# data-processing Published articles for data-processing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Everpure proposes reducing GPU idle time by improving AI data access DevFeed: [How Everpure proposes reducing GPU idle time by improving AI data access]() Original publisher: [Read original article]() Author: Chris Mellor Published: 2026-09-15T08:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Infrastructure](), [GPU](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [AI Agent](), [data-processing](), [data]() Tags: [agents](), [ai](), [ai-infrastructure](), [ai-ml](), [data-processing](), [gpu](), [hardware](), [rag](), [sponsored]() ### AI overview This sponsored feature describes Everpure's approach to reducing GPU idle time in AI systems by improving access to large-scale insurance data. It discusses central metadata indexing, storage performance, self-describing data, and integration with Nvidia GPU infrastructure for AI agents and retrieval-augmented generation. ### Source excerpt SPONSORED FEATURE: The vendor's AI solutions are dedicated to increasing GPU utilization and avoiding costly GPUs doing nothing while waiting for data ## Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale DevFeed: [Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale]() Original publisher: [Read original article]() Author: Aswin Vasudevan Published: 2026-09-14T21:22:45Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [threat detection](), [Amazon Bedrock](), [Security](), [Serverless](), [API](), [Amazon Web Services](), [AWS CloudTrail](), [Amazon CloudWatch](), [Amazon S3]() Tags: [advanced-300](), [agentic](), [agents](), [amazon-bedrock-agentcore](), [amazon-cloudwatch](), [amazon-s3](), [analysis](), [api](), [aws](), [aws-cloudtrail](), [code](), [customer-solutions](), [data](), [data-processing](), [security](), [serverless](), [threat-detection]() ### AI overview Abnormal AI uses Amazon Bedrock AgentCore Code Interpreter as an ephemeral, serverless compute scratch pad for real-time inline email threat detection. The article describes its sandbox isolation, networking and file-handling options, preloaded Python capabilities, observability integrations, and use at billion-message scale. ### Source excerpt Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ## Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack DevFeed: [Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-12T18:24:22Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AWS Outposts](), [Amazon Web Services](), [cloud-infrastructure](), [Network](), [Latency](), [data-processing](), [Security]() Tags: [5g](), [automation](), [aws](), [aws-outposts](), [cloud](), [compute](), [connectx](), [data-processing](), [enterprise](), [governance](), [latency](), [low-latency](), [networking](), [security]() ### AI overview AWS has generally released a second-generation single-rack AWS Outposts configuration: a self-contained 42U rack combining compute, storage, and networking with up to 2,688 vCPUs and 100 TB of Amazon EBS. The article describes its on-premises cloud compatibility, compact footprint, supported instance families, and accelerated networking options for trading floors and 5G cores. ### Source excerpt AWS has made second-generation single-rack AWS Outposts generally available, a self-contained 42U rack that puts compute, storage, and networking together with up to 2,688 vCPUs and 100 TB of Amazon EBS. It runs the same APIs, console, automation, governance policies, and security controls as the multi-rack second-generation Outposts and the parent AWS Region, so an The post Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack appeared first on StorageReview.com. ## OpenTelemetry proposes environment variables for context propagation across processes DevFeed: [OpenTelemetry proposes environment variables for context propagation across processes]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-11T11:01:22Z Content type: article Language: en Sources: [Blog on OpenTelemetry]() Topics: [context](), [tracing](), [OpenTelemetry](), [Processes](), [CI/CD](), [data-processing]() Tags: [ci-cd](), [data-processing](), [environment-variables](), [opentelemetry](), [processes](), [spans](), [tracing](), [w3c]() ### AI overview The OpenTelemetry Specification has a release candidate for using environment variables to carry trace context and baggage between processes. The article explains how this can connect spans across workflow runners, shells, build tools, test processes, and similar workloads when protocol