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Hey everyone, I am a frontend dev and design engineer with about 4.5 years of experience. I am currently looking for a full time role. I am hoping to join an agency or a product team that actually cares a lot about good design. I love building modern and clean websites with a minimalist touch. My approach to work is just based on first principles. I always try to think about how Apple would make things. Good design is not really about what you add, it is about what you refine. Here is what I use to build things: My stack: React, Next.js, TypeScript, and Tailwind CSS. Animations: Motion and GSAP for really smooth interactions. How I work: I focus heavily on the design intent and the core architecture to build pixel perfect layouts and animations from scratch. You can check out my work here: My Portfolio: https://deepbuilds.in Recent Build (Autumn): https://autumndev.vercel.app If your team is looking for someone who sweats the small details and wants to build some really cool stuff together, drop me a DM. Let's talk! submitted by /u/Party-Membership-597 [link] [留言]

Keeping validation schemas, OpenAPI docs, and test fixtures in sync requires manual effort. I built TriadJS as a POC to consolidate them in one place. You define the API once using a TypeScript DSL. The framework derives the following: Runtime validation OpenAPI 3.1 & AsyncAPI specs Database schemas via Drizzle Boundary tests (calling scenario.auto() reads schema constraints to generate fuzz tests) This is a pre-1.0 project with specific architectural compromises: Auto-generated tests abstract away edge cases, which can complicate debugging. Designed for AI: It optimizes for single-file context so LLMs can read the API without traversing multiple YAML and test files. This prioritizes scaffolding over standard modularity. There is are built in claude plugin with skills (schema DSL, endpoints, channels, BDD behaviors with the authoritative assertion phrase table, testing, adapters, Drizzle, CLI, DI) and 8 slash commands ( /triadjs:new , /triadjs:model , /triadjs:endpoint , /triadjs:channel , /triadjs:scenario , /triadjs:test , /triadjs:docs , /triadjs:validate ). I am looking for feedback on this architecture. Is this level of tight coupling an anti-pattern, or is the single source of truth worth the DSL requirement? Repo: TriadJS submitted by /u/justhamade [link] [留言]

anthropic just released fable 5 two days ago and i haven’t had a chance to properly dig in yet for context it’s basically a public version of mythos, the model they’d been keeping locked behind project glasswing for select partners only. now it’s out for everyone on pro/max/team plans until june 22 for free, after that it’ll need usage credits from what i’ve read it’s supposed to be insane at long agentic tasks… like multi-hour sessions where it spins up sub-models, gathers data, writes and tests its own code. someone gave it one prompt to build a travel-time map and it went off on its own for hours and just… built it the one catch is it has hard safety blocks in areas like cybersecurity, bio, chem. falls back to opus 4.8 when it hits those but i want to hear from people actually using it right now. what’s the best thing you’ve noticed? and what feels overhyped or still rough? drop your experiments in the comments, genuinely curious submitted by /u/NewMuffin3926 [link] [留言]

Hey HN. I'm a high schooler in Fremont, CA. Tuesday morning I got a county-wide AC Alert text telling everyone in Alameda County to prepare a go-bag for an East Bay Hills Red Flag Warning that starts tonight at 11 PM. The text went to ~half a million phones. The actual NWS warning polygon only covers East Bay Hills (NWS zone CAZ515). Most people who got the text don't need a go-bag tonight. Some in the hills don't realize how close they are. So I built this tool - https://redflag-check.info/ mit

Govern the AI skills your team depends on Discussion | Link

Hi, I want to use AI to check my grade with the mark scheme and see what grade it would give me. Now, after doing this, would the assignment be flagged by an AI detector? submitted by /u/No-Witness1045 [link] [留言]

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Hey everyone — I've been building The Drive AI, a file intelligence API, and wanted to share it. The problem: If you're building an AI agent, RAG pipeline, or any app that needs to understand documents, you end up duct-taping together 5-6 different libraries — one for PDFs, one for screenshots, one for Office docs, one for markdown conversion, one for OCR. Each breaks differently and none give you structured output. What this does: Send any file or URL, get structured JSON back. Define a schema of what you need, and the API extracts it with typed fields, confidence scores, and citations pointing to where in the document the data came from. 107+ file formats — PDFs, Office docs (Word, Excel, PPT), 40+ code languages, images, videos, websites. One API handles all of them. Not just extraction. You can also: Convert anything to clean markdown Generate screenshots of URLs (with device presets, dark mode, full-page capture) Ask analytical questions about documents and get reasoned, step-by-step answers Get Open Graph images for link previews What makes it different from competitor? Most "file to X" APIs do one thing — thumbnails OR markdown OR extraction. This handles the full pipeline. And the extraction isn't just OCR-and-dump — you define a JSON schema, and it returns typed data with confidence scores. Think of it as "SQL for documents." The simple path-based API is also something I haven't seen elsewhere: GET /md/example.com/report.pdf gives you markdown. GET /example.com gives you a screenshot. No auth needed for basic usage. Free tier: 100 credits/month, no card required. There's also an interactive playground where you can test every endpoint without writing code. Would love feedback from anyone building with documents or doing AI agent work. What's missing? What would make you switch from your current setup? Give it a try at https://dev.thedrive.ai submitted by /u/karkibigyan [link] [留言]

