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I'm a business student, not a developer. I shipped a working SaaS product in 10 days with Claude Code. (Draft for dev.to — edit anything that doesn't sound like you, then publish. Suggested tags: #ai #nextjs #supabase #buildinpublic) Ten days ago I couldn't have told you what a webhook was. Last night I published quidkit — a Next.js + Supabase + Stripe starter kit with working auth, subscription billing, and documentation — and this morning I'm writing this from holiday. I study business management. I'm not a CS student. I can't really "code" in the way that word usually means. What I can do, it turns out, is manage a very fast, very literal developer that lives in my terminal — and that changed what's buildable for someone like me. This is the honest write-up: what I built, how the AI workflow actually looked, every bug that nearly got me, and what it cost. What I built quidkit is a starter kit for developers building subscription apps. The pitch: before anyone can pay you monthly for your app idea, you need the boring foundation — accounts and login, taking payments, knowing WHO paid, emails that send themselves, security so users can't see each other's data. That's 2–4 weeks of tedious work that isn't your idea. quidkit is that foundation, pre-built: clone it, rename it, build your thing on top. Stack: Next.js 16, React 19, Tailwind v4, Supabase (auth + database with row-level security), Stripe (checkout, customer portal, webhook sync), Resend (email). Live demo at demo.quidkit.dev — you can sign up and "pay" with Stripe's test card and watch the whole pipeline work. £29. Because the established kits are £200–£300 and I'm literally the target market: someone without that kind of money. The actual workflow People imagine "AI builds your app" as one magic prompt. It's not. It's closer to being a project manager with one extremely capable, extremely literal employee: I wrote specs, not code. Every session started with me pasting a detailed brief into Claude Code — w

General Intuition, the startup building a foundation model that trains generalized AI agents how to move through space and time, is in talks to raise at a $6 billion pre-money valuation from new investors including Valor Ventures, Point72 Ventures, Seven Seven Six.

Looking to better protect your Kindle or add a little personality to your favorite e-reader? From cases and covers to page turners and even charms, this is the guide for you.

Home theater projectors are getting better and better, and have quickly become my favorite way to enjoy movies.

These WIRED-tested dual-purpose air purifiers also function as heaters, fans, art pieces, and more, offering the best of both worlds.

If you’re a designer, you’ve probably had the same problem I’ve had: there are dozens of UI/UX design...

I run TextTimeTools , a free site with speaking-time and reading-time calculators. It's a pure static site deployed to Cloudflare Pages — no backend, no database, no CMS. The calculators themselves are one page. But the site has 50+ pages , each targeting a different keyword like "how many words is a 5 minute speech" or "how long to read 1000 words". Every single one of those pages is generated by a build script. I've never written one by hand. Here's the pattern, and why it's the highest-leverage thing I've done for this site's organic traffic. The problem with a single calculator page A speaking-time calculator answers one query well: "how many words is my speech". But people don't search for tools — they search for answers : "how many words is a 5 minute speech" "how many words for a 3 minute speech" "how long to read 1000 words" "how long to read 5000 words" Each of those is a separate keyword with its own search intent and its own competition. One calculator page can't rank for all of them — a page titled "Speaking Time Calculator" has no reason to show up for "how long to read 1000 words". The classic fix is to write a page per keyword. That works, but it doesn't scale — every new keyword means hand-writing another page, and keeping them consistent is a nightmare. The fix: generate pages at build time The build script ( gen-longtail.cjs ) takes a list of keyword targets and emits a complete, keyword-specific HTML page for each one. The word count pages and reading time pages are both generated this way. const PAGES = [ { minutes : 1 , slug : ' how-many-words-is-a-1-minute-speech ' , variant : ' is-a ' }, { minutes : 2 , slug : ' how-many-words-is-a-2-minute-speech ' , variant : ' is-a ' }, { minutes : 5 , slug : ' how-many-words-is-a-5-minute-speech ' , variant : ' is-a ' }, // ... up to 15 minutes { minutes : 2 , slug : ' how-many-words-for-a-2-minute-speech ' , variant : ' for-a ' }, { minutes : 5 , slug : ' how-many-words-for-a-5-minute-speech ' , variant :