headers or message metadata are unavailable, and requests feedback before the specification becomes Stable. ### Source excerpt A trace does not always cross a network boundary. A workflow runner starts a shell, the shell launches a build tool, and the build tool starts test processes. Batch and data-processing systems create similar chains of child processes. Without a shared way to pass trace information across these boundaries, spans from each process can end up in separate traces. If context propagation is new to you, it is the mechanism that carries information from one service or process to the next. For tracing, this includes the trace and span identifiers that let new spans join the same trace. It can also carry baggage: application-defined key-value pairs that are passed to downstream work. ## Refreshing the Travel-Time Map Behind Lyft's Marketplace: Rebuilding Neighborhood Reachability... DevFeed: [Refreshing the Travel-Time Map Behind Lyft's Marketplace: Rebuilding Neighborhood Reachability...]() Original publisher: [Read original article]() Author: Manjunath Shettar Published: 2026-09-10T16:12:28Z Content type: article Language: en Sources: [Lyft Engineering - Medium]() Topics: [dataset](), [airflow]() Tags: [airflow](), [data-processing](), [feature-engineering](), [latency](), [lyft](), [offline](), [tea]() ### AI overview Lyft describes rebuilding its Neighborhood Reachability Signals: offline, regional travel-time matrices between geohash-6 cells and their associated neighborhood-center lists. The refresh replaces older static data and is intended to support marketplace pricing, driver guidance, and demand heatmaps, with future work aimed at time-aware travel times. ### Source excerpt Refreshing the Travel-Time Map Behind Lyft's Marketplace: Rebuilding Neighborhood Reachability Signals Every time Lyft calculates pricing to balance a market, nudges a driver toward an under-served pocket of a city, or paints a heatmap of where demand is building, there is a quiet lookup table doing work in the background. It answers a deceptively simple question: how long does it take to get from here to there?, for millions of pairs of places, across hundreds of regions. That lookup table is the Neighborhood Reachability Signal, and for years large parts of it were frozen in a snapshot of the world from 2018-2019. This is the story of how we rebuilt it, why a refresh substantial enough to be worth adopting was what finally moved Pricing to switch, the cleanly positive results that came out of that switch, and where we're taking it next, from one static file per region to time-aware travel times that change with the rhythm of the day. What is a Neighborhood Reachability Signal? A geohash is a compact way of carving the world into a grid of cells. At geohash-6 resolution, each cell is roughly the size of a few city blocks. Slice a region into geohash-6 cells and you get a clean, discrete coordinate system for "neighborhoods" that downstream systems can reason about. The Forecasting & Real-Time Optimization (FORTOP) team produces the Neighborhood Reachability Signals dataset, which consists of two companion files for each region: Neighborhood Reachability Matrix: the estimated travel time, in minutes, between the centers of pairs of geohash-6 cells. Think of it as a sparse origin-to-destination travel-time matrix for a region. Neighborhood Centers: the list of all geohashes that appear in the ETA files for that region, i.e. the "vocabulary" of cells that the marketplace is allowed to talk about. Both files are generated offline on a schedule by an Airflow DAG. They are static in the sense that they are precomputed and shipped, rather than queried live (which is exact ## Announcing 90-minute function timeout on AWS Lambda Managed Instances DevFeed: [Announcing 90-minute function timeout on AWS Lambda Managed Instances]() Original publisher: [Read original article]() Author: Tarun Rai Madan Published: 2026-09-09T19:08:14Z Content type: release Language: en Sources: [AWS Compute Blog]() Topics: [data-processing](), [Transcodings](), [Amazon EC2]() Tags: [ai-inference](), [announcements](), [aws](), [aws-lambda](), [batch](), [data-processing](), [event](), [event-driven](), [foundational-100](), [serverless]() ### AI overview AWS Lambda Managed Instances now support a 90-minute timeout for asynchronous and event source mapping invocations, increasing the previous 15-minute limit by six times. The update targets longer-running data processing, media transcoding, AI inference, and batch workloads. ### Source