# Platform Published articles for Platform. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Web Search Limitations and Duplicated Tooling Complicate Market-Signal Agents DevFeed: [Web Search Limitations and Duplicated Tooling Complicate Market-Signal Agents]() Original publisher: [Read original article]() Author: Charlie Klein; Bryan Smith Published: 2026-09-17T17:00:00Z Content type: opinion Language: en Sources: [Databricks]() Topics: [Web](), [Software](), [Claude Code](), [codex](), [API](), [Model Context Protocol](), [databricks]() Tags: [agent](), [api](), [claude-code](), [codex](), [databricks](), [databricks-ai](), [llms](), [mcp](), [orchestration](), [platform](), [product](), [search](), [web]() ### AI overview The article describes an agent that combines company data in Databricks with web-based market signals. Its enrichment logic is rebuilt across Claude Code, Codex, and a direct model API workflow because each provides different tools, search behavior, and configuration requirements. The article argues that inconsistent web search and the lack of a shared layer make reliable account enrichment difficult. ### Source excerpt An agent that needs the outside worldAn engineer at a software company is building... ## Build a unified CI/CD control plane with Red Hat Developer Hub DevFeed: [Build a unified CI/CD control plane with Red Hat Developer Hub]() Original publisher: [Read original article]() Author: Rishabh Singh Published: 2026-09-17T13:16:44Z Content type: tutorial Language: en Sources: [Red Hat Developer]() Topics: [internal developer portal](), [Backstage](), [argo-cd](), [CI/CD](), [Platform Engineering]() Tags: [argo-cd](), [backstage](), [ci-cd](), [cognitive-load](), [developer-portal](), [developer-productivity](), [guide](), [how-to](), [onboarding](), [openshift](), [platform](), [platform-engineering]() ### AI overview This guide explains how platform engineers can deploy and configure Red Hat Developer Hub as a unified developer portal for Red Hat OpenShift, Argo CD, Tekton, Quay, and related deployment information. It describes aggregating pipeline runs, deployment health, and container image tags in one view, along with software templates for self-service project bootstrapping. ### Source excerpt If your engineering teams waste time context-switching between Red Hat OpenShift, Argo CD, Tekton, and Quay, your platform needs a unified developer portal. The post Build a unified CI/CD control plane with Red Hat Developer Hub appeared first on Red Hat Developer. ## Australia's AI opportunity starts with data DevFeed: [Australia's AI opportunity starts with data]() Original publisher: [Read original article]() Author: Sean MacKirdy Published: 2026-09-17T01:00:00Z Content type: opinion Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [data](), [Artificial Intelligence](), [AI Strategy]() Tags: [agentic-ai-government](), [ai](), [ai-adoption](), [australia](), [data](), [platform](), [public-sector-media-entertainment]() ### AI overview Elastic's participation in Australia's first Parliamentary AI Industry Showcase emphasized that data is a central constraint on AI performance and a prerequisite for deploying AI that is effective, safe, and trusted. ### Source excerpt Elastic, a Founding Partner at Australia's first Parliamentary AI Industry Showcase, highlights that data, not just models, is the key to deploying safe, effective, and trusted AI. ## Rate limits on GitLab.com are changing DevFeed: [Rate limits on GitLab.com are changing]() Original publisher: [Read original article]() Author: Sam Wiskow Published: 2026-09-17T00:00:00Z Content type: release Language: en Sources: [GitLab]() Topics: [GitLab](), [Automation](), [Users](), [account]() Tags: [2026](), [automation](), [credentials](), [gitlab](), [limits](), [platform](), [product](), [user](), [users]() ### AI overview GitLab.com will align rate limits with subscription tiers beginning October 19, 2026. Free accounts and unauthenticated requests change first, while Premium and Ultimate limits change in January 2027. Unauthenticated requests are limited to 60 requests per hour per IP address, with preview windows scheduled for October 7 and 14. ### Source excerpt GitLab.com hosts millions of projects for teams of every size that need a platform they can rely on. Demand is climbing quickly, and we expect platform load to grow several times over this year. Predictable limits are what keep GitLab.com fast for everyone on it, including the automation and agent workloads teams are building on the platform. To hold that as we scale, we're updating how rate limits work. Starting October 19, 2026, rate limits on GitLab.com will align with your subscription tier. Free accounts and unauthenticated requests happen first, on October 19. Premium and Ultimate move in January 2027. What is changing Limits align with your subscription. Free, Premium, and Ultimate subscription plans get their own limits, applied per user and per top-level group. Free takes effect October 19; Premium and Ultimate in January 2027. Signing in gets