Finding software is easy. Finding the right software is not. Search for almost any category—email marketing, CRM, productivity, design, or AI—and you will find hundreds of options. Every product presents itself as the best choice, while many comparison articles repeat the same features without explaining which users each tool actually suits. That problem inspired me to build PickTool , a platform for discovering and comparing AI and SaaS tools. PickTool is still evolving. I am currently improving its content quality, tool coverage, comparison experience, performance, and SEO structure. This is not a polished launch announcement. It is an honest look at the architecture behind the project and some of the lessons I have learned while building it. What Is PickTool? The goal of PickTool is simple: Help people find the right software in minutes, not hours. Instead of creating a basic directory filled with product names and affiliate links, I want each important tool to include useful and structured information, such as: Core features Pricing model Best use cases Strengths and limitations Ratings and evaluation criteria Alternatives Direct comparisons Related guides and category pages The challenge is that this creates several interconnected types of content. A single product can appear on its own tool page, inside a category, in multiple comparisons, and in articles about the best software for a particular use case. Keeping all of this consistent requires more than publishing isolated blog posts. Why I Chose Next.js and Laravel PickTool uses a decoupled architecture: Next.js powers the public-facing website. Laravel powers the backend, API, database logic, and administration system. MySQL stores tools, categories, ratings, pricing information, and editorial content. I chose this combination because I wanted the frontend and content-management logic to evolve independently. Laravel provides a structured backend for managing relationships between tools and content. Next.js

Docking stations expand what your laptop can do, and I’ve been testing the best of the best to see which you should buy.

Buildroot builds a cross-compiler, a Linux kernel and a complete root filesystem from source, driven by one Kconfig-style configuration file. Starting from the qemu_arm_vexpress_defconfig that ships with Buildroot 2026.05.1, two commands produce a bootable ARM system you can run under QEMU. The images you ship are the ones in output/images/ ; output/target/ looks like a root filesystem but must never be copied to a device. This post starts a new hands-on series on Buildroot for embedded Linux. By the end of this part you will have built a working Buildroot root filesystem for an ARM target, booted it under QEMU, and understood which generated directories are safe to ship. Later parts add your own packages, a BR2_EXTERNAL tree, kernel and bootloader integration, and reproducible image output. If the choice between build systems is still open, our earlier Yocto vs Buildroot comparison covers it; this series assumes the decision is made. What you need A Linux host, several gigabytes of free disk space, and a network connection. No development board is needed for this part; QEMU stands in for the hardware. On a Debian or Ubuntu host, this covers the mandatory packages the manual lists, plus the ncurses development files that menuconfig needs: raghu@techveda.org:~$ sudo apt install build-essential diffutils patch gzip bzip2 perl tar cpio unzip rsync file bc findutils gawk wget libncurses-dev One rule from the manual is worth stating plainly: build everything as a normal user. Buildroot never needs root, and running it as root exposes your host to any package that misbehaves during installation. The command above is the only one in this post that uses sudo . Getting Buildroot and choosing a target Download and unpack the current stable release — 2026.05.1 at the time of writing — from buildroot.org/downloads , and work from that directory. Buildroot ships ready-made configurations for many boards and emulated machines, one file each in configs/ , and make list-defconfigs

On the latest episode of Equity, we wonder why the DOJ is investigating startup board seats.

A chatbot can sound convincing for five minutes without remembering anything. Then you mention the job interview you were stressed about last week, the name of your dog, or a small detail from a late-night conversation. It replies like none of it happened. That is where most "AI companion" demos fall apart. I am building Local Waifu , a desktop AI companion that runs on the user's own Mac or PC. One of the rules I set early was simple: conversations and memories should stay on the machine. No central chat database. No server that needs to be online for the character to remember someone. The rule sounds clean. Building it was not. Saving chats is not memory The first version of memory was the obvious one: save messages. That gives you history, which is useful, but it does not solve recall. A long chat history grows fast. Sending all of it back to a local language model on every message is slow, expensive in context space, and usually makes the reply worse. The model does not need to see every conversation from the last six months. It needs the few pieces that matter right now. If someone says, "I have to take Luna to the vet tomorrow," the character should be able to find that Luna is their dog. It should not need to reread hundreds of unrelated messages about work, movies, and dinner plans to get there. So I treated chat history and long-term memory as different things. Chat history is the recent conversation. It gives the model immediate context. Long-term memory is a small collection of facts, moments, preferences, and relationship details that may matter later. Those memories need to be searchable by meaning, not only by exact words. The memory data stays in SQLite I wanted the app to work without a hosted database, so the storage layer is local SQLite. Each character gets their own data. Chats, memories, extracted entities, and relationships are stored locally on the device. If a user creates two characters, one character does not quietly inherit the other one's

