# Retention Published articles for Retention. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Hobby projects now retain fewer deployments to free up storage DevFeed: [Hobby projects now retain fewer deployments to free up storage]() Original publisher: [Read original article]() Author: Pranav Kanchi Published: 2026-09-16T18:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Deployment](), [Disk image]() Tags: [3](), [deployments](), [retention](), [storage](), [upgrade]() ### AI overview Vercel has changed Deployment Retention for Hobby teams. Each Hobby project now keeps its three most recent production deployments and three most recent deployments of any type, while some protected deployments remain exempt. Deployments outside the exceptions are deleted immediately when a team exceeds the 10GB storage limit. ### Source excerpt Hobby projects now retain fewer deployments past the 30-day retention window. Hobby teams get 10GB of Deployment Storage. Every deployment you keep uses some of it, and going over the limit can block you from deploying until you free some up. Deployment Retention for Hobby teams now deletes old deployments sooner, so dormant projects stop holding storage that your active projects need. Each Hobby project now keeps its 3 most recent production deployments, plus its 3 most recent deployments of any type, regardless of age. Preview deployments no longer get their own protection. Your current production deployment is still never deleted, and aliased and active-branch deployments are still protected. See the full list of exceptions in the docs. If your team is over the 10GB limit, deployments outside those exceptions are now deleted immediately instead of after 30 days. To stay under the limit, see how to optimize your Deployment Storage usage, or upgrade to Pro, where storage is billed at $0.10 per GB per month. Read more ## Why Kafka retention.ms can delay message deletion DevFeed: [Why Kafka retention.ms can delay message deletion]() Original publisher: [Read original article]() Author: Rogerio Santos Published: 2026-09-16T13:05:06Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Kafka](), [Apache-Kafka](), [configuration](), [file](), [Event-Streaming]() Tags: [broker](), [configuration](), [diagnose](), [event-driven](), [how-to](), [kafka](), [lifecycle](), [retention](), [storage](), [stream-processing]() ### AI overview This guide explains why Kafka messages can remain readable beyond a topic's retention.ms setting. Kafka deletes closed log segments rather than individual messages, and the active segment and continuous writes can delay removal of older records. ### Source excerpt A customer opened a support case with a deceptively simple complaint: a Kafka topic was configured with a 12-hour retention (retention.ms), yet messages produced on July 24 were still readable 4 days later, on July 28. Nothing was broken. The broker logged no errors. The retention policy was working as designed, but the segment layout and continuous write pattern delayed when the old records could actually be removed. The post Why your Kafka topic ignores retention.ms (and how to fix it) appeared first on Red Hat Developer. ## Unify your marketing data with Lakeflow Connect DevFeed: [Unify your marketing data with Lakeflow Connect]() Original publisher: [Read original article]() Author: Sonia Bendre; Giselle Goicochea Published: 2026-09-11T18:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Software as a service](), [Databases](), [API](), [Automation](), [dashboards](), [hubspot](), [CI/CD]() Tags: [analytics](), [api](), [automation](), [churn](), [ci-cd](), [connectors](), [crm](), [customer](), [customers](), [dashboards](), [data](), [databases](), [databricks](), [engineering](), [marketing](), [product](), [retention](), [saas](), [salesforce](), [series]() ### AI overview This first post in a series presents Lakeflow Connect as a fully managed data-ingestion service for unifying fragmented marketing and Customer 360 data. It describes native connectors for SaaS applications, databases, and files, configured through a point-and-click UI or API, with governed tables in Unity Catalog and integrations such as HubSpot and Salesforce. The article highlights reduced maintenance compared with custom pipelines and support for downstream reporting, analytics, retention, and churn analysis. ### Source excerpt This is the first post in a new series exploring how Lakeflow Connect brings fully... ## Mixpanel Is Easy to Install. Trusting the Data Takes Work. DevFeed: [Mixpanel Is Easy to Install. Trusting the Data Takes Work.]