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Why Owl Post covers Technology

Big tech moves on a different timescale than the news cycle wants you to believe. What looks like a product launch is usually the public surface of an organizational bet made eighteen months earlier.

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Daily Dose of DevOps — SLO design: for high-change environments

SLO design: for high-change environments A platform capability becomes dangerous when ownership is implicit but authority is broad. For slo design for high-change environments, the decisive question is not whether a team can demonstrate the technology once. It is whether the organisation can operate it repeatedly, audit its decisions, and recover when assumptions fail. Treat slo design as part of an SLO-led telemetry platform that connects service behaviour to customer-visible outcomes. Define the consumer, owner, support boundary, change policy, and recovery objective before selecting implementation details. When operating slo design for high-change environments, undocumented authority and ambiguous ownership create more risk than a missing feature. A credible design measures error-budget burn, tail latency, cardinality growth, telemetry loss, and diagnostic time. Those measures should be visible to both the platform owner and consuming teams. If the measurements cannot distinguish adoption from coercion, or reliability from mere activity, the operating model is not yet falsifiable. Start with a narrow contract that can be tested automatically. The implementation should encode stable semantic conventions, tiered retention, cardinality budgets, sampling policy, and telemetry pipeline SLOs. The following fragment is illustrative; production values must be derived from workload evidence and organisational policy. processors: memory_limiter: limit_mib: 512 batch: send_batch_size: 1024 exporters: otlp: endpoint: telemetry-gateway:4317 Roll the control out to one representative service, observe failure behaviour, and exercise rollback before broad adoption. Record exceptions as expiring decisions with an accountable owner, not permanent bypasses. Standardisation reduces cognitive load and makes controls observable, but an excessively rigid path displaces complexity into workarounds. Flexibility improves local fit, but every variant expands the support surface and weakens

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Why every React Native upgrade takes longer than you estimated (and how to estimate honestly)

Almost every React Native upgrade I've worked on was estimated in days and delivered in weeks. "Bump react-native, fix whatever the Upgrade Helper diff says, run the app." React Native Upgrade Helper Native dependencies. Every library with an ios/ or android/ folder is a separate upgrade inside your upgrade. Each has its own compatibility story, maintainers and release cadence. Toolchain coupling. A React Native bump can pull in new requirements for Xcode, the Android Gradle Plugin, the JDK or Node. These fail late, in CI, on someone else's machine. Template drift. Your project's native files stopped matching the template years ago. A diff against a clean template can't know about your custom changes. Architecture changes. Newer React Native versions change defaults (for example around the New Architecture). Libraries that never adopted the new model become blockers. Check the release notes of your target version for the current state. Unknown unknowns. Abandoned packages, patched dependencies (patch-package) and forks. Count native dependencies. The number matters more than total dependencies. Classify each one: actively maintained / slow / abandoned, and "known compatible with the target version" / "unknown" / "known broken". Do a time-boxed dry run on a throwaway branch: bump, install, build iOS and Android, record every failure. Stop after a fixed time (say a day). Estimate from the dry run: your known failures plus a buffer proportional to the unknown count. "Two weeks if three unknown dependencies behave, five if they don't" is more useful and more honest than a single number. It also tells your manager where the risk is. I'm working on a tool, rn-upgrade-doctor, that automates steps 1 and 2 and produces a project-specific risk report. The CLI goes public on 20 Oct. If you want early access: [https://rnupgradedoctor.dev] What was the worst upgrade estimate you ever got wrong? I'd love to hear it in the comments.

