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Line 267 of a 427-line Terraform plan: # aws_rds_cluster.reporting must be replaced - /+ resource "aws_rds_cluster" "reporting" { ~ arn = "arn:aws:rds:us-east-1:842910557412:cluster:reporting" - > ( known after apply ) ~ cluster_resource_id = "cluster-D85642F9611A" - > ( known after apply ) ~ engine_version = "14.9" - > "15.4" ~ id = "reporting" - > ( known after apply ) ~ storage_encrypted = false - > true # forces replacement # (29 unchanged attributes hidden) } The merge request says "bump reporting Postgres to 15.4." The plan does exactly that. It also destroys the reporting database and creates an empty one in its place. Underneath the known-after-apply churn, two attributes are changing. One is the version bump, the thing your MR is about. The other is storage_encrypted flipping from false to true , and it isn't yours. Someone on another team that shares this repo merged it earlier in the week. You're just the one deploying. You review other people's Terraform MRs and have a feel for what each stack normally does; most weeks someone else shepherds the deploy. Today it's you. Your change goes out next, so you're carrying everything merged since the last deploy, including work you never reviewed and had no reason to know about. Nobody was negligent. The queue simply had someone else's change in it. It's a good change, by the way. You want encrypted storage. But there's no in-place path from unencrypted to encrypted on an RDS cluster. Terraform's only move is destroy and create. That's what -/+ means, and the comment at the end of the line says it in plain English: forces replacement . And the version bump alone would have failed. Going from 14 to 15 is a major version upgrade, and Aurora refuses those unless the config sets allow_major_version_upgrade = true . This one doesn't. That MR by itself would have died at apply, loudly, with an error naming the exact problem. A replacement doesn't upgrade anything. It creates a new cluster at 15.4 from scratch, so the f

Astronomers will test equipment that, if it’s successful, will one day be crucial for discovering Earthlike planets.

Installing a large language model on your personal computer gives you a handy digital assistant that won’t compromise your data privacy.

I recently finished building a small suite of puzzle and game solvers that all run entirely in the browser — no backend, no API calls, no machine-learning models. You paste in a Sudoku, a chess position, or a crossword pattern, and the answer comes back instantly, computed on your own device. The fun part wasn't the UI. It was that each puzzle turned out to be a textbook excuse to reach for a different classic algorithm. Nine solvers, and I got to use constraint propagation, adversarial search, heuristic search, brute-force scanning, and plain old pattern matching — the stuff that shows up in an algorithms course and then, in most day jobs, never again. This is a tour of which algorithm fits which puzzle, and a few of the potholes I hit along the way. Everything here is vanilla JavaScript running in a Web Worker. The one design constraint: no server Before the algorithms, the rule that shaped all of them: it has to run client-side. That's a privacy choice (your puzzle never leaves the tab) and a cost choice (no compute bill), but it's also a fun forcing function. You can't lean on a beefy backend or a hosted model — you get one browser thread (well, a Worker thread) and whatever you can compute in a few hundred milliseconds. That budget is exactly why classic algorithms shine here. They're fast, deterministic, and small enough to ship as a script. Let's group the solvers by the technique each one leans on. Family 1: Constraint propagation Sudoku Sudoku is the poster child for constraint propagation. A cell that can only be one value forces that value; that in turn shrinks its neighbours' options, which forces more cells, and so on. Most "easy" and "medium" boards fall over from propagation alone (naked singles + hidden singles), and only the hard ones need a backtracking search on top. The nice property: the same engine that solves the board also powers the hint feature (find the next forced cell and explain why it's forced) and a uniqueness check — count solutions,

Cisco ACE ilə işləyən administratorun qarşısında qəribə vəziyyət dayanır: cihaz zəngin funksiyalara malikdir, trafik yolunun tam ortasındadır, amma özü artıq keçmiş nəsil platformadır. Buna görə konfiqurasiyaya yalnız “request hansı serverə getsin?” sualı ilə baxmaq kifayət etmir. Tətbiqin sağlamlığı, session davranışı, SSL sərhədi və cihaz sıradan çıxanda baş verəcək hadisələr eyni xəritədə görünməlidir. Problem də budur. ACE 4710 ayrıca appliance kimi, ACE modulları isə şəbəkə avadanlığının daxilində application delivery funksiyası verirdi. Cisco bu iki məhsulu data center üçün load balancing və application delivery həlli kimi təsvir edir. Bu sinif cihaz client ilə backend arasında reverse proxy və ya Layer 4 load balancer rolunda dayanır; client virtual IP-yə qoşulur, ACE uyğun server farm-ı tapır, işlək real server seçir və bağlantını ora ötürür. Kağız üzərində sadədir. Production-da isə hər oxun öz state-i və nasazlıq ssenarisi var. Trafik ACE-dən necə keçir? Konfiqurasiyanı oxumağın rahat yolu ayrı-ayrı komandaları əzbərləmək deyil, obyektlər arasındakı yolu izləməkdir. Virtual IP xidmətin xarici ünvanıdır. Class map trafiki tanıyır, policy map həmin trafikə load balancing davranışı bağlayır, server farm backend hovuzunu saxlayır, real server isə konkret tətbiq instansiyasıdır. Health probe real serverin rotasiyada qalıb-qalmayacağına qərar verir. Diaqram — orijinal məqalədə Bu axında class map və policy map giriş trafikinin hansı xidmətə aid olduğunu müəyyən edir. Server farm seçildikdən sonra predictor işlək real serverlər arasından birini seçir. Cavab client-ə ACE üzərindən qayıdırsa, cihaz connection state-i saxlayır; asimmetrik routing yaranarsa paketlərin bir hissəsi bu state-dən yan keçə və bağlantı qırıla bilər. Deməli, routing dizaynı load balancer konfiqurasiyasından ayrı məsələ deyil. Predictor serverin həqiqi yükünü həmişə bilmir ACE-də round-robin və least connections davranışları fərqli məqsədlərə xidmət edir. Weighted round-robin standart predic

