标签：#RAM

submitted by /u/droppedasbaby [link] [留言]

You installed TypeScript 7, ran your build, and something broke. Maybe ESLint crashed with a cryptic TypeError: Cannot read properties of undefined (reading 'Cjs') . Maybe ts-jest stopped transforming your test files. Maybe your CI pipeline just went red for no reason you can point to. You're not doing anything wrong. TypeScript 7 shipped tsgo, a genuine Go port of the type-checker, not a rewrite from scratch. But the tools that plug into TypeScript don't talk to the type-checker directly, they talk to a programmatic API. That API isn't stable yet, it lands in 7.1. Until then, a chunk of the ecosystem throws errors the moment you point typescript at the new version. The 10-second version Don't replace typescript in your dependencies with the 7.x line if you use typescript-eslint, ts-jest, ts-morph, or any tool doing programmatic type-checking. Keep typescript pinned to 6.x for those tools, and install @typescript/native-preview alongside it purely for fast type-checking in CI or a manual tsgo --noEmit command. Two compilers, living side by side, each doing a different job. Why this is happening The TypeScript team calls this Project Corsa: a line-by-line port of the compiler from the old JavaScript codebase (Strada) into Go (Corsa), preserving identical type-checking behavior while getting roughly 10x faster builds from real OS threads instead of Node's single-threaded event loop. That preservation is impressive, but it's a port, not a reimplementation with a new API surface. Tools like typescript-eslint depend on the programmatic API to walk your AST and pull type information out of the compiler, and that API isn't ready until 7.1. What's actually broken right now typescript-eslint — npm refuses to install alongside typescript@7 at all (ERESOLVE error), because the published peer range only allows versions below 6.1.0. Force it through and ESLint crashes deep inside typescript-estree . Tracked as typescript-eslint issue #12518, closed as not planned since the real

Last month I finally did some math I'd been putting off: how much I was actually paying to keep a bunch of sites online. $39/month on Hostinger (about R$200, I'm in Brazil). For hosting 10+ sites: product landing pages, blogs, a couple of small tools. Every month, on autopilot, straight off the card. Then I asked myself the obvious question I'd been avoiding: out of those 10+ sites, how many actually need a server running 24/7? Answer: none. What these sites actually are A product landing page doesn't need PHP processing a request. A blog doesn't need a database query on every page view. A marketing site doesn't change its content every second. That's HTML, CSS, and JS you can generate once and serve from a CDN. In other words: a static site. A few real examples I migrated: eduardovillao.me → my personal blog, built with Astro formroute.dev → a SaaS landing page, plain HTML wpfeatureloop.com → a dev tool landing page, plain HTML Three different kinds of sites (blog, SaaS, dev tool), two different stacks, and none of them needed a server running around the clock just to exist. The reason I hadn't migrated sooner wasn't technical. It was inertia. "It's already paid for, it already works, leave it alone." Classic. The migration I moved everything to Cloudflare Pages . The reasoning is boring because it's so simple: it's free, global CDN, automatic SSL, Git-based deploys, custom domains at no extra cost. For static sites, there's really nothing to debate. The process, in short: Each site became a repo (or a folder inside a monorepo, depending on the case) Connected the repo to Cloudflare Pages Set up the build, mostly plain HTML, Astro for the blog where I wanted content collections and a proper writing workflow Pointed the domain, SSL came up on its own Cancelled hosting for that domain on Hostinger Repeated that site by site. No magic, just repetitive work, but each one took about 20-30 minutes. (If you want the technical deep dive on one specific migration, including

submitted by /u/Mynameis__--__ [link] [留言]

Take the opportunity to pilfer a goat. — The 36 Stratagems, Take the Opportunity to Pilfer a Goat Previously on this series: #5: Leo Walked Into a Burning House. He Walked Out With a Client. — At 1 AM, Leo received an anonymous message and drove across town to fix a competitor's outage. A second message followed — a screenshot with a name: Automated Compliance Lab. He didn't remember the acronym. He didn't delete the screenshot. #10: Lena Watched a Team Adopt Her AI Template. Leo Didn't Know the Knife Was in the Contract. — Lena joined CoreStack as a consultant and built Leo a reporting template. Leo thought she was there to help. Five weeks later the template went live. Six months later the data baseline was locked. He only then realized he'd been inside her palm the whole time. Taken down by a smile. This was a few months later. The Archive Cleanup SOC 2 Type II renewal had just passed. The auditors were gone. CoreStack's compliance team was doing the post-audit archive — classifying every record produced during the audit and tagging them with retention periods. Leo got the cleanup part. The training pipeline's cache directory. The cleanup cron job hadn't run for a week — nobody noticed. When he looked inside, the output folder had a few records with train_ prefixes mixed in among inference outputs. One of them had a model_version that wasn't CoreStack's own. model_version : " acl-train-2026q2-v3" Leo copied that line out. Didn't delete it. Didn't report it. Dropped it into a folder called _misc/ .Set a quiet keyword alert for "acl-train" before closing the terminal. He noticed the naming convention wasn't FinOptima's — FinOptima used fin-model- plus timestamps. acl- — he'd seen that prefix somewhere before. Couldn't place it. He didn't let himself try. He filed it away. Went back to archiving. The Trace Not every CTO digs through cache write logs during archive cleanup. He did. He spent two hours cross-referencing FinOptima's API call records against CoreStack's