you the full limit. An authenticated request is governed by your subscription plan below. A request that arrives with no credentials gets 60 requests per hour per IP address. The per-plan limits are published in the rate limits documentation. What happens on October 19 There will be two preview windows for Free and unauthenticated traffic, on October 7 and October 14 from 15:00 to 19:00 UTC. Signed-in Premium and Ultimate requests are not affected, since those limits do not change until January. Unauthenticated requests are capped no matter where they come from, including automation running against a paid account without credentials. A preview window (engineers call these brownouts) is a short, planned window where we switch the new limits on and then switch them back off. Nothing else about the service changes while it runs. The point is to give you a real look at how your own workloads behave under the new limits, weeks before they apply for good. On October 19 the new limits take effect. We set these limits by looking at how GitLab.com is actually used. Almost all users are already inside the new limits and won't n ## onsemi Embedded Power Platform Announced DevFeed: [onsemi Embedded Power Platform Announced]() Original publisher: [Read original article]() Author: Vic A Published: 2026-09-16T22:07:08Z Content type: release Language: en Sources: [ServeTheHome]() Topics: [Artificial Intelligence](), [systems](), [Simulation and Design]() Tags: [800v](), [ai](), [automotive](), [embedded](), [industrial](), [news](), [onsemi](), [platform](), [power]() ### AI overview onsemi announced its Embedded Power Platform, which embeds heterogeneous power dies in silicon and connects them with wafer-level redistribution layers instead of wire bonds. The company is targeting vehicle electrification, AI power infrastructure, and industrial applications, with 800V DC AI racks identified as an early use case. ### Source excerpt At its investor day, onsemi announced its new Embedded Power Platform for AI racks, automotive, and industrial applications The post onsemi Embedded Power Platform Announced appeared first on ServeTheHome. ## Platform Engineering in the Age of AI DevFeed: [Platform Engineering in the Age of AI]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-09-16T20:28:57.610955Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [Platform Engineering](), [Artificial Intelligence](), [AI Agent](), [internal developer portal](), [API](), [Deployment](), [sdlc]() Tags: [agent](), [ai](), [api](), [deployment](), [developer-experience](), [engineering](), [metrics](), [platform](), [sdlc]() ### AI overview This commentary examines how platform engineering is adapting as AI coding tools and agents become part of software delivery. It discusses the build-versus-provide decisions facing platform teams, how agents may consume internal developer platforms through APIs, the need for guardrails, and the challenge of measuring the results of AI investment. ### Source excerpt 94% of engineering leaders say their AI metrics are missing. Here's how platform engineering is changing to close that gap. | Blog ## ClickHouse is now available on the dbt platform DevFeed: [ClickHouse is now available on the dbt platform]() Original publisher: [Read original article]() Author: Aditya Chidurala; José Muñoz; Alex Francoeur Published: 2026-09-16T19:38:40Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [Rust](), [Open Source]() Tags: [adapter](), [announcements](), [beta](), [clickhouse](), [cloud](), [dbt](), [open-source](), [platform](), [release](), [rust]() ### AI overview ClickHouse announces a dbt v2 adapter powered by dbt's Rust engine, available in public beta. ClickHouse is also available as a natively supported data warehouse on dbt Platform in private beta, with support for open-source ClickHouse and ClickHouse Cloud. ### Source excerpt The ClickHouse adapter for dbt v2 is in public beta, powered by dbt's Rust engine. ClickHouse also joins the dbt platform in private beta, supporting open-source ClickHouse and ClickHouse Cloud. ## Shared Selective Persistent Memory for Agentic LLM Systems DevFeed: [Shared Selective Persistent Memory for Agentic LLM Systems]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [agent observability](), [Framework](), [Code](), [configuration](), [Access Control](), [Git](), [Model Context Protocol (MCP)](), [CSV](), [SQL](), [Grafana]() Tags: [access-control](), [agentic](), [agents](), [code](), [configuration](), [csv](), [dashboards](), [git](), [llm](), [mcp](), [memory](), [platform](), [replication](), [sql]() ### AI overview This research introduces shared selective persistent memory for agentic LLM systems. The architecture retains reusable task specifications, data schemas, tool configurations, and output constraints while discarding session-specific reasoning traces. Shared workspaces support role-based collaborative reuse, and experiments report higher task completion than no memory or full-history persistence, along with zero-token data refresh and lower token