# apache Published articles for apache. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## PDFx - Extract references and metadata from PDF documents, and download all referenced PDFs DevFeed: [PDFx - Extract references and metadata from PDF documents, and download all referenced PDFs]() Original publisher: [Read original article]() Author: Chris Hager Published: 2026-09-17T02:48:44.791916Z Content type: tutorial Language: en Sources: [Chris Hager]() Topics: [pdf](), [Python](), [Command-line interface](), [JSON](), [pip](), [Open Source](), [GitHub]() Tags: [apache](), [broken-links](), [command-line](), [download](), [github](), [install](), [json](), [library](), [open-source](), [pdf](), [python]() ### AI overview PDFx is an Apache-licensed open-source tool that extracts references and metadata from PDF documents. It detects PDF, URL, arXiv, and DOI references, can download referenced PDFs in parallel, identify broken hyperlinks, extract PDF text, and output results as text or JSON. It is available as a command-line tool and Python package, with support for local and online PDFs. ### Source excerpt Reading over this paper and its references recently, I thought it would be great to be able to download all the references at once... This inspired me to write a little tool to do just that, and now it's done and released under the Apache open source license: https://github.com/metachris/pdfx Features Extract references and metadata from a given PDF Detects pdf, url, arxiv and doi references Fast, parallel download of all referenced PDFs Find broken hyperlinks (using the -c flag) (more) Output as text or JSON (using the -j flag) Extract the PDF text (using the --text flag) Use as command-line tool or Python package Compatible with Python 2 and 3 Works with local and online pdfs Getting Started Grab a copy of pdfx with easy_install or pip and run it: ## Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads DevFeed: [Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-16T14:42:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [dropbox](), [Artificial Intelligence](), [API](), [Orchestration](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-ml-data-engineering](), [apache](), [apis](), [architecture](), [architecture-design](), [asynchronous-architecture](), [backend](), [caching](), [data-pipelines](), [development](), [distributed-systems](), [dropbox](), [dropbox-riviera-ai-platform](), [embeddings](), [enterprise-content-management](), [large-language-models](), [ml-data-engineering](), [model-context-protocol-mcp](), [news](), [orchestration](), [platform-engineering](), [plugins](), [rag](), [tika]() ### AI overview Dropbox has expanded Riviera from an internal file-preview service into a content-processing platform supporting more than 300 file formats and over 100 transformation capabilities. The platform supports Dropbox products including Search, Replay, Sign, and Dash, and provides APIs for asynchronous document conversion, media transcription, and structured metadata extraction for AI and RAG workflows. ### Source excerpt Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows. By Leela Kumili ## Unifying governance across engines and catalogs in the Open Lakehouse DevFeed: [Unifying governance across engines and catalogs in the Open Lakehouse]() Original publisher: [Read original article]() Author: Daniel Weeks; Ryan Blue; Andrei Tserakhau Published: 2026-09-10T15:05:10Z Content type: article Language: en Sources: [Databricks]() Topics: [Apache Iceberg](), [data](), [databricks](), [Apache Spark](), [DuckDB]() Tags: [apache](), [apis](), [catalog](), [data](), [databricks](), [duckdb](), [governance](), [open](), [product](), [spark]() ### AI overview The article examines two additions to the Apache Iceberg REST Catalog specification: read restrictions and catalog labels. It explains how they support governance across catalogs and engines, including centralized and delegated enforcement, and discusses trust requirements for engines such as Spark, DuckDB, and Trino. ### Source excerpt In our previous posts, we showed how open table formats, open APIs and unified governance... ## Troubleshoot Kafka issues across every layer of your stack with Kafka Console DevFeed: [Troubleshoot Kafka issues across every layer of your stack with Kafka Console]() Original publisher: [Read original article]() Author: Tori Engler; Shelly Matskel Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Kafka](), [configuration]() Tags: [apache](), [data-streams-monitoring](), [debugging](), [event-streaming](), [infrastructure](), [kafka](), [monitor](), [performance](), [root-cause-analysis]() ### AI overview An overview of Datadog Kafka Console for diagnosing Kafka health and performance issues, inspecting messages, and tuning configurations. ### Source excerpt Learn how Kafka Console helps you identify