The decision comes as India emerges as the world’s largest GCC market.

zxcvbn is the most widely used password strength estimator with 1M npm downloads a week. It's also 389KB gzipped and hasn't shipped a commit since 2017. Most sign-up forms are hauling that around just to block password123 . Poor password UX is a real conversion problem. A strength meter that adds 389KB to your bundle delays page load — on mobile, measurably so. Users who hit a slow registration page don't wait. They leave. The irony is that most of that weight goes toward catching passwords nobody is actually using to register on your site. So I built passcore - 3.0KB gzipped and 98.4% detection rate on real breach data - same as zxcvbn, benchmarked against a deduped list of passwords pulled live from RockYou, Adobe, HIBP, and other major leak lists. zxcvbn takes ~9.7ms to load — it's parsing 389KB of dictionary into memory on every cold start. passcore loads in ~0.2ms. It evaluates a password in ~2,600 nanoseconds. For a registration form, it's effectively invisible — no jank, no layout shift, no contribution to your Core Web Vitals score. The strength meter shows up before the user finishes typing their first character. How it works: passcore runs five detection layers on every password: Dictionary - All entries sourced directly from breach data, not a generic word list Keyboard patterns - qwerty , asdf , 1234 , numpad walks Repeats - aaaa , ababab Sequences - abcdef , 123456 L33t speak - decodes p@ssw0rd → password , m0nk3y → monkey , then dictionary lookup The dictionary is small by design. Every entry was chosen because it appears in real breach data - not because it's a common English word. Password1! is caught not by a 40k word list but by stripping the suffix and checking if the core word is in the breach list. It is. The scoring model: passcore returns a score from 0 to 4 - same scale as zxcvbn. The detection layers run first. A dictionary match, keyboard pattern, repeat, sequence, or l33t substitution scores 0 or 1 immediately - no further calculation. If

I mean human still operates but basically gets one joystick 🕹️ because machine will think for itself how to put its leg better. So some sort of spinal cord intuitive walking. With the library of objects one shouldn’t step on, no way. submitted by /u/Ubud_bamboo_ninja [link] [留言]

There is a moment, about ten minutes into using Lovable, where you feel like the future has arrived. You type a few sentences, and a real website appears. It looks good. It works. You did not write a line of code. We get it. That moment is genuinely impressive, and Lovable deserves the credit it gets for it. But a website is not a launch. It is a thing you live with. You update your hours. You add a blog post. You publish a case study. You change a price. You hire someone and want them to handle the news page without breaking anything. That is where the two platforms stop looking alike. So instead of comparing the first ten minutes, this post compares the next ten months. What Lovable actually builds Lovable is an AI coding tool. When you describe your site, it writes a React application: components, state, routing, build tooling. Your content, the actual words and images on your pages, lives inside that code. This is a fine architecture for a web app. It is an awkward one for a website, because every future change is a code change. Want to fix a typo in a testimonial? That sentence is a string inside a React component. You can ask the AI to change it, and it usually will. But you are editing software to edit a sentence. Your marketing person is not going to do that. Your client definitely is not. And there is a quieter problem underneath. The site Lovable generates depends on a specific framework, a specific set of packages, and a build pipeline. Frameworks move fast. The React app that builds cleanly today may need dependency updates a year from now just to keep working. Someone has to own that, and it is probably you. What SleekCMS builds SleekCMS starts from a different assumption: most businesses do not need a web application. They need a website, and a website is mostly content. So when you describe your site to SleekCMS, you get two things: First, your content as structured data. Your pages, your services, your team bios, your blog posts all live in a CMS, in