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On August 19 we launched LeadAce on Product Hunt. It was our first launch to an English speaking audience. I am writing down the numbers while they are still uncomfortable, because the posts I found most useful when I was preparing were the ones that did this. We are a small software company in Tokyo. LeadAce is an outbound sales agent that runs as a Claude Code plugin. The backend is open source. It has been in Public Beta since the launch. The numbers Product Hunt, 24 hours: 2 upvotes 1 comment, which was mine Day rank #160, week rank #758 3 followers on the product page Signups from the launch: 0. Site traffic for the four weeks up to launch day: 6 active users, 27 page views. Referrers were direct 4, producthunt.com 1, t.co 1. Our X account over the same four weeks: 48 posts, 576 impressions total, 2 link clicks, 2 new followers. So the launch did not fail at the landing page. It failed before that. Almost nobody arrived. Where we got stopped This is the part I did not plan for. I spent weeks on the product, the demo video, the gallery images and the copy. Every one of those was ready. What I did not have was accounts. Hacker News. I could not post Show HN at all. HN was limiting Show HN submissions from low karma accounts, and my account had karma 1. I created it years ago and never used it. There is no way to buy your way past this, and there should not be. r/ClaudeAI. My first attempt was removed by automod because the account was too new. I tried again from my older Reddit account, which has an age of 5 years but karma 1. A moderator locked it. The subreddit requires 50 total karma to post a Showcase on the feed. They pointed me to a megathread instead, which is the correct call on their side. My comment there got 67 views and 1 upvote in 19 hours. r/SaaS. The post went through, but Reddit's pre-submit check warned me that it might break the rules on vendor spam. I removed every link from the body and changed the ending to a real question. That version poste

This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK . What I Built I built an Autonomous Multi-Agent Handwritten Notes Generator . Students and educators often need clean, visual study guides that resemble real handwritten notes, but manually summarizing technical subjects and formatting them takes hours. This system solves that by combining autonomous web research, structured note extraction, and headless browser rendering. You enter any topic or question, and a coordinated team of AI agents researches the concept, formats it into a notebook layout using Google handwriting fonts ( Caveat ), and captures a high-resolution .png notebook page screenshot. Deployment & Repository Links: GitHub Repository: himanshuyeolecse-jpg / multi-agent-handwritten-notes An autonomous multi-agent system built with LangGraph, Tavily, and Playwright that researches complex topics and renders handwritten-style student study notes into PNG screenshots. multi-agent-handwritten-notes An autonomous multi-agent system built with LangGraph, Tavily, and Playwright that researches complex topics and renders handwritten-style student study notes into PNG screenshots. 🎓 Multi-Agent Handwritten Notes Generator An autonomous multi-agent workflow built using LangGraph , LangChain , Tavily Search , and Playwright . The system researches complex technical concepts and dynamically compiles the findings into styled, handwritten-notebook PNG screenshots. 🏗️ System Architecture [ User Input / Prompt ] │ ▼ [ Researcher Node ] ── (Tavily Web Search & Summarization) │ ▼ [ Note Renderer Node ] ── (HTML/CSS + Google Caveat Font + Playwright Screenshot) │ ▼ [ Critic Node ] ── (Validation Check: Is Output Complete?) │ Approved? ──► No ──► [ Researcher Node ] │ Yes ▼ [ PNG Screenshot Saved ] ⚡ Features Autonomous Research: Uses Tavily API to fetch up-to-date technical context. Dynamic HTML/CSS Rendering: Formats structured summaries into a paper-notebook layout utilizing… View o

The neon flying squid can fly in formation. The shoal of about 100 squid rose unexpectedly from a patch of the Pacific Ocean around 370 miles from Tokyo and glided near the boat for about 30 metres. The astonished researchers were the first to capture photographs of such a thing, which looked like the early stages of an alien invasion. They were probably neon flying squid ( Ommastrephes bartramii ), the subsequent study states , a species that is part of a 20-strong flying squid family that was known to leap from the water but, until then, was only rumoured to also be able to glide above it...