Kafka issues, inspect messages, and tune configurations with infrastructure and app context. ## Typst 0.15 adds variable fonts, MathML, and multiple bibliographies DevFeed: [Typst 0.15 adds variable fonts, MathML, and multiple bibliographies]() Original publisher: [Read original article]() Author: jake Published: 2026-09-09T15:37:55Z Content type: news Language: en Sources: [LWN.net]() Topics: [Typst](), [pdf](), [SVG](), [Variable Fonts](), [LaTeX](), [Rust]() Tags: [apache](), [features](), [fonts](), [latex](), [new-features](), [open-source](), [pdf](), [rust](), [variable-fonts]() ### AI overview Typst 0.15 adds support for variable fonts, MathML, multiple bibliographies, and other features. The article examines variable-font support and demonstrates it with Roboto Flex and Zycon. ### Source excerpt Typst is a system for typesetting documents into various formats: PDF, SVG, PNG, and, in progress, HTML. It is adept at handling technical material, and is often considered to be an eventual LaTeX replacement. We last looked in on Typst a year ago, when it had reached version 0.13. A new version, 0.15, was released in June with lots of new features, including support for variable fonts, MathML, multiple bibliographies, and more. Typst is free, Apache-2.0-licensed software, programmed in Rust. ## IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license DevFeed: [IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license]() Original publisher: [Read original article]() Author: Roman Vaculin; Wesley M Gifford; Jiri Navratil; Chandra Reddy; Ayhan Sebin Published: 2026-09-09T15:36:24Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [foundation-models](), [Temporal data](), [releases](), [benchmarking](), [Open Source Models & Datasets](), [Inference]() Tags: [apache](), [benchmarking](), [foundation-models](), [ibm](), [inference](), [model-architecture](), [open-source](), [releases](), [time-series](), [zero-shot]() ### AI overview IBM released Granite Time Series PatchTST-FM-r2, a roughly 385M-parameter time-series foundation model for zero-shot forecasting. The article covers its architecture, probabilistic forecasting, missing-value imputation, benchmark results, licensing, and available reproducibility resources. ### Source excerpt Time-series foundation models are changing the way forecasting systems are built. Instead of training and maintaining a separate model for every dataset, users can use a pretrained model and generate forecasts zero-shot. IBM has released Granite Time Series PatchTST-FM-r2, the latest model in the Granite TSFM family (github, blog). ## NeoMME: an efficient Multimodal-native and Multilingual Encoder DevFeed: [NeoMME: an efficient Multimodal-native and Multilingual Encoder]() Original publisher: [Read original article]() Author: Tony Wu; Aurélien Lac Published: 2026-09-03T13:13:48Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vlm](), [Fine-tuning](), [Large Language Model](), [GPU]() Tags: [apache](), [architecture](), [diffusion](), [embeddings](), [fine-tuning](), [gpu](), [hugging-face](), [multimodal](), [quantization](), [retrieval](), [training](), [transformers](), [vector](), [vision](), [vlm]() ### AI overview NeoMME is a family of multilingual multimodal encoders trained from scratch with a masked discrete-diffusion objective. It uses one bidirectional Transformer for text tokens and image patches, and is fine-tuned for visual document retrieval with dense and late-interaction embeddings. ### Source excerpt We introduce NeoMME, a family of 260M and 800M multilingual multimodal encoders. Unlike many generative visual language models, NeoMME does not use a separate pretrained vision tower or a causal language model. A single bidirectional Transformer processes both text tokens and raw image patches, and we train the entire model from scratch with a masked discrete-diffusion objective. We fine-tuned NeoMME for visual document retrieval using ColPali's page-image approach. ## Rerouting the Stream: How Lyft Moved to the Apache Flink Operator DevFeed: [Rerouting the Stream: How Lyft Moved to the Apache Flink Operator]() Original publisher: [Read original article]() Author: Maheep Myneni Published: 2026-08-31T19:08:16Z Content type: article Language: en Sources: [Lyft Engineering - Medium]() Topics: [Apache Iceberg](), [Streaming](), [Kubernetes](), [Open Source](), [autoscaling](), [Tooling](), [Architecture & Design]() Tags: [apache](), [apache-flink](), [autoscaling](), [data-platforms](), [infrastructure](), [kubernetes](), [lyft](), [open-source](), [production](), [streaming](), [streaming-data-processing](), [tooling]() ### AI overview Lyft describes its migration from an internally developed Flink Kubernetes operator to the open-source Apache Flink Kubernetes Operator. The move addressed maintenance burden, technical debt, feature gaps, outdated