() Original publisher: [Read original article]() Author: Jared Currie Published: 2026-09-11T12:00:56Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [data](), [dashboards]() Tags: [dashboards](), [data](), [developer-tools](), [metric](), [metrics](), [properties](), [retention](), [tracking]() ### AI overview This tutorial explains how to improve trust in Mixpanel analytics by defining business questions and metric terms, filtering noise such as employees, automated tests, scrapers, and duplicate events, and documenting each metric's scope and limitations. ### Source excerpt Mixpanel makes it easy to start collecting events and building dashboards. The harder part is knowing whether those dashboards represent real product usage. A report can look polished while counting employees, automated browser tests, web scrapers, duplicate events, or actions that users attempted but never completed. It can also be technically correct while answering a [...] The post Mixpanel Is Easy to Install. Trusting the Data Takes Work. appeared first on Atomic Spin. ## GHarchive data has become unreliable for measuring GitHub activity DevFeed: [GHarchive data has become unreliable for measuring GitHub activity]() Original publisher: [Read original article]() Author: KD (noreply@blogger.com) Published: 2026-09-03T16:00:00Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [data](), [datasets](), [GitHub](), [Crawler](), [GitHub API](), [Open Source](), [GraphQL]() Tags: [collect](), [data](), [dataset](), [datasets](), [github](), [google](), [google-open-source](), [graphql](), [open-data-sets](), [open-source](), [oss](), [pipeline](), [pull-requests](), [retention](), [stream](), [volume]() ### AI overview This article examines the reliability of open source data, focusing on GHarchive's coverage of GitHub events. It argues that GHarchive should not be used for real-time or volume-based metrics because event retention has declined and some activity is omitted by the GitHub Event stream and API limitations. ### Source excerpt by Sophia Vargas, Google Open Source & Andrew Nesbitt, Ecosyste.ms Every second, open source contribution quietly shapes the software we rely on, and yet our view of this open ecosystem is surprisingly opaque. Open source development is performed in public spaces -- we can see the commits, issues and comments, the APIs and endpoints are free to use -- the logs are just sitting there, so why can't we just collect all of the data? ...Said every researcher, everywhere. However in most cases of open source related data, we are only looking at part of the whole. Why am I writing this post? Because many of us (including many business decision-makers) are too comfortable with unsubstantiated data. We've gotten used to it. Our models assume that it's smelly and we adjust the logic and weights to compromise. When it comes to open source, our confidence is even lower, even though our resulting decisions can directly impact individuals whom we collectively depend on. Let's consider one of my favorite datasets: GHarchive. Started as a hobby project in 2011, this crawler has amassed more than 15 years of event data from GitHub. While this source provides a historical record of open source development on GitHub, as a real-time or comprehensive source of metrics, it's unreliable and should not be a source for volume-based metrics. In 2025, GHarchive captured 14% fewer events than in 2024, despite steady growth in platform adoption. Since 2025, we estimate that data retention in GHarchive has fallen to ~50% and in 2026 it may be as low as 20% for some event types (see figure below). Prior to 2025, you could make the general assumption that the majority of events would be represented in this pipeline. Since 2025, we must now assume we may be missing at least half of events and possibly more -- not to mention all of the additional activity that's left out of the event API (see GitHub's GraphQL API.) The crawler logic behind this dataset is simple: give me all the events from the GitHub Ev ## LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate DevFeed: [LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-02T15:22:26Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data](), [Disk image](), [Resilience](), [data centers](), [Artificial Intelligence](), [ibm]() Tags: [2026](), [ai](), [architectures](), [data](), [data-center](), [datasets](), [enterprise](), [enterprise-storage](), [hardware](), [ibm](), [report](), [resilience](), [retention](), [storage](), [training](), [upgrade]() ### AI overview LTO tape capacity shipments rose 57% year over year in Q1 2026, driven by continued LTO-9 adoption, the rollout of LTO-10, and growing enterprise data and archival needs. The article explains how tape supports economical cold