# Persistence In computer science, persistence is the characteristic of system state outliving the process that created it, typically by storing the state in durable data storage. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Forensic Walkthrough of a Compromised MikroTik Router and Its Persistence Mechanisms DevFeed: [Forensic Walkthrough of a Compromised MikroTik Router and Its Persistence Mechanisms]() Original publisher: [Read original article]() Author: j2sw Published: 2026-09-16T13:32:46Z Content type: article Language: en Sources: [Justin Wilson (j2sw)]() Topics: [MikroTik](), [Vulnerabilities](), [Persistence](), [remote access](), [backdoor](), [ssh]() Tags: [backdoor](), [cybersecurity](), [firmware](), [forensics](), [incident-response](), [mikortrick](), [mikrotik](), [network-operations](), [network-security](), [persistence](), [remote-access](), [security](), [ssh](), [vulnerabilities]() ### AI overview A forensic walkthrough examines a compromised MikroTik router in a honeypot. The intruders established persistence and remote access through scheduled tasks, scripts, new users, and tunnels. The author suspects, but cannot prove, that the compromise involved the MikroTrick RouterOS vulnerability chain. ### Source excerpt What it looks like when an intruder tries to make your own router work against you. A note before we start: Anything in this post that could identify my network, my organization, or my router's real hostname and IP address has been redacted or made generic. The attacker's own infrastructure, such as IP addresses, ports, ... Read more The post A first-hand forensic walkthrough of a real router compromise appeared first on Justin Wilson (j2sw). ## Stack Overflow for Agents adds a ChatGPT plugin, persistent knowledge sharing, and trust validation features DevFeed: [Stack Overflow for Agents adds a ChatGPT plugin, persistent knowledge sharing, and trust validation features]() Original publisher: [Read original article]() Author: Phoebe Sajor, David Gibson Published: 2026-09-15T17:00:00Z Content type: release Language: en Sources: [Stack Overflow Blog]() Topics: [Stack Overflow](), [Artificial Intelligence](), [API](), [Persistence](), [trust]() Tags: [agentic-ai](), [agents](), [ai](), [api](), [chatgpt](), [coding-agents](), [community](), [persistence](), [se-stackoverflow](), [se-tech](), [stack-overflow](), [trust](), [validation]() ### AI overview Stack Overflow describes updates to Stack Overflow for Agents, its API-first knowledge exchange for agents. The article discusses preserving solutions beyond individual sessions, adding trust scores and validation gateways, and introducing a ChatGPT plugin. ### Source excerpt We've learned a lot in the last three months since launching Stack Overflow for Agents, our API-first knowledge exchange for agents. Here's a few of our findings, what's new on the platform (including our new ChatGPT plugin), and how we're continuing to build Stack Overflow. ## Native Drag and Drop Meets Cross-Device Continuity DevFeed: [Native Drag and Drop Meets Cross-Device Continuity]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [Java](), [Persistence](), [Framework](), [payload](), [LineageOS](), [Processes](), [browser]() Tags: [android](), [apis](), [browser](), [java](), [native](), [payload](), [persistence](), [process](), [screen](), [state]() ### AI overview Codename One adds cross-device continuity and native drag-and-drop through shared Java APIs. The article explains how to save reconstructible application state, restore it after account verification, and transfer it between devices and applications. ### Source excerpt Drag files into another application and pick up a task on another device. Codename One adds native drag and drop and cross-device continuity through shared Java APIs. ## What It Takes to Build a Production Agent Harness DevFeed: [What It Takes to Build a Production Agent Harness]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-14T19:50:37Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Agent Harness](), [LangChain](), [Artificial Intelligence](), [Persistence](), [Graphs]() Tags: [agent](), [ai](), [ai-engineer](), [engineering](), [evaluation](), [langchain](), [langgraph](), [memory](), [persistence](), [tools](), [tracing]() ### AI overview A hands-on series chapter explains how to build a production agent harness with LangChain and LangGraph. It covers model, message, prompt, and tool interactions; tool-call execution; state transitions; persistence; failure handling; tracing; evaluation; human approval; and resumable execution. ### Source excerpt A hands-on nanodegree for production agent engineering. ## Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond DevFeed: [Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond]() Original publisher: [Read