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GitHub热门项目 | Common recipes to run vLLM | Stars: 864 | 9 stars today | 语言: JavaScript

GitHub热门项目 | A Git-based CMS for Static Site Generators | Stars: 19,157 | 5 stars today | 语言: JavaScript

GitHub热门项目 | GitHub 汉化插件，GitHub 中文化界面。 (GitHub Translation To Chinese) | Stars: 27,319 | 63 stars today | 语言: JavaScript

GitHub热门项目 | Extensions for the Zed editor | Stars: 1,758 | 3 stars today | 语言: JavaScript

GitHub热门项目 | StyleX is the styling system for ambitious user interfaces. | Stars: 9,351 | 26 stars today | 语言: JavaScript

GitHub热门项目 | Official Python inference and LoRA trainer package for the LTX-2 audio–video generative model. | Stars: 7,389 | 47 stars today | 语言: Python

GitHub热门项目 | Free and Open Source Machine Translation API. Self-hosted, offline capable and easy to setup. | Stars: 14,817 | 179 stars today | 语言: Python

GitHub热门项目 | | Stars: 31,754 | 480 stars today | 语言: Python

GitHub热门项目 | Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process. | Stars: 44,719 | 144 stars today | 语言: Python

GitHub热门项目 | GLM-5: From Vibe Coding to Agentic Engineering | Stars: 3,890 | 286 stars today | 语言:

GitHub热门项目 | A lightweight, lightning-fast, in-process vector database | Stars: 11,033 | 435 stars today | 语言: C++

GitHub热门项目 | Kilo is the all-in-one agentic engineering platform. Build, ship, and iterate faster with the most popular open source coding agent. | Stars: 21,484 | 1,339 stars today | 语言: TypeScript

Adobe is updating its Firefly AI assistant with new chops, and adding it to Premiere, Illustrator, InDesign and Frame.io.

For the past few days, I've been trying to parse a PDF (scanned and text based) which has the same contents. PDF has nested tables Tables start at one page and end at another Currently I have been using (docling)[ https://docling-project.github.io ] to help me out with this text and convert it to the formats I require. I have a few limitations that I have a limit of 30s per page (2 minutes for the 4 paged pdf). And the biggest limitation is that I have to optimize it for the CPU. It has to run at a maximum of around 30s per page on the CPU . I have been trying a lot but docling is always failing at figuring out the table breaking in between two pages, and one single table and information that spills out from one page to another, is created into different tables by docling. How do I resolve this ? I am not being able to find a solution that works well within my given constraints better than docling currently. I've tried PyMuPDF, I've tried camelot as well. Camelot gave very nice results in converting to CSV, but it fails when nested tables come into the picture. I even tried to integrate camelot + docling into a hybrid pipeline but that also failed with my PDF with nested tables. Has anyone faced this problem before? Does anyone know of resources that could help me out with this problem? Any recommendations? Anything? :sob: submitted by /u/Kakarot_DB [link] [留言]

Ok so first, let me be honest. This post is partly me venting. I've been maintaining node-dependency-injection for about 9 years now. 300 stars on GitHub. Not exactly viral. And every time I look at InversifyJS or tsyringe climbing in popularity I think "yeah, but at what cost". So let me explain my problem with decorators. The coupling nobody talks about When you write this: @ Injectable () export class UserService { constructor (@ Inject ( MAILER_TOKEN ) private mailer : IMailer ) {} } Where does that @Injectable() live? In your DI framework. Which means your UserService — which is domain logic, business rules, the thing that should outlive any framework decision — now has a direct dependency on your IoC container library. Your domain knows about your infrastructure. That's the wrong direction. I know, I know. "It's just a decorator, it doesn't do anything". But it's still an import. It's still coupling. And if you ever want to swap the container, or move that service somewhere else, or just test it without spinning up the whole container — you now have to think about it. With NDI, your service is just a class: export class UserService { constructor ( private mailer : IMailer ) {} } That's it. No imports from my library. No decorators. No metadata. The service doesn't know it's being injected. The wiring lives completely outside — in a YAML file or in a bootstrap file. Your domain stays clean. "But Symfony does decorators and it's fine" Symfony doesn't use decorators in services actually. The DI config is external — YAML, XML, PHP config files. Your service is just a PHP class. That's literally what inspired NDI from the beginning. What NDI actually does Quick example. You have two payment providers and you want to inject the right one based on context: services : payment.stripe : class : ' payments/StripePayment' keyed : group : payment key : stripe default : true payment.paypal : class : ' payments/PaypalPayment' keyed : group : payment key : paypal checkout.ser