storage, long-term retention, AI training datasets, and data center power constraints. ### Source excerpt The Linear Tape-Open (LTO) Program Technology Provider Companies, comprising Hewlett Packard Enterprise, IBM Corporation, and Quantum Corporation, have released their annual tape media shipment report. Following sustained enterprise data expansion, the report highlights a strong start to 2026, driven by continued LTO-9 adoption and the rollout and initial capacity ramp-up of LTO-10 media. According to The post LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate appeared first on StorageReview.com. ## How to recognize your team with GitLab Achievements DevFeed: [How to recognize your team with GitLab Achievements]() Original publisher: [Read original article]() Author: Lee Tickett Published: 2026-08-27T00:00:00Z Content type: tutorial Language: en Sources: [GitLab]() Topics: [GitLab](), [Open Source](), [Template](), [Markdown]() Tags: [collaboration](), [community](), [company](), [feature](), [how-to](), [markdown](), [open-source](), [product](), [profile](), [retention]() ### AI overview GitLab Achievements lets teams create reusable custom badges and award them for contributions, milestones, certifications, or other behaviors. Awards appear on recipients' profiles after they accept them, and optional personal messages support GitLab Flavored Markdown. The feature is presented as a way to reinforce recognition, retention, motivation, and belonging across companies, open source projects, and communities. ### Source excerpt Every team runs on people who go above and beyond. The engineer who fixes the flaky test nobody else will touch. The reviewer who turns your merge request around in an hour. The teammate who finishes their certification. Or the community member who shows up release after release. Whether your team is a company, an open source project, or a community, GitLab had no built-in way to say "we see you." GitLab Achievements changes that. What are GitLab Achievements? An achievement is a custom badge you create once and award to people for their behavior. That might be a contribution, a milestone, or simply getting the most out of GitLab. Each one has a name, a description, and an avatar. Think "First merged MR", "Certified", or "Contributor of the month". Awarded achievements appear on the recipient's profile, alongside their contribution history. There are two halves to the achievement. The achievement itself is a reusable template you define at the group level. Awarding that achievement to someone is a separate action, and it can carry an optional personal message. That message supports GitLab Flavored Markdown, so you can link straight to the merge request, issue, or event that earned it. The same "Contributor of the month" badge can then tell a different story every time you award it. Recipients stay in control. An award doesn't appear on anyone's profile until they accept it using a link from an email notification. Recognition is offered, never forced onto someone's profile. Achievements are generally available as of GitLab 19.2, across Free, Premium, and Ultimate, on GitLab.com, GitLab Self-Managed, and GitLab Dedicated. Why recognition matters Recognition is one of the least expensive, most effective tools you have for retention, motivation, and belonging. A visible badge on a profile costs you a minute to award and gives the recipient something lasting. And "team" is broad: It might be your company, your open source project, or your wider community. The value depen ## Deployment Storage keeps your deployments rollback-ready DevFeed: [Deployment Storage keeps your deployments rollback-ready]() Original publisher: [Read original article]() Author: Christian Le Published: 2026-08-21T05:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Deployment](), [Vercel]() Tags: [bug](), [deployment](), [pricing](), [production](), [retention](), [storage](), [vercel]() ### AI overview Vercel's Deployment Storage keeps files from previous deployments available for inspection and rollback. Production deployments can be restored in seconds without a rebuild, while retention policies control how long different deployment states remain available. ### Source excerpt Every deployment produces a set of files, including the pages, functions, and assets Vercel serves. Deployment Storage keeps those files available so you can inspect previous deployments and roll back when needed. Instantly roll back to previous deployments in seconds If a production deploy ships a bug or a change you want to reverse, rolling back restores the previous version in seconds. Open your project, click Instant Rollback on the Production Deployment tile, then