costs. ### Source excerpt Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is both token-inefficient and counterproductive--irrelevant context degrades generation quality. We introduce shared selective persistent memory, a memory architecture for agentic systems that identifies and retains four categories of reusable context--task specifications, data... ## SDL3 Ported To HarmonyOS / OpenHarmony DevFeed: [SDL3 Ported To HarmonyOS / OpenHarmony]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-15T16:54:08Z Content type: news Language: en Sources: [Phoronix]() Topics: [Simple DirectMedia Layer](), [huawei](), [Library](), [Operating system](), [cross-platform](), [Open Source](), [Software]() Tags: [applications](), [cross-platform](), [desktop-linux](), [games](), [huawei](), [library](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [platform](), [software](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview SDL3 has gained upstream support for Huawei's HarmonyOS and OpenHarmony. The port was sponsored by Outfit7, tested on real hardware, and is already used by some commercial games, according to Icculus. ### Source excerpt The SDL3 library commonly used for games and applications for cross-platform hardware and software abstractions now has upstream support for Huawei's HarmonyOS and OpenHarmony... ## Loyalty, Leveled Up: How We're Building the Next Generation of Rewards DevFeed: [Loyalty, Leveled Up: How We're Building the Next Generation of Rewards]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-09-15T13:14:46Z Content type: article Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Product Management](), [Mobile](), [personalization](), [App]() Tags: [app](), [customer-experience](), [loyalty-program](), [mobile](), [personalization](), [platform](), [product-management](), [technology](), [user-experience]() ### AI overview McDonald's describes how its teams redesigned the Rewards experience as a loyalty platform rather than a simple rewards list. The work focused on reducing friction, improving personalization, and integrating rewards into the mobile ordering journey. By September 2026, the redesigned experience was live across the company's lead Global Mobile App markets. ### Source excerpt From simpler experiences to smarter systems, this is how loyalty evolves at global scale. by Sara Robertson, Sr. Manager, Product Management Quick Bytes Loyalty operates at massive scale, but customer research showed that finding, understanding, and using rewards didn't always feel easy in the moments that mattered most By rethinking loyalty as a platform -- not just a rewards list -- teams reduced friction, introduced scalable personalization, and embedded value directly into the ordering journey The result is a simpler, more intuitive loyalty experience designed to grow with customers and support long-term global scale Loyalty at McDonald's operates at a scale few brands have ever achieved. Every day, millions of customers engage with McDonald's Rewards -- each with their own habits, preferences, and expectations. As the program grows, so does our ambition: deliver a loyalty experience that feels effortless, personal, and genuinely valuable for everyone. That ambition is tied to a clear leadership focus: continuing to grow our global loyalty customer base by making loyalty so intuitive, flexible, and valuable that it becomes a natural part of how customers use the app. As McDonald's continues its journey to build one of the world's leading loyalty programs, simplicity and customer value remain at the center of every decision. That's why teams across Product, Engineering, and Experience Design -- together with cross-functional partners across the Global team, segments, and markets -- collaborated to reimagine the loyalty experience. As of September 2026, the experience is now live across all of our lead Global Mobile App (GMA) markets. Designing for growth starts with reducing friction Customer research made one thing clear early on: loyalty was valuable, but engaging with it didn't always feel easy. Customers told us rewards could be hard to find, confusing to understand, or disconnected from the actual moment they were ordering food. Too often, rewards were remembered ## Dell Pro 5 16 (AMD) Laptop Review DevFeed: [Dell Pro 5 16 (AMD) Laptop Review]() Original publisher: [Read original article]() Author: Ryan Smith Published: 2026-09-14T16:25:36Z Content type: article Language: en Sources: [ServeTheHome]() Topics: [dell](), [Hardware](), [cpu](), [intel]() Tags: [amd](), [cpu](), [dell](), [external](), [hardware](), [intel](), [intel-core](), [laptop](), [laptops](), [mobile](), [modular](), [overview](), [performance](), [platform](), [portable](), [product](), [review](), [ryzen-ai](), [series](), [strix-point](), [thunderbolt]() ### AI overview A review of Dell's 16-inch Pro 5 business laptop powered by an AMD Ryzen AI 400 series processor. The article highlights its performance, modularity, business positioning, and comparability with Intel-based Dell models. ### Source excerpt