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Kimi K3 took first place in Arena's Frontend Code evaluation the week it launched, and it holds a 1M-token context — but Moonshot doesn't ship a coding agent, and your coding agent doesn't ship Kimi K3. Claude Code is locked to Anthropic's API by default, Cursor to its own backend, Cline to whatever key you hand it. LLM Gateway bridges that gap. It speaks both the Anthropic and OpenAI API formats, so the tools you already use can run Kimi K3 — or any of 200+ models — with a base-URL change. Here is the exact setup for each tool. Kimi K3 in Claude Code Claude Code talks to any endpoint that speaks Anthropic's /v1/messages format, which LLM Gateway does natively. Three environment variables: export ANTHROPIC_BASE_URL = https://api.llmgateway.io export ANTHROPIC_AUTH_TOKEN = $LLM_GATEWAY_API_KEY export ANTHROPIC_MODEL = kimi-k3 claude That's the whole migration. Every request now routes through LLM Gateway to Kimi K3, and every request shows up in your dashboard with its exact cost, token counts, and cache-hit rate. One refinement worth adding: Claude Code uses a second, smaller model for routine background work, and you can point it at something cheap — or free: export ANTHROPIC_SMALL_FAST_MODEL = glm-4.7-flash-free That puts K3 on the hard reasoning and a $0 model on the housekeeping. Kimi K3 in Cursor Cursor routes its chat / plan panel (Cmd/Ctrl + L) through a custom OpenAI-compatible endpoint. Setup: Open Cursor Settings → Models Add your LLM Gateway key under OpenAI API Key Enable Override OpenAI Base URL and set it to https://api.llmgateway.io/v1 Add kimi-k3 as a custom model and select it Be aware of the boundary: Cursor's Composer, inline edit (Cmd/Ctrl + K), and autocomplete are locked to Cursor's own backend and will not route through any external endpoint. Plan and chat with K3's full 1M context in Cursor; if you want K3 driving the actual agent loop, use Claude Code or Cline instead. Kimi K3 in Cline Cline is the straightforward one — it's built to bring y

How Normal Software Engineers Actually Use AI in Their Daily Work Let's cut through the hype. You're not building the next AGI. You're a working software engineer with deadlines, legacy code, and a backlog that never shrinks. So how do you actually use AI tools in your day-to-day work? After surveying hundreds of developers and reflecting on real-world usage patterns, here's what normal software engineers are doing with AI—no Silicon Valley theatrics required. The Mundane But Invaluable: Code Completion and Boilerplate The most common use case is the least sexy: letting AI handle repetitive code. GitHub Copilot, Cursor, and similar tools excel at generating boilerplate that you'd otherwise copy-paste from Stack Overflow or previous projects. Real example: Writing CRUD endpoints in Express/TypeScript: typescript // Type this comment and let AI complete: // Create a REST endpoint for user registration with email validation app.post('/api/users/register', async (req: Request, res: Response) => { try { const { email, password, name } = req.body; // Email validation const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/; if (!emailRegex.test(email)) { return res.status(400).json({ error: 'Invalid email format' }); } // Check if user exists const existingUser = await User.findOne({ email }); if (existingUser) { return res.status(409).json({ error: 'User already exists' }); } // Hash password and create user const hashedPassword = await bcrypt.hash(password, 10); const user = await User.create({ email, password: hashedPassword, name }); res.status(201).json({ userId: user.id, email: user.email }); } catch (error) { res.status(500).json({ error: 'Internal server error' }); } }); Did AI write perfect code? No. But it gave you scaffolding to refine, saving 10-15 minutes of typing. That's the real win. The Game-Changer: Explaining Legacy Code and Obscure APIs Every developer inherits someone else's mess. AI tools shine when deciphering undocumented code or unfamiliar libraries. Pract

Nostalgia for a time before smartphones Discussion | Link

Brain-like knowledge base with AI writing & deep research Discussion | Link

13,000+ MCP servers, skills & plugins for AI coding agents Discussion | Link

Let Agents Autonomously Build Better Agents for $0.02 Discussion | Link

A request starts at 10:00:00.900 and finishes 200 ms later. Your latency log says -800 ms . That sounds impossible until the machine corrects its clock between the two reads. Then ordinary code turns an adjustable wall clock into a broken stopwatch. The subtraction that works—until it does not This is probably hiding in one of your metrics helpers: async function timed ( operation ) { const startedAt = Date . now (); const result = await operation (); return { result , durationMs : Date . now () - startedAt , }; } Most of the time, this reports a sensible number. That is what makes it dangerous. Date.now() answers a calendar question: how many milliseconds have passed since the Unix epoch according to this machine? The answer must remain comparable with logs, users, and other computers, so synchronization software is allowed to correct it. A correction can change its rate, jump it forward, or move it backward. If either correction lands between the two calls, the subtraction measures the clock adjustment as if it were work. A timestamp is a coordinate. A duration is a distance. They need different instruments. Your computer already has two kinds of clock Operating systems expose several clocks, but two mental buckets cover most application code: Question Clock JavaScript example “When did this happen?” Wall clock Date.now() , new Date() “How long did this take?” Monotonic clock performance.now() A wall clock follows civil time. It has an epoch and can be serialized as an ISO timestamp. A monotonic clock starts at an arbitrary origin and promises that later readings will not be lower than earlier readings. wall: 1000 ── 1001 ── 0998 ── 0999 clock correction monotonic: 40 ───── 41 ───── 42 ───── 43 On Linux, CLOCK_REALTIME is settable wall time . CLOCK_MONOTONIC cannot be set and does not make discontinuous jumps backward, although gradual frequency adjustment can affect its rate. Linux even has CLOCK_BOOTTIME for a monotonic counter that includes suspended time. Thre

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Orchestrate agents at scale while reducing cost Discussion | Link

These locks, lights, and other smart home upgrades let you add automation without messing up your home’s vibe.

