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Forms are everywhere in mobile apps - authentication flows, data entry, support requests, onboarding... If your app has a login screen, a form is likely the first thing a new user interacts with. That makes accessibility here not just a nice-to-have, but a first impression. The problem is that forms are consistently one of the most broken areas for assistive technology users. Missing labels, keyboard traps, silent validation errors, focus going nowhere after submission - these are issues that make an app unusable for a significant portion of your users. This guide is a complete reference for building forms that work for everyone in React Native, whether users are navigating with their fingers, an external keyboard, a screen reader or voice input. Code examples throughout show both what to do and why . A fully working demo repo is available to fork and test on a real device - check it out at rn-accessible-form-demo . Labels Every form field needs a label, whether a text input, checkbox or radio/submit buttons. No exceptions. Don't rely on placeholders Placeholder text disappears the moment a user starts typing. Screen readers will read it initially, but once it's gone, there's no way for them to recall what the field was for without clearing their input. Placeholders are useful as hints, not as labels. Use a visual label + accessibilityLabel To avoid screen readers announcing the same information twice (once for the visual label, once for the input), hide the visual label from assistive technology and put the full label on the input itself. < Text importantForAccessibility = "no" accessibilityElementsHidden > Email address* </ Text > < TextInput accessibilityLabel = "Email address, required" /> importantForAccessibility="no" handles Android, and accessibilityElementsHidden handles iOS. Together they tell assistive technology to skip the visual label entirely - the accessibilityLabel on the TextInput is the single source of truth for screen readers. Required fields De

Most web scraping projects are not unique snowflakes. Track competitor prices. Enrich a list of leads. Audit a site for SEO. Pull training data for a model. It is the same handful of recipes, over and over. A web scraping template is one of those recipes, pre-wired: a ready-to-use JSON config that chains the right tools in the right order, so you copy it, point it at your targets, and run. CrawlForge ships 24 of them in the templates gallery . This guide is about using them well — not just copy-paste, but read, adapt, and cost them out before you scale. TL;DR: A CrawlForge template is a copy-paste JSON config that chains multiple MCP tools into one workflow (price monitoring, lead enrichment, SEO audits, market research, AI training data). There are 24 across 9 categories, each costing 3–19 credits per run. Run them from Claude/Cursor, the crawlforge CLI, or the REST API. Free tier = 1,000 credits, no credit card. Table of Contents What Is a Web Scraping Template? Templates Gallery vs the scrape_template Tool How to Use a Template the Right Way 8 Templates Worth Copying First The Other 16 Templates Customizing or Building Your Own FAQ What Is a Web Scraping Template? A template is a saved configuration that orchestrates two or three CrawlForge tools into one workflow with a business outcome attached. Instead of wiring search_web then scrape_structured then analyze_content yourself — and guessing every parameter — you copy a config that already does it. Each template in the gallery carries: A category — E-commerce, Research, Data Collection, Monitoring, AI & LLM, Sales, SEO, Content, or Advanced Scraping (nine in total). A difficulty — beginner, intermediate, or advanced. The tool chain it runs and a fixed credit cost per run (3–19 credits). A copy-paste JSON config with sensible default parameters. You run that config from any MCP client (Claude, Cursor, Windsurf), the crawlforge CLI, or the REST API. Same config, same shape of result. Templates Gallery vs the scrap

This is a submission for the June Solstice Game Jam What I Built Solstice Cipher: Enigma of the Longest Day is a browser-based puzzle game built around the Caesar cipher — the same substitution cipher technique used in ancient cryptography. On the theme of the June Solstice, I tied the longest day of the year to an Enigma Machine-inspired challenge: decode encrypted messages before time runs out, with the difficulty scaling as the sun climbs higher. The game features a real-time animated sky that shifts through dawn, noon, and dusk to reflect the solstice theme. Players are given a cipher shift key and must decode encrypted phrases by working through the Caesar cipher manually or by reasoning out the pattern — no brute-force tools allowed in-game. This connects to the June Solstice theme because the game is literally set on the longest day: the puzzles grow harder as the day progresses, and the sky animation mirrors real solstice light from sunrise to sunset. Video Demo Play it here: gtxpoffic-developer.github.io Code GTXPOFFIC-developer / Solstice-Chiper-Enigma-of-the-Longest-Day This is a Enigma based June Solstice game feel free to include your own code or tinker this project just mention the orignal developers name pls Solstice Cipher — Enigma of the Longest Day A browser-based Enigma machine puzzle game set on the June solstice. Decode (or encode) encrypted transmissions before the daylight runs out. Built By Sudipto — Original developer Feel free to fork, tinker, and include this in your own projects. Just mention the original developer's name. How to Play Objective Configure the Enigma machine correctly to decode each level's ciphertext (or encode the plaintext) before the sun sets. Each wrong guess costs 45 minutes of daylight; correct guesses pause the timer for 30 seconds. Controls Control What it does Rotor dropdowns Select which 3 rotors (I–V) are used ▲ / ▼ buttons Adjust each rotor's starting position Plugboard Drag from one letter to another to connec

