Hey everyone, I’m the author of prose, an MIT-licensed SDK/engine for building reading apps: https://prose-reader.com/ It’s a TypeScript, web-based reader engine that can be used to build anything from a simple image/archive-based comic or manga reader to a more complex EPUB reader with navigation, search, annotations, gestures, zoom, pagination, and custom reading behavior. It supports EPUB and archive-style formats like ZIP/CBZ/CBR, and can run in the browser or inside mobile apps through WebView/React Native. One of the main ideas behind Prose is its enhancer system , which works like a plugin system. Enhancers let you add or modify reader capabilities without changing the core engine, so apps can build their own UX and features on top. The project has been around for about 6 years, but I haven’t promoted it much. It’s also used in Oboku , an open-source reading app I’ve been building: https://oboku.me/ AI disclaimer: I have about 15 years of experience as a developer, and I use AI as a tool to help me iterate faster and improve quality, not as “vibe coding” or a replacement for my own judgment. It has helped me deal with many small problems and move the project forward faster. I’m looking for developers who might be interested in trying it, giving ideas, or providing feedback to help improve it. GitHub: https://github.com/mbret/prose-reader Docs: https://doc.prose-reader.com/ Demo: https://demo.prose-reader.com/ Discord: https://discord.com/invite/dffDEgwNc5 Would love to hear what you think. submitted by /u/yabai90 [link] [留言]

Why is it that most organization are in a hurry to integrate AI agents in process that don't really need the advancement, is that they don't want to be left behind or they are just following the hype submitted by /u/Quiet-Brilliant-1455 [link] [留言]

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

Go from 4K resolution for Crimson Desert to 680Hz for Counter-Strike 2.

submitted by /u/EUobs [link] [留言]

Link: https://nova3d.xyz It's free but you'll need to bring your own API Key (BYOK). For open source enthusiasts, check out the github repo at: https://github.com/RareSense/Nova3D This project is a big deal if you see limitations of current AI 3D generators (like Meshy or Tripo). They produce solid, monolithic 3D objects that look good but are practically useless, because: - Want to change the arm of a robot you generated? Regenerate the entire asset. - Want to edit something manually? The whole thing collapses because it's not actually structured. - Want to rig or animate it for a game? Can't easily do that, because it’s a dead, monolithic blob instead of a functional, modular asset. This is a flutter front-end with a python, fastAPI and headless blender back-end. Would love to get community feedback! submitted by /u/mhb-11 [link] [留言]

Arm has open-sourced Metis, an agentic AI security framework designed to autonomously uncover complex software vulnerabilities. Unlike traditional pattern-based tools, Metis applies semantic reasoning to analyze cross-component dependencies and provides clear, natural language explanations for its findings. By Sergio De Simone

The same issues keep showing up — the kind that look fine but quietly cost signups or search ranking. Sharing in case it helps your own site: Forms with placeholder text instead of real labels. "Email"/"Password" shown inside the box looks labeled, but it isn't — the text vanishes when you type, and screen readers + browser autofill don't read it. Quiet signup killer. Heading levels that skip. Jumping from an H1 straight to an H3 with no H2. Invisible to the eye (it's in the markup), but it confuses screen readers and how Google reads your page. A main CTA that doesn't actually go anywhere. More common than you'd think — the button looks fine but the click loops or dead-ends. Worth literally clicking your own "Get started" and following it through. Missing title / meta description. Google then writes its own (usually worse) snippet for you, costing click-throughs. No social preview tags (Open Graph). Share your link on X or Reddit and it shows a blank, ugly preview instead of a title + image. Founders share links constantly, so this one stings. Most take 5 minutes to fix once you know they're there. Full disclosure: I built a tool that checks for this ([Sweep]( http://www.usesweep.app) ) — happy to run yours and send the report if you drop a URL below. But honestly, just checking these five by hand is worth an afternoon. submitted by /u/Hairy_Wash_9086 [link] [留言]

Hi! I want to share a project that I work for a while. It started from idea to get rid off manual copying data from game design documents to game engine. Here you can define your game objects, their props, relations and everything will be stored in structural JSON format that can be read by Unity, Godot, Unreal and other engines. What we have now? construct **wiki-like documents **using a block editor and template system (markdown is supported too) design dialogues of your game in special graph editor create maps and prototype levels on canvas store and manage database of game objects use created objects inside engine directly or export data to customizable data formats (arbitrary JSON, CSV) Made it free and open source. Please try (have Windows and Mac builds) and give your feedback Source code: https://github.com/ImStocker/ims-creators Itch.io: https://nordth.itch.io/imsc-desktop

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback for improving the project. You might not have noticed, but Chrome quietly started shipping a local AI model called Gemini Nano bundled right into the browser. No API keys. No cloud round-trips. No per-token cost. It just runs on your machine. The interface to talk to it is called the Prompt API , and it landed in Chrome 138. I spent some time going through the full API surface and built a playground that lets you experiment with every feature session management, streaming, structured output, multimodal input, response prefixing, and more in one page. This post walks you through all of it. Why does this matter? On-device AI flips the usual tradeoffs: Free at runtime — the model runs on the user's hardware, not your servers Private by default — no data leaves the device once the model is downloaded Works offline — after the initial download, no network required Low latency — no round-trip to a data centre The catch is that Gemini Nano is a small model. It's great for classification, summarization, Q&A on focused content, and structured extraction. It won't replace GPT-4 for complex reasoning. Think of it as a smart, free, always-available layer you can add on top of your existing product. Enabling the API The Prompt API isn't on by default in all Chrome builds. Enable two flags: Step 1 — Go to chrome://flags/#optimization-guide-on-device-model and set it to Enabled BypassPerfRequirement . Step 2 — Go to chrome://flags/#prompt-api-for-gemini-nano and enable both the base API and the multimodal option. Relaunch Chrome. Then visit chrome://on-device-internals to check the model download status. First use will trigger a download — Gemini Nano is a few gigabytes. The Playground I put together a single-file HTML playground that covers the entire API

