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android doze kills your react native background tasks--here's why and how to fix it

android doze kills your background tasks and nobody explains why properly been building a react native app that schedules stuff to run later. worked fine every time i tested it. shipped it, and it started missing schedules. only when the phone had been sitting idle for a while. never on my desk. took me way too long to figure out what was going on so writing it up here. what happens you schedule something for 1am. check logs next morning: 01:00:00 alarm fired 01:00:02 connected (while back grounded, 2 seconds) 01:18:xx the actual send ran the connection came up fine. in 2 seconds. while the phone was back grounded. but the code that was supposed to do something with that connection ran 18 minutes later when something else woke the phone up. why doze mode freezes javascript timers. setTimeout, setInterval, any polling loop on the js thread-all frozen. but native events (connection callbacks, lifecycle events, native module bridges) keep firing. i had a setInterval checking "are we connected yet" every second. doze froze that loop. the connection came up, nobody noticed for 18 minutes because the thing checking for it was asleep. the phone could do the work. my code just couldn't tell. stuff i tried that didn't fix it foreground service — keeps the process alive but doesn't unfreeze js timers. not the problem. more setTimeout/setInterval variations, literally the thing causing it. spent two days making the problem worse. HeadlessJS dropped in without changes compiled, never ran on newer RN. lost a few hours there. the actual fix move everything off timers. put your work directly in the event handler. instead of polling to check if you're connected: js // this is frozen by doze. don't. setInterval (() => { if ( isReady ()) doWork () }, 1000 ) do this : jsconnection . on ( ' status ' , ( state ) => { if ( state === ' connected ' ) { doWork ( job ) } }) native events survive doze. timers don't. that's the whole thing. for waking up at the right time — native AlarmManager

2026-06-16 原文 →
AI 资讯

Trump’s Anthropic shutdown just made the case for non-American AI

At Washington's request, Anthropic suddenly took its newest and most powerful AI models offline over the weekend. The American company said it had little choice after the White House demanded it block access for all foreign nationals, including its own employees. Abroad, the incident offered a sobering reminder that the US not only dominates frontier […]

2026-06-16 原文 →
AI 资讯

A load balancer inspired by how Emperor Penguins survive Antarctic winters

Why I modeled a load balancer after Emperor Penguin huddles A few months ago I was reading about how emperor penguins survive Antarctic winters. Temperature drops to -40°C, wind hits 120km/h, and somehow these birds make it through. Not because they're individually tough. Because they rotate. Cold penguins on the outside push inward. Warm ones from the center move out to rest. Nobody coordinates this. No penguin is in charge. It emerges from one simple rule: if you're cold, push in. If you're warm, you'll get pushed out eventually. I couldn't stop thinking about this. I was working on a service mesh at the time and dealing with the usual problem — one slow server quietly dragging down the whole cluster. Round robin doesn't care. Least connections helps but not always. Weighted approaches need manual tuning that goes stale immediately. The penguin thing kept nagging at me. What if servers had a "temperature"? What if hot servers rotated out to rest? That's HuddleCluster. The basic structure Two rings: Inner ring (deque): Active servers. Requests go to them round-robin. Simple, fair, zero overhead for normal traffic. Outer ring (min-heap): Resting servers. Keyed by temperature — coolest server sits at the top, ready to rotate back in first. When a server in the inner ring runs hot past a threshold, it moves out. When an outer ring server cools down, it comes back in. That's the entire rotation logic. About 50 lines of Python. What is "temperature"? This took me a while to get right. My first attempt was just raw latency. That was bad. A server handling one slow database query looks terrible even when it's completely healthy. I needed something more composed. Current formula: pythontemperature = EMA( 0.7 * relative_latency_anomaly + 0.1 * cpu_score + 0.1 * memory_score + 0.1 * (error_rate + connection_score) ) Three decisions here worth explaining. EMA over simple moving average EMA weights recent measurements more heavily. If a server just had a bad spike but recovere