choose an earlier production deployment. Vercel immediately repoints your domains to that deployment, with no rebuild required. Deployment Storage pricing Deployment Storage is billed at $0.10 per GB per month Hobby teams include up to 10 GB Existing teams continue with their current pricing, with no change to their bill at this time. Control how much Deployment Storage you keep: Edit your Deployment Retention Policy to set how long Pre-Production, Production, Canceled, and Errored deployments stay available. Shorter retention means less storage, but you can't roll back to a deleted deployment The Usage page shows Deployment Storage and Functions Storage by project Keep future deployments smaller by trimming your output directory, moving large files to Vercel Blob, and reducing Function bundle size Learn more about Deployment Storage here. Read more ## Canada's next phase of ACA enforcement: Are you ready? DevFeed: [Canada's next phase of ACA enforcement: Are you ready?]() Original publisher: [Read original article]() Author: Glenda Sims Published: 2026-08-20T17:02:15Z Content type: article Language: en Sources: [Deque]() Topics: [Accessibility](), [Web](), [User interface design]() Tags: [aca](), [accessibility](), [accessibility-compliance](), [accessible-canada-act](), [business](), [canada](), [canada-laws](), [compliance](), [public-sector](), [retention](), [training](), [transportation](), [web]() ### AI overview This article explains the next phase of Accessible Canada Act enforcement, including upcoming deadlines for accessibility statements, web pages, training, mobile apps, digital documents, and procurement. It recommends beginning with a self-assessment and documenting technical evidence, retention, barriers, actions, and remaining gaps. ### Source excerpt There's a new deadline looming for the Accessible Canada Act (ACA), and it's a significant one. The work required to meet the new requirements is substantial, and you need to start now if your business is going to safely meet the new deadline. The post Canada's next phase of ACA enforcement: Are you ready? appeared first on Deque. ## Offering Zero Data Retention for frontier models DevFeed: [Offering Zero Data Retention for frontier models]() Original publisher: [Read original article]() Published: 2026-08-19T19:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [Responsibility & Safety](), [Artificial Intelligence](), [Security, Privacy and Abuse Prevention](), [API](), [Monitoring]() Tags: [agentic](), [ai](), [ai-agents](), [ai-safety](), [api](), [company](), [data](), [enterprise](), [monitoring](), [openai](), [privacy](), [retention](), [safety](), [security]() ### AI overview OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing, which analyzes patterns across related interactions through automated systems without exposing prompts or responses to OpenAI personnel. The approach is intended to support safety monitoring for complex and agentic tasks while preserving customer control over sensitive content. ### Source excerpt OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing for advanced AI safety without compromising data privacy. ## Introducing memory retention for agentic memory in OpenSearch DevFeed: [Introducing memory retention for agentic memory in OpenSearch]() Original publisher: [Read original article]() Author: Erfan Ballew Published: 2026-08-13T22:29:25Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [AI Agent](), [audit trail](), [model-deployment]() Tags: [agentic](), [audit-trail](), [blog](), [compliance](), [cost](), [experimental](), [lifecycle](), [memory](), [opensearch](), [precision](), [retention](), [storage](), [technical]() ### AI overview This article explains OpenSearch 3.8's experimental memory retention feature for agentic memory. It describes age-based and count-based limits for different memory types, how policies prevent stale context and uncontrolled storage growth, and how to enable retention on an existing cluster. ### Source excerpt Learn how the memory retention policy in OpenSearch automatically manages the lifecycle of agentic memory, controlling storage growth while preserving specific memories. The post Introducing memory retention for agentic memory in OpenSearch appeared first on OpenSearch. ## Introducing advanced Kubernetes control plane configuration in Amazon EKS DevFeed: [Introducing advanced Kubernetes control plane configuration in Amazon EKS]() Original publisher: [Read original article]() Author: Ashok Srirama Published: 2026-08-12T17:23:45Z Content type: article Language: en Sources: [Containers]() Topics: [Amazon EKS](), [Kubernetes](), [configuration](), [API](), [Availability](), [Provisioning]() Tags: [advanced-300](), [ai-ml](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [announcements](), [api](), [availability](), [ci-cd](), [kubernetes](), [packing](), [retention](), [storage]() ### AI overview Amazon EKS introduces advanced Kubernetes control plane configuration, allowing users to set API server, scheduler, and controller manager parameters directly. The article covers pod placement scoring, event retention, and HPA synchronization settings through feature explanations and hands-on walkthroughs. ### Source excerpt With Amazon EKS, you can now configure Kubernetes control plane components (the API server, scheduler, and controller manager) directly through EKS APIs. This post explains what's configurable and includes two hands-on walkthroughs: enabling MostAllocated bin-packing to optimize pod placement, and tuning event retention duration. ## Investigate account-level churn risk with Product Analytics account segments DevFeed: [Investigate account-level churn risk with Product Analytics account segments]() Original publisher: [Read original article]() Author: Sharon Ye; Adam Virani Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Software as a service](), [data]() Tags: [analytics](), [b2b](), [churn](), [conversion](), [digital-experience-monitoring](), [product-analytics](), [real-user-monitoring](), [retention](), [saas]() ### AI overview Datadog Product Analytics account segments combine account attributes with user activity to investigate account-level churn risk. The article describes applying reusable segments to funnels, retention analysis, and Datadog Pathways, with examples involving SaaS, enterprise accounts, ARR, feature adoption, and renewal engagement. ### Source excerpt Learn how Product Analytics account segments combine business context and product behavior to identify accounts that may be at risk of churn. ## AI Gateway is now available on AWS Marketplace DevFeed: [AI Gateway is now available on AWS Marketplace]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-05T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [aws-marketplace](), [Amazon Web Services](), [API](), [Inference]() Tags: [ai-gateway](), [api](), [aws](), [aws-marketplace](), [cost](), [governance](), [inference](), [models](), [pricing](), [retention]() ### AI overview AI Gateway is now available on AWS Marketplace, allowing teams to procure it through their existing AWS accounts and consolidate inference spending on their AWS bills. It provides one API endpoint for hundreds of models, with automatic fallbacks, regional inference, cost controls, governance, and Zero Data Retention. AWS Marketplace purchases support private annual contracts and usage-based pricing, with no markup over provider pricing. ### Source excerpt AI Gateway is available on AWS Marketplace. Teams can procure AI Gateway through their existing AWS account, consolidating inference spend onto their AWS bill and streamlining procurement. Purchases are available as private offers with annual contract terms, plus usage-based pricing beyond the contract. AI Gateway gives you one API to hundreds of models through a single endpoint. Reliability, cost controls, and governance, including automatic fallbacks, regional inference, and Zero Data Retention, are built in. You pay provider pricing with no markup; buying through AWS doesn't change what you pay per token. View the listing on AWS Marketplace for details. Read more ## Pinner Progression: Better Use-Case Representation Driving Weekly Active User Growth at Pinterest DevFeed: [Pinner Progression: Better Use-Case Representation Driving Weekly Active User Growth at Pinterest]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-07-27T16:01:02Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [recommendation systems](), [Data Science](), [data]() Tags: [engineering](), [growth](), [interest-exploration](), [metrics](), [personalization](), [pinterest](), [recommendation-system](), [retention](), [retrieval](), [understanding-user]() ### AI overview Pinterest introduces Pinner Progression, a recommendation-system program that uses persistent User Interest Clusters to improve discovery and make retention a first-class objective alongside engagement. ### Source excerpt Part 1 of 2 Authors Personalization (Homefeed): Yuke Yan, Chuxi Wang, Andreanne Lemay, Olafur Gudmundsson, Anna Kiyantseva, Krystal Benitez, Jongho Kim, Jiacong He, Rahul Goutam, James Li, Dylan Wang User Understanding: Simin Li, Sufyan Suliman, Yingjian Ding, Hongbo Deng Data Science: Armando Ordorica, Yan Chen, Ellie Zhang, Karim Wahba Introduction Pinterest's mission is to help people discover the inspiration to create a life they