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While designing the database and Product Requirements Document (PRD) for an online school project, I ran into a problem I was not expecting. The school had multiple subscription plans. For simplicity, imagine: Live Class Plan:₦50,000 per term Video On Demand Plan: ₦30,000 per term Hybrid Plan (Live Classes + Video On Demand):₦70,000 per term. Initially this looked simple. Students subscribe. System charges them. Done. Then I asked: What happens if somebody changes plans halfway through the term? Suppose: A student already paid: Live Class Plan ₦50,000 Two months later: They decide: Upgrade to Hybrid Plan Do we charge: ₦70,000 again? That would be unfair. Do we charge: ₦20,000 difference? Maybe. But what if they already used most of their subscription period? This question led me to something called: Proration What Is Proration? Proration simply means: Charging customers only for the portion they actually use. Instead of pretending subscriptions always begin and end perfectly. Proration tries to answer: "How much value remains in the current subscription?" and "How much should the customer pay for the new one?" Simple Example Assume: Term Length: 100 Days Student buys: Live Plan ₦50,000 After: 40 Days they upgrade. This means: Used: 40 Days Remaining: 60 Days Value remaining: Remaining Value = Remaining Days / Total Days = 60 / 100 = 60% Remaining credit: 60% × ₦50,000 = ₦30,000 Hybrid costs: ₦70,000 Therefore: Amount to bill = New Plan Price − Remaining Credit = ₦70,000 − ₦30,000 = ₦40,000 Student pays: ₦40,000 instead of: ₦70,000 This feels fairer. Downgrades Are More Complicated What if: Hybrid user: ₦70,000 moves to: ₦30,000 plan Should the system: Refund money? Create account credits? Apply discount later? Ignore downgrades until renewal? This is where: Proration Rules become important. What Are Proration Rules? Proration calculations are useless without rules. The business must decide: Rule 1: How Is Remaining Value Calculated? Options: Daily basis Weekly basis

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

A proposed $2 billion data center has become a political flashpoint in the small city of Shelbyville, Indiana. And the controversy has only grown more intense after the mayor, Scott Furgeson, was caught on camera saying of the "No Data Center" signs going up that, "I've seen a lot of these all over town, but […]

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 […]

These newer social apps offer alternatives to Big Tech’s feeds, focusing on interests, creativity, and community.

82-0 marries the stat nerd fun of fantasy basketball with instant gratification and a bit of dumb luck. The goal is to draft a team of players that could (theoretically) have a perfect 82-0 season. Obviously, if you just had free rein to pick whoever you wanted from throughout history, there would be little challenge. […]

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 […]

Whether you hate subscriptions like Kindle Unlimited or simply want to get away from Amazon for whatever reason, you'll find plenty of solid options available.

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]

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