# S3 Published articles for S3. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## CTERA Data Archiving Solution Pairs InsightAI With CTERA Archive to Move Inactive Files Off Primary Storage With an Audit Trail DevFeed: [CTERA Data Archiving Solution Pairs InsightAI With CTERA Archive to Move Inactive Files Off Primary Storage With an Audit Trail]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-17T17:29:28Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data-processing](), [Artificial Intelligence](), [Security](), [Amazon S3]() Tags: [ai](), [audit-trail](), [data-protection](), [data-storage](), [enterprise](), [object-storage](), [restore](), [retention](), [s3]() ### AI overview CTERA launched a Data Archiving Solution that combines InsightAI with CTERA Archive to identify inactive files and move them from primary storage under retention policies and an audit trail. The solution keeps archived data governed and accessible within the CTERA Intelligent Data Platform, using on-premises or cloud storage tiers. ### Source excerpt CTERA has launched the CTERA Data Archiving Solution, which pairs its InsightAI data service with CTERA Archive so the platform can recommend which files to move off primary storage and then execute the move under retention policy and an audit trail. It's the product follow-through to the Cold Data Storage Report CTERA published a day The post CTERA Data Archiving Solution Pairs InsightAI With CTERA Archive to Move Inactive Files Off Primary Storage With an Audit Trail appeared first on StorageReview.com. ## Perplexity's AI agents helped build a database. They weren't allowed to run it. DevFeed: [Perplexity's AI agents helped build a database. They weren't allowed to run it.]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T21:51:15Z Content type: article Language: en Sources: [The New Stack]() Topics: [Database](), [DynamoDB](), [Rust](), [Artificial Intelligence](), [rocksdb](), [Amazon S3]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [coding](), [coding-agents](), [database](), [databases](), [dynamodb](), [open-source](), [performance](), [perplexity](), [rocksdb](), [rust](), [s3]() ### AI overview Perplexity built CobbleDB, a Rust key-value store, after finding DynamoDB too costly and insufficiently controllable for its search workload. Coding agents helped develop it, but were not allowed to run it in production. Perplexity measured lower read latency and expects lower costs, with plans to open-source the database. ### Source excerpt Perplexity decided it was paying too much for DynamoDB and wasn't getting the control it wanted over read performance. So The post Perplexity's AI agents helped build a database. They weren't allowed to run it. appeared first on The New Stack. ## Appwrite Init 2026 recap: Everything we shipped DevFeed: [Appwrite Init 2026 recap: Everything we shipped]() Original publisher: [Read original article]() Author: Aishwari Pahwa Published: 2026-09-15T00:00:00Z Content type: release Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [releases](), [PostgreSQL](), [Amazon S3](), [Firewall](), [OAuth 2.0](), [Self-hosted]() Tags: [2](), [firewall](), [init](), [oauth2](), [postgresql](), [recap](), [release](), [releases](), [s3](), [self-hosted]() ### AI overview An Appwrite Init 2026 recap covering five days of launches, including Appwrite 2.0, a redesigned Console, native PostgreSQL, VectorsDB, DocumentsDB, MySQL, S3-compatible Storage, Firewall, OAuth2, and Domains. ### Source excerpt An Appwrite Init 2026 recap of all five days of launches, from Appwrite 2.0 and native PostgreSQL to VectorsDB, S3 support, Firewall, OAuth2, and Domains. ## Appwrite 2.1 is now available for self-hosting DevFeed: [Appwrite 2.1 is now available for self-hosting]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Amazon S3](), [hosting](), [API](), [API keys](), [Command-line interface](), [SDKs](), [Compression](), [Encryption]() Tags: [api](), [api-documentation](), [aws](), [cli](), [compression](), [encryption](), [hosting](), [products](), [s3](), [self-hosting](), [sign-in]() ### AI overview Appwrite 2.1 is available for self-hosted deployments. It adds the S3-compatible Storage API and AutoGravity image previews, along with TikTok and Kakao sign-in and Appwrite Console fixes. The release uses the existing upgrade command and migration process from Appwrite 2.0. ### Source excerpt Appwrite 2.1 brings the S3 API and AutoGravity to self-hosted instances, adds TikTok and Kakao sign-in, and fixes for Appwrite Console. ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## How AWS Lambda logs every flow across thousands of microVMs per host with eBPF and Rust DevFeed: [How AWS Lambda logs every flow across thousands of microVMs per host with eBPF and Rust]() Original publisher: [Read original article]() Author: Prashant Kumar Singh Published: 2026-09-11T12:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [AWS Lambda](), [Architecture & Design](), [SIEM, Security, Observability](), [telemetry](), [Amazon EC2](), [Amazon S3](), [VPC]() Tags: [architecture](), [aws](), [aws-lambda](), [aws-marketplace](), [compliance](), [ebpf](), [firecracker](), [incident](), [incident-response](), [logs](), [monitoring](), [observability](), [post-contributed](), [rust](), [s3](), [scale](), [security](), [server](), [serverless](), [sponsor-aws-marketplace](), [sponsored-post-contributed](), [vpc]() ### AI overview AWS Lambda describes replacing an aging network-capture system with an eBPF and Rust pipeline that records network flows across short-lived, tenant-isolated microVMs. The system prioritizes complete, correctly attributed records with minimal overhead for security investigation, metering, audit, observability, and monitoring. ### Source excerpt On any compute platform, when a security alert fires, the question is always the same. Which workload talked to that The post How AWS Lambda logs every flow across thousands of microVMs per host with eBPF and Rust appeared first on The New Stack. ## Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0 DevFeed: [Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0]() Original publisher: [Read original article]() Author: Eric Kim Published: 2026-09-10T21:15:39Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI search](), [Retrieval Augmented Generation (RAG)](), [Amazon S3](), [AWS IAM]() Tags: [amazon-bedrock](), [amazon-bedrock-knowledge-bases](), [announcements](), [audio](), [embedding](), [how-to](), [images](), [intermediate-200](), [multimodal](), [rag](), [s3](), [search](), [video]() ### AI overview A walkthrough for building an Amazon Bedrock Knowledge Base with TwelveLabs Marengo Embed 3.0 to perform natural-language semantic search across video, images, and audio. ### Source excerpt TwelveLabs Marengo Embed 3.0 is now generally available as an embedding model in Amazon Bedrock Knowledge Bases, bringing fully managed natural language search to video, image, and audio content. This walkthrough shows how to build a knowledge base powered by Marengo 3.0 and run semantic queries against your media. ## Backblaze B2 x Suite Studios: S3 Native File Streaming Turns B2 Cloud Storage Into a High-Performance Drive DevFeed: [Backblaze B2 x Suite Studios: S3 Native File Streaming Turns B2 Cloud Storage Into a High-Performance Drive]() Original publisher: [Read original article]() Author: Dave Simon Published: 2026-09-10T14:04:38Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [Amazon S3](), [Cloud](), [Streaming](), [Filesystems](), [mount](), [data](), [migration]() Tags: [architecture](), [automation](), [b2cloud](), [cloud](), [cloud-storage](), [data](), [featured](), [featured-cloud-storage](), [media](), [media-workflow](), [migration](), [mount](), [nas](), [object-storage](), [s3](), [storage](), [streaming]() ### AI overview Backblaze B2 integrates with Suite Studios' S3 Native File Streaming to provide drive-like, high-performance access to cloud-stored data. Teams can mount B2 buckets, stream only the file portions applications need, and use standard S3-compatible objects without duplicating or relocating datasets. The integration supports media production and other workflows involving large cloud datasets. ### Source excerpt Backblaze B2 now integrates with Suite Studios S3 Native File Streaming, giving teams high-performance, drive-like access to cloud data. Work directly with standard S3-compatible objects across media, scientific, geospatial, and engineering workflows without duplicating datasets or creating new storage silos. The post Backblaze B2 x Suite Studios: S3 Native File Streaming Turns B2 Cloud Storage Into a High-Performance Drive appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Improving Lakebase Postgres compute cache DevFeed: [Improving Lakebase Postgres compute cache]() Original publisher: [Read original article]() Author: David Wein; Sunil Kamath; Haoyu Huang Published: 2026-09-10T13:47:03Z Content type: article Language: en Sources: [Databricks]() Topics: [Caching](), [databricks](), [Databases](), [autoscaling](), [Amazon S3](), [Filesystems](), [cloud-infrastructure]() Tags: [autoscaling](), [cache](), [caching](), [data](), [data-engineering](), [databricks](), [engineering](), [filesystem](), [latency](), [low-latency](), [memory](), [s3](), [storage]() ### AI overview This Databricks article describes improvements to compute-side caching for Lakebase Postgres, a disaggregated storage system backed by object storage such as Amazon S3. It explains PostgreSQL shared buffers, the operating system page cache, and the planned use of dynamically autoscaling shared buffers consuming up to 75% of compute memory. It also introduces a local file cache as an incremental solution for fixed compute instances. ### Source excerpt The disaggregated storage model of Lakebase Postgres provides a feature rich, flexible... ## Loading Parquet data into MySQL with ClickHouse DevFeed: [Loading Parquet data into MySQL with ClickHouse]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [MySQL](), [parquet](), [Databases](), [Amazon S3]() Tags: [clickhouse](), [mysql](), [parquet](), [s3](), [sql]() ### AI overview A tutorial on using ClickHouse to load Parquet data into MySQL and query MySQL through ClickHouse table functions. ### Source excerpt Use ClickHouse to load Parquet files into MySQL, explore remote data, and run MySQL queries with table functions and named collections. ## Improving Lakebase Postgres Compute Cache on Neon, Part 1 DevFeed: [Improving Lakebase Postgres Compute Cache on Neon, Part 1]() Original publisher: [Read original article]() Author: Sunil Kamath Published: 2026-09-09T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Caching](), [Databases](), [Operating system](), [Amazon S3](), [cpu](), [Latency](), [autoscaling]() Tags: [autoscaling](), [cache](), [compute](), [cpu](), [engineering](), [latency](), [memory](), [os](), [postgres](), [s3](), [storage]() ### AI overview Neon describes a Lakebase Postgres compute-cache change that allocates most memory to shared buffers backed by huge pages. The goal is to keep hot pages in DRAM, reducing storage reads, CPU use, and latency; the article reports up to roughly 2x throughput on specified fixed-size computes. ### Source excerpt On large fixed-size Lakebase Postgres computes on Neon, we now put most of the machine's memory into Postgres shared buffers and back that cache with huge pages. Hot pages stay in DRAM instead of falling through to a local disk cache, so the same working set is served faster and with less CPU. ## Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3 DevFeed: [Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3]() Original publisher: [Read original article]() Author: Tzach Livyatan Published: 2026-09-08T21:32:47Z Content type: release Language: en Sources: [ScyllaDB]() Topics: [releases](), [Amazon S3](), [AI search](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [product](), [rag](), [release](), [s3](), [search](), [storage]() ### AI overview ScyllaDB 2026.3 adds full-text search, a preview object-storage backend, OCI integration, and experimental table and migration capabilities. It also introduces large-data guardrails and cluster-wide restoration from object-storage backups. ### Source excerpt new updates should help you move even more workloads to ScyllaDB, at a fraction of the cost. ## Introducing chdb Postgres extension: High-performance imports from cloud storage DevFeed: [Introducing chdb Postgres extension: High-performance imports from cloud storage]() Original publisher: [Read original article]() Author: David Wheeler Published: 2026-09-08T15:42:52Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [cloud-infrastructure](), [Amazon S3](), [parquet](), [CSV]() Tags: [benchmark](), [clickhouse](), [cloud](), [duckdb](), [extension](), [json](), [parquet](), [performance](), [postgres](), [s3](), [storage]() ### AI