Today we are taking a look at Dell's 16-inch laptop for mainstream business segment, the AMD-based Dell Pro 5 16. The pro laptop hits all the high notes, pairing an AMD Ryzen AI 9 HX PRO 470 processor with great modularity and a good balance between size, weight, and performance The post Dell Pro 5 16 (AMD) Laptop Review appeared first on ServeTheHome. ## Claude Managed Agents: How They Work and Where They Fit DevFeed: [Claude Managed Agents: How They Work and Where They Fit]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-09-14T11:29:41Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Agent Harness](), [Claude](), [Artificial Intelligence](), [sdlc](), [context window](), [Network](), [Processes]() Tags: [agent](), [agents](), [ai](), [claude](), [context-window](), [infrastructure](), [network](), [platform](), [sandbox](), [sdlc](), [software](), [tools]() ### AI overview This article explains Claude Managed Agents, a hosted runtime and managed agent harness operated by Anthropic. It describes how the harness coordinates tools and execution, the sandbox provides isolated command and file access, and the session preserves durable task history outside the model's context window. It also discusses the additional platform layer needed to connect multiple agents across a business process and the SDLC. ### Source excerpt What Claude Managed Agents are, how the runtime works, and how platform teams connect several agents across the SDLC. ## MIT spinout turns plastic waste into resilient building materials DevFeed: [MIT spinout turns plastic waste into resilient building materials]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [systems]() Tags: [3-d-printing](), [ai](), [aj-perez](), [alumni-ae](), [artificial-intelligence](), [atlas-composites](), [cleaner-industry](), [engineering](), [homes](), [housing](), [manufacturing](), [materials-science-and-engineering](), [matt-pouliot](), [mechanical-engineering](), [platform](), [pollution](), [production](), [recycled-plastic-building-materials](), [robotics](), [school-of-engineering](), [startups](), [sustainability](), [u-s-army](), [water]() ### AI overview MIT spinout Atlas Building Composites is commercializing an AI-powered robotic manufacturing platform that recycles single-use and low-grade plastic into durable building components. Its waterless process has been used for structures including a bridge supplied to the U.S. Army Corps of Engineers. ### Source excerpt Atlas Building Composites is commercializing MIT research to turn plastic waste into parts for buildings and other infrastructure. ## How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons DevFeed: [How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons]() Original publisher: [Read original article]() Author: Fatjon Nebiu Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana]() Tags: [analytics](), [auth](), [best-practices](), [cloud](), [cost](), [data](), [deployment](), [enterprise](), [grafana](), [grafana-alloy](), [grafana-cloud](), [how-to](), [kubernetes](), [logs](), [metrics](), [monitoring](), [observability](), [opentelemetry](), [platform](), [production](), [prometheus](), [scale](), [telemetry](), [testing](), [traces]() ### AI overview A practical guide to scaling Grafana Alloy as a centralized telemetry gateway. It covers capacity planning, load testing, and production considerations for collecting metrics, logs, and traces and forwarding them to Grafana Cloud. ### Source excerpt Running Alloy as a single-instance sidecar is simple. Running it as a centralized gateway that absorbs the full telemetry stream of an enterprise platform--tens of millions of active series, terabytes of logs per day, and tens of thousands of trace spans per second--is a different challenge altogether. To get it right, you need deliberate capacity planning, honest load testing, and a monitoring setup that doesn't rely on the very thing you're testing. As part of the Professional Services team here at Grafana Labs, we've seen this firsthand working with customers. In this post, we'll walk you through the best practices we follow to help them find success, and we'll do so using real, anonymized data from a recent engagement. We'll cover how we sized and load tested a production Alloy central collector deployment on Kubernetes, what the numbers looked like under real stress, and how the cluster behaves today handling the full production telemetry workload for a large enterprise platform. By the end, you should have a better sense for how you can create your own central gateway for collecting telemetry in Grafana Cloud. Why a central gateway? Before diving into numbers, it's worth explaining the pattern. In a central gateway setup, all telemetry from application teams--metrics, logs, and traces--flows to a shared Alloy fleet via OTLP or native Prometheus/Loki write protocols. Alloy buffers, processes, batches, and forwards everything to Grafana Cloud. This gives you several things that per-team sidecar deployments struggle to provide: A single control plane: Auth, rate