding automation. ### Source excerpt Your SOPs Are Already Code... You Just Can't Run Them Yet Paul Gut | CX & Trust Engineering Every support org has them: thousands of pages of branching, conditional logic scattered across flowcharts, docs, and the heads of your most tenured agents. "If the label's been generated, check the tracking status; if it shows delivered, verify the address on file..." The Standard Operating Procedures (SOPs) grow until they can no longer be managed. And when you're the fastest-growing live commerce platform in history, it all catches up with you even faster. At Whatnot, our agents handle everything from shipping questions on vintage sneaker orders to address changes on trading card shipments. Until recently, they were spending the majority of their time on work that required no human insight, like checking order prices, validating addresses, looking up tracking statuses -- routine data checks interspersed with the actual decision-making. Even worse, our more experienced agents had memorized the SOPs and stopped referencing them altogether, which made propagating even the slightest changes a nightmare. We needed SOPs to behave like code: automatable, deployable, version-controlled. And there's no better way to make something behave like code than to simply turn it into code. So in mid-2025, we built Atlas: our new workflow execution and automation engine. Formalize first, automate later The idea behind Atlas is simple: for every ticket, the engine traverses a directed graph of nodes, eventually reaching a resolution the agent can execute. The graph is represented in code (more on that decision later). For every ticket, the engine traverses a directed graph of nodes, eventually reaching a resolution the agent can execute. We built a small Python DSL to represent these graphs. Each node could be a decision ("Has a label been generated for this order?"), an action ("Generate a shipping label"), or a resolution ("Send the label to the user"). We also made data points -- reusable pieces ## New ways to learn math and science in ChatGPT DevFeed: [New ways to learn math and science in ChatGPT]() Original publisher: [Read original article]() Published: 2026-03-10T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [math](), [Artificial Intelligence](), [real-time](), [Graphs]() Tags: [ai](), [chatgpt](), [concepts](), [explore](), [graphs](), [learning](), [math](), [product](), [real-time](), [science]() ### AI overview ChatGPT introduces interactive visual explanations for more than 70 core math and science concepts. Learners can adjust variables and manipulate formulas to see how changes affect graphs and outcomes in real time, making abstract ideas more tangible. ### Source excerpt ChatGPT introduces interactive visual explanations for math and science, helping students explore formulas, variables, and concepts in real time. ## Aloha, Redpanda FY27 company kick-off DevFeed: [Aloha, Redpanda FY27 company kick-off]() Original publisher: [Read original article]() Author: Jenny Medeiros Published: 2026-02-24T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [AI Infrastructure](), [Redpanda-Connect](), [Serverless](), [Amazon Web Services](), [Disaster Recovery](), [MCP Server](), [Graphs](), [Google]() Tags: [ai](), [ai-infrastructure](), [aws](), [disaster-recovery](), [google](), [graphs](), [mcp-server](), [redpanda-connect](), [serverless]() ### AI overview Redpanda's FY27 company kick-off brought more than 160 employees together in San Diego to reflect on the company's progress and direction. The article highlights the Agentic Data Plane, Redpanda Cloud, Redpanda Serverless on AWS, disaster recovery in Redpanda 25.3, MCP server development, and new Redpanda Connect connectors, alongside talks about company culture and using AI to reshape work. ### Source excerpt From AI hackathons to hakas, here's what happened at our fourth company kick-off. ## netlab 26.02: KinD support, more EVPN/VXLAN DevFeed: [netlab 26.02: KinD support, more EVPN/VXLAN]() Original publisher: [Read original article]() Published: 2026-02-11T09:15:00Z Content type: release Language: en Sources: [ipSpace.net blog]() Topics: [Kubernetes](), [evpn](), [VXLAN](), [configuration](), [Containers](), [Graphs]() Tags: [configuration](), [container](), [docker](), [evpn](), [graphs](), [kubernetes](), [netlab](), [vxlan]() ### AI overview The netlab 26.02 release adds KinD-based Kubernetes cluster support, Layer 2 EVPN/VXLAN support on several platforms, graph generation for selected nodes or links, fabric plugin link parameters, and OSPFv3 reports. It also introduces additional container configuration methods, including direct Linux scripts and containerlab startup configurations, along with SSH readiness checks and a device boot timer. ### Source excerpt netlab release 26.02 is out, including the usual potpourri of goodies: Support for Kubernetes (KinD) clusters based on work by @wnagele Layer-2 EVPN/VXLAN support on Cat8000v, IOL, and IOLL2 netlab graph command can create graphs from a subset of nodes or links You can specify the parameters of core links in the fabric plugin OSPFv3 reports The fun part, however, are the new container configuration methods: Read more ... ## \[RevEng\]\[CS\] Fuzzy topological sorting DevFeed: [\[RevEng\]\[CS\] Fuzzy topological sorting]() Original publisher: [Read original article]() Published: 2026-01-08T23:00:00Z Content type: article Language: en Sources: [Dennis Yurichev]() Topics: [Sorting](), [Graphs](), [Algorithms](), [C++](), [Linux](), [Kernel](), [Terminal]() Tags: [algorithms](), [c-plus-plus](), [graphs](), [kernel](), [linux](), [sorting](), [terminal]() ### AI overview The article describes a fuzzy topological sorting method for graphs that may contain cycles. It uses simulated annealing to minimize backward references and maximize forward references, then applies the resulting ordering to call graphs from Linux 0.99.15 and the Boolector SMT solver to estimate function abstraction levels. ### Source excerpt [RevEng][CS] Fuzzy topological sorting ## An MCP Agent for Querying Link-State Routing Graphs DevFeed: [An MCP Agent for Querying Link-State Routing Graphs]() Original publisher: [Read original article]() Published: 2025-11-19T06:15:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [Model Context Protocol](), [Large Language Model](), [Graphs](), [networking](), [GitHub](), [IS-IS]() Tags: [ai](), [github](), [graphs](), [is-is](), [llm](), [mcp](), [network](), [routing]() ### AI overview The article highlights a demo that uses an LLM through an MCP agent to query collected link-state routing graphs. It points readers to the source code and related Topolograph, OSPF watcher, and IS-IS watcher projects. ### Source excerpt Vadim Semenov created a nice demo that allows you to use an LLM to query the collected link-state graphs through an MCP agent (SuzieQ would probably be faster and easier to deploy, but hey, AI). If you want to kick the tires, you'll find the source code on GitHub (Network AI assistant, MCP server for Topolograph service). You'll also need Vadim's previous projects: Topolograph and OSPF watcher or IS-IS watcher. [Next page]()