love. Our recommendation system serves hundreds of millions of users, surfacing billions of Pins across interests ranging from home renovation to meal planning to wedding decor. The home feed , where much of that discovery happens, is powered by a multi-stage pipeline spanning retrieval, lightweight scoring, ranking, and re-ranking [1][2][3]. Most of our prior work on this pipeline has been optimized for engagement: clicks, saves, downloads, closeups. These are strong signals of immediate relevance, and optimizing for them has driven significant gains across the system [4][5]. The problem is that engagement and retention are different things. A user can save ten sourdough recipes today and churn next month anyway. All we did was feed them more of what they already liked: we never helped them find something new for next time. This post is one of two that introduces Pinner Progression, a program that reframes the home feed recommendation system around retention as a first-class objective. Our core insight is that by augmenting sequential, action-by-action user understanding with holistic, persistent use-case representation, we can reliably anticipate the user's next moves and start to serve recommendations that ignite their serendipitous discovery. In this post, we introduce the key use-case representation signal: User Interest Clusters (UICs) -- and describe its construction, integration into the recommendation stack, and impact on engagement on retention metrics. A follow-up will cover how we predict unseen UICs and conduct systematic us ## Kimi K3 and Kimi K3 Fast with ZDR and US-based providers now on AI Gateway DevFeed: [Kimi K3 and Kimi K3 Fast with ZDR and US-based providers now on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-27T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [Inference](), [Latency](), [data centers](), [Vercel](), [baseten]() Tags: [ai](), [ai-gateway](), [baseten](), [coding-agents](), [compliance](), [data-centers](), [inference](), [latency](), [retention](), [sdk](), [us](), [vercel]() ### AI overview Vercel's AI Gateway now supports Moonshot AI's Kimi K3 and Kimi K3 Fast through US-based providers including Baseten and Fireworks. The release adds Zero Data Retention, US-only inference routing, provider failover, model endpoint details, coding-agent setup, and playground access. Kimi K3 Fast offers lower latency at a higher per-token cost. ### Source excerpt Kimi K3 from Moonshot AI and its faster serving path, Kimi K3 Fast, are now available from US-based providers on AI Gateway, including Baseten and Fireworks. Zero Data Retention (ZDR) is also supported for both models. Running Kimi K3 on US-based providers lets teams with data residency and compliance requirements use the model on US infrastructure. Because AI Gateway serves the models from multiple providers, it automatically routes across them for failover, higher uptime, and more available throughput than any single provider offers. You call the same moonshotai/kimi-k3 model ID, and the gateway handles provider selection and fallback. Kimi K3 Fast trades a higher per-token cost for lower latency. Request it with the speed option on the base model, which stays on moonshotai/kimi-k3 and falls back to standard speed when the fast tier is unavailable. Alternatively, use moonshotai/kimi-k3-fast. The fast variant costs ~50% more than the base model. To use Kimi K3, set model to moonshotai/kimi-k3 in the AI SDK: US inference To route Kimi K3 requests to use only US data centers for inference, set inferenceRegion. Regional pricing is ~10% more than the regular variant. Zero Data Retention Zero Data Retention for Kimi K3 is also available. Turn on Zero Data Retention for every request from the AI Gateway dashboard settings, or set it per request with zeroDataRetention: Providers and endpoints To see every provider serving Kimi K3, along with per-provider pricing, supported parameters, uptime, throughput, and latency, call the model endpoints API: Model prices vary by provider and variant type. Use Kimi K3 in your coding agent Run vercel ai-gateway coding-agents setup and select Kimi K3. This will detect the agents on your machine, provision an AI Gateway key, and write their config. See how to set it up via the Vercel CLI. Try Kimi K3 in the model playground. Read more ## Designing For Distressed Users: Why Mental Health Apps Shouldn't Follow Every UI Fashion DevFeed: [Designing For Distressed Users: Why Mental Health Apps Shouldn't Follow Every UI Fashion]() Original publisher: [Read original article]() Author: hello@smashingmagazine.com (Kat Homan) Published: 2026-07-09T15:00:00Z Content type: article Language: en Sources: [Articles on