标签：#t

GitHub热门项目 | The free and privacy-friendly screen recorder with no limits 🎥 | Stars: 18,664 | 86 stars this week | 语言: JavaScript

GitHub热门项目 | Node.js & React bindings for Zed’s GPUI. Build memory efficient native apps with React and no Electron | Stars: 1,631 | 76 stars today | 语言: Rust

GitHub热门项目 | A free + OSS logo generator powered by Flux on Together AI | Stars: 8,593 | 111 stars today | 语言: TypeScript

GitHub热门项目 | Standalone iOS app to spoof GPS location without jailbreak. Includes Shadowrocket/Surge/Loon/QX/Stash module. | Stars: 3,856 | 20 stars today | 语言: JavaScript

GitHub热门项目 | Claude-first paid-media operations skill for Claude Code across 12 ad platforms (Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, X): source-grounded audits, deterministic scoring, versioned JSON reports, and capability-gated account changes. | Stars: 8,948 | 94 stars today | 语言: Python

GitHub热门项目 | Production-grade MCP server giving Claude 27 security intelligence tools across 21 APIs — CVE lookup, EPSS scoring, CISA KEV, MITRE ATT&CK, Shodan, VirusTotal, and more. | Stars: 1,458 | 46 stars today | 语言: Python

GitHub热门项目 | 本项目采用 CC BY-NC-SA 协议，禁止任何商业化行为，任何衍生项目必须保留本项目地址并以相同协议开源 | Stars: 9,585 | 171 stars today | 语言: TypeScript

Article 1 of 3 · vLLM Internals This English edition is adapted from the published Chinese article on Zhihu . It preserves the source-code references, experimental boundaries, and reproducible artifacts while adapting the structure for an international engineering audience. Series: Part 1 · Request lifecycle · Part 2 · CUDA kernels and paged attention · Part 3 · FlashAttention from PyTorch to Triton This article follows one offline inference request through vLLM V1: from LLM.generate() and inter-process communication to scheduling, input flattening, GPU model execution, paged KV-cache access, sampling, and resource reclamation. The goal is to answer one concrete question: what happens behind the call to llm.generate() before the completed result reaches the caller? The discussion assumes familiarity with Transformer inference, including prefill, decode, KV caching, and autoregressive generation. It focuses on how those concepts appear in vLLM source code rather than reteaching the model architecture. Version scope. The source references were verified against vLLM 0.22.0; this edition was checked on September 3, 2026. vLLM evolves quickly, so some filenames and call boundaries will move. The long-lived ideas—continuous batching, token budgets, paged KV allocation, and the separation between scheduling and execution—are the real subject of the article. Why Read the Source Instead of Another API Guide? Many introductions stop at the useful analogy that PagedAttention manages the KV cache much like virtual memory manages pages. The analogy does not tell us how a request is admitted, how variable-length requests become a flat token batch, or what the page table looks like at the kernel boundary. The answers are in the source. This article follows vLLM 0.22's V1 execution path from the public entry point to the CUDA boundary. It is a source-code walkthrough, not an API tutorial. Start with a System Map Start with the process boundary and the engine loop. In vLLM V1, Engin

One skeleton, many screens argued that admin screens should be declared as typed data rather than coded, and it ended by claiming the idea was independent of the stack: draw the boundary as a one-way dependency — domains depend inward on a framework that knows nothing about them — and validate it with a zero-diff refactor of a screen you already trust. That was React and TypeScript. This is the same claim re-run in Rust, where a descriptor can be a compile-time constant and a template is a macro. Because the first result is already published, the second stack is a replication with a control rather than a fresh opinion — which is rare enough to be worth doing properly. Companion to Topcoat and the shrinking cost of full-stack Rust . That post was written from the announcement and promised a follow-up reporting where the rough edges actually show. This is it, from the pilot that followed: a small admin panel built on Topcoat 0.6.2 and Toasty 0.10.0, and the four questions that post committed to answering. The pilot is open source — a clean clone runs both screens and the test that decides the argument. That phrase, a compile-time constant , is where the title comes from, so it is worth saying now what it buys and why I wanted it. A TypeScript descriptor is an array of objects assembled when the module loads. A Rust one can be more than that: &'static , Copy , allocated never, fully checked before the program starts. Going in, that looked to me like the same idea in a stricter form — if declaring a screen as data is good, then declaring it as data the compiler can see through and verify must be better still. I treated that property as the thing worth protecting, and the pilot was partly a test of whether it could be. The stack is deliberately a young one. Topcoat is six weeks old: Tokio's team announced it on 22 July 2026, the pilot pins 0.6.2, and the project still expects breaking changes. It is not the only full-stack Rust framework — Leptos and Dioxus have been at