dependencies, and growing real-time streaming demands, while providing capabilities such as autoscaling, memory tuning, safer upgrades, and automatic rollbacks. ### Source excerpt Written by Maheep Myneni, Arda Kuyumcu, and Prem Santosh Udaya Shankar at Lyft. Why We Migrated: Technical Debt Meets Modern Streaming Demands Over the past several quarters, Lyft's Streaming Compute team retired our internally developed Flink Kubernetes operator and moved our entire streaming fleet onto the open-source Apache Flink Kubernetes operator. This post is about why we made the switch, how we pulled it off incrementally without disrupting users, and the follow-on work it took to actually get the benefits we were after. Back in 2020, when we first architected the Lyft Flink Kubernetes Operator, it was exactly what we needed. At that time, the open-source community hadn't yet built a dedicated control plane, so we built our own to manage all streaming applications on Kubernetes. It worked well for our initial workloads, but as our real-time data needs increased and our engineers' scope of ownership grew in both breadth and complexity, the cracks started to show. First came the maintenance burden. Our operator had become a relic of Lyft's early Kubernetes days, kept alive by a growing pile of custom code. Every Flink version upgrade meant carefully picking through layers of accumulated technical debt and hoping nothing broke on the way through. Second came the feature gap. Streaming tooling kept moving, and our engineers kept asking for capabilities that had become table stakes elsewhere, such as autoscaling to right-size jobs, automatic rollbacks on failed deploys, and an end to hand-tuning CPU and memory. Each request left us with two options, neither of which was ideal. We could explain why we couldn't support it yet, or spend weeks rebuilding something the open-source community had already shipped. Third was the dependency problem. We were pinned to outdated libraries. That doesn't break anything today, but it almost always creates new issues down the line. Security patches lagged, modern Kubernetes features stayed out of reach, and every quarter we waite ## Moving from Minimus to Docker Hardened Images DevFeed: [Moving from Minimus to Docker Hardened Images]() Original publisher: [Read original article]() Author: Vishrut Iyengar Published: 2026-08-25T22:27:06Z Content type: article Language: en Sources: [Docker]() Topics: [Docker Hardened Images](), [migration](), [Docker](), [supply-chain-security](), [Docker Hub](), [Open Source](), [Debian](), [Artificial Intelligence]() Tags: [apache](), [ci](), [community](), [debian](), [docker](), [docker-hardened-images](), [docker-hub](), [enterprise](), [guide](), [migration](), [open-source](), [products](), [security](), [solutions](), [supply-chain](), [vulnerabilities]() ### AI overview Docker explains how Minimus customers can migrate to Docker Hardened Images before the Minimus registry goes offline on October 22, 2026. The article highlights the 60-day maintenance window, free migration assistance, DHI's open-source Apache 2.0 catalog, and a drop-in migration process centered on updating Dockerfile FROM lines. ### Source excerpt The Minimus registry goes offline on October 22. Here is the migration path, the free help Docker is offering, and where to start. ## Granite 4.2 LLMs: How They're Built DevFeed: [Granite 4.2 LLMs: How They're Built]() Original publisher: [Read original article]() Author: Yousaf Shah; Swanand Kadhe; Riddhiman Moulick; Ashish Sunil Agrawal; Santosh Borse Published: 2026-08-25T15:14:14Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Language models](), [AI Chat](), [AI-assisted coding](), [releases]() Tags: [agentic](), [agents](), [apache](), [chain-of-thought](), [code](), [fine-tuning](), [grouped-query-attention](), [ibm](), [llms](), [models](), [post-training](), [reasoning](), [reinforcement-learning](), [sglang](), [tool](), [training](), [transformer-architecture](), [vllm]() ### AI overview Granite 4.2 is a family of 3B, 8B, and 30B dense decoder-only reasoning language models. The article covers their training pipeline, thinking modes, native tool calling, and agentic reinforcement learning for the 8B and 30B models. ### Source excerpt Authors: Granite Team, IBM TL;DR: Granite 4.2 is our first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B. These models are post-trained from Granite-4.1 base models. Granite-4.1 base models were pre-trained from scratch on roughly 15T tokens with a five-phase strategy that extends the context window to 512K tokens, supervised fine-tuned on chain-of-thought, reasoning, and agentic-trajectory data, then post-trained with a multi-stage reinforcement... ## Granite 4.2 brings native reasoning to enterprise agents DevFeed: [Granite 4.2 brings native reasoning to enterprise agents]() Original