Smashing Magazine -- For Web Designers And Developers]() Topics: [App](), [User interface design](), [Accessibility]() Tags: [cognitive-load](), [design](), [evaluation](), [experimentation](), [health](), [innovation](), [mental-health](), [research](), [retention](), [ui](), [ux]() ### AI overview This article argues that mental health apps should prioritize simplicity, reduced cognitive strain, accessibility, trust, and retention over fashionable interface trends. It presents trend-driven design as a potential source of friction for distressed or overwhelmed users and introduces an evaluation framework grounded in research, product audits, and app-store evidence. ### Source excerpt Many UI trends are designed to capture attention and signal innovation, but those goals often conflict with the needs of mental health apps: reducing cognitive strain, fostering trust, and providing a sense of refuge. Kat Homan introduces an evaluation framework that helps designers assess whether trendy visual and interaction patterns support or undermine the unique goals of mental health experiences. ## Claude Fable 5 access restored on AI Gateway DevFeed: [Claude Fable 5 access restored on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-01T07:01:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Fable](), [Anthropic Claude](), [API](), [Artificial Intelligence](), [debugging]() Tags: [anthropic-claude](), [api](), [coding](), [debugging](), [docs](), [fable](), [government](), [model](), [models](), [real-time](), [retention](), [safety]() ### AI overview Access to the Claude Fable 5 Mythos-class model has been restored on AI Gateway after the US Government lifted export controls. The release updates its safety classifiers and documents model fallbacks, API usage, and 30-day retention of prompts and completions. ### Source excerpt Access to Claude Fable 5, the Mythos-class model, has now been restored on AI Gateway following the US Government's decision to lift the export controls. Fable 5 is the same model that was available between June 9 and June 12. What has changed is the safety classifiers, which are now updated and more robust. In the near term, some routine tasks such as coding and debugging may trigger safety classifiers. To ensure requests are still serviced when the safety classifiers are triggered, use model fallbacks. AI Gateway will try each model in models in the stated order if Anthropic refuses the request to Fable 5. To call Fable 5, use model name anthropic/claude-fable-5: Model fallbacks work on every API format: for more information on how to configure these, see the docs. Anthropic does not support Zero Data Retention for the model, because some misuse patterns are only visible across cumulative requests, which real-time filters cannot catch on their own. Prompts and completions are retained for 30 days and are not used to train Claude. Read more in the data retention whitepaper. Read more ## How Redpanda Cloud Topics rethinks Kafka compaction DevFeed: [How Redpanda Cloud Topics rethinks Kafka compaction]() Original publisher: [Read original article]() Author: Willem Kaufmann Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [cloud-infrastructure](), [Cloud](), [Streaming](), [Algorithm]() Tags: [apache](), [apache-kafka](), [architecture](), [cloud-native](), [cloud-storage](), [clusters](), [cpu](), [cycles](), [engineering](), [kafka](), [object-storage](), [reduce](), [retention](), [scale](), [storage](), [streaming]() ### AI overview The article explains how Redpanda Cloud Topics redesigns Kafka log compaction for cloud-native streaming. It describes how the architecture reduces redundant processing and CPU use, lowers cloud storage costs, and preserves Kafka behavior while addressing scaling challenges such as limited memory, tombstone removal, and rewriting large volumes of object storage data. ### Source excerpt Compaction can overwhelm poorly sized Kafka clusters, leading to full disks and maxed-out CPUs. Learn how Redpanda's Cloud Topics architecture redesigns compaction to cut redundant work, reduce cloud storage costs, and preserve the Kafka semantics you rely on. ## Sandboxes now expire based on last use DevFeed: [Sandboxes now expire based on last use]() Original publisher: [Read original article]() Author: Marc Codina Segura Published: 2026-06-29T01:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Persistence]() Tags: [persistence](), [retention](), [sandboxes](), [snapshots](), [time](), [vercel](), [workflows]() ### AI overview Vercel Sandbox snapshots now expire according to their last use rather than their creation time. Using a snapshot resets its expiration timer, allowing active workflows to keep