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The renewal notice came and I decided to let it go. threadkeeper.io was my first idea and my first website. I bought the domain in August 2025, about six weeks after a community college AI summer camp where I was writing files with names like ccc-ai-pdf-project and describing them in my own README as a beginner Python project. Then I shipped a domain, a blog, a CLI, and a manifesto. Before I let it lapse I went back to look at it one more time. Sentimental. Five minutes, tops. Then I tried to figure out where the source code lived, and realized it did not live anywhere. The site was on Spaceship. I had built it there, in the browser, and never put it in version control. Not once. There was no repo to clone, no local folder, no backup. The only copy of my first website that existed in the world was the one running on a server I had four days left on. The tagline on that site, in cyan, at the top of the page, was "Don't commit without context." I never committed it at all. I did not have the source code to my own website So the first job was not nostalgia. It was extraction. I pulled all eight pages and every asset off the live server before it went dark: the landing page, the blog, three posts, the Dr. Kahlo page, and the Ariadne Clew recap app I built for an AWS hackathon. Nineteen files. sitemap.xml claimed there were four pages, which tells you how much I trusted my own sitemap in 2025. The rest I found by following links. That archive is now public, with a SHA-256 for every original file so anyone can verify nothing drifted in the rescue: earlgreyhot1701d.github.io/threadkeeper-archive It is committed now. A year late. I named a file dom_js.js and did not blink Here is the first thing I found once I could actually read my own code. The Ariadne Clew app had seven JavaScript modules. Two of them were named with snake case and a suffix: api_js.js , dom_js.js , main_js.js . Four were camelCase with no suffix: utils.js , theme.js , exportMarkdown.js , dragDrop.js . Tw

This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK . Finding the perfect, thoughtful gift shouldn't feel like a chore. Whether it's for a birthday, anniversary, or holiday, we all experience gift-buying paralysis: Generic suggestions : "Just buy them a mug or a generic gift card." Budget anxiety : Falling in love with an idea only to find out it costs 3x what you planned to spend. Missing the subtle nuances : Forgetting that someone dislikes clutter, lives in a tiny apartment, or prefers practical experiences over physical objects. To solve this, I built GiftAdvisor . It is an intelligent, consumer-friendly gift recommendation system built with Google Agent Development Kit (ADK) , Gemini ( gemini-3.1-flash-lite ) , and deployed seamlessly to Google Cloud Run . Live Demo & Links Live Cloud Run App : https://gift-advisor-1008832068452.us-central1.run.app GitHub Repository : https://github.com/inusha-thathsara/Multi-Agent-Gift-Idea-Generator-with-Google-ADK What I Built GiftAdvisor transforms unstructured descriptions of a person into tailored, ranked, and strictly budget-compliant gift recommendations. Instead of dumping everything into a single monolithic prompt, GiftAdvisor splits the cognitive load across three specialized AI agents orchestrated via Google ADK: Profile Analyzer Agent : Understands the human behind the prompt (lifestyle, hobbies, aesthetic preferences, and explicit anti-preferences ). Idea Finder Agent : Brainstorms creative, thoughtful candidate gifts across multiple categories with estimated market prices. Budget Filter Agent : Audits estimated prices, filters out anything exceeding the user's hard budget limit, swaps in budget-friendly alternatives, and delivers a ranked curation. Key Highlights & Features Pure Multi-Agent Pipeline : Built using Google ADK's LlmAgent , SequentialAgent , and InMemorySessionService . Zero-Overhead Scale-to-Zero : Deployed to Google Cloud Run with min-instances=0 (scales to zero w