excerpt AWS Lambda now supports a 90-minute function timeout for asynchronous and event source mapping (ESM) invocations on Lambda Managed Instances, a 6x increase from the previous 15-minute limit. Data processing, media transcoding, AI inference, and batch workloads can now run on Lambda without re-architecting. ## 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 ## AI on Kubernetes: Building the Cloud-Native Foundation for Intelligent Applications DevFeed: [AI on Kubernetes: Building the Cloud-Native Foundation for Intelligent Applications]() Original publisher: [Read original article]() Author: Urolime Technologies Published: 2026-07-20T12:54:45Z Content type: tutorial Language: en Sources: [Kubernetes Archives - Urolime Blogs]() Topics: [Artificial Intelligence](), [Kubernetes](), [Cloud](), [AI Platform](), [Scalability]() Tags: [ai](), [cloud](), [cloud-native](), [data-processing](), [inference](), [kubernetes](), [kubernetes-consulting-services](), [scalability]() ### AI overview This article explains why Kubernetes is useful as cloud-native infrastructure for deploying AI applications. It discusses dynamic workload scaling, GPU-based training, inference, CI/CD, large-scale data processing, and consistent management across on-premises, private-cloud, and public-cloud environments. ### Source excerpt Artificial Intelligence (AI) has grown past its experimental stage and is now becoming a strategic business enabler. With AI-powered customer experiences, predictions, and generative AI technologies, businesses have begun adopting AI solutions to stay ahead of the game. However, deploying AI at scale requires more than sophisticated AI models and algorithms. To use AI successfully [...] ## NVIDIA Vera CPU Boosts AI Factory Throughput to Accelerate Agentic Workloads DevFeed: [NVIDIA Vera CPU Boosts AI Factory Throughput to Accelerate Agentic Workloads]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-07T18:10:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Vera CPU](), [AI Factory](), [Artificial Intelligence](), [systems](), [Inference](), [Orchestration](), [model-deployment](), [Cache](), [data-processing](), [Neural Network](), [PyTorch]() Tags: [agentic](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-factory](), [cache](), [cpu](), [data-processing](), [featured](), [inference](), [model](), [nvidia-vera](), [orchestration](), [performance](), [rl](), [top-stories](), [vera-cpu]() ### AI overview This NVIDIA developer article explains how the NVIDIA Vera CPU can improve AI factory throughput for agentic workloads. It emphasizes sustained per-core performance for CPU tasks between model steps, including tool calls, code execution, sandbox evaluations, data processing, orchestration, KV-cache coordination, and result handling. The article also describes how CPU performance affects reinforcement learning rollouts, user response time, and cached-context efficiency. ### Source excerpt Agentic systems turn model reasoning into action through multi-step workflows that combine inference, tool use, code execution, retrieval, orchestration, and... ## How to Scale Kubernetes DevFeed: [How to Scale Kubernetes]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-06-16T07:15:28Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [Kubernetes](), [autoscaling](), [Scalability](), [cpu](), [data-processing](), [jobs]() Tags: [autoscaling](), [cpu](), [data-processing](), [how-to](), [kubernetes](), [scalability]() ### AI overview This tutorial outlines Kubernetes scaling strategies, including Horizontal Pod Autoscaling for changing pod replica counts based on metrics, Vertical Pod Autoscaling for adjusting pod CPU and memory requests or limits, and Cluster Autoscaling for changing the number of worker nodes. It also notes practical benefits and limitations of these approaches. ### Source excerpt Top Strategies to Know About ## Vercel Sandbox can now run for up to 24 hours DevFeed: [Vercel Sandbox can now run for up to 24 hours]() Original publisher: [Read original article]() Author: Rob Herley Published: 2026-06-16T00:01:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [data-processing](), [Persistence]() Tags: [agentic](), [data-processing](), [documentation](), [e2e-testing](), [enterprise](), [persistence](), [sandboxes](), [scale](), [vercel]() ### AI overview Vercel Sandboxes now support uninterrupted sessions of up to 24 hours, increased from 5 hours. The longer runtime supports large-scale data processing, end-to-end testing pipelines, and long-lived agentic