Psychedelics are often associated with disconnecting from reality, but a recent neuroimaging study found that psilocybin can actually make our brain activity more connected to the world around us.

RFK Jr. went to Samoa to spread vaccine fears. The measles outbreak after killed 83.

Un déploiement devrait être la chose la plus ennuyeuse de ta semaine. S'il est excitant, c'est mauvais signe. Au début de ma carrière, les mises en production étaient des événements. On retenait son souffle, on croisait les doigts, quelqu'un exécutait de mémoire une séquence d'étapes manuelles, et on regardait les journaux avec une boule au ventre. C'était palpitant. C'était aussi terrifiant, et le côté palpitant était précisément le problème : chaque déploiement était un pari, parce que chaque déploiement était un peu différent du précédent. Un bon déploiement est répétable. La même chose, de la même façon, à chaque fois — automatisée, pas récitée par un humain fatigué à la fin d'une longue journée. Quand le processus est un script plutôt qu'une cérémonie, l'ennui remplace l'angoisse. Tu ne pries plus. Tu appuies sur un bouton, et le résultat est prévisible parce qu'il a déjà été prévisible cent fois. L'automatisation fait ici plus que gagner du temps. Elle supprime toute une catégorie d'erreurs : l'étape oubliée, le mauvais paramètre, le « je croyais que tu l'avais fait ». La machine ne se fatigue pas, ne saute pas de ligne, ne se laisse pas distraire à mi-chemin. Elle rend le déploiement fiable au point d'en être ennuyeux — et l'ennui, en production, est un luxe. Alors, si tes mises en production font encore monter le rythme cardiaque, ce n'est pas de la prudence. C'est un signal. Rends-les répétables, rends-les automatiques, rends-les ennuyeuses. Garde le frisson pour ta vie ; ton système de production, lui, mérite l'ennui. – Serguey Shinder

Fraudsters are exploiting enterprise chat apps like Teams and Webex to trick Chinese victims into transferring large sums of money, fueling a wave of complaints.

"The public square is open again, and definitely not affiliated with X in any way."

Just as new data centers face growing backlash from neighboring communities, the US Environmental Protection Agency (EPA) is about to make it harder for people to weigh in on any pollution those centers create. The EPA plans to toss out a federal rule requiring public notice and an opportunity to comment when certain industrial sites […]

Retro, a friend-focused photo-sharing app built by former Instagram employees, has raised more than $21 million in Series A funding.

A recent paper argues that AI is often better at doctoring than doctors. Guess who isn't thrilled.

Sandhya Devanathan will oversee some OpenAI operations across Southeast Asia and Australia in her new role.

The art portfolio platform Cara, designed for creators who don’t want their work used to train AI, has been under assault by trolls seizing and publishing its data.

The company is testing robots that can swap cables, reset servers, and take on other tasks performed by technicians, fueling concerns among some workers that their jobs could be at risk.

Influential right-wing conspiracy theorists and MAGA-aligned pundits are falsely claiming that the Covid vaccine killed Dolly Parton, the vaccination advocate and philanthropist.

