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@ctrl_
Software Dev, .NET and zig. My wife’s YT: https://t.co/xh3PTBpt0w
A simulation study by Chinese scientists shows how 🇨🇳 PLA could attempt to block Starlink across Taiwan. Their findings — published on Nov 5 in the Chinese peer-reviewed journal Systems Engineering and Electronics — suggest that jamming Starlink across a region as large as Taiwan is technically feasible, but only at an immense scale that would require 1,000 to 2,000 electronic warfare drones. The paper, titled “Simulation research of distributed jammers against mega-constellation downlink communication transmissions”, was written by a team from Zhejiang University and Beijing Institute of Technology (BIT) — the latter a top player in China’s defense research. “The orbital planes of Starlink are not fixed, and the movement trajectories of the constellation are highly complex, with the number of satellites entering the visible area constantly changing.” “This spatiotemporal uncertainty poses a significant challenge for any third party attempting to monitor or counter the Starlink constellation.” Traditional satellite communication relies on a handful of large, geostationary satellites fixed above the equator. To block them, the Chinese military just needs to overpower their signal from the ground. But Starlink is different. Its satellites are low, fast and numerous. A single user terminal does not connect to one satellite — it rapidly hops between multiple ones, creating a mesh network in the sky. Even if you manage to block one signal, the connection jumps to another within seconds. Moreover, Starlink uses advanced phased-array antennas and frequency-hopping techniques that adapt in real time, much of which is controlled remotely by SpaceX engineers in the US. Starlink could be countered by a distributed jamming strategy. Hundreds or thousands of small, synchronized jammers would need to be deployed across the sky — on drones, balloons or aircraft — forming an electromagnetic shield over the battlefield. Using actual Starlink satellite data, the team simulated the dynamic positioning of satellites over a 12-hour period above eastern China. They modelled the downlink signal strength from Starlink satellites, reception pattern of user terminals, propagation of interference from ground to sky and sky to ground, and the cumulative effect of multiple jammers hitting the same terminal from different angles. Then they introduced a grid of virtual jammers, flying at 20km altitude, spaced between 5 and 9km apart like a chessboard in the sky.  Each jammer emitted noise at various power levels, mimicking realistic electronic warfare payloads. Two types of antennas were tested — one with a wide beam which covered more area but spread energy thinly, and a narrow-beam one that was focused and powerful, but required precision. The simulation calculated, for every point on the ground, whether a Starlink terminal could maintain a usable signal. Under optimal conditions — using a powerful but costly 26 decibel-watt (dBW) jamming power (400 watt) source, a narrow-beam antenna and 7km spacing — each jammer node suppressed Starlink reception across an average area of 38.5 sq km. Taiwan covers around 36,000 sq km. To blanket the island with reliable Starlink suppression would require at least 935 coordinated interference nodes, and this number does not include redundancy for failures, compensating for terrain such as mountains that block signals and countering Starlink’s future anti-jamming upgrades. Using a weaker but more affordable 23 dBW power source with 5km spacing would double the drone deployment scale to around 2,000 units. The results were preliminary because Starlink kept some key technology confidential. “If it becomes possible in the future to obtain actual measurements of the radiation pattern data of Starlink user terminals, and to acquire empirically measured values of the suppression coefficients for these terminals, it would help achieve more accurate assessment results.” amp.scmp.com/news/china/sci…
We spend a lot of time helping customers unwind database-per-customer architectures.
Does anyone have more of this genre of image of old and new cultures colliding
Your app is slow. Your users are waiting 3, 4, maybe 5 seconds for a page to load. You check your logs. The problem isn't your code. It's your database. It's on fire. It's spending all its time running the same complex 'SELECT' queries with 'JOINs' and 'ORDER BY' clauses, over and over, just to fetch a user's profile and their 10 most recent posts from a slow, spinning disk. You've heard of Redis. You think 'cache'. You also think, 'it's in-memory, so if my server reboots, all my data is gone'. You think it's just a temporary, volatile speed boost, a fragile layer you can't really depend on for anything important. That's not the full story. I was digging into it, I was expecting a simple cache. What I got was a Swiss Army Knife for my data. Here was my journey from slow app to superpower. Step 1: The 'Hello World' (The Obvious Cache) This is what everyone does, and it's brilliant. - My Problem: That slow 3-second SQL query. - My New Workflow: I fetch the data 'once', 'SET' it in Redis (which lives in blazing-fast RAM), and set an expiration of 5 minutes. - For the next 5 minutes, 10,000 requests for that profile are served in 'sub-milliseconds'. - My database load just dropped 99%. My app is fast. But the real magic begins when you stop treating it like a dumb string store. Step 2: The Click Moment (Data Structures) The real power is that Redis is a high-speed data 'structure' server. - My Problem: I need a real-time leaderboard. My 'ORDER BY score' query is killing my 10-million-row 'users' table. - My New Workflow: I use a Redis 'Sorted Set'. When a user's score changes, I 'ZADD leaderboard-main 19850 'user:123''. Redis handles the sorting 'instantly' on insertion. Fetching the top 10 is near-instant. Step 3: The 'Wait, what?' Features - My Problem: I need a list of recent notifications. 'SELECT ... LIMIT 20' is still a waste. - My New Workflow: I use a Redis 'List'. When a new notification comes in, I 'LPUSH' it. Then I 'LTRIM' the list to keep it at 20 items. It's a perfect, high-speed stack. - My Problem: I need a background job queue to send welcome emails. - My New Workflow: I don't install RabbitMQ. I just 'LPUSH' a job to a 'List', and my worker 'BRPOP's (blocking pop) to wait for the next job. It's simple and reliable. - My Problem: I need a real-time chat app. - My New Workflow: I don't build a complex WebSocket server. I just use 'Pub/Sub'. It's a high-speed, lightweight message broker built-in. Step 4: The "Pro Escape Hatch" (Debunking the Myth) This is what sold me. - My Fear: 'But if it reboots, my data is gone!' - The Reality: Redis has 'persistence'. It's not on by default (for speed), but it's there. - It can take 'snapshots' (RDB) of your dataset. Or, it can keep an 'Append-Only File' (AOF) log of every single write command. - It's not a replacement for Postgres as your 'source of truth', but it's far from a 'throwaway' cache. It's your cache, your message queue, your session store, your leaderboard, and your real-time engine, all in one. I thought Redis was a 'nice-to-have' performance boost. It's not. It's the high-speed multi-tool for your entire application.
According to far left Wikipedia, pride is bad only for White people.
“People who use their middle initial are narcissists.” - @DrDJWilde
Lung cancer is now the most common cancer in men in Indonesia Nasopharyngeal carcinoma (NPC) is now the most common cancer in men in Bali, with environmental factors like air pollution being a significant risk factor I wonder if trash burning has something to do with it? 🤷♂️
@levelsio I loved Bangkok and Bali, but man I just cant stay there for more than a vacation because of the air quality Sitting on a scooter behind cars feels like downing a pack of cigarettes every ride 🚬🚬🚬
Yep, if you are only using EC2 then Hetzner makes total sense.... But when you add managed services (queues, web sockets, autoscaling - all that shit I don't want to manage myself 😬), cloud pricing isn't just infra, and in Azure at least, those extras ain't that pricey. Though in his case it was mostly moving VPS. 👍
