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Hi there, this is your daily ☕️ DevShot.

In today's DevShot:

☁️ Google Cloud outage hits UK banks

📱 Mac.apk runs Android apps on Apple Silicon

⚡ Mercury 3 beats GPT-6 Luna on speed

🐋 DeepSeek open-sources its inference engine

🔧 Google's TorchTPU runs PyTorch on TPUs

🕸️ Cilium 1.20 opens its eBPF datapath

Plus: 🎁 8 more stories you might like, 🛠️ 6 tutorials, 🧰 7 tools, and 📚 5 papers.

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☁️ Google Cloud outage hits UK banks LINK
  • A power event in Google Cloud's europe-west2-b zone in the London region took down services for UK customers including Lloyds Bank, HSBC, Royal Mail, Revolut, and Cloudflare, with Google pledging full recovery within the hour.
  • Downtime began at 0608 UTC when the availability zone faltered, knocking out BigQuery Data Transfer Service, Cloud Filestore, Cloud Run, App Engine, GKE, and Managed Service for Apache Kafka, an estimated 38 services in total.
  • Google mitigated the power event and prioritized recovering existing workloads, though customers may still be unable to create new ones; one academic noted true resilience needs multi-region deployments and tested disaster recovery, not just moving to the cloud.
📱 Mac.apk runs Android apps on Apple Silicon LINK
  • Mac.apk, now in alpha, runs Android apps natively on Apple Silicon Macs with no emulator, virtual machine, or Linux kernel underneath, reimplementing the Android application environment directly on top of macOS.
  • Apps launch as ordinary macOS processes, draw through the Mac's GPU, and run on the CPU at native speed, appearing in the Dock with resizable windows, a back button by the traffic lights, and light/dark mode that follows the system.
  • The catch: there's no phone-style sandbox, so an APK runs with your full macOS user permissions and can read and write your files like any app, meaning you should only install APKs from sources you trust.
⚡ Mercury 3 beats GPT-6 Luna on speed LINK
  • Inception's Mercury 3, a diffusion-based language model, reportedly outperforms speed-optimized rivals GPT-6 Luna and Claude Haiku 5.5 on quality benchmarks while running four to eight times faster at over 1,000 tokens per second.
  • Unlike sequential LLMs, the model refines multiple tokens in parallel and ships roughly 40x larger than Mercury 1 without losing speed, extending the context window to 1 million tokens, nearly quadrupling Mercury 2.5's capacity.
  • Mercury 3 is compatible with the OpenAI API format for drop-in integration and is available via the Inception API, Baseten, OpenRouter, and Vercel, with Inception targeting agents, live translation, and tutoring where cumulative latency hurts usability.
🐋 DeepSeek open-sources its inference engine LINK
  • DeepSeek has open-sourced DeepInfer, the inference engine it runs in production, under Apache 2.0, letting developers serve its V4.1 Flash model on their own hardware with the same optimizations behind its API and support for future models planned.
  • The framework bundles DeepSeek's FlashMLA, DeepGEMM and DeepEP kernels, multi-chip distribution, speculative decoding, async scheduling and structured outputs, and ships alongside deepseek-recipe, a Rust and Python library that reproduces its official API's request and response handling.
  • DeepInfer supports Nvidia Blackwell and Huawei's Ascend 950 NPUs from day one but omits Hopper, currently handles offline inference only, and leaves KV cache management, load balancing and stage separation for developers to supply themselves.
🔧 Google's TorchTPU runs PyTorch on TPUs LINK
  • Google has launched TorchTPU, a specialized build of PyTorch that lets developers create and train AI models on its TPUs rather than being locked into Nvidia's GPUs, which run the majority of AI workloads today.
  • The tool lets developers run existing PyTorch apps on TPUs with a few config tweaks and no code rewrites, and Google worked with Meta, Hugging Face, and the teams behind vLLM and SGLang to keep those libraries compatible.
  • Google also shipped a TPU agent suite that migrates AI apps to Google Cloud and auto-fixes migration errors, and updated LiteRT so PyTorch-based apps can run on-device; AWS released a rival build, TorchNeuron, for its Trainium chips earlier this week.
🕸️ Cilium 1.20 opens its eBPF datapath LINK
  • Cilium 1.20 shipped with an extensible eBPF datapath that lets cloud providers plug in custom networking without forking the project or merging changes upstream, alongside broader Gateway API support and progress on sidecarless mutual TLS.
  • Plugins now run as separate processes, independently built and versioned, talking to Cilium over a custom resource and gRPC; the release also jumps Gateway API from 1.4 to 1.6, adding ExternalAuth, ListenerSets, CORS filtering, TCPRoute and UDPRoute.
  • The plugin mechanism is beta and aimed at operators who know Cilium's internals, with docs warning a plugin can break Cilium or weaken security and may need updates as the architecture shifts; ordinary cluster users aren't the target.