I thought embedding a web UI into a native desktop application would be the easy part. After all... macOS has WebKit. Linux has GTK WebKit. Windows has WebView2. One API per platform, smaller installers, native look & feel. Sounds perfect. Then reality arrived. macOS 🍎 Honestly, this was the easiest platform. System WebKit is there. It behaves consistently. No additional runtime. No installer surprises. Exactly what you'd expect from a platform component. 10/10 Linux 🐧 Things became... more interesting. GTK WebKit works, but suddenly packaging starts to matter. An AppImage built on one distribution may refuse to start on another because some required WebKitGTK library isn't available. Your application itself is perfectly fine. The user's system just doesn't happen to provide exactly the version your build expects. You quickly discover that "works on my machine" has many regional dialects. 7/10 Windows 🪟 This one surprised me the most. Unlike macOS, the web view isn't really just "there." Using WebView2 means depending on the Edge WebView runtime. If the runtime isn't installed, congratulations—you now need another installer. So your installer may install something whose purpose is to allow your application to display HTML. Not exactly the dependency story I was hoping for. 2/10 Meeting in the middle At some point I asked myself: Why am I spending time debugging operating-system packaging instead of building my application? So I tried CEF (Chromium Embedded Framework). Yes... The application becomes larger. Quite a bit larger. But in exchange: • Same rendering engine everywhere. • Same JavaScript engine everywhere. • Same debugging experience. • Same HTML/CSS behavior. • No Linux WebKit dependency lottery. • No separate WebView runtime installation on Windows. • One code path across all desktop platforms. Ironically, shipping your own browser turned out to be simpler than relying on the browser already "provided" by the operating system. It's one of those engineering

TL;DR Every mobile app is forms underneath: signup, checkout, onboarding, KYC. The UI is an afternoon; the invisible stack (keyboard geometry, validation, migrations, RLS, typed writes) is where weeks disappear. Most AI form builders generate a pretty <TextInput> and stop. The useful pattern is generating the whole pipeline from one prompt: SQL migration, RLS policies, regenerated types, controlled state, visible errors, and a real Supabase insert. Five silent-failure patterns ship broken forms constantly: Alert.alert on web, unchecked { error } , RLS with no policy, stale generated types, and guard clauses that swallow crashes. Iterate additively (point-and-edit, follow-up prompts) instead of regenerating. Full regenerations lose per-field polish. Why "just add a form" is never just a form Ask any React Native developer what's slow about mobile development and forms will be near the top of the list. Not for the reasons the UI suggests. The visible part (labels, inputs, a submit button) is an afternoon. The invisible part is where the calendar goes: Keyboard geometry. iOS pushes content up; Android resizes; the submit button ends up under the keyboard on one platform and floats wrong on the other. Every screen with a TextInput needs a KeyboardAvoidingView with the correct behavior prop and a ScrollView with keyboardShouldPersistTaps="handled" , or it ships broken. Controlled state. Every field wants a useState slice, an onChangeText handler, a value prop, and a clean way to reset. Formik and react-hook-form abstract this, but they add a dependency graph, and neither handles the mobile-specific ergonomics. Validation with visible errors. A validator that fails silently is worse than none. Errors have to render on the correct field, at the correct time. The database half. A form that doesn't persist is a demo. Persisting means a table, columns of the right type, RLS policies (or every query returns zero rows with no error), a typed client, and error handling on the mu

The idea of whether machines will dominate us is generally treated as a new one in current discussions on AI risk, since in 1863 Samuel Butler published a letter titled 'Darwin Among the Machines' which made an argument closely resembling the one in today's debate. He stated that the real danger was not that machines would gain control of power, but that people would find machines so useful that it would become practically impossible to get rid of them, so that by that stage the question of who was actually in charge would have already been answered — no matter who seemed to be in control. He said that a society which, in principle, could not destroy its machines had already "created a race of beings towards whom it is beyond our power to destroy" and had thus become "acquiescent in our bondage." This is a quite different claim from the one that says 'superintelligence takes over'; it's similar to the sorts of arguments currently being made regarding algorithmic dependence, attention economy lock-in, and infrastructure that is too deeply embedded to be undone — arguments that can be applied to any technology that is sufficiently indispensable, not just to intelligent machines. The full letter, together with the historical background (it is also the immediate source of the "Butlerian Jihad" in Dune, a fact that most people are unaware of) can be found here: https://romaricjannel.substack.com/p/the-rise-of-mechanical-life I'm wondering if people here consider "acquiescence through indispensability" to be a real and separate risk category or whether it's just a rewording of dependence anxiety that appears each time a major technology is introduced. submitted by /u/Smart_Fly_5783 [link] [留言]

