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We've been testing coolers for years, and our top picks are on sale for Amazon Prime Day.

Cross-posted from noflattery.com/decide — where I ran this exact question through a council of four different frontier models and let them argue it out. You're a solo founder at ~$8K MRR. You have runway for exactly one full-time hire. Which role unlocks the most growth? (A) a second engineer to ship features faster (B) a marketer to build a real acquisition channel (C) a customer-success / support hire to cut churn and free your time (D) a salesperson to chase larger deals The intuitive answer for most technical founders is A — more shipping velocity. The case below is for C , and it's stronger than it looks. (With one caveat that can flip the whole thing — stick around for it.) TL;DR: At ~$8K MRR solo, hire customer success first if churn is real or support is eating your week . If voluntary churn is under ~3% and support is light, hire a marketer instead. Engineer and sales come later. The case for customer success first 1. Churn quietly eats growth before features can add it. At $8K MRR, 5% monthly churn is ~$400/month bleeding out before you grow an inch. Across bootstrapped SaaS in the $5–15K MRR band, the strongest predictor of reaching $50K isn't feature velocity or channel — it's net revenue retention above 90% . That's a customer-success function, not an engineering one. 2. You are the bottleneck, and support is eating you. As a solo founder you're doing product, sales, billing, and support. If support takes ~15 hours a week, that's nearly 40% of your capacity — and it's the cheapest thing to hand off. A CS hire costs less than a senior engineer or an experienced salesperson, and it buys back the hours (and the headspace) you need to think strategically again. 3. It's a research department in disguise. A CS hire generates the highest volume of qualitative signal: why people leave, what they actually use, what they'd pay more for. An engineer builds what you think users want. CS tells you what they actually need — which means the engineer you hire next buil

It’s been hot in London this week. Really hot. A dangerous heat wave has hit Western Europe. Yesterday, the UK recorded its highest ever June temperature at 36.1 °C (about 97 °F). But as the weather app on my phone confirmed, it felt like 39 °C. It’s frightening that we are seeing such temperatures in…

Where's the Trump phone? We're going to keep talking about it every week. We still don't have the phones we preordered yet, but this week the T1 hit open sale, no deposit required. Trump Mobile's T1 Phone is now available for anyone to buy directly. The phone has previously trickled out to a small number […]

A fast, beautiful, and native FTP/SFTP client for macOS Discussion | Link

We've gone from A to Z to find Amazon's best Prime Day deals on the gear worth owning.

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

Times are hard in 2026. These Amazon Prime Day deals under $100 on earbuds, Kindles, and other tested products should help make life just a little bit easier.

There’s no better time to get a Kindle than during Amazon's own sale event.

Move over, spring cleaning. These vacuum deals will give your home the cleanest summer yet thanks to Amazon Prime Day.

Apple deals abound for Amazon Prime Day. We've rounded up the best deals on Apple Watches, iPhones, MacBooks, iPads, and accessories.

From MacBooks to gaming laptops, these are the very best deals on some of my very favorite laptops for Amazon Prime Day.

AI community manager that hypes up your Discord server Discussion | Link

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

I've been working on a different approach to giving AI coding agents context about large codebases. Instead of indexing files by embeddings or feeding hundreds of lines into the model, the idea is to statically classify code into structured semantics. For each file it produces things like: -Primary semantic role (service, repository, controller, DTO, etc.) with the evidence used to reach that conclusion. -Behavioral traits (transaction handling, business rule enforcement, orchestration, event emission, in-memory state, database interaction, etc.). -Architectural relationships and dependency direction. It also works at the function level, so individual methods get their own behavioral classification and relationship hints. The output is structured JSON rather than summaries, so an agent can query it instead of rereading source files. One thing I found interesting is that this often gives agents enough architectural context without spending tokens on large files. Instead of inferring "what is this class?", they already know something like: Domain service, performs transactional DB writes, enforces business rules, emits events, depends on persistence and event layers. Beyond agent context, the same information seems useful for architectural analysis—tracking responsibility drift, identifying layering violations, or seeing when a service gradually accumulates unrelated behaviors. I've been testing it on Medusa (TypeScript) so far, and the results have been promising, although there are still plenty of edge cases. I'm curious how others are approaching this. If you're building coding agents or working with large monorepos, what's been the hardest part of codebase understanding? Context size, architectural reasoning, stale indexes, something else? submitted by /u/Zealousideal_Ant4747 [link] [留言]

I just published Fintech Engineering Handbook distilled from 6 years of tears, sweat and swears. It’s a free ~25-page resource with various hints and patterns around handling money in software systems. Tell me what you think! submitted by /u/Krever [link] [留言]