Inspired by a similar project called GenosDB and Cloudflare’s initiative to rebuild Next.js, I decided to rebuild GunDB with a modern coding style, incorporating improvements and addressing shortcomings in the original technology. I used the OpenCode tool with the Big Pickle model to rewrite the project in a new graph database called Garfo (the Portuguese word for “fork”), and I was impressed with the results and its practical applications. In this article, I’ll explain the technology and its improvements over GunDB. Introduction Garfo is a modern, browser-first fork of GUN.js — the decentralized, offline-first graph database. A fork of the original project that keeps the familiar GUN graph API while bringing meaningful improvements to the modern JavaScript ecosystem. Why a Fork? GUN.js is a revolutionary technology — a graph database that syncs in real time, works peer-to-peer, resolves conflicts automatically, and runs in the browser. However, the JavaScript ecosystem has evolved. TypeScript has become the standard, ES modules are the norm, and new transport layers like Nostr have emerged as promising decentralized protocols. Garfo was born to fill these gaps: a GUN rewritten with modern typing, designed with the browser as a first-class citizen, and with native support for the Nostr protocol. Key Features Familiar Graph API If you've used GUN before, you'll feel right at home. Garfo exposes the same chainable API: import Garfo from ' garfo ' ; const db = new Garfo ({ localStorage : true }); db . get ( ' users ' ). get ( ' alice ' ). put ({ name : ' Alice ' , status : ' online ' }); db . get ( ' users ' ). get ( ' alice ' ). on ( profile => { console . log ( ' Update: ' , profile ); }); All the classic methods are there: get() , put() , set() , on() , once() , map() . Optional Nostr Transport This is one of the most exciting additions. Garfo can use Nostr relays as a transport layer, allowing peers to exchange graph messages through public or private relays: const

1. Stale closure & primitive capture What is the output of the following code? function createIncrement () { let count = 0 ; const message = `Count is ${ count } ` ; function increment () { count ++ ; } function log () { console . log ( message ); } return { increment , log }; } const { increment , log } = createIncrement (); increment (); increment (); log (); Test your understanding of closures, lexical scope, and primitive value capture. ✅ Output Count is 0 🧠 Explanation This is a classic stale closure trap — but not in the way most developers expect. Step-by-step execution: createIncrement() is invoked → new lexical environment created: count = 0 (mutable binding) message = "Count is 0" (primitive string, interpolated immediately at assignment) Inner functions increment and log are defined. Both close over the same lexical environment. increment() is called twice: count mutates: 0 → 1 → 2 ✓ This works as expected. log() is called: It references the variable message message still holds the original string value "Count is 0" The template literal was evaluated once, at the moment of assignment — not re-evaluated when log() runs. 🔑 Core Concept > Closures capture variables , not expressions . > But if a variable holds a primitive value (string, number, boolean), that value is fixed at assignment time. message is not a live reference to count . It is a snapshot . 🛠 How to fix it (if dynamic output is desired) Re-evaluate the template literal inside log() : function log () { console . log ( `Count is ${ count } ` ); } 🎯 What this question tests Concept Why it matters Template literal evaluation timing They run at assignment, not at access Primitive vs reference types Primitives are copied by value; objects/arrays are referenced Closure capture semantics Closures close over bindings, but the value of a primitive is immutable once assigned Mental model of "live" variables Not all variables in a closure are "live views" — only the bindings themselves are 2. JavaScript co

I go over some basics and implement a simple feature live on the Wookash Podcast. It might be interesting to those who have tried File Pilot and wondered why its UI is so fast and responsive. I do some actual UI programming. Not much, since we were short on time, but enough to give you a glimpse into how it works. submitted by /u/vkrajacic89 [link] [留言]

Large language models are good at sounding structured. That is not the same as being structured. Ask an AI assistant to "use first principles" and it may produce a confident answer with the phrase "first principles" near the top. Ask it to "red-team this plan" and it may list generic risks. Ask it to "apply OODA" and it may give you four headings without doing the hard part: orienting against assumptions, constraints, and evidence. That failure mode is subtle because the answer looks responsible. It has the right vocabulary. It has the right shape. But the method did not actually control the analysis. I built methodology-toolkit to target that gap. The goal is not to add more clever prompts to Claude Code. The goal is to add a small layer of discipline around non-trivial decisions: classify the problem, choose methods that fit, apply those methods explicitly, verify load-bearing claims, and stress-test plans before they harden into action. Repository: https://github.com/gagharutyunyan1993/methodology-toolkit The Problem: Methodology Theater Methodologies are useful because they constrain attention. First Principles asks you to strip assumptions and rebuild from base facts. ACH asks you to compare competing hypotheses by disconfirming evidence, not by collecting confirmations for your favorite answer. OODA asks you to separate raw observation from orientation, where bias and context do most of the work. Pre-mortem asks you to imagine the plan has already failed so optimism does not screen out obvious risks. When an AI assistant merely names those methods, you get the cost without the benefit. The answer becomes longer, more formal, and more convincing, but not necessarily more correct. That is worse than a short intuitive answer because the structure creates false confidence. methodology-toolkit treats that as the core anti-pattern: If a method is named, its steps must be walked. Not hinted at. Not summarized. Applied. Methodology theater: right vocabulary, no method