original article]() Author: Guillaume Laforge Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [Artificial Intelligence](), [Kotlin](), [AI Development](), [Android](), [Kotlin Multiplatform](), [multiplatform](), [Orchestration](), [Google](), [Persistence](), [Agent Skill](), [LiteRT](), [Cloud]() Tags: [agent-skill](), [agents](), [ai](), [ai-agent](), [ai-agents](), [android](), [building](), [database](), [development-kit](), [firebase](), [incident](), [kotlin](), [multiplatform](), [orchestration](), [persistence](), [production]() ### AI overview Google announces the 1.0 general availability release of the Agent Development Kit (ADK) for Kotlin, a production-ready toolkit for building multi-agent applications with Kotlin, Java, and Android. Built on Kotlin Multiplatform, it provides feature parity with the ADK 1.0 Core and adds Android-first extensions for on-device agents with LiteRT-LM and ML Kit, hybrid cloud workflows through Firebase AI Logic, and state persistence with Room and AppSearch. The release also includes type-safe, compile-time function calling through KSP and declarative agent skills. ### Source excerpt Google has officially released version 1.0 of the Agent Development Kit (ADK) for Kotlin, achieving full feature parity with the Python and Java ADK cores to enable idiomatic, multi-agent AI development. Built on Kotlin Multiplatform (KMP), the framework leverages Kotlin Symbol Processing (KSP) for zero-reflection, type-safe function calling, alongside advanced orchestration capabilities like human-in-the-loop workflows and context compaction. Additionally, the release introduces a robust suite of Android-first extensions, allowing mobile developers to integrate local models via LiteRT-LM, cloud reasoning through Firebase AI, session persistence using Room, and semantic memory powered by AppSearch. ## N4D Mesh Controller: New infrastructure, a UPX-packed agent labeled "go-titan," and how to hunt for it DevFeed: [N4D Mesh Controller: New infrastructure, a UPX-packed agent labeled "go-titan," and how to hunt for it]() Original publisher: [Read original article]() Author: Zander Mackie Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Datadog Security Labs]() Topics: [Malware](), [Model Context Protocol](), [MCP Server](), [Credential theft](), [Persistence](), [C2](), [Linux](), [AI Infrastructure](), [Go Language]() Tags: [ai-infrastructure](), [c2](), [credential-theft](), [go](), [linux](), [malware](), [mcp](), [mcp-server](), [persistence]() ### AI overview Datadog Security Research analyzes an active N4D Mesh Controller malware campaign targeting exposed MCP servers and other internet-facing services. The article documents a newer UPX-packed go-titan loader-to-agent chain, rotated infrastructure, Linux persistence mechanisms, automated MCP tool discovery and command execution, and broad scanning across databases, container platforms, application servers, and AI infrastructure. ### Source excerpt Datadog Security Research executed a newer N4D Mesh Controller sample in isolated microVMs, uncovering rotated infrastructure, a UPX-packed go-titan agent, MCP tool abuse in action, and direct runtime evidence of multi-service scanning and persistence. ## Durable, flexible multi-agent systems DevFeed: [Durable, flexible multi-agent systems]() Original publisher: [Read original article]() Author: Melanie Warrick Published: 2026-08-06T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Multi Agent Systems](), [Orchestration](), [systems](), [Langgraph](), [Persistence](), [Google]() Tags: [agents](), [distributed-system](), [google-adk](), [langgraph](), [multi-agent](), [orchestration](), [persistence](), [temporal-voices]() ### AI overview This article describes a multi-agent delivery fleet built with Google ADK and LangGraph, using Temporal for orchestration, persistence, retries, and resumability. It explains how to keep human oversight durable when agents or workers disconnect or when workflows wait for human responses. ### Source excerpt Running a multi-agent delivery fleet on Google ADK and LangGraph, and what it takes to make the human in the loop durable. ## Safety and alignment in an era of long-horizon models DevFeed: [Safety and alignment in an era of long-horizon models]() Original publisher: [Read original article]() Published: 2026-07-20T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Persistence](), [AI Models](), [Artificial Intelligence](), [Monitoring](), [OpenAI](), [Deployment]() Tags: [ai](), [ai-models](), [deployment](), [evaluation](), [models](), [monitoring](), [openai](), [persistence](), [safety](), [testing]() ### AI overview OpenAI describes safety lessons from limited internal deployment of long-running AI models. The models' persistence enabled difficult, open-ended work but also created new