overview chdb is a new Postgres extension that uses the in-process ClickHouse engine to import and export data across cloud storage systems and formats. The article presents import benchmarks, format support, and usage through a query function and a COPY hook module. ### Source excerpt The chdb Postgres extension brings fast imports and exports across cloud storage platforms and data formats, powered by the embedded ClickHouse engine. ## Cut GPU inference cold start from 8 minutes to less than a minute DevFeed: [Cut GPU inference cold start from 8 minutes to less than a minute]() Original publisher: [Read original article]() Author: Sajjan Gundapuneedi Published: 2026-09-03T18:30:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [GPU](), [Inference](), [model-deployment](), [Amazon Elastic Kubernetes Service](), [CUDA](), [Optimization](), [Amazon S3](), [Kubernetes]() Tags: [ai-infrastructure](), [amazon-eks](), [aws-marketplace](), [cpu](), [cuda](), [gpu](), [inference](), [kubernetes](), [large-language-models](), [optimization](), [post-contributed](), [s3](), [sponsor-aws-marketplace](), [sponsored-post-contributed]() ### AI overview The article measures GPU model startup from pod creation to the first inference response and identifies six sequential phases. It reports that CUDA kernel recompilation dominates startup for a 64 GB model, while S3 weight downloads dominate for a 203 GB model. Configuration and platform changes reduced warm-node startup times by 80-93%, with additional cold-node improvements requiring Amazon EKS Auto Mode. ### Source excerpt We instrumented the full path from pod creation to first inference response on a GPU node running a 70B-class model. The post Cut GPU inference cold start from 8 minutes to less than a minute appeared first on The New Stack. ## Announcing the S3 API: Use any S3 client with Appwrite Storage DevFeed: [Announcing the S3 API: Use any S3 client with Appwrite Storage]() Original publisher: [Read original article]() Author: Torsten Dittmann Published: 2026-09-03T00:00:00Z Content type: release Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Amazon S3](), [API](), [Amazon Web Services](), [SDKs](), [Command-line interface](), [migration]() Tags: [announcements](), [api](), [aws](), [backup](), [cli](), [code](), [commands](), [data](), [files](), [migration](), [reuse](), [s3](), [sdk](), [sdks](), [server](), [storage](), [tools]() ### AI overview Appwrite Storage now exposes an S3-compatible API that supports AWS CLI, AWS SDKs, rclone, s3cmd, and other S3 clients. Existing buckets and files remain available through both the native Storage API and the S3 API, with support for standard operations, multipart uploads, and presigned URLs. ### Source excerpt Appwrite Storage now exposes an S3-compatible API. Point the AWS CLI, the AWS SDKs, and tools like rclone at your Appwrite buckets, with no migration required. ## Fast model loading for AI inference on Amazon EKS DevFeed: [Fast model loading for AI inference on Amazon EKS]() Original publisher: [Read original article]() Author: Sajjan Gundapuneedi Published: 2026-09-01T15:48:15Z Content type: article Language: en Sources: [Containers]() Topics: [Inference Performance](), [Amazon Elastic Kubernetes Service](), [Amazon S3](), [Language models]() Tags: [advanced-300](), [ai](), [ai-inference](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [best-practices](), [cuda](), [gpu](), [inference](), [kubernetes](), [language-models](), [pytorch](), [s3](), [sglang](), [startup](), [vllm]() ### AI overview The article analyzes cold-start delays for AI inference pods on Amazon EKS. It finds that startup bottlenecks vary by model size: torch.compile dominates for smaller models, while loading weights from S3 to GPU memory dominates for larger models. Configuration changes to Run:ai Model Streamer reduce model-loading time on repeat launches. ### Source excerpt When you scale AI inference on Amazon EKS, every new pod must load model weights into GPU memory before serving traffic. We investigated where cold-start time goes and found two configuration-only changes to Run:ai Model Streamer that cut model startup time by 80-93% on subsequent launches, with no code changes. ## AWS PrivateLink is now available on Pro