Benn Jordan may have initially gained notoriety for his music as Flashbulb and later, reviewing synths and effects pedals on YouTube under Benn and Gear. But about five years ago, Benn decided to take his YouTube channel in a different direction. He didn't stop covering music gear overnight, but as time progressed, his channel became […]

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

Back in '24, there was a story about GPT-2 being run on excel https://arstechnica.com/information-technology/2024/03/once-too-scary-to-release-gpt-2-gets-squeezed-into-an-excel-spreadsheet/ How hard/$/time would it be to recreate GPT-4 (or equivalent)? GPT-4 was released in '23, since then there have been more/better chips, etc. Is this something a competent S&P500 company could do on its own? submitted by /u/tjdogger [link] [留言]

Now I myself have not used AI a ton beyond making a funny picture or two on ChatGPT/Gemini and maybe asking it a few things on the fly if I need a second opinion on something - and sometimes it's been helpful. The biggest thing I hear from the "Fuck AI" crowd is that it ruins the creative circles like artists, authors, etc. because it copies their work. I sympathize with their hate, but I've heard an argument that it's not doing anything different than what we do when/if AI didn't play a role in anything: look at other people's work for inspiration then create something. Like we can't create a song in a vacuum, we need to learn and be exposed to music theory, notes, other styles of music, instruments, etc. So someone starting a band didn't make something brand new, it took pieces from other artists. And the part that makes me sing AIs praises, so to speak, is its use in the medical field. Doctor Mike posted a video about a year ago talking about this. Like, if it's improving healthcare to the point that it's detecting life threatening things to help doctors treat and cure us more effectively and efficiently, why are we trying to get rid of it? Maybe that's not what people are saying when they want AI gone or saying how 'awful' it is, but I just hope we don't end up throwing the baby out with the bathwater with AI because I genuinely think it's an astonishing thing that's clearly helpful in certain circles. submitted by /u/SeaGlass_7 [link] [留言]

Facebook has long been filled with feeds of clickbait articles. Now, Meta is making its own clickbait articles with AI. The standalone Meta AI app now has a "For You" section that populates a list of clickbait-style stories for you to read. But the topics, images, and text are all AI-generated - and as questionable […]

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For over a decade, the standard approach to bot mitigation has relied on a fundamentally flawed premise: tracking every micro-movement a user makes. The industry standard "invisible" CAPTCHAs ingest your mouse curves, touch pressure, scrolling behavior, and browser history to calculate a "human score." At Conversion.business , we took the opposite approach. We built a zero-telemetry, privacy-first Gamified CAPTCHA platform that tracks no behavioral interactions. Instead, we rely on a mathematically rigorous Game Randomization Strategy and a strict cryptographic handshake. Here is the architectural teardown of why standard machine learning fails against our engine, and why discarding behavioral telemetry actually increases security. The Flaw in "Invisible" Telemetry Standard CAPTCHA systems rely on security through obscurity. They collect massive amounts of user telemetry and run it through proprietary risk-analysis models. This creates two massive problems: The Privacy Tax: You are forcing your users to surrender behavioral biometric data just to log in. The ML Training Loop: If an attacker can reverse-engineer the "human" mouse-curve threshold, they can train a bot to inject fake cursor paths. Once the model is trained, the security layer is completely compromised until the vendor updates their algorithm. The Conversion.business Approach: Zero Telemetry Our OopsSDK does not track mouse movements, touch pressure, or cross-site cookies. We rely on a lean, transparent Verification Signature that collects only: solveTimeMs : The exact duration from puzzle initialization to completion. webglFingerprint : Hashed hardware renderer info. userAgent : To identify known headless browsers. How do we stop bots without tracking behavior? By attacking the core requirement of machine learning: predictability . The Game Randomization Strategy Machine learning models, specifically reinforcement learning and computer vision bots, require a predictable environment to train effectively