Hello all: I am just about finished with my first production-quality app. I developed it for macOS and Windows with full platform parity and am about to port to Ubuntu/Debian before releasing. I have licensing setup through Keygen.sh. I am happy with that decision so far; however, I have questions regarding that issue and the issue of telemetry. Privacy is a focus of mine and a commitment I made to prospective subscribers. That said, I need some form of telemetry and a DRM that can take subscription payments. I am trying to think of the best way to implement the Keygen.sh API for privacy. The app has, well, an app--the client--and it also has a remote server component. It will mostly be free but the more labor intensive developed elements and the tools that require the remote server will be paid at either a monthly or annual subscription rate. I am so confused about how to best implement the licensing API while remaining true to my privacy commitments. Part of me says, "Well, their information is going to be on Keygen's servers anyway, might as well host the store through their website/portal." The other inclination is to host the store on my server/website, so I can control how much information PII is required. The answer can be “very little,” but a much bigger question arises surrounding what to do with the information once accepted. (I’ve considered maintaining the subscriber data in some kind of encrypted space on the server with hardcoded, volatile keys. That way not even I can see their data, but they’d still be able to search for and regenerate their licenses, management payment methods, etc.) The other question I have is along that same vein and involves telemetry. What I would like to do is keep the telemetry deliverables locally stored. When the user has an issue, experiences a bug, or has some other issue requiring my troubleshooting assistance, I can request it and they can choose to release their telemetry data to me. In between those disclosures, using

SPOILER: De 1 à 2000 Durante minha jornada como jogador , existiu uma coisa que me despertou muita curiosidade: os créditos. Quando eu jogava coisas como Final Fantasy, ficava completamente arrepiado quando via aquelas cenas antes do menu inicial, com CGIs bem bonitões e um monte de nomes em japonês — muitos deles que, honestamente, até hoje não sei quem são. Esse arrepio também acontecia quando eu chegava ao final de algum jogo e começavam a aparecer nomes e mais nomes. Quando eu entendi o que eram aqueles nomes, a primeira coisa que me veio à mente foi: peraí, precisa de tudo isso de gente pra fazer um jogo? Too long, didn't read : Sim e não. Skyrim levou aproximadamente 6 anos com uma equipe de 100 pessoas (e ainda é dito que é uma equipe enxuta), enquanto Stardew Valley levou 4,5 anos com uma "equipe" de apenas 1 pessoa. Eles têm quase o mesmo tamanho em horas jogadas na campanha principal. Not too long, I'll read it : É um pensamento lógico que trabalhos maiores no mundo da tecnologia envolvam uma quantidade maior de pessoas trabalhando E tempo. Dessa forma, seria correto assumir que um trabalho menor exige menos tempo e menos pessoas trabalhando. Mas o que os dados dizem? King of Fighters 94: 2 anos, começou com 6 pessoas, aumentou para 60. Clair Obscure - Expedition 33: aproximadamente 6 anos, 30 pessoas. TES V - Skyrim: 6 anos, 100 pessoas. Super Mario Bros: 2 anos, 7 pessoas na equipe principal. Stardew Valley: 4,5 anos, 1 pessoa. Kenshi: 12 anos, 1 pessoa por 6 anos (trabalhando meio período), aproximadamente 8 pessoas por mais 6 anos (tempo integral). Final Fantasy VII (PS1): aproximadamente 2 anos, de 100 a 150 pessoas (uma das maiores equipes da indústria na época). Final Fantasy VII Remake: Cerca de 5 anos, e não se tem números exatos, mas os créditos citam mais de 2000 pessoas, incluindo muitos -istas e -ores (vou tentar confirmar esse número depois). É claro que existem muitos fatores envolvidos aí, como época, demandas de mercado, prazos, tecnologia