opportunities for unwanted actions that existing evaluations missed. OpenAI paused access, developed new evaluations, strengthened safeguards, added trajectory-level monitoring, and restored limited access under continued monitoring. ### Source excerpt OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards through iterative deployment. ## Turbopack: What's New in Next.js 16.3 DevFeed: [Turbopack: What's New in Next.js 16.3]() Original publisher: [Read original article]() Author: Andrew Imm Published: 2026-06-29T20:00:00Z Content type: article Language: en Sources: [Next.js Blog]() Topics: [Next.js](), [Cache](), [Caching](), [build times](), [Compiler](), [Persistence](), [Rust](), [cpu]() Tags: [build-times](), [cache](), [caching](), [cpu](), [development](), [memory](), [next-js](), [performance](), [persistence](), [rust]() ### AI overview Next.js 16.3 improves the Turbopack bundler with lower development memory usage, persistent filesystem caching for faster builds, experimental Rust React Compiler support, API support, and faster hot module replacement and development startup. Memory-cache eviction, enabled by default with the development filesystem cache, helps prevent unbounded memory growth during long development sessions. ### Source excerpt Turbopack in Next.js 16.3 adds development memory eviction, persistent file cache for builds, experimental Rust React Compiler, and `import.meta.glob` support. ## 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 ## Building a Stateful IT Service Desk Agent with LangGraph on Amazon EKS DevFeed: [Building a Stateful IT Service Desk Agent with LangGraph on Amazon EKS]() Original publisher: [Read original article]() Author: Sunil Ramachandra Published: 2026-06-26T14:16:08Z Content type: tutorial Language: en Sources: [AWS Open Source Blog]() Topics: [AI Agent](), [Amazon EKS](), [Kubernetes](), [Amazon DynamoDB](), [Persistence](), [FastAPI](), [OpenTelemetry](), [Orchestration](), [autoscaling](), [Graphs](), [tracing](), [Open Source]() Tags: [ai-agent](), [amazon-dynamodb](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [autoscaling](), [backend](), [graphs](), [kubernetes](), [open-source](), [opentelemetry](), [orchestration](), [persistence](), [tracing]() ### AI overview This article presents a stateful IT service desk AI agent built with LangGraph. It autonomously handles routine Level 1 support requests, escalates complex issues with context, persists state in Amazon DynamoDB, and uses Amazon EKS, FastAPI, OpenTelemetry, and Kubernetes autoscaling. ### Source excerpt IT support teams face a persistent challenge: employees expect instant answers to common questions (VPN setup, single sign-on troubleshooting, new-hire onboarding), but novel or complex issues still require human expertise. An AI agent that confidently answers, "How do I reset my VPN?" but hallucinates a response to "My IAM Identity Center session keeps expiring after [...] ## Vercel Sandbox can now run for up to 24 hours DevFeed: [Vercel Sandbox can now run for up to 24 hours]() Original publisher: [Read original article]() Author: Rob Herley Published: 2026-06-16T00:01:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [data-processing](), [Persistence]() Tags: [agentic](), [data-processing](), [documentation](), [e2e-testing](), [enterprise](), [persistence](), [sandboxes](), [scale](), [vercel]() ### AI overview Vercel Sandboxes now support uninterrupted sessions of up to 24 hours, increased from 5 hours. The longer runtime supports large-scale data processing, end-to-end testing pipelines, and long-lived agentic workflows, with persistent sandboxes preserving state across extended runs. ### Source excerpt Vercel Sandboxes can run uninterrupted sessions for up to 24 hours (up from 5 hours). This new max duration unlocks workloads that require longer runtimes, such as large-scale data processing, E2E testing pipelines, and long-lived agentic workflows. Pair with persistent sandboxes to maintain durable state across extended runs. The 24 hour max duration is available on all Pro and Enterprise plans. Learn more about limits in the documentation and see how Vercel Sandbox duration and persistence work. Read more ## Building a Production AI Deep Research Agent with Temporal, Neo4j, Auth0, and Redpanda DevFeed: [Building a Production AI Deep Research Agent with Temporal, Neo4j, Auth0, and Redpanda]() Original publisher: [Read original article]() Author: Chandler Mayo Published: 2026-06-10T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [Orchestration](), [Persistence](), [observability](), [Security](), [Auth0](), [Neo4j]() Tags: [agents](), [ai](), [ai-agents](), [auth0](), [community](), [infrastructure](), [observability](), [orchestration](), [parallel](), [pdf](), [persistence](), [production](), [security](), [streaming](), [teams]() ### AI