Most "is this plugin safe?" advice is vibes. I wanted numbers, so I built a dataset. Here's what it found, and exactly how, so you can check my work or build your own. The finding first Of 8,010 WordPress plugins with a publicly documented vulnerability since 2023 (15,534 vulnerability records in total): 3,780 have been removed from the wordpress.org plugin directory. Removal stops updates but doesn't uninstall — affected sites keep running the code. 277 carried a critical (CVSS ≥ 9.0) flaw on record before removal. 2,115 are still installable today with a known vuln and no update in 12+ months — roughly 6.7M active installs combined. The part that surprised me most: "removed from the directory" is nearly invisible to a site owner. No dashboard warning, no email. The plugin just quietly stops getting fixes while sitting on the site. How I built it (no paid APIs) The whole thing runs on two public sources and no API keys. 1. Vulnerability data — the GitHub Advisory Database. It mirrors CVE records including the Patchstack and Wordfence CNA assignments that cover almost all WordPress plugin CVEs. It's a git repo, so a shallow, sparse clone of the advisories/unreviewed/{year} folders gets you the raw JSON: git clone --depth 1 --filter = blob:none --sparse \ https://github.com/github/advisory-database.git Each advisory carries the CVE ID, a CVSS vector string, CWE IDs, and reference URLs. The plugin slug isn't a first-class field — you recover it from the Patchstack/Wordfence reference URLs with a couple of regexes. That alone attributes the large majority of WordPress advisories to a specific plugin. 2. Maintenance signals — the wordpress.org plugin API. For each slug: https://api.wordpress.org/plugins/info/1.2/?action=plugin_information&request[slug]=SLUG That gives install count, last-updated date, tested-up-to version, and support-thread resolution ratio. A 404 (or an {error} body) means the plugin isn't in the directory — but that's ambiguous: it could be removed ,

If you're shopping for LLM APIs in 2026, Chinese vendors are impossible to ignore. As of August 21, 2026 (always check official pricing pages for the final word), flagship Chinese models charge between ¥4.00 and ¥12.00 per million input tokens — with ERNIE 5.1 at ¥4.00, GLM-5.1 at ¥6.00, Kimi K2.6 at ¥6.50, DeepSeek V4 Pro at ¥9.00, and Qwen3.7 Max at ¥12.00. Budget-tier input can be as low as ¥0.20 (Qwen3.5 Flash), and value models like DeepSeek V4 are 80–98% cheaper than GPT-5.5-class peers. But don't pick a model on sticker price alone. Cache hit rates, endpoint access, and tool-calling fit often matter more than nominal list prices. The data below was verified against official pricing pages by llmabacus on 2026-08-21. Chinese vendors have turned quarterly price cuts into a structural competitive weapon: DeepSeek V4 Flash, for example, offers cached input at ¥0.10 per million tokens — just 1/30th of its standard input price. 2026 Chinese LLM API Pricing Landscape: An Overview The 2026 Chinese LLM market is shaped by three forces: Hardware cost deflation — cheaper compute keeps pushing prices down. Escalating domestic price wars — vendors undercut each other every quarter. Aggregator endpoints — services that arbitrage price gaps and unify access. As of Aug 2026, tracking firm pricepertoken lists 610+ models globally, 43 of them free. Paid input prices range from roughly $0 to $150 per million tokens. Chinese vendors sit in the lowest price band, and many update prices quarterly — as Morph noted in its 2026-06-28 analysis: "LLM prices change every quarter." Final prices are subject to each vendor's official pricing page: DeepSeek Alibaba Cloud Bailian/Qwen Moonshot/Kimi Zhipu GLM Baidu ERNIE Tencent Hunyuan The main camps remain unchanged: DeepSeek and Alibaba's Qwen dominate the extreme value tier. Kimi (Moonshot) differentiates on ultra-long context. GLM (Zhipu) , Doubao , and Tencent Hunyuan serve the domestic enterprise market. OpenAI , Claude , and Gemini hol

Classic Machine Learning Through the Eyes of an SRE — Part 9: Isolation Forest The algorithm in one line: Isolation Forest scores how anomalous a point is by how few random cuts it takes to separate that point from everything else. No model of normal, no loss function, nothing optimized. ← Previous: Part 8 — Hierarchical Clustering Fails Beautifully · Next: this is the series finale — start at Part 1 . Every anomaly detector I had studied models what NORMAL looks like, then calls the leftovers outliers. K-Means: far from every centroid. DBSCAN: in the noise bucket. Sensible, and intuitive. Isolation Forest does not bother. It never models normal at all. It goes straight at the rare points with a single question: how few random cuts does it take to isolate you? Random cuts, literally. Pick a feature at random, pick a split value at random between that feature's min and max, repeat. A point that separates from the crowd in three cuts is anomalous. A point buried in the middle of a dense mass takes thirty. Grow hundreds of these random trees, average the isolation depth for each point, and you get an anomaly score. There is no loss function here. No optimization, not even the local kind that decision trees do at every split. Every cut is a coin flip, and the power comes entirely from averaging, which is the forest trick from the supervised half of this series now applied to pure randomness. Cheap randomness plus averaging beats careful modeling, as long as the target is something randomness naturally exposes. Rarity is exactly that. Sometimes the winning move is to optimize less. That sentence would have gotten me laughed out of my first ML study session. It is also this finale's thesis. The part I had completely backwards Here is the thing I did not know until I read the original paper properly, and it is the opposite of every instinct a decade of ops gave me. Isolation Forest deliberately trains each tree on a small subsample of your data, and this is not a performan