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A deep dive on Amazon's robotics efforts, resulting in 1M+ active warehouse robots; Amazon is still the world's 2nd-largest corporate employer with 1.5M workers (Jeremy Hsu/Ars Technica)

Jeremy Hsu / Ars Technica: A deep dive on Amazon's robotics efforts, resulting in 1M+ active warehouse robots; Amazon is still the world's 2nd-largest corporate employer with 1.5M workers — No organization in the world operates more robots than Amazon. The company already has more than 1 million robots working …

techmeme.com

ARKit vs ARCore vs WebXR: Choosing Your AR Stack (2026)

The four options ARKit is Apple's native AR framework for iPhone and iPad. ARCore is Google's, primarily for Android. Unity AR Foundation lets one Unity project target both. WebXR delivers AR in a web browser with no install. As of September 2026, Safari on iPhone does not support WebXR augmented reality. That single limitation shapes almost every AR decision, because in many markets a large share of the people you want to reach are on iPhone. It means a browser-only AR experience gives iPhone users either no AR or a reduced fallback. If your audience skews to iPhone, that is a serious constraint on web AR. Best accuracy and features on iPhone. Direct access to surface detection, image and object tracking, face tracking, location anchors, and LiDAR-powered scene reconstruction on devices that have LiDAR, such as iPhone 18 Pro. The trade-off: iPhone only, and users must install an app. Best choice for Android-first projects, and home to the Geospatial API for location-based AR using Google's visual positioning. The trade-off mirrors ARKit: Android-focused, and an install is required. One codebase, both platforms. It wraps ARKit and ARCore, so you get native AR tracking on both while writing most of the app once. For most commercial AR apps that need to work well on iPhone and Android, this is usually the right default. The trade-offs are an app install and the occasional platform-specific feature that needs native work. Maximum reach, zero install — a link or a QR code opens it instantly. Excellent for campaigns, events and quick demos. The trade-off is the iPhone gap described above, plus generally less capability than native. Campaign, event, packaging, "try it in five seconds": WebXR, accepting a reduced iPhone experience or a separate iPhone path. Commercial app that must be accurate on every phone: Unity AR Foundation. iPhone-only, needs the deepest Apple features: native ARKit. Location-based AR at city scale: consider ARCore Geospatial, or ARKit location ancho

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Sources: NTT Data is in advanced talks to build a data center for Anthropic in Osaka of up to 500 MW, among Japan's largest, potentially costing $6.3B (Nikkei Asia)

Sources: NTT Data is in advanced talks to build a data center for Anthropic in Osaka of up to 500 MW, among Japan's largest, potentially costing $6.3B (Nikkei Asia)

Nikkei Asia: Sources: NTT Data is in advanced talks to build a data center for Anthropic in Osaka of up to 500 MW, among Japan's largest, potentially costing $6.3B — TOKYO/OSAKA — NTT Data Group is in the final stages of talks to build a data center in western Japan for U.S. AI company Anthropic, multiple sources have told Nikkei.

techmeme.com

Japanese manufacturer Omron says it will acquire US industrial PC firm OnLogic and its parent company, Interlogic, for $675M, to boost its "physical AI" efforts (Anton Bridge/Reuters)

Anton Bridge / Reuters: Japanese manufacturer Omron says it will acquire US industrial PC firm OnLogic and its parent company, Interlogic, for $675M, to boost its “physical AI” efforts — Japanese manufacturer Omron (6645.T) will buy US industrial PC firm Onlogic and its parent company, Interlogic, for $675 million, the company said on Tuesday.

techmeme.com

How Styx keeps coding agents away from production

Agents run as you, with your logged-in CLIs. This is how Styx puts a person between them and production, where that stops, and a bug I fixed the day before launch. Every coding agent I use runs as me. Claude Code, Codex, Gemini CLI and Cursor's agent start with my PATH, my files and my logged-in CLIs. If I've run vercel login, gcloud auth login or supabase login on this machine, the agent has too. Nothing in that setup knows the difference between vercel deploy and vercel deploy --prod, or between a supabase db push to a scratch project and one to the database people are using. I run several at once, across several projects, and I don't read every command. One wrong --prod and it's done before I look. Styx is the app I built to run those agents. This is how the part between an agent and your deploy targets works, and where it stops. The code is Apache-2.0; I've linked the files. A command like supabase db push takes this path: A shim named supabase, first on the agent's PATH, catches it. The shim asks a local broker. Its connection is bound to one agent session. A pure policy function decides: approve, ask or deny. On ask, the agent waits and you get a request, in its chat and as a notification. You grant it for a set time. Production writes, deploys and deletes need Touch ID, Windows Hello or your system password first. The real command runs with that grant's credentials in its environment, and nothing else. Every step is written to an append-only, hash-chained audit log. When Styx starts an agent, a directory of small scripts goes first on its PATH: vercel, gh, aws, gcloud, supabase and ssh. This is the vercel one, from shim-service.ts: #!/bin/sh # Styx shim: authorises the command through the grant broker, then execs the real vercel with injected credentials. exec env ELECTRON_RUN_AS_NODE=1 "$STYX_EXE" "$STYX_CLI" wrap vercel "$@" styx wrap connects to the broker, calls exec_authorize with the tool, its arguments and the working directory, and waits up to ten min