Hello Reddit, I'm 33, currently in school for my Bachelor's in IT (~2 years from graduation). I am taking Calculus (Math 110 at Penn State) for the 3rd time. I failed it twice before. I am currently a nurse of 10 years going back to school. I am taking a summer class version of it, which I did not realize is accelerated / asynchronous, and am slowly starting to do poorly in it. My end goal after school is to work remotely in a programming-related job. But math has always been hard for me. So I am asking: -Any tips / resources / insights for passing Calculus -Any way I can pursue a degree in programming / computers without having to take Calculus -what other degree options that aren't math heavy will allow me to pursue tech-related careers? Open to suggestions, but my necessity list is being able to work remotely and earn a comfortable living abroad. Thanks! submitted by /u/Born_Cod_8931 [link] [留言]

submitted by /u/Independent_Pitch598 [link] [留言]

It actually even removes code. submitted by /u/Kal1boy [link] [留言]

Patreon sends billions of notifications each year. As our largest creator audiences grew, a legacy task responsible for generating millions of recipient-specific notifications began consistently timing out. This post explains how our team introduced a two-stage fanout architecture, isolated email, push and in-app processing, improved observability, and migrated more than 200 notification types across a 13-year-old codebase. submitted by /u/patreon-eng [link] [留言]

submitted by /u/davidalayachew [link] [留言]

The Quest Begins (The "Why") I still remember the first time I stared at a LeetCode problem that asked me to count the number of islands in a grid. My initial instinct? Run a BFS/DFS from every unvisited land cell, mark everything reachable, and repeat. It worked, but each query felt like I was re‑exploring the same territory over and over again—like walking the same hallway in a dungeon every time I wanted to open a new door. Then a friend tossed me another problem: “Given a list of friendships, tell me if two people are in the same social circle.” Again, the naive solution was to rebuild the whole graph for every query. I felt like I was stuck in a grind‑fest, repeating the same low‑level work while the real challenge—understanding the structure of the connections—remain. That frustration sparked a question: Is there a way to remember what we’ve already discovered about connectivity, so future queries are instant? The answer, as many of you have guessed, lives in a humble but mighty data structure called Union‑Find (also known as Disjoint Set Union, DSU). The Revelation (The Insight) At its core, Union‑Find is about two simple ideas : Each element starts in its own set – think of every person as a lone adventurer. When we learn that two elements belong together, we merge their sets – we call that a union . The magic isn’t just in merging; it’s in how we find the representative (or “root”) of a set later on. If we naïvely walked up a chain of parents every time, we could end up with O(n) per find—still a grind. Two optimizations turn this into near‑constant time: Union by rank (or size) – always attach the smaller tree under the root of the larger one. This keeps the overall tree shallow, guaranteeing that the height never exceeds log n. Path compression – during a find operation, we make every node we pass point directly to the root. It’s like handing every traveler a map that instantly shows the shortest route to the campfire, so next time they don’t need to trek

On 23 June 2026, AWS shipped Lambda MicroVMs : isolated VMs you launch, suspend, resume and terminate through an API, built explicitly for "workloads that execute user- or AI-generated code." Up to 16 vCPUs, 32 GB of memory, 8 hours of runtime, a dedicated HTTPS endpoint per VM. We've been shipping that product for a while. So has E2B, so has Modal. The interesting thing about the launch isn't that AWS caught up - it's that the biggest infrastructure company in the world looked at agent sandboxes, agreed with the design, and then shipped it with one European region and a price roughly three times ours per vCPU . That's the whole post. If you want an AWS Lambda MicroVMs alternative, "can anyone else do this" isn't the question - the isolation technology is literally the same on both sides. The questions are who operates the machine, where in Europe you can put it, and what it costs to leave running. AWS wins some of these outright, and we'll say where. The short verdict Pick Lambda MicroVMs if you're already deep in AWS, need more than 4 vCPUs or 8 GB in a single sandbox, or your agent has to reach private resources inside your VPC. The IAM integration and the size of the fleet are real advantages. Pick orkestr sandboxes if you're an EU company that wants execution, snapshots and logs inside one EU legal entity, you want to pay for CPU you actually burned rather than CPU you reserved, and your sandboxes are small and numerous rather than huge. The same primitive Both products run on Firecracker. AWS says so on the docs page - "Lambda MicroVMs deliver these core capabilities through Firecracker virtualization" - and so do we. Each sandbox is a hardware-isolated VM with its own kernel and rootfs, not a container sharing the host kernel. That distinction matters exactly when an LLM is writing shell commands you haven't read yet. The lifecycle is the same too. Create, exec, read and write files, pause, resume, terminate. Here's ours: from orkestr import Sandbox with Sand

the most useful habit i have hasn't changed in years: your reviewer should never be the first person to read your code. it doesn't matter if the change is one line. you read it first. the trick is reading it in the diff view, the same tool your reviewer will use, not your editor. the diff is a different lens. the debug print you forgot, the block you commented out, the variable you renamed everywhere but one place, all of it looks fine while you're writing and wrong sitting next to everything else that changed. this matters more now that an agent writes the code, not less. you're no longer the author who remembers every keystroke. you're the first reviewer of a change you mostly watched happen. so read it like a stranger wrote it. in a sense, one did. https://starikov.co/commit-lifecycle/ submitted by /u/iGotYourPistola [link] [留言]