limiting, and routing in one place so application teams don't need to manage Grafana Cloud credentials Centralized buffering: Ensure a transient Grafana Cloud slowdown doesn't immediately cause data loss at the source Cost visibility: Configure the gateway to only accept telemetry data containing the label or attribute that is mandatory for cost-attribution Protocol normalization: Send OTLP, Prometheus Remote ## CrowdStrike Delivers the Next Evolution of the Agentic SOC DevFeed: [CrowdStrike Delivers the Next Evolution of the Agentic SOC]() Original publisher: [Read original article]() Author: Brandon Benke Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Agentic SOC](), [SOC](), [Security](), [Artificial Intelligence](), [Automation](), [data](), [real-time](), [Reconnaissance](), [Cloud](), [Network]() Tags: [2026](), [agentic](), [agentic-soc](), [agents](), [ai](), [automation](), [cloud](), [data](), [identity](), [network](), [operations](), [platform](), [real-time](), [saas](), [security](), [soc]() ### AI overview CrowdStrike describes the next evolution of its agentic SOC, where analysts and AI agents work together in a unified system to investigate and respond to threats in real time. The Falcon platform combines data generation, enrichment, investigation, orchestration, and governance, with capabilities for detection-ready third-party data and coordinated specialist agents. ### Source excerpt Expert agents that reason together, learn your environment, and run on data CrowdStrike owns. See how we deliver the agentic SOC. Learn more! ## CrowdStrike Falcon Guardian Defines the Next Generation of AI Security DevFeed: [CrowdStrike Falcon Guardian Defines the Next Generation of AI Security]() Original publisher: [Read original article]() Author: Michael Devins Published: 2026-09-12T11:17:51.295154Z Content type: release Language: en Sources: [Blog]() Topics: [AI Bots]() Tags: [agentic](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-gateway](), [ai-security](), [architecture](), [autonomous](), [platform](), [securing-ai](), [security](), [telemetry](), [threat-detection]() ### AI overview CrowdStrike announces Falcon Guardian, an AI detection and response solution for discovering, monitoring, investigating, and securing AI agents at runtime. It adds an AI gateway and connects agent activity with endpoint telemetry to support threat response. ### Source excerpt A new flagship AI detection and response solution delivers runtime protection for AI agents, introduces a new AI gateway, and extends expert-led defense. ## Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA DevFeed: [Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA]() Original publisher: [Read original article]() Author: Alex Martin; Dima Melnyk Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [ai observability](), [SDKs](), [ci]() Tags: [agent](), [agents](), [ci](), [cli](), [development](), [enterprise](), [evaluation](), [experiments](), [gemini](), [llm](), [metrics](), [model](), [platform](), [production](), [telemetry](), [testing](), [traces]() ### AI overview Gemini Enterprise Agent Platform's evaluation service is generally available. It provides consistent evaluation of agents and models across local experiments and production traffic, with pre-built metrics, adaptive rubrics, custom metrics, simulators, and workflow integrations. ### Source excerpt Agent Platform's evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic. You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry. The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines. ## How to build a member portal with login and a dashboard in Webflow Cloud DevFeed: [How to build a member portal with login and a dashboard in Webflow Cloud]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [dashboards](), [Authentication](), [Database](), [SQLite](), [Nextra](), [Node.js](), [App]() Tags: [auth](), [authentication](), [cloud](), [data](), [database](), [guide](), [guides](), [how-to](), [next-js](), [node-js](), [platform](), [sqlite](), [webflow]() ### AI overview This tutorial explains how to build a Webflow Cloud member portal with login, a SQLite-backed data model, session-scoped queries, a member dashboard, and profile editing. It emphasizes deriving the member ID from the session rather than from the request so users can access only their own data. ### Source excerpt Learn how to build a member portal on Webflow Cloud where every query is scoped to the signed-in member. ## Evolving Pinterest's Embedding Retrieval Platform DevFeed: [Evolving Pinterest's Embedding Retrieval Platform]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-11T15:01:03Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [Architecture & Design](), [Algorithms](), [Scalability](), [Latency](), [IO]() Tags: [ann](), [cost](), [embedding](), [embeddings](), [engineering](), [infrastructure](), [latency](), [memory](), [models](), [pinterest](), [platform](), [quantization](), [real-time](), [retrieval](), [scalability](), [search]() ### AI overview Pinterest