publisher: [Read original article]() Author: Mike Murphy; Kim Martineau Published: 2026-08-25T15:00:00Z Content type: release Language: en Sources: [IBM Research]() Topics: [ibm](), [Large Language Model](), [Artificial Intelligence](), [Loop Engineering](), [Reinforcement learning](), [foundation-models](), [Software Engineering](), [Cloud]() Tags: [agentic](), [agents](), [ai](), [apache](), [cloud](), [fine-tuning](), [ibm](), [large-language-models](), [reasoning](), [reinforcement-learning](), [release](), [terminal]() ### AI overview IBM is releasing Granite 4.2 language models in 3B, 8B, and 30B sizes for enterprise agentic workflows. The models provide native reasoning, tool calling, instruction following, coding support, and deployment across cloud, on-premises, and edge environments. They are released under the Apache 2.0 license and trained with a multi-stage reinforcement learning process. ### Source excerpt IBM's new open Granite models are designed for agentic AI, combining reasoning, tool use, coding, instruction following, and speech capabilities. ## AWS Glue 6.0 now available with 30% lower price and full Apache Iceberg v3 support DevFeed: [AWS Glue 6.0 now available with 30% lower price and full Apache Iceberg v3 support]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-21T18:53:26Z Content type: release Language: en Sources: [AWS News Blog]() Topics: [Apache Iceberg](), [Amazon Web Services](), [Apache Spark](), [Python](), [Scala](), [Geographic Information System](), [Internet of things]() Tags: [analytics](), [apache](), [apache-iceberg](), [aws](), [aws-glue](), [launch](), [news](), [performance](), [pricing](), [python-3-13](), [real-time](), [release](), [scala](), [spark](), [streaming]() ### AI overview AWS Glue 6.0 is generally available with 30% lower pricing, a modernized Spark 4.1 runtime, Python 3.13 and Scala 2.13 support, and full Apache Iceberg v3 support. The release adds improved handling of semi-structured data, declarative ETL pipelines, faster PySpark execution, and real-time streaming with single-digit millisecond latency. ### Source excerpt AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.13, and Scala 2.13, delivering 30% lower pricing than previous AWS Glue versions. ## Develop and test MCP in Postman: jhipster-mcp in action DevFeed: [Develop and test MCP in Postman: jhipster-mcp in action]() Original publisher: [Read original article]() Author: Anthony Viard Published: 2026-08-19T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [Model Context Protocol](), [Postman](), [Spring Boot](), [Node.js](), [TypeScript](), [Angular](), [React](), [Vue.js](), [JSON](), [Remote Procedure Call (RPC)]() Tags: [ai-agents](), [angular](), [apache](), [cli](), [general](), [jhipster](), [json](), [mcp](), [node-js](), [open-source](), [post](), [react](), [rpc](), [spring-boot](), [typescript](), [vue]() ### AI overview This Postman developer article demonstrates jhipster-mcp, an open-source MCP server that exposes the JHipster generator to MCP clients. It uses Postman's MCP client to test tools, prompts, and resources for generating and evolving Spring Boot applications from JDL. ### Source excerpt Drive the jhipster-mcp server from Postman's MCP client to test tools, prompts, and resources before handing them to an AI agent. Try it today. The post Develop and test MCP in Postman: jhipster-mcp in action appeared first on Postman Blog. ## From Search to Search & Apache Lucene: Growing the heart of OpenSearchCon DevFeed: [From Search to Search & Apache Lucene: Growing the heart of OpenSearchCon]() Original publisher: [Read original article]() Author: Kris Freedain Published: 2026-08-14T21:57:12Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Library](), [Maintainers]() Tags: [2026](), [apache](), [blog](), [community](), [event](), [opensearch](), [search]() ### AI overview Community feedback led OpenSearchCon to expand its Search track into Search & Apache Lucene, creating a dedicated forum for Lucene practitioners, maintainers, and search relevance experts. The article describes the revised track and the strong response to its call for presentations for OpenSearchCon North America 2026. ### Source excerpt Learn how community feedback transformed OpenSearchCon's Search track into a dedicated home for Apache Lucene practitioners. Discover the new Search & Apache Lucene track at OpenSearchCon North America 2026, September 22-24 in San Jose. The post From Search to Search & Apache Lucene: Growing the heart of OpenSearchCon appeared first on OpenSearch. ## Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets DevFeed: [Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets]() Original publisher: [Read original article]() Author: Sundar Raghavan; Steven Palma; Cagatay Cali; Arron Bailiss; Yin Song Published: 