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How Can One Physical Server Run Hundreds of Virtual Machines at Once?

Imagine walking into a data center and seeing rows of powerful physical servers. Each machine contains processors, memory, storage devices, and network interfaces. Now consider something surprising: a single one of these servers might run dozens or even hundreds of virtual machines, each with its own operating system, applications, and configuration. How is that possible? A computer has a limited number of CPU cores, a fixed amount of RAM, and only so much storage. If every virtual machine behaves like an independent computer, where does all the additional computing power come from? The answer is server virtualization. Instead of assigning a separate physical machine to every application or workload, virtualization allows multiple computing environments to share the resources of one physical server. Software manages how those resources are allocated, when they are used, and which virtual machines can access them. This technology is one of the reasons modern data centers can operate efficiently without requiring a separate physical computer for every service. Let's look at what actually happens behind the scenes. Every virtual machine ultimately depends on physical hardware. Before virtualization can do anything, there must be a real machine providing the resources needed to execute instructions, store data, and communicate over a network. A typical server contains CPU cores for processing, RAM for holding active data, storage for operating systems and applications, and network interfaces for sending and receiving information. Consider a company running a website, a database, an internal application, and several background services. Traditionally, the company might deploy each workload on a separate physical server. The problem is that these applications rarely use all their available resources continuously. One server might be almost idle while another is busy processing requests. Virtualization allows these workloads to share the same physical machine instead. Each

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One User, Two UIDs: NFSv4, Kerberos, and Docker on a Domain-Joined TrueNAS

Introduction In our shared GPU lab, several things broke in the same few weeks. Files on a user's NFS home showed up as nobody:nogroup. Once we tightened the share, a user could no longer write to their own home, even though ls showed them as the owner. A freshly provisioned home dataset came out owned by root:root with mode 755 instead of belonging to the user with mode 700. A student's Jupyter container opened with the prompt I have no name! and sudo refused to work. Most of this came from one root cause. The same Active Directory user had one UID on the Linux clients and a different UID on the storage server. Neither side was misconfigured on its own terms. In the end, we computed the base the clients used and set the storage server's ID range to start at the same number. Two more lookups on the same path, one on the client and one on the server, also had to be fixed before access worked. This post follows one identity from login to an NFS file, shows where the mismatch hurts, and covers the fix, a checklist, and alternatives. The environment has four parts: Domain controller: a Samba-based AD appliance for the EXAMPLE.LOCAL realm. Storage: TrueNAS SCALE at nas.example.local (192.0.2.10), joined to the same domain. Every user gets a separate ZFS dataset, exported over NFSv4 with sec=krb5 only. Each dataset is owned by its user with mode 0700. The owner has full access and everyone else has none. Clients: Ubuntu workstations joined to the domain with SSSD. pam_mount mounts the user's dataset at login, and rpc.gssd handles the Kerberos side of NFS. Docker: users run their work (Jupyter, training jobs) in containers. The user's NFS directory is bind-mounted into the container as /workspace. The isolation goal is simple: user A must not be able to read user B's dataset, even with Docker in the picture. The diagram shows which identity is in effect at each hop, before the fix. The UIDs are examples. Diagram: see the original post. The client's number never reaches the

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