describes evolving its Manas embedding-retrieval platform to address the cost, scale, and flexibility challenges of serving billions of embeddings. The excerpt covers ANN search, vector quantization, and SSD-based serving. ### Source excerpt Authors: Bowen Zhou | Staff Software Engineer; Shan Gao | Senior Software Engineer; Jingwen Hu | Software Engineer II; Wenjiang Chu | Staff Software Engineer The Billion-Embedding Challenge At Pinterest, the "signal" is our lifeblood. Whether it's a home decor enthusiast finding the perfect rug or a fashion seeker discovering a new aesthetic, our discovery engine relies on understanding deep semantic relationships to help our users find inspirations. Over the last few years, the explosive growth of embedding-based retrieval has fundamentally transformed how we surface these signals -- and at the heart of that transformation is Manas, Pinterest's in-house distributed search platform. Embedding Retrieval is one of the core capabilities of Manas, supporting multiple approximate nearest neighbor search algorithms, hybrid queries with both token and embedding clauses, as well as real-time updates to ensure fresh contents become searchable within seconds. Deployed on over 80 clusters and serving billions of embeddings, Manas embedding retrieval powers all major product surfaces at Pinterest including Home Feed, Search, Related Pins, Ads, and Notifications. However, as our corpus scales toward tens of billions of embeddings and our models capture increasingly complex interactions, we face mounting challenges around cost efficiency, scalability, and flexibility. On the infrastructure side, traditional ANN algorithms like HNSW are notoriously memory-hungry -- they require the entire index to reside in RAM to maintain low query latency, making cost grow linearly with corpus size. On the modeling side, the classic two-tower retrieval paradigm is too restrictive: it reduces each candidate to a single embedding and scores relevance through a simple dot product, leaving little room to express richer, context-dependent notions of similarity. To tackle these challenges, our team has been evolving Manas's embedding retrieval stack across three fronts: Quantization. We reduce the memor ## Rapidly scaling online storage to serve over 1 billion ChatGPT users DevFeed: [Rapidly scaling online storage to serve over 1 billion ChatGPT users]() Original publisher: [Read original article]() Published: 2026-09-11T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [cloud-infrastructure](), [Database](), [data]() Tags: [chatgpt](), [data](), [database](), [engineering](), [global](), [infrastructure](), [openai](), [platform](), [python](), [scale](), [storage]() ### AI overview OpenAI describes scaling Habitat from a Python client-side library into a distributed online storage platform for global product traffic. The article focuses on reliability, performance, and capacity decisions made during rapid growth. ### Source excerpt Learn how OpenAI evolved Habitat from a Python library into a globally distributed storage platform serving 1 billion ChatGPT users and 22M requests per second. ## The 15 Best Chinese Live Streaming Platforms (ICP Licensed and Native) for 2026 DevFeed: [The 15 Best Chinese Live Streaming Platforms (ICP Licensed and Native) for 2026]() Original publisher: [Read original article]() Author: Jon Whitehead Published: 2026-09-11T07:30:31Z Content type: comparison Language: en Sources: [DaCast]() Topics: [Streaming](), [Firewall](), [Sports]() Tags: [2026](), [china](), [comparison](), [e-commerce](), [firewall](), [gaming](), [live-streaming](), [platform](), [selection](), [streaming](), [the-video-experts-blog]() ### AI overview This comparison reviews 15 live-streaming platforms for reaching audiences in China, covering international ICP-licensed services and native Chinese platforms. It discusses market scale, platform selection, the Great Firewall, and legal and regulatory considerations. ### Source excerpt Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 Live streaming in China isn't a niche channel, it's one of the most competitive, fastest-moving live-streaming markets in the world, and which platform you choose to reach it matters just as much as the decision to enter it in the [...] The post The 15 Best Chinese Live Streaming Platforms (ICP Licensed and Native) for 2026 appeared first on Dacast. ## How to calculate DevOps platform total cost of ownership DevFeed: [How to calculate DevOps platform total cost of ownership]() Original publisher: [Read original article]() Author: GitLab Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [GitLab]() Topics: [CI/CD]() Tags: [ai](), [ci-cd](), [complexity](), [compute](), [cost](), [devops](), [devsecops](), [devsecops-platform](), [drivers](), [how-to](), [infrastructure](), [integration](), [model](), [platform](), [reduce](), [software-delivery](), [tools]() ### AI overview A guide to modeling the total cost of ownership of a DevOps platform, including subscriptions, CI/CD compute, AI usage, infrastructure, tools, and