Okay so I've been thinking about this a lot lately and I feel like everyone's still stuck on the "which model is best" debate when there's a completely different cost problem creeping up on companies actually deploying this stuff. It's not the model. it's the steps. Like... a browser agent doing something that sounds simple: fill out a form, grab data from a dashboard, submit a thing. that's not 3 steps. that's observe, click, wait, observe again, oh there's a modal now, handle that, screenshot is stale, retry, login broke, start over. easily 30-50 tool calls for a task a human would do in 90 seconds. At a small scale you don't care. annoying but whatever. at company scale? If you're running agents across customer ops, internal tooling, research, travel booking, job pipelines, etc., that inefficiency compounds really fast. I came across something called ego lite which apparently takes a different approach: isolated sessions per task, reusable login state, better page snapshots, JS-level orchestration so agents can chain actions instead of calling tiny tools one by one. they're claiming 20-50% faster completion on comparable tasks which honestly if true is not a small number when you're paying per token per call. idk maybe I'm in the weeds on this and most companies aren't at the scale where it bites yet. but it feels like one of those things where by the time people notice the bill, the architecture decisions are already locked in. the smartest model running in a bad environment is still a slow expensive agent. Anyone else actually tracking execution efficiency as a real cost metric or is it still mostly vibes and benchmarks out there? submitted by /u/babyb01 [link] [留言]

Pretty basic question, I’m curious to know what the most useful thing you’re using AI for? Are you using things like Claude cowork for tasks, Codex or Claude code for programming, script writing, homework? Do you use it as a regular chat for companionship, are you using it for life advice? Really just curious how individuals are finding it useful to them Thanks submitted by /u/thomas_unise [link] [留言]

sit down because this is going to bother you. cast your mind back 18 months. deepseek dropped and the internet lost its mind. "china just ended openai." it was everywhere. people were running it locally, posting benchmarks, losing sleep over geopolitics. then... nothing. it just kind of stopped being talked about. it didn't lose. it didn't win. it just... evaporated from the conversation. sora. remember sora? openai dropped that video generation demo and we were all convinced cinema was dead, hollywood was cooked, every creative job on earth had 18 months left. there were congressional hearings being threatened. think pieces everywhere. and now? when's the last time you actually heard someone say the word sora? not in a demo. in real life. used by a real person. i'll wait. github copilot was supposed to make every programmer 10x more productive. there were developers posting that they'd never write code from scratch again. entire job categories were being eulogised in real time. and now most developers i know have a complicated and slightly embarrassed relationship with it, like someone who got really into a mlm for three months and doesn't want to bring it up. llama was going to democratise ai forever. open source was going to eat everything. the big labs were cooked because you could run intelligence locally on a macbook. and you still can. but do you? does anyone you know actually do that regularly? it became a thing that's theoretically amazing and practically used by like eleven people on hacker news. cursor was the future of coding. perplexity was going to kill google search. both are still around, both are fine, both have paying customers. neither changed anything at the level the discourse suggested they would. here's what i think actually happened. we were living through a hype cycle so fast and so layered that each new thing would go through the entire arc - discovery, mania, backlash, abandonment - in about six weeks. and because the next thing arrived be

I built AIPDFKit because I kept running into the same friction: needing to do something simple with a PDF -- redact some sensitive info, pull out a table, or convert a document to Markdown -- and every tool either required an account, put the good stuff behind a paywall, or made me wonder what was happening to my files afterward. PDFKit is my answer to that. PDFKit -- Free AI-Powered PDF Tools PDFKit is a free, browser-based PDF utility suite powered by AI, built for developers and technical professionals who need fast, reliable document processing without the friction of paid plans or mandatory accounts. Whether you're parsing data out of PDFs, sanitizing sensitive information, or converting documents into developer-friendly formats, PDFKit gets the job done in seconds. What it does AI-assisted PII redaction -- automatically detect and mask emails, phone numbers, names, and more Table extraction to Excel -- pull structured data out of PDFs without copying and pasting PDF to Markdown conversion -- especially useful for feeding document content into LLMs or RAG pipelines These aren't just format converters. The AI layer means the output is clean, structured, and actually ready to use. Privacy first No account creation required. PDFKit stores no user data and automatically deletes all uploaded files after one hour. For developers handling client documents or sensitive data pipelines, this is a meaningful differentiator over SaaS tools that retain files indefinitely. Who it's for Developers preprocessing PDFs before feeding them into RAG pipelines Anyone automating document workflows People who need to quickly extract structured data without spinning up a Python script Anyone dealing with sensitive documents who can't afford to have files sitting on someone else's servers It's the kind of utility you bookmark and reach for constantly. Built to be fast, free, and frictionless. Check it out: https://www.aipdfkit.com/ Would love to hear what features you'd find most usefu