The conditional operator ( ?: ) — The Only Ternary Operator is one of the most useful operators in Java. It lets you write simple decision-making logic in a single line, making your code cleaner and more concise. It's also a favorite topic in Java interviews because of its syntax, nesting behavior, and type compatibility rules. In this article, you'll learn: What the conditional operator is Why it's called a ternary operator Syntax and working Nested conditional operators Difference between ?: and if-else Practical examples Interview questions Memory tricks What is the Conditional Operator? The conditional operator is represented by: ? : It is the only ternary operator in Java . A ternary operator takes three operands , unlike: Operator Type Number of Operands Example Unary 1 ++x , !flag , ~5 Binary 2 a + b , a > b , a && b Ternary 3 (a > b) ? a : b Syntax result = ( condition ) ? valueIfTrue : valueIfFalse ; How It Works condition │ Is it true? / \ Yes No │ │ valueIfTrue valueIfFalse │ │ └────── Result ──────┘ If the condition is true , Java returns the value before the colon ( : ). If the condition is false , Java returns the value after the colon ( : ). Example 1 int x = ( 10 > 20 ) ? 30 : 40 ; System . out . println ( x ); Output 40 Step-by-Step Evaluate the condition: 10 > 20 ↓ false Since the condition is false, Java selects the value after : . 40 Therefore, x = 40 Example 2: Finding the Maximum int a = 10 ; int b = 20 ; int max = ( a > b ) ? a : b ; System . out . println ( max ); Output 20 This is one of the most common uses of the conditional operator. Example 3: Even or Odd int number = 7 ; String result = ( number % 2 == 0 ) ? "Even" : "Odd" ; System . out . println ( result ); Output Odd Example 4: Absolute Value int x = - 5 ; int absolute = ( x < 0 ) ? - x : x ; System . out . println ( absolute ); Output 5 Nested Conditional Operators One of the biggest advantages of the conditional operator is that it can be nested . Example int x = ( 10 > 20 ) ? 30 :

I Ran 10 AI Coding Models Through 5 Tasks: A Data Scientist's Take I'll be honest — I went into this expecting a clear winner. I came out with a scatter plot, three regressions, and a deeper appreciation for why "best" is the most dangerous word in machine learning. Over the past three weeks I've been grinding through prompts with ten different LLMs, all routed through the same endpoint, scoring every output on a 1–10 rubric that I tried very hard not to bias. The pricing data is pulled directly from the provider pages. The scores are mine. If you disagree with a score, you're probably right — n=1 per task per model is a laughably small sample size, and I say that as someone who publishes papers with bigger samples. But trends still emerged. Let me walk you through what I found. The Lineup Before I touch a single benchmark, here's the cast. I've grouped them by family so you can see the obvious concentration in the open-source Chinese ecosystem, which personally I find fascinating — three of the top five are DeepSeek or Qwen variants. # Model Provider Output $/M Category 1 DeepSeek V4 Flash DeepSeek $0.25 General (strong code) 2 DeepSeek Coder DeepSeek $0.25 Code-specialized 3 Qwen3-Coder-30B Qwen $0.35 Code-specialized 4 DeepSeek V4 Pro DeepSeek $0.78 Premium general 5 DeepSeek-R1 DeepSeek $2.50 Reasoning (code thinking) 6 Kimi K2.5 Moonshot $3.00 Premium general 7 GLM-5 Zhipu $1.92 Premium general 8 Qwen3-32B Qwen $0.28 General purpose 9 Hunyuan-Turbo Tencent $0.57 General purpose 10 Ga-Standard GA Routing $0.20 Smart routing One quick note on Ga-Standard — it's a routing layer that picks a backend model per request. So the score fluctuates. I averaged across runs. How I Tested Five prompts. Each one designed to probe a different cognitive layer: Function implementation — flatten a nested list recursively in Python Bug fix — chase down an async/await race condition in JavaScript Algorithm — Dijkstra's shortest path in TypeScript with proper types Code review — sec

submitted by /u/matijash [link] [留言]

submitted by /u/cachebags [link] [留言]

submitted by /u/IanisVasilev [link] [留言]