If you've ever tried to use an AI resume builder, you've probably hit the same wall I did. You sign up, poke around, find the one feature you actually need — and then boom: "Upgrade to Pro for $29/month." It's frustrating. Resume help shouldn't be locked behind a paywall. So I built my own. Meet Persona Persona is an AI-powered resume builder that you run completely on your own machine . No deployment required. No subscription. No account on some third-party service. You clone the repo, set it up, and it's yours. It's a fork of the excellent open-source project ResumeLM , but I've added a bunch of features I couldn't find anywhere else — especially around local AI and template variety. 👉 GitHub: github.com/nithiin7/persona (Drop a ⭐ if you find it useful!) The Big Deal: Run AI Completely Offline with Ollama This is the feature I'm most proud of. Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine. Here's how simple it is: Install Ollama on your computer Pull any model ( ollama pull llama3 , for example) Open Persona's settings, point it to your local Ollama URL Done — the AI now runs entirely on your machine No OpenAI key. No Anthropic key. No usage limits. Your resume data never touches an external server. If you do want to use cloud models, Persona supports those too — GPT-5, Claude Opus 4.7, Claude Sonnet 4.6, and a handful of open-source models via OpenRouter. But the Ollama path is what makes this genuinely different from everything else out there. It's 100% Free — Everything Unlocked The original ResumeLM had Stripe payments baked in. I ripped all of that out. Every single feature in Persona is available to every user, always. There's no "Pro plan." There's no feature gating. You self-host it, you own it, you use all of it. 10 Resume Templates Persona ships with ten distinct templ

I’m on the $200 subscription plan. Previously, using the Opus 4.8 model, I would only use up 80% of my total quota over the course of a week; however, yesterday alone, I consumed 45% of the quota just by using the Fable model to solve a problem and conduct a code review.

I built a knowledge graph out of my own work sessions. Hundreds of them — transcripts of me building a system with LLMs, extracted into concepts, decisions, findings, and the edges between them. For a while it felt like the thing was working. I'd query it, get back a clean structured answer, and move on. Then I ran a foreign model against it. I gave a different model my concept definitions and asked it to reconstruct the system, both the vocabulary and the relationships. It recovered 97.7% of the words. It recovered 61.1% of the structure. That 36-point gap was the first time I could see the problem instead of just living inside it. The vocabulary transferred because the definitions were written carefully. The edges didn't, because the edges were the part I'd let the extraction handle. And the whole time, querying the graph had felt complete. The structure came back typed, connected, confident-looking — so I stopped looking. I started calling it premature retrieval closure: the retrieval returns something shaped like a whole answer, which is exactly why I didn't notice the parts that were missing. Part 10 of Building at the Edges of LLM Tooling . If you're running a long-term project through an LLM-backed memory system (anything that turns raw sessions into structured, persistent memory), this is about the step where the structure starts lying about how complete it is. Start here . Why It Breaks Every memory system of this kind does the same move. An LLM reads raw interaction (a conversation, a document, a session log) and lifts structured memory out of it: entities, facts, rules, summaries. That structured memory becomes the thing the agent reads later, instead of the raw record. The lift is where fidelity goes. Pulling clean structure out of messy text means making decisions the text didn't make explicit: which entity this pronoun refers to, whether a relationship is real or inferred, what to keep and what to drop. Those decisions can be wrong, and when they are,

My first time trying to localize a technical video for a global audience was an absolute nightmare. I wanted to translate a programming tutorial into Spanish and Portuguese. I opened up a few trendy AI video tools. I spent 15 minutes uploading a massive file. And then... a giant pop-up hit me: "Sign up to view your clip." Then another: "Upgrade to premium to remove watermark." Then, after giving away my email, I realized the lip-sync looked like a badly dubbed 1970s kung-fu movie. I felt cheated, gatekept, and exhausted. I ended up closing the tabs, thinking global content distribution was only for big-budget marketing agencies. Sound familiar? The Problem with Traditional "Free" AI Video Tools Here is what I learned after lurking in video editing and creator communities: most "free AI video translators" aren't actually built for creators or independent developers. They are often: ● Held hostage behind sign-up walls just to harvest your data. ● Cluttered with massive watermarks that ruin your professional branding. ● Prone to creepy or robotic lip-syncing that completely kills audience retention. ● Server-heavy privacy nightmares where you have no idea where your media is being stored or trained. I knew there had to be a better way — a space where anyone could break language barriers instantly, securely, and without pulling out a credit card or giving away an email. So I built it. Introducing AIVideoTranslator I created AIVideoTranslator (aivideotranslator.ai) — a browser-based tool designed from the ground up for people who want to scale their content globally without the corporate friction. What's Different? Typical AI Video Tools AIVideoTranslator Aggressive Sign-up Walls Zero Friction: No account required, ever. Hidden Watermarks Pro Quality: Clean, watermark-free output. Robotic, Laggy Dubbing Pro Lip-Sync: Precise, natural-looking mouth alignment. Data Scraping / Storage Privacy-First: Files loaded directly into your browser. What's Inside ● 30+ Global Languag