dependent snapshots available while unused snapshots follow their retention policy. ### Source excerpt Vercel Sandbox snapshots now expire based on when they were last used, not when they were created. Active snapshots stay alive as long as workflows depend on them, while unused snapshots expire on their retention policy. Every time a snapshot is used, its expiration timer resets. This lets you set shorter retention windows without worrying that a snapshot will disappear between sessions, making it safer to build long-running workflows on top of Sandbox persistence. Learn more about Sandbox snapshots in the documentation. Read more ## Subscription Management Software: A 2026 Buyer's Guide DevFeed: [Subscription Management Software: A 2026 Buyer's Guide]() Original publisher: [Read original article]() Author: Aarthi Poonia Published: 2026-06-28T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [Software](), [Software as a service](), [Finance]() Tags: [2026](), [billing](), [churn](), [compare](), [compliance](), [features](), [guide](), [lifecycle](), [payment](), [pricing](), [recovery](), [reporting](), [retention](), [revenue](), [saas](), [subscription-billing](), [subscriptions](), [tax]() ### AI overview A 2026 buyer's guide to subscription management software, covering its role across the recurring-customer lifecycle, must-have billing and operational features, leading options, and platform selection for SaaS businesses. ### Source excerpt What subscription management software does, the must-have features, top tools compared, and how to choose the right platform for your SaaS in 2026. ## Customer Analysis for SaaS: A Framework for Real Decisions DevFeed: [Customer Analysis for SaaS: A Framework for Real Decisions]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-06-13T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [data](), [Data Infrastructure](), [Framework]() Tags: [analytics](), [churn](), [data](), [data-infrastructure](), [growth](), [guide](), [how-to](), [retention](), [saas]() ### AI overview This guide presents a framework for SaaS customer analysis, covering segmentation, customer behavior, willingness to pay, retention drivers, and the data infrastructure needed to turn customer data into actionable business decisions. ### Source excerpt Customer analysis framework for SaaS founders. Segmentation, behavior, willingness-to-pay, retention drivers, and the data infrastructure to make it actionable. ## Cloud Topics: the Metastore DevFeed: [Cloud Topics: the Metastore]() Original publisher: [Read original article]() Author: Andrew Wong Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Cloud](), [rocksdb](), [data]() Tags: [architecture](), [cloud](), [consumer](), [engineering](), [kafka](), [learn](), [mapping](), [memory](), [object-storage](), [retention](), [rocksdb](), [scale](), [serialization](), [storage]() ### AI overview This article explains Redpanda's metastore for Cloud Topics. The metastore maps Apache Kafka offsets to byte ranges in L1 objects stored in object storage, while tracking metadata such as leader-term boundaries and compaction state. Redpanda built a general-purpose key-value store, inspired by LevelDB and RocksDB and implemented as an LSM tree, to scale metadata independently of memory, local disk, and metadata formats. ### Source excerpt Learn how Redpanda's metastore powers Cloud Topics, from offset lookups and whole cluster restore to cross-region read replicas, and why it's built to be a foundational primitive for the future. ## Datadog and ClickHouse partner to bring full-fidelity data to modern observability DevFeed: [Datadog and ClickHouse partner to bring full-fidelity data to modern observability]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-10T17:21:47Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [telemetry](), [observability pipelines](), [OpenTelemetry]() Tags: [clickhouse](), [cost](), [ecosystem](), [integrations](), [logs](), [observability](), [observability-pipelines](), [opentelemetry](), [partnership](), [performance](), [retention](), [scale](), [search]() ### AI overview ClickHouse and Datadog announce a partnership that lets organizations route logs to ClickHouse through Datadog Observability Pipelines and search them in Datadog Log Explorer. The integration combines ClickHouse's performance and cost efficiency for high-volume, long-term telemetry retention with Datadog's observability workflows and supports OpenTelemetry-compatible schemas. ### Source excerpt ClickHouse and Datadog are partnering to combine full-fidelity log retention at scale with the powerful search and investigation experience engineers rely on every day. [Next page]()