2026-06-15 原文 →
AI 资讯

Building a Low-Latency Polymarket Bot for Earnings Markets: A Real-World Attempt (Lessons & Technical Breakdown)

A bot on Polymarket quietly extracted $32k in near risk-free profits by sniping “Will Company XYZ Beat Earnings?” markets. It waits for the official release, then instantly buys the winning side. Many limit orders from retail traders remain uncancelled, creating a post-announcement arbitrage window. Two developers decided to challenge it. Here’s what they learned while trying to build a faster version. Infrastructure Choices Location : Polymarket’s CLOB runs in AWS eu-west-2 (London). They deployed from Ireland (eu-west-1, Dublin) — the closest realistic option without IP tricks. UK IPs are blocked. Language : Rust for type safety and speed. The author notes you can achieve competitive latency in Python if you strip unnecessary network calls. Key Warning : Avoid the official Polymarket SDKs for ultra-low latency. They include helpful but slow pre-trade checks. Build lean custom clients. The Data Feed Challenge (The Real Bottleneck) The critical edge is getting earnings announcements faster than competitors. Source Performance Verdict Scraping Newswires Too slow Failed Benzinga Low-Latency Slower than manual clicking Failed Paid ultrafast feed ~500ms after release Still too slow EDGAR Consistently slower than newswires Backup only Even at 500ms, the order book was already swept by faster bots. The top players are likely using extremely expensive dedicated feeds or custom setups. Technical Lessons Learned Network > Code Most latency lives in the network round-trip, not in language choice. Optimize transport first. Custom Execution Layer Skip heavy SDK abstractions. Direct signed orders with minimal validation. Post-Event Sniping Logic Monitor newswire feeds aggressively Parse EPS vs. estimate instantly Place aggressive limit/market orders on the winning side Handle cases with ambiguity (multiple interpretations of “beat”) Reality Check They made some wins during EPS ambiguity or when faster bots hit size limits, but never won on pure speed against the leader. Why This

2026-06-15 原文 →
AI 资讯

Presentation: Practical Performance Tuning for Serverless Java on AWS

AWS Hero Vadym Kazulkin explains how to overcome Java’s enterprise hurdle on AWS Lambda: cold starts and memory footprints. He shares a technical deep dive into performance tuning, comparing fully managed AWS SnapStart (with pre-snapshot priming hooks) against GraalVM ahead-of-time compilation, while addressing the latest architectural implications of Project Leyden and Java 25. By Vadym Kazulkin

2026-06-15 原文 →
AI 资讯

The FCC Wants to Eliminate Burner Phones

A proposed FCC rule would kill burner phones: phones whose accounts are not attached to a particular person. The FCC plans to do this by legally forcing the country’s telecoms to store a wealth of personal information about essentially all phone customers, including a government issued identification number and their physical address, alarming privacy advocates and civil rights activists who compare the measures to those from authoritarian countries where it can be difficult to buy a mobile phone plan without giving up your identity. The proposed change would drastically shake up how people obtain phone plans in the U.S., and have all sorts of privacy and cybersecurity knock-on effects. The FCC is proposing the data collection partly as a way to combat scammers, with telecoms being required to collect other information on business and foreign customers like the intended use case of their bulk phone plan purchase and their IP address. But the changes would mean telecoms collect data on all new and renewing customers, and the FCC provides a long list of other things that the collected data could help authorities with...

2026-06-15 原文 →
AI 资讯

Article: Governing AI in the Cloud: A Practical Guide for Architects

In this article, the author outlines a practical approach to AI governance in the cloud, covering discovery of shadow AI, data classification at creation, IAM-based enforcement, policy-as-code, and operational controls. The article shows how organizations can embed governance into delivery pipelines, balancing security, compliance, and developer productivity without relying on manual processes. By Dave Ward

2026-06-15 原文 →
AI 资讯

A Merchant Center disapproval wiped 40% of our SKUs the day a 6-week promo launched