pgrac is an open attempt to rebuild Oracle RAC's core machinery (shared-everything storage, multiple active nodes all writing one database, a cluster-wide change number) on top of PostgreSQL 16. Build log #1 laid out the four problems that fight back. This one is about the problem that turns a node failure into silent data corruption, and the first, deliberately modest, layer pgrac ships against it. The failure mode In a shared-nothing cluster an evicted node is mostly harmless: it owns its own disks, so the cluster routes around it. In a shared-everything cluster the same event is dangerous, because every node writes the same storage. Picture the classic split: node 2 misses heartbeats, the cluster declares it dead and remasters its work elsewhere, but node 2 is not actually dead. It is frozen on a long GC pause, or its interconnect NIC flaked, and it is about to wake up and finish the write it started. Now two nodes believe they own the same blocks, and shared storage will accept both writes. That is not a crash. It is corruption you find three days later. Oracle RAC's answer is I/O fencing: before remastering a dead node's resources, you make certain it can no longer touch the storage, with STONITH, SCSI-3 persistent reservations, or a hardware watchdog. The node is fenced at a layer below its own software, because the whole point is that you cannot trust the dead node's software to behave. That hardware layer is real work, and it is not what pgrac built first. What it built first is the layer above it: an in-process cooperative write-fence, now default-ON. The rest of this is precise about what that does and does not buy you, because "we have fencing" is the kind of claim that is worth less than nothing if it is overstated. A fence needs an authority everyone can agree on You cannot fence on local opinion, because the whole problem is that the dead node disagrees about being dead. Authority has to live on durable, shared, quorum-backed storage. pgrac writes a sm

📝 Originally published in Japanese on Zenn. This is the English version. Canonical: https://zenn.dev/uya0526_design/articles/satellite3_metrics-rationale 📚 This is satellite article #3 in my "Read-Aloud Speed Meter dev log" series. For the whole picture, see the main article . Where This Sits The read-aloud speed meter converts speaking speed into an evaluation label like "slightly fast," and stagnation rate into one like "few." Those labels ultimately become the foundation for Claude Haiku's feedback. So — on what basis did I draw the thresholds (the dividing lines)? This article digs into that "basis." The short answer from my research: I couldn't find a paper that defines an academic threshold for "N characters/min = fast/slow." This is a record of how I drew the lines honestly once I'd learned there was no firm basis. More than the metric numbers themselves, I believe being transparent about why I chose those numbers is what makes an evaluation app trustworthy. 💡 I'm an ex-Java engineer learning TypeScript in public. This one is mostly about design decisions. Why Obsess Over the "Basis"? An evaluation app passes judgment on the user: "your reading is slightly fast." Once you're passing judgment, if you can't explain "why we can say that," it's just guesswork. This app in particular passes the labels straight to Claude Haiku to generate coaching. If the foundational label has an unclear basis, the feedback built on top of it is a castle on sand. So I decided to nail down the basis for the thresholds first. Two things to research: The judgment basis for speaking speed (characters/min) The judgment basis for stagnation rate (the proportion of silence) As it turned out, these two had completely different kinds of basis. Speed Thresholds: No Academic Threshold → Draw From General Rules of Thumb What I found For speaking speed, I first looked for academic backing. Here's what I found: Speaking speed has traditionally been measured against mora count, but prior researc

📝 Originally published in Japanese on Zenn. This is the English version. Canonical: https://zenn.dev/uya0526_design/articles/satellite2_haiku-coaching 📚 This is satellite article #2 in my "Read-Aloud Speed Meter dev log" series. For the whole picture, see the main article ; for the AmiVoice integration, see satellite #1 . Where This Sits This article covers the part of the read-aloud speed meter that hands the measured numbers to Claude Haiku to generate coaching as "one compliment + one improvement." Many articles that use generative AI just dump it on the model: "here's the recognized text, evaluate it nicely." This article is the opposite. Don't let the LLM do the math. All computation and rule-based decisions are settled in code, and Haiku only does the wording. I'll go through how I designed this "two-stage" split, and which swamps I sank into during implementation. Four things: Why "code does the math, Haiku only does the wording" (the role-split design) The finalized prompt, and the messages / system / cache_control implementation First-hand findings: the prompt cache that didn't work The moment real data slapped me with "written in the prompt ≠ obeyed" 💡 I'm an ex-Java engineer learning TypeScript in public, so I drop in Java comparisons. Why Not Let Haiku Do the "Math"? Up front: Haiku is more than enough for feedback generation — in fact, you can shape the task into something Haiku is good at. The key is the role split. I settle all the metric computation (speaking speed, stagnation rate, threshold decisions) entirely in code. So by the time it reaches Haiku, it's already settled facts like this (below is from running labelMetrics on a real measurement — reading the Heike sample during development; stagnationRate is a number for percentage display): { "pureSpeakingSpeed" : 322 , "pureSpeakingSpeedEvaluation" : "slightly fast" , "stagnationRate" : 0 , "stagnationRateEvaluation" : "few" } Haiku's only job is to translate these numbers and labels into warm, c