workflows, with persistent sandboxes preserving state across extended runs. ### Source excerpt Vercel Sandboxes can run uninterrupted sessions for up to 24 hours (up from 5 hours). This new max duration unlocks workloads that require longer runtimes, such as large-scale data processing, E2E testing pipelines, and long-lived agentic workflows. Pair with persistent sandboxes to maintain durable state across extended runs. The 24 hour max duration is available on all Pro and Enterprise plans. Learn more about limits in the documentation and see how Vercel Sandbox duration and persistence work. Read more ## How an astrophysicist uses Codex to help simulate black holes DevFeed: [How an astrophysicist uses Codex to help simulate black holes]() Original publisher: [Read original article]() Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [codex](), [Algorithms](), [data-processing](), [Programming](), [Simulation and Design]() Tags: [algorithms](), [applied-ai](), [codex](), [data](), [data-processing](), [event](), [images](), [model](), [relativity](), [scale](), [space](), [time](), [tools](), [workflows]() ### AI overview Astrophysicist Chi-kwan Chan uses Codex to refine and test algorithms for simulating electrons and ions around black holes. The article explains how these simulations, data processing, and large-scale computing workflows help interpret Event Horizon Telescope observations and study extreme physics and general relativity. ### Source excerpt Discover how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein's theory of general relativity. ## Introducing Apache Arrow Support in mssql-python DevFeed: [Introducing Apache Arrow Support in mssql-python]() Original publisher: [Read original article]() Author: Saumya Garg Published: 2026-05-04T04:33:00Z Content type: release Language: en Sources: [Microsoft Python Engineering]() Topics: [sql-server](), [data-processing](), [interoperability](), [data](), [pandas]() Tags: [apache-arrow](), [arrow](), [azure](), [azure-sql](), [client-driver](), [data-processing](), [interoperability](), [pandas](), [python](), [python-driver-for-azure-sql](), [python-driver-for-sql-server](), [sql-server](), [sql-server-2025](), [zero-copy]() ### AI overview Microsoft introduces Apache Arrow support in mssql-python, enabling SQL Server data to be fetched directly into Arrow structures for Polars, Pandas, DuckDB, and other Arrow-native libraries. The approach is intended to reduce Python object creation and memory overhead during data processing. ### Source excerpt Reviewed by Sumit Sarabhai Fetching a million rows from SQL Server into a Polars DataFrame used to mean a million Python objects, a million GC allocations, and then throwing it all away to build a DataFrame. Not anymore. mssql-python now supports fetching SQL Server data directly as Apache Arrow structures - a faster and more [...] The post Introducing Apache Arrow Support in mssql-python appeared first on Microsoft for Python Developers Blog. ## Payment Compliance: GDPR and PSD2 Obligations for SaaS DevFeed: [Payment Compliance: GDPR and PSD2 Obligations for SaaS]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [data-processing](), [Security, Privacy and Abuse Prevention](), [Authentication](), [tokenization]() Tags: [authentication](), [compliance](), [data-processing](), [gdpr](), [merchant-of-record](), [payment](), [saas](), [tokenization]() ### AI overview A guide to GDPR and PSD2 obligations for SaaS companies handling payments for European customers. It covers personal-data handling, Strong Customer Authentication, data minimization, retention, breach notification, tokenization, and merchant-of-record arrangements. ### Source excerpt Understand how GDPR and PSD2 affect your SaaS payment flows. Covers data handling obligations, Strong Customer Authentication, and how a merchant of record simplifies compliance. ## Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience DevFeed: [Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience]() Original publisher: [Read original article]() Author: Kubeflow SDK Team Published: 2026-03-19T05:00:00Z Content type: release Language: en Sources: [Kubeflow]() Topics: [SDKs](), [MLOps](), [Apache Spark](), [data-processing](), [Hyperparameter optimization](), [Machine learning](), [Kubernetes](), [Python]() Tags: [apache-spark](), [data-processing](), [hyperparameter-optimization](), [kubernetes](), [mlops](), [python](), [release](), [sdk]() ### AI overview Kubeflow SDK v0.4.0 introduces a Model Registry Client, SparkClient with SparkConnect