Three days into November, a disapproval cascade pulled 40% of active SKUs from Shopping and Performance Max simultaneously — on day one of a promotional window we'd spent six weeks building. No feed changes on our side triggered it. Here's the part most guides miss: Google's automated review threshold for certain policy categories (health claims, price accuracy, before/after imagery) tightens as platform ad volume increases heading into Q4. I've watched this happen across accounts running ₩50M–₩120M/month in combined Google spend, three years in a row, with zero feed-side changes preceding it. Same feed that sailed through August catches 15–20% disapprovals on recheck in September. The products didn't change. The enforcement did. When it hits during a live window, fix order matters more than fix speed. Price mismatches go first — not because they're the most dramatic, but because they cascade silently. One bestseller disapproved during a flash sale means Performance Max quietly reallocates budget to lower-performing products. By the time ROAS visibly drops, you've lost 48 hours of peak traffic. The specific failure mode I've seen twice on Cafe24 with direct API feeds: a site-wide price update propagates to the feed before the landing page CDN cache clears. Google crawls the feed, sees the new price, crawls the landing page, sees the old cached price. Mismatch. Disapproval. Fixing it is one line — force a manual fetch and verify sale_price_effective_date formatting — but finding it at 2am during a live sale is a different problem. Prohibited content disapprovals are deprioritized by most teams because they're rare. That's exactly wrong. A single escalation during Black Friday week can trigger account-level review, not just product suspension. Pull the SKU yourself within the hour if you can't fix the content immediately. Suspending your own SKU is recoverable. A suspended account during peak is not. GTIN and identifier issues — despite getting the most attention in s

2026-06-15 原文 →
AI 资讯

Anthropic Releases and Temporarily Suspends Claude Fable 5

On June 9, 2026, Anthropic launched Claude Fable 5, a model designed for long-horizon tasks, but it was taken offline shortly after due to a U.S. government export directive. It shares architecture with Claude Mythos 5, supporting extensive token usage. The model includes mandatory data retention requirements, which have affected its deployment with partners like Microsoft. By Andrew Hoblitzell

2026-06-15 原文 →
AI 资讯

🐍 When to choose ansible roles over playbooks

When to choose ansible roles over playbooks depends on the need for reusable structure, clear separation of concerns, and scalable maintenance across many environments. In a deployment that touches 1,200 servers, the early design decision determines whether the codebase remains maintainable or devolves into ad‑hoc tasks that require weeks of debugging. 📑 Table of Contents 📦 Modularity — Why Structure Matters 🧩 Reusability — When Scaling Demands Roles 🔧 Example: Deploying a Database Across Multiple Environments ⚙️ Dependency Management — How Requirements Influence Choice 🔗 Role Dependency Example 📁 File Layout — Organizing Artifacts for Maintenance 📊 Performance & Execution — Impact on Runtime 🔍 Comparison – Roles vs. Playbooks 🟩 Final Thoughts ❓ Frequently Asked Questions When should I still use a flat playbook? Can I mix roles and tasks in the same playbook? How do I test a role without affecting production? 📚 References & Further Reading 📦 Modularity — Why Structure Matters Roles enforce a predictable directory hierarchy that isolates tasks, variables, handlers, and files. What this does: # roles/webserver/tasks/main.yml - name: Install Nginx apt: name: nginx state: present - name: Deploy configuration template: src: nginx.conf.j2 dest: /etc/nginx/nginx.conf mode: '0644' notify: Restart Nginx # roles/webserver/handlers/main.yml - name: Restart Nginx service: name: nginx state: restarted tasks/main.yml: defines the ordered steps the role performs. handlers/main.yml: runs only when notified, preventing unnecessary restarts. The directory roles/webserver groups all related artifacts, making the role portable. Because the role encapsulates its logic, a playbook can invoke webserver without repeating internal steps. This eliminates duplication and aligns with the DRY principle. Key point: Enforced structure turns a loose collection of tasks into a self‑contained unit that can be shared across multiple playbooks. 🧩 Reusability — When Scaling Demands Roles Roles enable r

2026-06-15 原文 →