2026-08-13T17:16:04Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [lerobot](), [hugging face](), [cloud-infrastructure](), [dataset](), [SDKs](), [Command-line interface](), [Open Source]() Tags: [agents](), [apache](), [aws](), [cli](), [code](), [data](), [datasets](), [hub](), [hugging-face](), [lerobot](), [models](), [open](), [open-source](), [robots](), [storage](), [strands-agents](), [train](), [xet]() ### AI overview This article describes a continuous robotics data loop using Strands Agents, LeRobot, Hugging Face Hub, and Hugging Face Storage Buckets. It covers recording demonstrations, collecting episodes, training policies on growing datasets, deploying checkpoints, and using mutable Xet-backed storage to reduce repeated data transfers. ### Source excerpt Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets You have an agent that can already record a demonstration and push it to the Hugging Face Hub. Now you want to run that loop continuously: collect episodes through the day, train a policy on the growing dataset, deploy it, and pull the next batch back to improve it. Run that loop once and every piece works. Run it every day and you start paying for the same byte transfers over and over. ## How and Why Netflix Built a Real-Time Distributed Graph: Part 3 -- Querying the graph with gRPC... DevFeed: [How and Why Netflix Built a Real-Time Distributed Graph: Part 3 -- Querying the graph with gRPC...]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-08-07T16:01:02Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Graphs](), [gRPC](), [Netflix](), [real-time](), [Latency](), [API](), [Streaming](), [personalization](), [Security](), [apache-flink]() Tags: [apache](), [apache-flink](), [api](), [data](), [data-processing](), [events](), [latency](), [netflix](), [personalization](), [real-time](), [security](), [streaming]() ### AI overview Netflix's third-part blog post describes the serving layer for its Real-Time Distributed Graph and explains how gRPC enables efficient graph querying. It focuses on serving diverse workloads, including security lookups and personalization traces, with sub-100ms responses across a billion-edge graph. ### Source excerpt How and Why Netflix Built a Real-Time Distributed Graph: Part 3 -- Querying the graph with gRPC execution API Authors: Nilesh Mishra and Ajit Koti This is the third entry of a multi-part blog series describing how we built a Real-Time Distributed Graph (RDG). In Part 1, we discussed the motivation for creating the RDG and the architecture of the data processing pipeline that populates it. In Part 2, we discussed how we designed the storage layer to handle billions of nodes and edges while maintaining single-digit-millisecond latency. In Part 3, we will explore how we designed a fast, flexible serving layer to efficiently query the graph. Introduction In Part 1 of this series, we described why Netflix needed a Real-Time Distributed Graph (RDG) and how we used Apache Flink to build an ingestion and processing pipeline that turns streaming events into graph primitives. In Part 2, we explored how we designed a storage layer capable of handling billions of nodes and edges while still delivering single-digit-millisecond latency. In this post, we focus on the next challenge: querying the graph efficiently to power real-time insights for our internal partners. All of the work on ingestion and storage only matters if we can actually ask complex questions and get answers back quickly. As we optimized for lower latency, we found that the serving layer posed its own set of challenges, distinct from those of ingestion and storage. How do we turn a constantly evolving, billion-edge graph into sub-100ms responses across a wide variety of workloads? This is the problem we tackle in this post. The Real World Needs As we integrated the RDG into Netflix's ecosystem, we realized that "querying the graph" is not a one-size-fits-all operation. We needed to handle a wide range of access patterns: from high-volume security lookups to deep, exploratory personalization traces. Let's revisit our example from Part 1 and expand on it slightly. In the earlier posts, we focused on accounts, device ## Introducing Shieldstral. DevFeed: [Introducing Shieldstral.]