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built After being abandoned for several months, I have come back to build and complete Claude with LeetCode, which is a DSA learning system that automates daily algorithm education with Claude code directly inside GitHub repo. Every time I submit an accepted solution on Leetcode, the Github workflow fetches my Leetcode account data and commit the problem with the solution to the repo. Claude will then run on a fixed schedule and automatically generates a full structured lecture, covering the DSA topic, brute force through optimal solutions in Python, complexity analysis, and a YouTube video packaged in a GitHub Issue. This project means a lot to me because it merges two things I care about daily: now not only can I solve Leetcode problem, my solution is automatically analyzed by a powerful AI agent mentor. Demo Link to my project: https://github.com/Stewie-pixel/claude-with-leetcode.git Link to my application walkthrough: https://youtu.be/ClWdW3v9JJ0 The Comeback Story At first this was only a project to store the Leetcode questions I have solved. The process required manual pushing the problem to the repo and nothing special. Later I have added the automation workflow to fetch data from my Leetcode account, Claude will be prompted like an experienced dsa mentor from Claude and skill.md file to give a thorough analysis on that problem. And at the end of the day, Github Copilot workflow will give a daily summary report to cover my daily progress. My Experience with GitHub Copilot I built a DSA Mentor skill that gives Copilot the full context of what a lecture should contain: topic identification, the brute force to optimal approach structure, complexity analysis requirements, and the YouTube search step. Without Copilot, writing the dsaMentor.js orchestration logic and getting the agent to consistently produce structured markdown output would have taken significantly longer. I then use Copilot cli

If you're a networking instructor — at a university, technical college, boot camp, or corporate training program — you know the frustration of teaching the OSI Model. Static PowerPoint slides can only do so much. Students nod along in class, but when exam time arrives, the layers blur together. The PDU names become a confusing jumble. The OSI Model Simulator by Roboticela was built with educators in mind. It transforms a passive lecture into an interactive demonstration that students engage with, remember, and take home to explore on their own. Classroom Use Cases Live Demonstration Project the simulator on a classroom screen. Have students suggest messages to send and protocols to use. Step through each layer together as a class, stopping to ask questions: "What's happening here? What header was added? What device would operate at this layer?" The interactive format maintains attention far better than any lecture. Lab Assignments Assign students to run specific simulations and document their findings: "Run HTTP and HTTPS simulations. Screenshot the Presentation Layer for each. Explain in writing what differs and why." This assignment tests both tool usage and conceptual understanding. Flipped Classroom Send students to app.osi-model-simulator.roboticela.com before class. Ask them to run three simulations and come prepared to discuss what they observed. Class time becomes richer discussion rather than basic concept delivery. Protocol Comparison Exercise Have students run simulations for all five protocols — HTTP, HTTPS, SMTP, DNS, FTP — and create a comparison chart noting the differences at each OSI layer. This develops deep protocol literacy that traditional instruction rarely achieves. Why It Works: The Science of Active Learning Research in educational psychology consistently shows that active learning produces dramatically better retention than passive instruction. The "Learning Pyramid" (Edgar Dale's Cone of Experience) suggests: Lecture: ~5% retention after 2

While Python dominates the AI research space, TypeScript is the engine of the modern full-stack web. From high-performance Node.js backends to complex React frontends, TypeScript provides the structure and safety that large-scale engineering teams demand. When we built the apcore-js SDK, we didn't just want to "port" the Python logic. We wanted to create a first-class, type-safe experience that leverages the unique strengths of the JavaScript ecosystem. In this twentieth article of our series, we explore how to build AI-Perceivable modules in TypeScript using apcore and TypeBox . The Goal: Cross-Language Parity A core tenet of the apcore protocol is that a module’s behavior should be identical regardless of the implementation language. A module named executor.user.get must accept the same JSON input and produce the same output in both Python and TypeScript. apcore-js achieves this by using TypeBox as its core schema engine. TypeBox allows us to define JSON Schemas that double as TypeScript types, giving us compile-time safety and runtime validation in a single definition. Defining Type-Safe Schemas In apcore-js, you define your module's contract using TypeBox's Static and Type primitives: import { Type , Static } from ' @sinclair/typebox ' ; export const InputSchema = Type . Object ({ userId : Type . String ({ description : " The unique UUID of the user. " }), includePrivate : Type . Boolean ({ default : false , description : " Whether to include sensitive fields. " }) }); export type Input = Static < typeof InputSchema > ; By adding the description field directly into the TypeBox definition, you are creating the "Cognitive Interface" for the AI while simultaneously providing type hints for your IDE. Building the Module apcore-js supports both class-based and functional module definitions. The class-based approach is idiomatic for TypeScript developers: import { ClassModule , ModuleAnnotations , Context } from ' @apcore/core ' ; export class GetUserModule extends Cl