and Enterprise DevFeed: [AWS PrivateLink is now available on Pro and Enterprise]() Original publisher: [Read original article]() Author: Bryan Mishkin Published: 2026-09-01T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Network](), [Amazon Web Services](), [AWS IAM](), [Amazon S3](), [MongoDB]() Tags: [aws](), [enterprise](), [iam](), [mongodb](), [networking](), [pricing](), [s3](), [vercel]() ### AI overview Vercel has made AWS PrivateLink available to Pro and Enterprise teams through Advanced Networking. Vercel Functions and builds can privately access AWS-hosted databases, SaaS services, internal services behind an AWS Network Load Balancer, and S3 or DynamoDB without using the public internet. ### Source excerpt AWS PrivateLink is now available for Pro and Enterprise teams as part of Advanced Networking. It allows Vercel Functions and builds to connect to AWS-hosted services without sending traffic over the public internet. Use it to reach AWS-hosted databases such as RDS, Aurora, and Neon; SaaS services such as Snowflake and MongoDB Atlas; internal services behind an AWS Network Load Balancer; and S3 or DynamoDB through gateway endpoints. To configure PrivateLink, open your project's Networking settings and enable Advanced Networking. Select New Connection, then enter the service name and region. The service must accept connections from all AWS principals or allowlist the IAM role Vercel provides for your team. Vercel creates the connection and provides a stable hostname. Use this hostname from your Functions and builds to reach the service privately. Pricing: The first AWS PrivateLink connection is included with Advanced Networking. Each additional connection costs $30 per month. PrivateLink data transfer costs $0.04 per GB. Learn more in the AWS PrivateLink documentation. Read more ## Announcing Appwrite 2.0: a new foundation for your apps DevFeed: [Announcing Appwrite 2.0: a new foundation for your apps]() Original publisher: [Read original article]() Author: Eldad Fux Published: 2026-08-31T00:00:00Z Content type: release Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Databases](), [Amazon S3](), [OAuth](), [Firewall](), [MySQL](), [PostgreSQL]() Tags: [feature](), [firewall](), [mysql](), [oauth](), [postgresql](), [products](), [release](), [s3]() ### AI overview Appwrite 2.0 is a major platform release with a new engine, rebuilt Console, relational, schemaless, and vector data support, native PostgreSQL and MySQL engines, S3-addressable storage, OAuth 2.1 and OpenID Connect support, and organization-level Domains and Firewall. ### Source excerpt Appwrite 2.0 brings a new engine, a rebuilt Console, five database types, an S3 API, an OAuth 2.1 server, and organization-level Domains and Firewall. ## Detecting multi-stage attacks on AWS: A guide to cross-service signal correlation DevFeed: [Detecting multi-stage attacks on AWS: A guide to cross-service signal correlation]() Original publisher: [Read original article]() Author: Nisha Kashyap Published: 2026-08-26T17:39:19Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services (AWS)](), [Security](), [Detection engineering](), [Amazon CloudWatch Logs](), [AWS CloudTrail](), [VPC Flow Logs](), [Amazon S3](), [data]() Tags: [advanced-300](), [amazon-cloudwatch](), [amazon-cloudwatch-logs](), [amazon-guardduty](), [amazon-route-53](), [amazon-web-services-aws](), [analysis](), [aws-cloudtrail](), [aws-security-hub](), [best-practices](), [cybersecurity](), [guide](), [incident-response](), [logs](), [s3](), [security](), [security-blog](), [security-identity-compliance](), [shared-responsibility-model](), [technical-how-to](), [threat-detection](), [vpc-flow-logs]() ### AI overview This article explains how security engineers can detect multi-stage attacks on AWS by correlating signals across services with business context. It presents examples using CloudWatch Logs Insights and discusses expanding the correlations into an automated pipeline. ### Source excerpt A single alert from one security service tells you something happened. Read that signal alongside activity from other services and your own business context, and you will know whether what happened is part of a multi-stage attack. Consider a short sequence. An identity calls GetCallerIdentity from a source address it