📝 Originally published in Japanese on Zenn. This is the English version. Canonical: https://zenn.dev/uya0526_design/articles/satellite1_amivoice-bff 📚 This is satellite article #1 in my "Read-Aloud Speed Meter dev log" series. For the whole picture, see the main article . Where This Sits In the read-aloud speed meter app, this article covers the part that sends browser-recorded audio to the AmiVoice API to get back recognized text plus timestamps. The theme is calling an external API without exposing your API key to the browser — in other words, implementing a BFF (Backend for Frontend). The main article only touched the highlights, so here I go down to a level you can reproduce yourself. Specifically, four things: Why you must not call AmiVoice directly from the browser (why a BFF is needed) AmiVoice's synchronous HTTP auth, parameters, and multipart order Why the browser's MediaRecorder output (WebM/Opus) passes through as-is Reshaping the raw JSON with a pure-function mapper and testing it with fixtures 💡 I'm an ex-Java engineer learning TypeScript in public, so I drop in comparisons to Java here and there. Why a BFF Is Needed Using the AmiVoice API requires an API key. And that key must never appear in browser-side code. Frontend JavaScript is fully inspectable by the user, so writing the key there leaks it instantly. So I insert a relay that holds the key. [Browser] ──audio Blob──▶ [Next.js API Route (BFF / holds key)] ──▶ [AmiVoice API] record / display audio field reshape into u / d / a speech → text The browser only calls my own API Route ( /api/recognize ), and that Route attaches the key server-side and forwards to AmiVoice. The key is just read from process.env and never ends up in the bundle shipped to the browser. ☕ Java comparison: This is the same as a Spring @RestController reading an external API key from application.yml (env vars) and relaying without showing it to the client. Think "a thin Servlet that hides the secret and relays an external API."

A few months ago I started building small tools as single HTML files - no npm, no React, no backend. Just one file that opens in a browser and works offline. I built 4 real products this way: DarkenAmber IT Tools - 17+ developer tools in 194KB ZeroOffice - PDF, image, AI tools in one file PrivacyKit - Photo privacy tools, no upload required ElectroKit - Electrical calculator + cost estimates for CIS market Every single one: one .html file. Works offline. Opens instantly. No server. The problem with AI coding assistants Every time I asked Claude or Copilot to build something simple, I got: A React project with src/ folder package.json with 12 dependencies webpack config TypeScript setup ...before writing a single line of actual logic. I kept manually correcting it. "No, one file. No npm. Vanilla JS." Then I realized - I should just teach it once and reuse that knowledge. What is a Claude Skill? A skill is a Markdown file with YAML frontmatter that changes how Claude thinks for a specific context. It is not a prompt. It is not a system message. It is a reusable set of rules that shapes how Claude reasons, what it prioritizes, and what it avoids. yaml--- name: single-file-app description: "Build complete web tools as a single HTML file - vanilla JS, inline CSS, localStorage, offline-first." tags: html vanilla-js offline version: 1.2 --- The two skills I built single-file-app Teaches Claude to build complete web tools in one HTML file. What changes: No React, no npm, no build tools unless truly justified Vanilla JS first, always localStorage for data persistence Dark/light theme with system preference detection Accessibility built in (labels, aria, keyboard nav) XSS prevention for user input Export/import for user data Anti-patterns it prevents: ❌ "Let me set up a React project" ❌ Creating src/ folder for a simple tool ❌ Suggesting npm install for a calculator ✅ "Here is your complete HTML file" ship-it Teaches Claude to bias toward shipping over planning for early-stag