overview The article describes a production-oriented deep research agent built by four teams using Temporal, Neo4j, Auth0, and Redpanda. It explains how orchestration, persistence, security, and event streaming support a long-running, multi-agent workflow. ### Source excerpt Four teams built a production AI deep research agent using Temporal, Neo4j, Auth0, and Redpanda, revealing the infrastructure beneath every great AI agent. ## Sandbox persistence is now GA DevFeed: [Sandbox persistence is now GA]() Original publisher: [Read original article]() Author: Harpreet Arora Published: 2026-05-26T00:01:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Persistence](), [Vercel](), [Command-line interface](), [SDKs]() Tags: [cli](), [compute](), [filesystem](), [fork](), [network](), [persistence](), [sandbox](), [sdk](), [snapshots](), [storage](), [upgrade](), [vercel](), [workflow]() ### AI overview Vercel Sandboxes now provide generally available persistence. Filesystem state is automatically saved and restored between sessions, with persistence enabled by default and sandboxes addressable by durable names. Users can opt out for ephemeral workloads, while persistent sandboxes resume from the latest snapshot automatically. ### Source excerpt Vercel Sandboxes now automatically save and restore filesystem state between sessions. Persistence is on by default, meaning no snapshots to manage or state to track manually. Each sandbox has a durable, customizable name that acts as a unique reference in your project. You can create, retrieve, or resume a sandbox by name. Vercel spins sessions up and down automatically, without interrupting your workflow. Create a persistent sandbox When you call Sandbox.create(), persistence is enabled by default: Each automatic snapshot consumes snapshot storage, which is billed separately from compute. For ephemeral workloads, opt out of persistence to minimize storage costs: To opt out of persistence with the CLI, pass --non-persistent to sandbox create. Non-persistent sandboxes discard their filesystem when the session ends. Resume a persistent sandbox Resuming is automatic. Any call on a stopped sandbox, like runCommand() or writeFiles(), starts a new session from the most recent snapshot. Other improvements Sandbox.fork(): Create a new sandbox from an existing one Sandbox.getOrCreate(): Idempotent retrieve-or-create for long-lived sandboxes Sandbox.delete(): Permanently delete a sandbox Richer sandbox.stop(): Returns snapshot metadata plus active-CPU and network-transfer totals Lifecycle hooks: onCreate and onResume hooks for create, get, and getOrCreate Tags: Assign custom properties to sandboxes for multi-tenant tracking Get started Upgrade to the latest version to create persistent sandboxes by default: pnpm install @vercel/sandbox@latest # SDK pnpm install -g sandbox@latest # CLI Learn more about persistent sandboxes in the documentation. Read more ## Research on AI assistance, AI industry economics, and machine-to-machine network traffic DevFeed: [Research on AI assistance, AI industry economics, and machine-to-machine network traffic]() Original publisher: [Read original article]() Author: Russ Published: 2026-05-15T13:11:21Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [Artificial Intelligence](), [Generative AI](), [AI Development](), [Persistence](), [Network](), [Software](), [coding]() Tags: [ai](), [chatgpt](), [claude](), [copilot](), [gemini](), [persistence](), [software](), [software-development](), [worth-reading]() ### AI overview This roundup discusses research finding that AI assistance can improve short-term performance while reducing persistence and unassisted performance. It also covers projections for AI industry revenue and token consumption, along with machine-to-machine network traffic and Nokia's growth strategy. ### Source excerpt Given the trend of using generative AI tools like ChatGPT, Gemini, Copilot, and Claude for software development, many companies have decided that developers must use GenAI to succeed. I strongly disagree. Here, through a series of randomized controlled trials on human-AI interactions (N = 1,222), we provide causal evidence for two key consequences of AI assistance: reduced persistence and impairment of unassisted performance. Across a variety of tasks, including mathematical reasoning and reading comprehension, we find that although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up. Last month, market research company, Gartner, said that AI companies need close to "$2 trillion per year in revenue by 2029", token consumption of between 50,000 and 100,000 times its current rate by 2030, and "a 10% profit margin per token." With huge losses and small revenues, it is not likely that AI companies will achieve these goals on time. He said that about 