hasn't previously used. Within minutes, [...] ## DuckDB and the changing physics of analytics DevFeed: [DuckDB and the changing physics of analytics]() Original publisher: [Read original article]() Author: werner@allthingsdistributed.com (Dr. Werner Vogels) Published: 2026-08-26T14:00:00Z Content type: opinion Language: en Sources: [All Things Distributed]() Topics: [DuckDB](), [data](), [Databases](), [Amazon S3](), [Amazon Web Services](), [Computer science]() Tags: [analytics](), [aws](), [data](), [databases](), [duckdb](), [posts](), [s3](), [software](), [systems]() ### AI overview The article describes how changing relative costs for compute, memory, and networking are enabling more data processing to remain within applications. It presents DuckDB as a database supporting this shift and discusses its relationship to AWS data offerings, including S3 Files, S3 Tables, and S3 Vectors, alongside DuckLabs joining AWS. ### Source excerpt In this post, Andy Warfield explains how databases like DuckDB are enabling a new way to build with data, why they matter right now, and how they complement the work we've been doing in S3 (e.g., S3 Files, S3 Tables, S3 Vectors). And most importantly, why DuckLabs, the team behind DuckDB, is joining AWS ## Git was built for humans -- agents need an upgrade DevFeed: [Git was built for humans -- agents need an upgrade]() Original publisher: [Read original article]() Author: Jessica Taylor Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [AI-assisted coding](), [Amazon S3]() Tags: [agents](), [api](), [architecture](), [backend](), [concurrency](), [engineering](), [features](), [git](), [product](), [s3](), [scm]() ### AI overview GitLab describes next-generation source code management designed for large numbers of agents. Its proposed design preserves Git-protocol compatibility while replacing full repository clones with targeted server-side queries and purpose-built read/write APIs. ### Source excerpt The industry is now racing to rebuild source code management for agents. We showed our answer at GitLab Transcend, but let's reiterate why rebuilding the Git backend is only half the problem. Three things break when agents become the primary users of a Git server. Every developer running hundreds of agents hits the same wall, regardless of tools: The clone tax. An agent clones an entire repository to read a single file, then does it again for the next agent, and the next retry, transferring far more data than the task requires and burning context on a local grep or blame it shouldn't have needed to run. One agent invocation today can mean 5GB to 10GB transferred and 30+ seconds of setup, just to answer a single question. Concurrency collapse. Thousands of sessions hit a backend that was originally designed for human scale, producing bottlenecks and unpredictable availability. No isolation. Agents share accounts and one branch space, so they overwhelm the repository, leave no clean way to discard abandoned work, and keep no record of which agent did what. Our platform data shows how fast the pressure is building. Over the past year, our customers created 40% more CI/CD pipelines, and code pushes to GitLab.com were up 50%. While the secure repositories grew by 60%, codebase sizes have also grown by up to 500%. When we announced next-generation source code management (next-gen SCM) at GitLab Transcend in June, we walked through how Git, as an operational model, was not designed for the load agents placed on it. A few weeks later, new entrants, including Git hosts built specifically for agent-scale concurrency, are validating that claim independently. That convergence sharpens why rebuilding the Git backend is necessary, but on its own, it's still not enough. What we're building for agent scale Next-gen SCM runs on the Git protocol for backward compatibility, with a redesigned backend and interfaces built for agents. Instead of cloning a full working tree, agents query ## How Suprema Gaming made its data platform agent-ready with ClickHouse Cloud DevFeed: [How Suprema Gaming made its data platform agent-ready with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-08-25T18:52:53Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [migration](), [real-time](), [Cloud](), [data](), [Latency](), [Database](), [Artificial