100+ ChatGPT Prompts to Revolutionize Your Day

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  • Challenge Buster: Overcome common AI hurdles with expert strategies

Plus, in-depth sections on email composition, content creation, customer support, and data analysis.

📚 Tutorials & Advice

> How to build an AI agent harness from scratch in TypeScript: Build an AI agent framework where the harness, not the model, validates tool calls, enforces execution limits, and isolates the workspace so failures stay safe and testable.
> The 'generic' service circular dependency problem: Keep shared services like notifications or analytics ignorant of other domains' logic, so dependencies point one way and business rules stay where they belong.
> How to make your coding agent check its work in the browser: Explains how to make a coding agent truly verify frontend changes by giving it a browser through Playwright and instructions to test what it changed.
> Making a website AI friendly: what the bots fetched: Server logs reveal that AI crawlers mostly fetch ordinary pages, sitemaps and Markdown copies, so clean static HTML matters far more than trendy files like llms.txt.
> How I use skills for everything: Store a project's living memory in reusable agent "skills" the AI reads and updates each session, giving portable, version-tracked context you can inspect across tools.
> AI agents vs. Kubernetes: where each belongs in the control loop: Let AI agents investigate and propose fixes while Kubernetes operators execute the changes, because reasoning is uncertain but reconciliation must stay predictable.

🔗 Other News & Articles

  • Microsoft previews dotnetup: Toolchain manager for .NET SDKs and runtimes LINK
  • TornadoVM 7 enables GPU-native workloads from Java applications LINK
  • New in DevTools - November 2026 | Blog LINK
  • Letting Rust support models bigger than 4GB in the browser LINK
  • Xcode 27 replaced Simulator.​app. Expo CLI and expo-device-hub already know. LINK
  • Your coding agent loses its cache while it waits for you LINK
  • Apple stops pretending macOS is UNIX LINK
  • Popular screenshot tool gets its biggest update ever, adds native screen recording LINK

📄 Papers & Resources

> Agent task playbooks can be auto-tuned into a family of specialized versions that match each job to the right approach, lifting success on long business and tool-use workflows by up to ~15 points. LINK
> Self-improving agents can now safely edit their own prompts and tools without human oversight, but only when a platform measures two things, how trustworthy its automated reviewer is and how cheaply a bad change can be reversed. LINK
> Self-improving AI claims rarely come with proof, as only 22% of 90 studies on systems that upgrade themselves actually test whether the improvement loop repeats, so most "gets better over time" claims stay unverified. LINK
> AI code detection can now pinpoint the exact lines or blocks a model wrote inside a mixed file, not just flag the whole file, scoring ~72 on accuracy at a strict false-alarm setting. LINK
> Open-source AI training tools adopt only 37 of 1,598 proposed reinforcement learning methods, with just 5 to 7 reaching code per quarter, favoring big-model and simple tweaks, meaning most published advances never become usable options. LINK

🧰 Latest Tools

Die With Me: a Mac app that tracks your Claude and Codex usage, compares it with friends, and opens a shared chatroom when your allowance runs out. LINK
Kilo Code for JetBrains: open-source AI coding agent for JetBrains IDEs that lets you build and iterate across projects with flexible model choices. LINK
Mastra Factory: a TypeScript framework for building AI agents with staged workflows, memory, streaming, evals, tracing, and a Studio UI for testing. LINK
Kilo Code: open-source mobile AI coding agent that notifies you when sessions need input, letting you dictate prompts and attach files remotely. LINK
Floot MCP: connects Claude and ChatGPT to build full-stack apps from chat, handling databases, logins, hosting, emails, and publishing automatically. LINK
OpenSwarm: an AI-first operating system where agent swarms run the machine, letting you orchestrate dozens of parallel tasks instead of working sequentially. LINK

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