The reason I started this project is to learn more about C++, as we all know the best way of learning a programming language is to do projects, DO PROJECTS!! I used Austin Morlan's website to learn how to build it, it's quite good ( https://austinmorlan.com/posts/chip8_emulator/ ). I made some tweaks which I found to be better for me. I will not be posting the whole codebase here, it's too long. What I will be sharing are snippets of code, what I learned from it, and what I found amazing or funny (projects can have their own jokes). What is an Emulator ? An emulator is just hardware or software that lets the host system replicate conditions like the CPU, memory systems, clock cycles, etc., of the guest system whose functions/behaviour they want to simulate. It helps to bridge the architectural gap by making sure that each instruction code can be executed. In the case of Chip8, we have to simulate the hardware restrictions of the 1970s: a 64x32 screen, a 16-key keypad, timers, and a buzz sound. If you google Chip8, you will see that it is not actually a real physical device. It is a virtual machine/interpreter where you can interpret games (that was the intended purpose), like Pong or Space Invaders. It was a virtual language created in 1977 AD for a computer called COSMAC VIP. Building in C++ I wanted to get familiar with C++, that's why I am here. Building a Chip8 emulator in C++. Well, I learned you need headers, classes to define objects, the standard library, built-in objects like std::ifstream, std::streampos, and so on. I will explain some parts that left a mark in my memory. Header Files Well, before C++, I had only used a header file for an FPGA (Tang Nano 9K) project which I did. It made the LED blink in intervals. But now I understand more, such as how we create a blueprint of the class which we will be using to create objects in the future. Two modes: Public: The attributes and methods of the said class can be accessed by other functions or parts of the p

You open a hotel page. It looks like one product: a search grid, a featured stay, local highlights, reviews, a sticky trip summary. Under the hood it is eight HTTP servers and three UI runtimes . That is the experiment behind HarborStay , a demo booking app I built to answer a stubborn question: Can independent teams ship independent UI, in independent frameworks, and still give the browser a single, paint-ready HTML page? The punchline: yes — if the shell never imports a component. It only fetches HTML. The one rule The browser never talks to a fragment. It talks to the composer on port 3100 . The composer owns routes, layout, and the booking flow. Everything else is a fragment server that returns a chunk of HTML. flowchart LR Browser["Browser"] --> Composer["Composer :3100"] subgraph fragments["Fragment servers"] Nav["Navigation Marko :3101"] Search["Hotel search Marko :3102"] Details["Hotel details Marko :3103"] Reviews["Reviews Marko :3104"] Recs["Recommendations Marko :3105"] Highlights["Local highlights React :3106"] Disco["Experiences discovery Riot :3107"] Itin["Experiences itinerary Riot :3108"] end Composer --> Nav Composer --> Search Composer --> Details Composer --> Reviews Composer --> Recs Composer --> Highlights Composer --> Disco Composer --> Itin Composer --> CDN["CDN :3200"] This is the opposite of the usual microfrontend story (Module Federation, shared React, a host that import() s widgets). HarborStay is HTML composition . The shell does not know whether a fragment was rendered by Marko, React, or a hand-rolled Riot string. It only knows a URL. That one constraint buys a lot: Fragment teams can pick a runtime without asking the shell. A fragment outage becomes a fallback box, not a blank page. You can deploy search without redeploying reviews. It also forces honesty. If two fragments need to share a Redux store, the architecture is already leaking. What the user actually sees HarborStay models a small premium catalog: Harbor View Lodge in Lisbon