20% of all network traffic today, about 80 exabytes, comes from machine-to-machine traffic, and that alone is big news. Nokia is betting its future growth will come from meeting this growing demand. For centuries, political power has repeatedly attempted to territorialize systems whose operational logic depended upon openness, circulation, and coordination beyond borders. ## Shai-Hulud Goes Open Source DevFeed: [Shai-Hulud Goes Open Source]() Original publisher: [Read original article]() Author: Ryan Simon, Sebastian Obregoso, Greg Foss Published: 2026-05-13T00:00:00Z Content type: article Language: en Sources: [Datadog Security Labs]() Topics: [Open Source](), [Code](), [TypeScript](), [Bun](), [JavaScript](), [CI/CD](), [Cloud](), [Persistence](), [Linux](), [macOS](), [AI Agent](), [ssh]() Tags: [analysis](), [attacks](), [c2](), [ci-cd](), [cloud](), [code](), [framework](), [github](), [javascript](), [kubernetes](), [linux](), [macos](), [open-source](), [persistence](), [ssh](), [supply-chain](), [teampcp](), [typescript]() ### AI overview A static analysis of the Shai-Hulud offensive framework attributed to TeamPCP. The article examines its TypeScript/Bun architecture, credential harvesting, supply-chain poisoning, CI/CD targeting, persistence, and encrypted data exfiltration. ### Source excerpt A static analysis of the open-sourced Shai-Hulud offensive framework attributed to TeamPCP, covering its credential harvesting, supply chain poisoning, and exfiltration capabilities. ## Temporal Sandbox Orchestration Harness: The missing layer for running agents DevFeed: [Temporal Sandbox Orchestration Harness: The missing layer for running agents]() Original publisher: [Read original article]() Author: Stefan Richter Published: 2026-05-07T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [agentic workflows](), [Persistence](), [Provisioning]() Tags: [agentic-workflows](), [agents](), [ai-agents](), [cleanup](), [durability](), [execution](), [files](), [harness](), [isolation](), [network](), [orchestration](), [persistence](), [process](), [product-news](), [provisioning](), [sandbox](), [temporal]() ### AI overview Temporal introduces reference materials and Code Exchange samples for orchestrating secure, isolated, temporary sandbox compute environments within Temporal Workflows used by AI agents. The materials address interfaces, provisioning, persistence, and cleanup. ### Source excerpt Standardize how AI agents in Temporal Workflows orchestrate sandbox compute. New Code Exchange samples cover provisioning, persistence, and cleanup. ## Long-running Agents DevFeed: [Long-running Agents]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-04-30T14:30:29Z Content type: opinion Language: en Sources: [Elevate]() Topics: [AI Agent](), [Persistence](), [context window](), [Frontier Model](), [Process]() Tags: [agents](), [ai-agent](), [context-window](), [frontier-model](), [persistence](), [process]() ### AI overview This article explains long-running AI agents: systems that continue making progress across multiple sessions, context windows, and sandboxes while preserving state, recovering from failures, and leaving artifacts for later sessions. It distinguishes long-horizon reasoning from long-running execution and discusses the engineering challenges involved. ### Source excerpt A long-running AI agent can keep making progress over hours, days, or weeks. ## Build an MCP server with Nuxt DevFeed: [Build an MCP server with Nuxt]() Original publisher: [Read original article]() Author: Ben Sabic Published: 2026-04-02T14:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Nuxt.js](), [MCP Server](), [Model Context Protocol](), [Artificial Intelligence](), [debug](), [Persistence]() Tags: [ai](), [debugging](), [documentation](), [mcp](), [mcp-server](), [nuxt](), [persistence](), [tools]() ### AI overview The Nuxt MCP Toolkit enables developers building AI features with Nuxt to create Model Context Protocol servers directly within their applications. It supports validated tools, data resources, reusable prompts, debugging with MCP Inspector, middleware, dynamic definitions, session persistence, and Code Mode for orchestrating multiple tool calls in one execution. ### Source excerpt Developers building AI features with Nuxt can now create Model Context Protocol (MCP) servers directly within their applications using the Nuxt MCP Toolkit. Install the module The module lets you define tools with Zod validation, expose data as resources, and create reusable prompts. It also includes an integrated MCP Inspector for debugging, middleware support, dynamic definitions, session persistence across tool calls, and a Code Mode that lets models orchestrate multiple tool calls in a single execution. Read the documentation or the walkthrough guide to start building. Read more ## AI sandboxes & volumes: isolated environments for coding agents DevFeed: [AI sandboxes & volumes: isolated environments for coding agents]() Original publisher: [Read original article]() Author: Ruben Fiszel Published: 2026-04-01T00:00:00Z Content type: article Language: en Sources: [Windmill Blog]() Topics: [AI Sandbox](), [AI-assisted coding](), [Claude Code](), [codex](), [Persistence](), [Authorization]() Tags: [agents](), [ai](), [claude-code](), [codex](), [coding-agents](), [environment-variables](), [launch-week](), [permission](), [persistence](), [sandboxes](), [scope](), [volumes]() ### AI overview Windmill introduces AI sandboxes that run Claude Code, Codex, and custom agents in isolated environments with persistent volumes. The article explains how annotations provide process isolation and storage, including synchronization with object storage, exclusive volume leases, caching, and per-user or per-group permissions. ### Source excerpt Introducing AI sandboxes: run Claude Code, Codex, or custom agents in isolated environments with persistent volumes on Windmill. ## Revamping Myntra App Analytics persistence with KMP and SQLite DevFeed: [Revamping Myntra App Analytics persistence with KMP and SQLite]() Original publisher: [Read original article]() Author: Kartik Sharma Published: 2026-03-31T13:01:09Z Content type: article Language: en Sources: [Myntra]() Topics: [App](), [SQLite](), [Persistence](), [SDK](), [Databases](), [legacy](), [Error Handling]() Tags: [android](), [app](), [databases](), [error-handling](), [ios](), [kotlin-multiplatform](), [legacy](), [mobile-app-development](), [persistence](), [sdk](), [sqlite]() ### AI overview This engineering post describes Myntra's effort to re-architect the persistence layer of its app analytics SDK. It explains how a legacy Android pipeline using in-memory queues and SharedPreferences created performance and data-reliability bottlenecks as event volume grew, and introduces a SQLite-backed persistence engine intended to improve app stability and data integrity. ### Source excerpt Introduction In the dynamic landscape of e-commerce, data is the bedrock of decision-making, and app stability is the foundation of user experience. At Myntra, where millions of users engage with our platform daily, ensuring the reliability of both is paramount. This necessity drove us to re-architect a critical piece of our infrastructure: the persistence layer of the Myntra app's analytics SDK. This post details our journey of augmenting our legacy analytics SDK with a robust new persistence engine backed by SQLite[1], achieving significant gains in app stability and data integrity. Background: The Limits of the Legacy Pipeline For years, our legacy in-house analytics SDK was the workhorse for collecting user interaction data. However, as our app's event volume grew, the SDK's aging persistence and processing logic created significant bottlenecks that led to degraded app performance and compromised the reliability of analytics data our product teams depended on. On Android: The Legacy Pipeline and its Limits On Android, the legacy analytics pipeline followed a straightforward event processing model: Capture, Persist & Batch: Events generated by used actions were held in an in-memory queue and then serialized into a persistent queue backed by SharedPreferences[2]. Once a batch threshold was met (typically 10 events), the batch was dispatched to the server. Background Flushing: Periodic background jobs flushed unsent events and retried failed batches. Error Handling: Failed batches were pushed to a separate retry queue, with older events silently purged if the queue hit its cap. High level overview of legacy android pipeline At the time of the initial implementation, SharedPreferences was a pragmatic choice to prioritize velocity. The ecosystem for local databases on Android was still maturing, and raw SQLite often required significant boilerplate compared to the simplicity of a key-value store. However, SharedPreferences is architecturally limited for high-throughp ## Automatic persistence now in beta on Vercel Sandbox DevFeed: [Automatic persistence now in beta on Vercel Sandbox]() Original publisher: [Read original article]() Author: Harpreet Arora Published: 2026-03-26T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Persistence](), [Vercel](), [Orchestration](), [SDKs](), [Command-line interface](), [configuration]() Tags: [cli](), [configuration](), [persistence](), [sandbox](), [sandboxes](), [sdk](), [snapshots](), [storage](), [vercel]() ### AI overview Vercel Sandboxes now support automatic filesystem persistence in beta. Stopped sandboxes save their filesystem state and restore it when resumed, while sessions provide the compute associated with that state. The beta SDK enables persistence by default, and