support, namespaced TrainingRuntimes, dataset and model initializers, and new documentation. The release targets a unified Python interface for AI workloads on Kubernetes across data processing, model management, and ML pipelines. ### Source excerpt Explore the full documentation at sdk.kubeflow.org ## How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse DevFeed: [How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-03-17T12:18:28Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [dashboards](), [data-processing](), [Database](), [Node.js](), [event driven]() Tags: [analytics](), [clickhouse](), [dashboards](), [data](), [data-processing](), [event-driven](), [node](), [postgres]() ### AI overview Socialpruf migrated its analytics workload from Neon to Postgres managed by ClickHouse Cloud. The platform uses ClickHouse for customer-facing social analytics, aggregating millions of rows in milliseconds to power near-instant dashboards, while Node.js and Python components collect and process data from multiple social platforms. ### Source excerpt Socialpruf migrated from Neon to Postgres managed by ClickHouse, eliminating network transfer costs and achieving up to 5x faster query performance while powering real-time social analytics dashboards that aggregate millions of rows in milliseconds. ## Redpanda pushes the envelope on NVIDIA Vera DevFeed: [Redpanda pushes the envelope on NVIDIA Vera]() Original publisher: [Read original article]() Author: Travis Downs Published: 2026-03-16T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [NVIDIA Vera](), [Benchmark](), [cpu](), [Streaming](), [Latency](), [PyTorch]() Tags: [benchmark](), [cpu](), [data-processing](), [latency](), [nvidia-vera](), [performance](), [reinforcement-learning](), [streaming]() ### AI overview Redpanda benchmarks NVIDIA Vera against five other systems on Kafka-compatible streaming workloads. Vera delivers the lowest streaming latencies, better interconnect scaling, faster build times, and up to 73% higher throughput than AMD EPYC "Turin," with the article positioning it for data-intensive enterprise, AI, and agentic workloads. ### Source excerpt NVIDIA Vera provides 5.5x lower latencies and up to 73% higher throughputs than other leading CPU models. ## How Agoda Load Balanced Kafka DevFeed: [How Agoda Load Balanced Kafka]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-03-10T08:01:37Z Content type: article Language: en Sources: [System Design Codex]() Topics: [Kafka](), [data-processing](), [systems]() Tags: [data-centers](), [data-processing](), [kafka](), [parallelism](), [partitioning](), [round-robin]() ### AI overview A deep dive into how Agoda uses Kafka to process large volumes of real-time supplier price updates. It explains the distributor, processor, and Kafka components, then examines how partitions, partitioners, and consumer assignors support parallel processing and expose challenges when workloads and consumer capabilities are uneven. ### Source excerpt Deep Dive ## Protect your AI workloads from supply chain attacks DevFeed: [Protect your AI workloads from supply chain attacks]() Original publisher: [Read original article]() Published: 2026-01-30T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [Containers](), [chainguard](), [Vulnerabilities](), [AI Strategy](), [Open Source](), [Security](), [PyTorch](), [Tensorflow](), [MLOps]() Tags: [ai](), [ai-containers](), [chainguard](), [chainguard-containers](), [chainguard-for-ai](), [chainguard-libraries](), [containers](), [cudnn](), [cves](), [data-processing](), [deployment](), [kserve](), [legacy](), [libraries](), [mlops](), [nemo](), [ollama](), [python](), [pytorch](), [security](), [security-vulnerabilities](), [supply-chain](), [supply-chain-attacks](), [zero-cve-containers]() ### AI overview The article discusses security and operational challenges in AI/ML workloads, including complex dependencies, bloated artifacts, infrastructure sprawl, and unremediated CVEs. It presents Chainguard Containers' minimal images for AI workloads as a way to reduce attack surface, storage needs, and deployment overhead, and cites a 50 MB gpu-operator image compared with a 170 MB upstream equivalent. ### Source excerpt Chainguard secures AI adoption with minimal, zero-CVE containers and source-built libraries that prevent supply chain malware while keeping developers fast. ## Astro 5.17 DevFeed: [Astro 5.17]() Original publisher: [Read original article]() Author: Erika Published: 2026-01-29T00:00:00Z Content type: release Language: en Sources: [The Astro