() Original publisher: [Read original article]() Published: 2026-08-04T12:00:26Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [Inference](), [GPU](), [Nvidia]() Tags: [ai](), [apache](), [evaluation](), [gpu](), [inference](), [model](), [models](), [multimodal](), [nvidia](), [open](), [safety]() ### AI overview Shieldstral is a 3B open-weights multimodal safety classifier that evaluates text and images using plain-language policies supplied at inference time. The article describes its policy-adaptive question-answering design, calibrated safety scores, benchmark performance, and Apache 2.0 release. ### Source excerpt Shieldstral introduces a 3B open-weights multimodal safety classifier that outperforms models up to 7x its size. ## How Jump Trading uses ClickHouse with Iceberg for analytics DevFeed: [How Jump Trading uses ClickHouse with Iceberg for analytics]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-07-30T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Apache Iceberg](), [log management](), [data](), [Latency](), [Open Source](), [real-time](), [Database]() Tags: [analytics](), [apache](), [apache-iceberg](), [business](), [clickhouse](), [data](), [database](), [industry](), [latency](), [logging](), [logs](), [low-latency](), [observability](), [open](), [open-source](), [performance](), [real-time](), [scale](), [technology](), [trading]() ### AI overview Jump Trading uses a self-managed ClickHouse platform to capture and analyze petabyte-scale financial trading logs. The platform ingests hundreds of terabytes daily with a sub-20-second p99 and supports real-time analytics across hundreds of billions of events. To support large-scale batch reporting and research without affecting the real-time cluster, Jump added a parallel Apache Iceberg pipeline. ### Source excerpt Jump Trading captures petabyte-scale financial trading logs on a self-managed ClickHouse platform, where zero data loss and low latency are critical requirements. ## 10X more data, same 4 seconds: single-query scaling in Redpanda SQL on 1TB DevFeed: [10X more data, same 4 seconds: single-query scaling in Redpanda SQL on 1TB]() Original publisher: [Read original article]() Author: Marcin Grzebieluch Published: 2026-07-29T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [SQL](), [Benchmark](), [dataset](), [Apache Iceberg](), [Streaming]() Tags: [apache](), [benchmark](), [dataset](), [kafka](), [sql](), [streaming]() ### AI overview This article benchmarks how a single analytical query in Redpanda SQL scales as the dataset grows from 100 GB to 1 TB and as cluster resources increase. Redpanda SQL combines live-streaming topics with historical Apache Iceberg tables through bridge queries, and the benchmark uses skewed NYC Taxi trip data to evaluate strong scaling and query feasibility. ### Source excerpt A benchmark on how a single analytical query behaves in Redpanda SQL as the dataset and the cluster grow. ## Harness AgentTrace: An Observability and Guardrail Framework DevFeed: [Harness AgentTrace: An Observability and Guardrail Framework]() Original publisher: [Read original article]() Author: Sunil Gattupalle Sanjay Nagaraj Published: 2026-07-21T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [agent observability](), [ai observability](), [Framework](), [Testing](), [tracing]() Tags: [agents](), [ai](), [ai-observability](), [apache](), [ci](), [evaluation](), [observability](), [testing](), [tracing]() ### AI overview Harness describes AgentTrace, an internal framework for observing, evaluating, and governing AI agents in production. It connects production monitoring with evaluation, allows failures to become regression test cases, and includes open-source harness-sdk and harness-evals layers under Apache 2.0 that work with any OpenTelemetry backend. ### Source excerpt Harness AgentTrace unifies AI observability, evaluation, and guardrails to detect failures, improve quality, and secure AI agents in production. | Blog ## Khronos Sponsors Open-Source glTF Importer/Exporter for Autodesk 3ds Max DevFeed: [Khronos Sponsors Open-Source glTF Importer/Exporter for Autodesk 3ds Max]() Original publisher: [Read original article]() Author: jphilips (jeff@khronosgroup.org) Published: 2026-07-14T13:00:00Z Content type: release Language: en Sources: [Blogs Khronos Blog]() Topics: [Open Source](), [3D](), [Tool](), [Documentation]() Tags: [3d](), [animation](), [apache](), [api](), [blog-glt](), [cameras](), [compression](), [documentation](), [gltf](), [lighting](), [mesh](), [open-source](), [plugin-development](), [plugins](), [render](), [standards]() ### AI overview The Khronos Group sponsored and released open-source glTF 2.0 importer and exporter plugins for Autodesk 3ds Max under the Apache 2.0 license. The plugins support round-trip glTF workflows, including scene content, materials, textures, compression, animation, lighting, and instancing. ### Source excerpt The Khronos Group has sponsored the glTF 2.0 Importer/Exporter for Autodesk 3ds Max open-source project under the Apache 2.0 license, and today has released the resulting glTF importer and exporter plugins, built to professional standards in collaboration with the community, and free for all to use. ## Operating AI/ML Workloads on Kubernetes: A Headlamp Plugin for Kubeflow DevFeed: [Operating AI/ML Workloads on Kubernetes: A Headlamp Plugin for Kubeflow]() Original publisher: [Read original article]() Author: Alok Dangre Published: 2026-07-13T20:00:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [dashboards](), [AI