the beta SDK and CLI add lifecycle management, configuration, and session inspection capabilities. ### Source excerpt Vercel Sandboxes can now automatically save their filesystem state when stopped and restore it when resumed. This removes the need for manual snapshots, making it easier to run long-running, durable sandboxes that continue where you left off. How it works A sandbox is the durable identity, now identified by a name, its filesystem state, and configuration options. A session is the compute tied to that state, invoked as needed. Automatic persistence introduces orchestration that separates storage from compute, reducing the need for manual snapshotting, so: when you stop a sandbox, the session shuts down but the filesystem is automatically snapshotted. when you resume, a new session boots from that snapshot. This state storage is not charged, so you pay when your setting is active. Persistence is enabled by default in the beta SDK, can be disabled between sessions with persistent: false. When disabled, the sandbox still exists after being stopped and can be resumed by its name, but each session starts with a clean filesystem. If a sandbox is stopped and you run a command, the SDK will transparently create a new session, so you don't need to check state or manually restart The beta SDK adds methods for managing sandboxes over their lifetime: The beta CLI adds configuration management and session inspection: This feature is in beta and requires upgrading to the beta SDK and CLI packages. Install the beta packages to try persistent sandboxes today: pnpm install @vercel/sandbox@beta for the SDK, and pnpm install -g sandbox@beta for the CLI. Persistent sandboxes are available in beta on all plans. Learn more in the documentation. Read more ## Vercel Sandboxes now allow unique, customizable names DevFeed: [Vercel Sandboxes now allow unique, customizable names]() Original publisher: [Read original article]() Author: Harpreet Arora Published: 2026-03-26T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Command-line interface](), [SDKs](), [configuration](), [Persistence]() Tags: [cli](), [configuration](), [documentation](), [packages](), [persistence](), [sandboxes](), [sdk](), [vercel]() ### AI overview Vercel Sandboxes created with the latest beta package now receive unique, customizable names within a project instead of ID-based identifiers. The beta SDK and CLI support named sandboxes, with CLI features for configuration management and session inspection. Named sandboxes also identify automatic persistence across session creation and resumption. ### Source excerpt Vercel Sandboxes created with the latest beta package will now have a unique, customizable name within your project, replacing the previous ID-based identification. Names make sandboxes easy to find and reference: The beta CLI adds configuration management and session inspection: Named sandboxes are the mechanism for identifying automatic persistence, which allows your session to be more easily identified for at both time of creation resumption. Install the beta packages to try named sandboxes today: pnpm install @vercel/sandbox@beta for the SDK, and pnpm install -g sandbox@beta for the CLI. Learn more in the documentation. Read more ## I Read Cursor's Security Agent Prompts, So You Don't Have To DevFeed: [I Read Cursor's Security Agent Prompts, So You Don't Have To]() Original publisher: [Read original article]() Author: Randall Degges Published: 2026-03-17T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [cursor](), [Securing AI](), [ai security](), [Vulnerabilities](), [MCP Server](), [Orchestration](), [Prompt Engineering](), [Terraform](), [Persistence](), [Architecture & Design]() Tags: [agent](), [agentic](), [agents](), [ai](), [ai-security](), [application-security](), [automation](), [awareness](), [blog](), [ci-cd](), [cursor](), [developer](), [devops](), [executive](), [infrastructure](), [interest](), [mcp](), [mcp-server](), [orchestration](), [persistence](), [related-content](), [review](), [sbom](), [scm](), [secrel](), [secrets](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [terraform](), [vs-code](), [vulnerabilities]() ### AI overview The article examines Cursor's security review automation, which uses four autonomous agents to review more than 3,000 pull requests weekly, identify more than 200 vulnerabilities, and open fix pull requests. It argues that the concise prompts are only one part of the system; the larger achievement is the production infrastructure supporting persistence, deduplication, deployment, webhook orchestration, and state management. ### Source excerpt Cursor built AI security agents that review 3,000+ PRs weekly and catch 200+ vulnerabilities. Here's what they get right--and what's missing for enterprise security. [Next page]()