Blog]() Topics: [configuration](), [data-processing]() Tags: [astro](), [data-processing](), [privacy](), [release]() ### AI overview Astro 5.17 adds configurable dev-toolbar placement, asynchronous loader parsing, and partitioned-cookie support for embedded contexts. ### Source excerpt Astro 5.17 brings configurable dev toolbar placement, support for partitioned cookies, and powerful new image optimization options. ## Trusting AI agents: A reinsurance case study DevFeed: [Trusting AI agents: A reinsurance case study]() Original publisher: [Read original article]() Author: Sophia Barnes Published: 2026-01-22T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [data-processing](), [datasets](), [decision-making](), [parallel](), [file]() Tags: [agents](), [ai](), [ai-agents](), [automate](), [case-study](), [data-processing](), [excel](), [how-to](), [insurance](), [multi-agent](), [pricing](), [risk](), [workflows]() ### AI overview This case study explains how a multi-agent AI system with human-in-the-loop safeguards automates reinsurance data workflows. The system parses unstandardized Excel submission packs, matches catastrophe events with historical records, creates cedant loss records, and flags changes to existing data. ### Source excerpt Learn how to build a reliable multi-agent AI system with human-in-the-loop safeguards using Temporal. A detailed case study on automating complex reinsurance data workflows. ## Zero-ETL lakehouses for Postgres people DevFeed: [Zero-ETL lakehouses for Postgres people]() Original publisher: [Read original article]() Author: George MacKerron Published: 2026-01-12T18:43:54Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Data analysis](), [data-processing](), [datasets](), [Databases](), [databricks](), [API](), [Ruby](), [Linux]() Tags: [analysis](), [api](), [data-processing](), [database](), [databases](), [databricks](), [linux](), [postgres](), [ruby]() ### AI overview An explanation of data lakehouses and enterprise data tooling for readers familiar with Postgres. It contrasts OLTP transactions with OLAP analysis, describes composable lakehouse stacks, and discusses tools that move or analyze data across Postgres and lakehouses. ### Source excerpt Neon is made by Postgres people. Since Neon became part of Databricks, we Postgres people also find ourselves part of a larger organisation of enterprise data people. This post is about what I've learned as a result. It aims to explain 'data lakehouses' and related enterprise-dat... ## DS-STAR: A state-of-the-art versatile data science agent DevFeed: [DS-STAR: A state-of-the-art versatile data science agent]() Original publisher: [Read original article]() Published: 2025-11-06T17:50:36Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Data Science](), [Data Mining & Modeling](), [data-processing](), [LLM evaluation / benchmarking](), [Benchmark]() Tags: [agents](), [benchmark](), [code](), [data-mining-modeling](), [data-processing](), [data-science](), [google](), [json](), [large-language-models-llms](), [machine-intelligence](), [markdown](), [natural-language-processing](), [statistics](), [verification](), [visualization]() ### AI overview DS-STAR is a data science agent from Google Cloud that automates tasks including statistical analysis, visualization, and data wrangling across varied data types. It uses file analysis, LLM-based verification, and iterative sequential planning to produce verifiable insights, achieving state-of-the-art results on the DABStep, KramaBench, and DA-Code benchmarks. ### Source excerpt Data Mining & Modeling ## Toward provably private insights into AI use DevFeed: [Toward provably private insights into AI use]() Original publisher: [Read original article]() Published: 2025-10-30T10:56:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [Confidential Computing](), [Google](), [trusted-execution-environment](), [data-processing](), [Large language models (LLMs)](), [gemma](), [Open Source]() Tags: [ai](), [confidential-computing](), [data-processing](), [gemma](), [generative-ai](), [google](), [large-language-models-llms](), [mobile-systems](), [on-device](), [open-source](), [research](), [security-privacy-and-abuse-prevention](), [software-systems-engineering]() ### AI overview Google Research introduces provably private insights, a system that combines large language models, differential privacy, and trusted execution environments to analyze aggregate patterns in on-device generative AI use without exposing individual data. ### Source excerpt Generative AI [Next page]()