internal labor. ### Source excerpt There's nothing like budget pressure to put your DevOps platform under a microscope. But subscription fees and license costs only tell one part of the story. The total cost of ownership (TCO) for a DevOps platform also includes variable costs like CI/CD compute and AI usage, along with the infrastructure, tools, and employee time required to keep software delivery moving. That wider view matters when you're tasked with defending platform spend or comparing options with a head of finance. Designing a useful TCO model can: Make those costs transparent for stakeholders Shine a light on the reasoning (or lack thereof) behind each cost Identify areas to reduce spend without negatively impacting software delivery What total cost of ownership really includes The core challenge of calculating TCO is that DevOps platforms package and price capabilities differently. For example, one platform may bundle CI/CD or AI capabilities into a per-seat subscription, while another could price usage separately. A third may appear less expensive upfront but require additional tools and ongoing integration work. That's why list prices or pricing tiers alone won't give you a useful comparison. Start with the capabilities and workloads your organization actually needs, then calculate what it takes to support them on each platform. Use the same scope and time period for every option -- often one year -- and define which teams, applications, environments, and delivery stages are included. Separate recurring costs from one-time expenses and external spend from internal labor, so finance can audit the assumptions and forecast future years. A useful TCO model, therefore, answers two questions: What does it cost to meet our requirements today? Which variables will cause that cost to rise or fall as our usage changes? The cost categories that drive your bill Most DevOps platform costs fit into the following categories: Cost categoryWhat it includesMain cost driverPlatform accessPaid seats, role-based ## Scality Maestro 1.1 Manages ARTESCA Fleets From One Console With No VPN Into Customer Sites DevFeed: [Scality Maestro 1.1 Manages ARTESCA Fleets From One Console With No VPN Into Customer Sites]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-10T20:17:01Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [cloud-infrastructure](), [Monitoring](), [Tooling](), [Deployment](), [Server](), [Firewall](), [Network](), [Disk image]() Tags: [data-protection](), [deployment](), [enterprise](), [firewall](), [infrastructure](), [monitoring](), [network](), [platform](), [production](), [scale](), [server](), [software](), [tooling](), [updates](), [upgrade]() ### AI overview Scality has generally released Maestro 1.1, a fleet management platform for service providers operating many Scality ARTESCA object storage deployments. It provides centralized monitoring, software and server firmware updates, capacity tracking, and automated pay-as-you-go billing without requiring VPN tunnels or customer-side network reconfiguration. Each deployment connects outbound to Maestro, which does not connect back into customer networks. ### Source excerpt Scality has made Maestro 1.1 generally available, a fleet management platform for service providers running large numbers of Scality ARTESCA object storage deployments on behalf of their customers. The target is the backup-as-a-service business that starts as a handful of ARTESCA clusters and grows to tens or hundreds, each sitting inside a different customer's network The post Scality Maestro 1.1 Manages ARTESCA Fleets From One Console With No VPN Into Customer Sites appeared first on StorageReview.com. ## TikTok Is Quietly Putting Web Apps Inside Its App DevFeed: [TikTok Is Quietly Putting Web Apps Inside Its App]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-09-10T17:33:18Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [modern web development](), [Web Development](), [interactive website](), [App](), [React Native]() Tags: [app-development](), [app-ecosystem](), [browser](), [digital-platforms](), [embedded-apps](), [framework](), [future-of-apps](), [in-app-experiences](), [javascript](), [mini-apps](), [mobile-apps](), [mobile-web](), [platform](), [react](), [social-commerce](), [social-media](), [technology](), [tiktok](), [tiktok-apps](), [tiktok-developers](), [ui-design](), [ux-design](), [web](), [web-apps](), [web-design](), [web-designers](), [web-development]() ### AI overview TikTok Minis let third-party, web-based experiences run inside TikTok, using technologies such as HTML, JavaScript, React, and Vue. The article examines how this could blur the boundaries between websites, social networks, and apps while noting TikTok's platform, configuration, and approval constraints. ### Source excerpt TikTok isn't just hosting videos anymore--it's quietly letting web-based experiences live inside the app. TikTok Minis could blur the line between social network, browser, and app platform, and web designers should be paying attention. [Next page]()