Every small task used to mean a new tab. JSON formatter on one site, GST calculator on another, PDF merger somewhere that wanted my email before it would merge two pages. Ads everywhere, slow UIs, and that low-grade worry about uploading a payslip or invoice to a server I do not control. I got tired of juggling twenty bookmarks for work that should take thirty seconds — so I started building one place for all of it. What ToolReign is ToolReign is a free online toolbox: 260+ utilities across 15 categories , all running in your browser. Developer tools (JSON formatter, JWT decoder, API client), text utilities, SEO helpers, PDF and image tools, spreadsheets, and a finance section I built with India in mind — GST with CGST/SGST/IGST splits, EMI and SIP calculators, HRA exemption, gratuity, income tax estimates, and more. The idea is straightforward: open a tool, do the work, leave. No signup wall, no file uploads to a backend, no account to manage. I am Anirudha Sonwane , a Senior Software Engineer at Giant Leap Systems in Pune. ToolReign is a side project I build around my day job — not a pitch deck, just something I wished existed. The tech stack decisions Next.js 14 App Router and static export Each tool lives at its own route under src/app/{category}/{tool-slug}/ . That maps cleanly to SEO: one URL, one search intent, one page of metadata. The site exports statically ( output: 'export' ), so production deployment is uploading an out/ folder to static hosting — no Node server to babysit. The App Router made this scale. Add a page component, register the slug in tool-registry.json , and the sitemap, category hubs, and search index pick it up automatically. At 260+ tools, hand-maintaining URLs would have broken within a month. 100% client-side — the decision that shaped everything This was the core architectural bet, and it is also the privacy story: your data never leaves the browser. Finance calculators are plain TypeScript math with useMemo . PDF merge and split use

React dialogs often start simple. You add an isOpen state, then a selected item state, then confirm/cancel callbacks, then another dialog after the first one. Eventually, a simple flow can become scattered across multiple components. For example, a user flow like this: Select a user Confirm the action Add the user often becomes multiple pieces of state: const [ isUserSearchOpen , setIsUserSearchOpen ] = useState ( false ); const [ isConfirmOpen , setIsConfirmOpen ] = useState ( false ); const [ selectedUser , setSelectedUser ] = useState < User | null > ( null ); This works, but the actual flow is harder to read. What if dialogs could be handled as async flows? I wanted the code to read closer to the user flow: const user = await openAsync ( UserSearchDialog ); if ( ! user ) return ; const confirmed = await openAsync ( ConfirmDialog , { title : `Add ${ user . name } ?` , }); if ( confirmed ) { await addUser ( user . id ); } The dialog opens, waits for a result, and the caller continues based on that result. This is about orchestration, not UI This is not meant to replace Radix, MUI, Headless UI, shadcn/ui, or custom dialog components. Those libraries solve the dialog UI problem well. The idea here is to manage the flow around dialogs: opening dialogs from anywhere under a provider resolving typed result values handling nested dialogs distinguishing completed vs dismissed supporting dismissal reasons guarding close behavior with shouldClose So the actual dialog UI can still be your own component. I packaged the pattern I turned this idea into a small open-source library called react-dialog-flow . It provides a headless dialog stack, Promise-based openAsync , typed results, nested dialogs, closeTop , closeAll , dismissal reasons, shouldClose , and optional UI primitives. GitHub: https://github.com/CHOKANGYEOL/react-dialog-flow npm: https://www.npmjs.com/package/react-dialog-flow Docs: https://dialog-flow.kangyeol.com/ It is still early, so I am mainly looking for feed

I spent 3 months building a tool that solves the most annoying part of working on a dev team. You know the drill: → New dev joins → "hey check your DMs" → Someone pastes the .env in Slack → That message sits there forever → 6 months later someone screenshots it by accident or worse — → git add . → git commit -m "added env" → git push → you just leaked your prod database URL to the internet I got tired of it. So I built DotSync. ─────────────────────────────────── Here's what it looks like in practice: $ dotsync push 🔒 Encrypting 10 secrets for team access... 📤 Uploading... ✅ Version : v7 Secrets : 10 keys encrypted Teammates can now run: dotsync pull That's it. New dev joins? They run dotsync pull. Secrets updated? dotsync push. Moving between your laptop and work machine? dotsync pull. ─────────────────────────────────── The part I actually care about — security: Everything is encrypted ON your machine before it hits the network. The server stores a blob it literally cannot read. I'm not asking you to trust my infrastructure. You don't have to. Stack if you're curious: • Argon2id key derivation (64MB memory cost — brute force isn't happening) • AES-256-GCM encryption • Zero-knowledge server (stores ciphertext only) • Single Go binary, no runtime deps ─────────────────────────────────── Other things it does that I use daily: dotsync diff → shows exactly which keys changed vs remote (never shows values) dotsync history → full version history, who pushed what and when dotsync rollback → restore any previous version in one command dotsync run -- node server.js → injects secrets as env vars, nothing written to disk dotsync scan → scans your codebase for accidentally committed secrets ─────────────────────────────────── Free tier covers: • 1 project • 3 team members • 7 days history That handles most small teams completely free. ─────────────────────────────────── Now the part where I need your help: I'm looking for 100 engineers to actually stress test this. Not "give i

Hi folks! I’m the architect behind WLOADCTL, a commercial workload scheduling system for enterprise automated task orchestration and RPA docking. A quick share of my daily work focus: Distributed task scheduling core development with Java & C Cross-system automation scripts built by Python & Shell Backend frontend based on SpringBoot + Vue3 Edge traffic protection & access optimization using Cloudflare Enterprise RPA integration to automate repetitive backend operations I’ve been tackling a lot of real-world pain points like cross-Linux distro compatibility, high-frequency API access security and mass task concurrency control recently. If you’re working on workload scheduling, backend automation or Cloudflare security tuning, feel free to leave a comment to chat!