Intelligence](), [Amazon S3](), [Amazon Simple Queue Service (SQS)]() Tags: [agentic](), [ai](), [ai-agents](), [analytics](), [b2b](), [brazil](), [clickhouse](), [cloud](), [data](), [database](), [latency](), [migration](), [modernization](), [real-time](), [s3]() ### AI overview Suprema Gaming migrated its analytics platform from Snowflake to ClickHouse Cloud to support agentic operations. The move reduced warehouse costs by 62%, cut query latency from minutes to milliseconds, and improved data freshness from four hours to real time. The company now has an early production deployment of ClickHouse Agents serving teams across the group. ### Source excerpt Suprema Gaming migrated its analytics platform from Snowflake to ClickHouse Cloud to power a company-wide shift toward agentic operations. ## AWS Extends NixOS Infrastructure Credits for 2026-27 DevFeed: [AWS Extends NixOS Infrastructure Credits for 2026-27]() Original publisher: [Read original article]() Author: ron Published: 2026-08-24T16:22:07Z Content type: news Language: en Sources: [Announcements - NixOS Discourse]() Topics: [Amazon Web Services](), [Nix](), [Resilience](), [Amazon S3]() Tags: [announcements](), [aws](), [partnership](), [resilience](), [s3]() ### AI overview AWS has committed $20,000 per month in credits for 2026-27 to support cache.nixos.org and releases.nixos.org. The announcement describes this as a continuation of a multi-year partnership with the NixOS Foundation and Community. ### Source excerpt Happy Monday, and hopefully a good 4 weeks before NixCon week to everyone! Some very good news for us today. AWS has committed the next chapter of its support for the NixOS Foundation & Community: $20,000 per month in AWS credits for the 2026-27 period, nearly a quarter of a million dollars, to help keep cache.nixos.org and releases.nixos.org running. A few years ago, this partnership started as an urgent effort to close an immediate infrastructure funding gap. Today, it has grown into a durable multi-year partnership, with AWS making its largest financial commitment to date. Huge thank you to the AWS Open Source and Open Data teams, the Nix Infra team, and everyone who shared their Nix + AWS stories with us over the last few months (y'all know who you are!). Your experiences genuinely helped us make the case. Some of your stories appeared in the initial thread and a lot more came in via DMs! Looking for Nix + AWS usage stories We still have important work ahead on cost efficiency, resilience, and the long-term sustainability of this infrastructure. Lots of progress has been made by the infra team over the last 12 months! More to come here as well! Steve (Staff Writer @ Flox) has been volunteering with the Infra & AWS teams and will publish a much deeper blog very soon covering the scale, technical work, and what comes next. I don't want to steal his thunder, so more on that shortly. For now: big heart to everyone who helped get this over the line. Lots of Nixy Love, Ron & The Foundation Our incredible Infrastructure Team - @hexa @vcunat @jfly @arianvp @Mic92 @ra33it0 @Infinisil @ryantrinkle @lassulus @ethancedwards @bme 2 posts - 2 participants Read full topic ## WAL + S3: Lakebase storage for the era of agents DevFeed: [WAL + S3: Lakebase storage for the era of agents]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-08-24T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Database](), [Amazon S3](), [Transactions](), [data](), [migration]() Tags: [agents](), [data](), [database](), [engineering](), [migration](), [product](), [s3](), [storage](), [transactions]() ### AI overview The article argues that PostgreSQL workloads associated with agents need storage built around transaction history rather than only the current state. It presents the write-ahead log as an existing timeline of database modifications and examines placing S3 beneath PostgreSQL to support isolated copies, point-in-time restoration, historical queries, and many disposable environments more efficiently. ### Source excerpt Agents treat code as lightweight: branch it, deploy it, throw it away. The infrastructure under that code should work the same way. Working with an OLTP database is traditionally heavy and full of friction, but very little of that is a Postgres problem. [Next page]()