Is the spec optional if the model is free? I keep seeing that assumption in pull requests. A free coding model shows up in the workflow. A free remote server shows up beside it. Then people drop the checklist without a fight. Why write a failing test for a cheap loop? Just rerun the agent until something compiles, right? That mental model is quietly expensive for teams. Free compute does not purchase a behavioral contract. It only purchases another place to be wrong. This FAQ names five claims I still hear. Each entry has the claim, the evidence, and a corrected model. Then I attach a small artifact you can run. None of this needs paid quotas I will not invent. Who this is for You already ship product patches with coding agents. You also distrust a fluent chat transcript from agents. You want a workflow that survives a free box vanishing. Skip this path if you need a hard SLA. Skip it if the box will hold production secrets. Skip it if "works on the agent host" is the release bar. The setup I actually mean I am talking about a narrow, boring stack. You can call a coding model without a purchase. You can use a remote server without a purchase. I use MonkeyCode when I want that pairing in one place. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I will not name models, hardware, or duration. Those details move, and the myths do not. The method still works on a laptop you already own. The free box is optional in every step below. The spec is not optional in any step. Myth 1: Free retries replace a failing test The claim It's free, so I can loop until the tree compiles. The evidence Compilation is not behavior, and it never was. A green compiler can still ship the wrong function. Retrying a prompt does not freeze an oracle for later. Did that extra retry actually get cheaper for you? The sample got cheaper, but no assertion appeared. The corrected model The failing test is the spec you keep. The agent is a patch generator you distrust. F

macOS and Windows both have their own advantages. If you're planning to move to Windows, make sure you know what you're giving up.

Originally published on lockmargin.com Last weekend I asked myself an uncomfortable question: could someone post my app and a valid license key on a torrent, and anyone use it for free? The answer is yes. That question sat with me all weekend. I couldn't build anything else until I mapped it, so this is the map. Context: I'm building LockMargin, a local-first invoicing app for freelancers. $49 once. No account. No telemetry. No phone-home of any kind. The license is a signed token the app verifies locally. The threat model First, I narrowed the problem. I'm not trying to stop professional cracking crews or commercial resellers. I'm trying to survive the casual kind: a key shared in a chat, an installer plus key on a torrent, "send me your key" between friends. Then I drew a hard line. If a mechanism breaks on a new laptop, a wiped disk, a VM - it's out. If it needs a server - out. If it needs an account - out. The app has to keep working even if my company disappears. Every option on my list had to pass that test. The honest ceiling Here is the fact most DRM marketing hides: if an application can locally answer "this token is valid," then anyone holding the same token gets the same answer. Copying the authorization is always possible when verification is local. That is not an implementation flaw. It is the architecture. Two different things get conflated here. Authenticity: did I publish this binary? Code signing answers that. Authorization: does this person own a license? A signed local token answers that. Neither prevents a buyer from handing a valid token to somebody else. And two different threats. Sharing is when a buyer gives their key to another person - the contractual and social layers help with that. Cloning is when the installer and a valid key travel together. That is where the hard limit appears: if a real installer plus a real key spreads, an offline application cannot tell the new user from the owner. I say that plainly, because pretending otherwise i

In two weeks, I built Retro Dynamics Agent, an app that generates retrospective activities for teams, facilitates them on a real-time collaborative board, and turns the outcomes into Jira or Azure DevOps tickets. I built it working with an AI coding agent, Claude Code, throughout almost the entire process: design, implementation, production debugging, and documentation. I do not want to tell another “I used AI and it wrote the code for me” story. We have heard that one enough. What I found more interesting were the parts of the project where there was no obvious answer in a tutorial, and how the work was divided in those situations. I defined the constraints and made the underlying decisions. The agent proposed concrete technical solutions and implemented them. Then the responsibility for verifying that everything actually worked, not just that it compiled, came back to me. Here are three examples from the project. 1.- Connecting to Jira without server-side sessions or frontend memory I wanted any team to be able to connect its own Jira account through OAuth, instead of relying on a global token that only I could configure. The problem was that my application runs entirely on serverless functions. Nothing stays in memory between requests, and the frontend does not maintain its own state either. No localStorage. No router. An OAuth login means leaving the application, authenticating with Atlassian, and then coming back. But coming back to what, if nothing remembers which screen you were on? Before touching the code, I asked the agent to create a complete implementation plan, including the files that would need to change, the design decisions, and the scope. I reviewed that plan as if it were a pull request from another developer. I made decisions such as: For now, only Jira would use OAuth. Azure DevOps would keep its manual token flow because setting up OAuth there is considerably more involved. Tokens would be encrypted before being stored in the database, never sa

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How much hydrogen awaits us underground? A flurry of exploration efforts is searching for underground stores of hydrogen gas, which could provide a valuable source of zero-carbon fuel. The hunt has…

Dark energy might be getting weaker. Scientists are wondering if interactions with dark matter in a “dark dimension” may be responsible.