Development](), [API](), [Web](), [data]() Tags: [ai-ml](), [apache](), [api](), [dashboards](), [kubernetes](), [site-reliability]() ### AI overview This article introduces a Headlamp plugin for Kubeflow that surfaces Kubeflow custom resources and Kubernetes-level workload information in a general-purpose Kubernetes UI. It helps operators troubleshoot notebooks, training jobs, experiments, pipelines, Pods, and related resources through the Kubernetes API. ### Source excerpt Kubernetes has quietly become the default platform for AI and machine learning. Whether you run notebook servers for data scientists, schedule distributed training jobs, tune hyperparameters, or orchestrate multi-step ML pipelines, those workloads increasingly land on a Kubernetes cluster. Kubeflow is one of the most popular ways to assemble that stack, and it does so the Kubernetes-native way: every capability is exposed as a Custom Resource Definition (CRD). That design is a gift to cluster operators, because it means ML workloads can be observed and managed with the same primitives as everything else in the cluster. But in practice the specialized ML dashboards that ship with these platforms hide the Kubernetes layer underneath. When a notebook is stuck or a training run fails, the operator is often left dropping back to kubectl to find out what actually happened at the Pod level. This post introduces the Headlamp Kubeflow plugin, which closes that gap by surfacing Kubeflow's custom resources directly inside a general-purpose Kubernetes UI. It is a worked example of a pattern any CRD-heavy platform can follow: meet operators where they already work, and show them the cluster-level truth. Headlamp itself is an extensible Kubernetes web UI maintained under Kubernetes SIG UI and licensed under Apache 2.0. It runs as a desktop app or in-cluster, and its plugin system lets anyone add first-class views for custom resources. Why operators need a different view Purpose-built ML dashboards help data scientists submit experiments, pipelines, and notebooks. Cluster operators and site reliability engineers (SREs) troubleshoot the Kubernetes resources underneath, and they ask different questions: Why is a notebook stuck? Is it ImagePullBackOff, OOMKilled, or a Pod waiting on a PersistentVolumeClaim? Which Run resources failed recently across namespaces? Which parameter set does a Katib Experiment report as optimal? Do TrainJob resources reference the expected TrainingRuntime ## One Driver, One Format, Every Language: ADBC DevFeed: [One Driver, One Format, Every Language: ADBC]() Original publisher: [Read original article]() Author: Luke Gannon Published: 2026-07-10T15:00:55Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Databases](), [data](), [Data Science](), [R](), [Ruby](), [Go Language](), [JavaScript](), [Python]() Tags: [apache](), [api](), [c](), [clickhouse](), [data](), [data-infrastructure](), [data-science](), [database](), [databases](), [go](), [java](), [javascript](), [python](), [ruby]() ### AI overview ClickHouse has introduced an official ADBC driver, providing Arrow-native, zero-conversion access to ClickHouse across multiple programming languages. Built with Rust and distributed through the ADBC Driver Foundry, it gives languages such as Ruby, R, and C standardized database access without separate ClickHouse drivers. ### Source excerpt ClickHouse now has an official ADBC driver, giving Ruby, R, C, and every other ADBC-aware tool zero-conversion, Arrow-native access to ClickHouse without a dedicated client for each language. ## Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T DevFeed: [Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-07T17:05:42Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Robotics](), [Model Development](), [Open Source](), [Training AI Models](), [datasets](), [multimodal](), [Deployment](), [Simulation and Design](), [Tooling]() Tags: [ai-foundation-models](), [apache](), [building](), [data](), [deployment](), [developers](), [development](), [evaluation](), [featured](), [humanoid-robots](), [images](), [integrations](), [isaac](), [isaac-sim](), [manufacturing](), [model](), [multimodal](), [nvidia](), [open-source](), [robotics](), [robotics-simulation](), [robots](), [simulation-modeling-design](), [software](), [tooling](), [training](), [workflows]() ### AI overview NVIDIA Isaac GR00T Development Platform unifies humanoid robot development workflows, combining data collection, simulation-based training, evaluation, and deployment. The article highlights the open Isaac GR00T 1.7 vision-language-action model, which accepts language and images and can be adapted to robots, tasks, and environments through post-training. ### Source excerpt As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing. Building humanoids... [Next page]()