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Presentation: Rewriting All of Spotify's Code Base, All the Time

Jo Kelly-Fenton and Aleksandar Mitic explain how Spotify created "Honk," an AI coding agent, to handle complex fleet-wide codebase migrations. They share key architectural insights on decoupling CI verification runtimes from AI agents, dealing with automated pull request bottlenecks, and driving aggressive standardization across thousands of engineering repositories. By Jo Kelly-Fenton, Aleksandar Mitic

2026-08-07 原文 →
AI 资讯

Why does Apple keep banning Telegram, but never X?

For roughly an hour this week, Telegram vanished from Apple's App Store. Even during that blip, it was a stunning absence for such a major app: an avenue of communication for more than 1 billion users around the world, used widely as a secure platform for people who live in countries under censorship. Apple later […]

2026-08-07 原文 →
AI 资讯

Tapo H100: Cellar Humidity Monitoring in Home Assistant

Cellars sweat. On a warm humid day the air you let in is warmer than the cold concrete, and the moment it touches a cold surface it gives up its water. That's condensation, and over enough summers it's how a basement grows mould in the corners you never look at. I wanted a number that warned me before that happened. The catch I already knew going in: a raw relative-humidity reading isn't that number. This is Part 08 of the series. The hub was already in the house running other things, and the install was genuinely the easy 20 minutes. The part worth writing about is what came after the sensor showed up: turning its two raw readings into a dew-point spread, and deciding when that spread means "act" versus "ignore the spike." Why the H100, and why 868 MHz is the whole point The hub is a TP-Link Tapo H100 , a little smart hub that acts as a radio bridge for TP-Link's battery sensors — the T100 motion, the T110 contact, and the one I care about here, the T310 temperature/humidity sensor. Here's the load-bearing detail, and the reason I reached for this hub instead of a WiFi sensor: the Tapo sensors don't talk WiFi. They talk to the H100 over 868 MHz sub-GHz radio . That matters in a cellar more than anywhere else. Sub-GHz is long-range and punches through concrete and floors in a way 2.4 GHz WiFi simply doesn't. There's no WiFi worth having down in my cellar, and no interest in running a repeater into a damp room just to read a sensor. The T310 sits down there on a battery, the H100 upstairs where the network is, and the radio link between does the work. One H100 supports up to 64 sensors — for a house, more headroom than I'll ever use. Getting the sensors into Home Assistant The native TP-Link integration doesn't expose the H100's child sensors — it's built for the plugs and bulbs. The one that works is the community Tapo Controller integration (petretiandrea's TP-Link Tapo ), installed through HACS , the same custom-integration store I've leaned on throughout this ser

2026-08-07 原文 →
AI 资讯

The Real-Time Fetish: Why You (Probably) Don't Need Streaming

In modern Data Engineering, there is an unspoken fetish for "Real-Time." If you ask any business stakeholder how fast they need their dashboard to update, the default answer will always be: "As fast as possible." This drives well-intentioned engineers to design incredibly complex architectures. We spin up Kafka clusters, implement Flink, and wrestle with latency, late-arriving data, and tumbling windows. All to have data flowing in milliseconds. But the harsh reality is that the vast majority of companies are building Ferraris just to sit in rush-hour traffic. 1. The Actionability Gap (The Golden Question) The biggest mistake when choosing a streaming architecture isn't technical; it's a business mistake. Before implementing real-time pipelines, the only question that matters is: "Does the company have the operational capacity to make a decision in milliseconds?" If you are building a credit card fraud detection system or a live e-commerce recommendation engine, yes, every millisecond counts. But if the data is feeding a financial dashboard that the executive board only reviews during their Monday morning meeting, updating that screen every second is a colossal waste of money and effort. Real-time data has zero value if the human action is batch. 2. The Hidden Complexity and the Cloud Bill Batch processing is forgiving. If a pipeline fails at 3 AM, you trigger a rerun, and by 8 AM, everything is fine. Batch is cheap, predictable, and easy to debug. Streaming, on the other hand, is unforgiving. Handling application state, event duplication (exactly-once semantics), out-of-order events, and sudden traffic spikes requires a senior engineering team dedicated solely to keeping the infrastructure alive. Furthermore, the cloud bill for 24/7 continuous processing is orders of magnitude higher than spinning up your compute clusters on a schedule. 3. "Micro-Batch" Solves 99% of Your Problems There is a perfect middle ground that the hype industry tries to ignore: the micro-ba

2026-08-07 原文 →
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Canadian Man Pleads Guilty in Snowflake Extortions

A 26-year-old Canadian man once described as one of the most consequential cybercrime threat actors of 2024 has pleaded guilty to computer fraud and conspiracy to hack and extort more than 165 organizations that used the cloud data storage provider Snowflake. Connor Riley Moucka, of Kitchener, Ontario, also admitted to stealing call and text history records of more than 100 million AT&T customers.

2026-08-07 原文 →