Simple computers for agent w/ full VMs Discussion | Link

Event-Driven Architecture (EDA) is one of the most popular approaches for building scalable distributed systems. Instead of tightly coupling services through synchronous APIs, services communicate by publishing and consuming events. It sounds perfect. Until your consumers start making API calls for every event they receive. At that point, you've reintroduced the very coupling Event-Driven Architecture was supposed to eliminate. This is where Event-Carried State Transfer (ECST) comes in. The Hidden Problem in Event-Driven Architecture Imagine an e-commerce platform. A customer places an order. The Order Service publishes an event: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Several services subscribe to this event: Inventory Service Notification Service Analytics Service Shipping Service Billing Service Everything looks asynchronous. But here's what actually happens. Order Created Event │ ▼ Inventory Service │ GET /orders/ORD-10234 │ Order Service Notification Service does the same. Analytics Service does the same. Shipping Service does the same. Suddenly, one event generates dozens of synchronous API calls. Congratulations, You've Recreated a Monolith Your architecture now looks like this: Order Service ▲ ┌───────────┼───────────┐ │ │ │ Inventory Shipping Notification │ │ │ └───────────┼───────────┘ API Requests Although events are being used, every consumer still depends on the Order Service. If the Order Service is unavailable: Notifications fail Inventory updates fail Analytics stop processing Shipping cannot continue The event broker isn't the bottleneck anymore. The originating service is. Event-Carried State Transfer Solves This Instead of publishing only an identifier, publish the data consumers actually need. Instead of this: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Publish: { "event" : "OrderCreated" , "orderId" : "ORD-10234" , "customerId" : "USR-1001" , "customerName" : "John Doe" , "totalAmount" : 249.99 , "currency" : "USD" , "i

One identity layer for your customers, APIs, and agents Discussion | Link

I’m 14 and I got tired of spending hours (sometimes days) setting up new machines. Different distros, different package managers, fragile shell scripts, no rollback. So I built Nexus — a cross-platform environment provisioning engine. One command turns a fresh OS into a fully productive development machine. The Problem Setting up a dev machine is painful: Hundreds of Linux distros with different package managers (apt, pacman, dnf, apk). Shell scripts break easily with no rollback or state management. Tools like Ansible are overkill for laptops. Docker doesn’t configure your host. Dotfile managers don’t install dependencies. Result: Developers lose dozens of hours per year on setup issues. What Nexus Does One static Go binary. Detects your OS, chooses the right package manager, applies declarative YAML profiles, and handles everything with security gates and rollback. No scripts. No manual steps. Works on Linux and Windows (with WSL2 support). Standout Features Cross-distro support — apt, pacman, dnf, apk behind one interface. 7-step orchestrator with rollback — PreFlight → Refresh → Execute → Verify → Audit. Failed foundation packages trigger full rollback. Security gate (SanitizeAndExecute) — Allowlist, metacharacter rejection, timeouts. No raw shell execution. 10 built-in profiles — Go dev, Rust dev, Frontend, Data Science, Ethical Hacking, etc. WSL2 setup in ~60 seconds on Windows. Dotfiles + age-encrypted vault + Distrobox containers . Community profile registry . Optional Tauri GUI dashboard. Architecture Nexus is organized in bounded contexts: BRAIN — Cobra CLI + core engine (Go) DNA — YAML profiles with JSON Schema + struct validation + SHA256 integrity BRIDGE — WSL2 handling (cross-compiled) CONTAINER — Distrobox management VAULT — age encryption REGISTRY — Community profiles Every command goes through a strict security gate. State is crash-safe with atomic writes and append-only logs. Quick Start # Install go install github.com/Sumama-Jameel/nexus-engine/cm

I’ve been building a small set of calculator pages for freelancers and small-business owners. The first version was pretty simple: put the formula on a page, add a few inputs, show the result. That works for a demo. It does not always work for a real user. The more I worked on it, the more I realized the hard part is not the math. The hard part is making the page feel trustworthy when someone is using it for a business decision. Here are the changes that mattered most. 1. Explain the assumption close to the input At first I wrote explanations below the calculator. Most people never read them. So now I try to put small notes near the input itself. For example, if a calculator asks for payment processing fees, the user should not have to scroll down to understand whether that means percentage fee, fixed fee, or both. This is boring UI work, but it reduces bad inputs. 2. Show intermediate numbers A single final result can feel like a black box. For business calculators, I’ve found it helps to show a few intermediate numbers: subtotal estimated fees estimated tax net amount break-even number Even if the final number is the same, users trust it more when they can see how the result was built. 3. Avoid pretending the answer is exact Small-business math has a lot of messy edges. Taxes vary. Fees vary. Local rules vary. Some people include owner salary in cost. Some do not. A calculator should not act like it knows every detail. I now prefer wording like: estimated monthly amount or rough break-even point That feels less flashy, but it is more honest. 4. Make the empty state useful A blank calculator page is not very helpful. I started adding realistic default numbers or examples where it makes sense. Not because the default is “correct”, but because it helps the user understand what kind of number belongs in each field. This is especially useful for people who are not spreadsheet-heavy. 5. Keep the result easy to copy A lot of users are not trying to stay on the page. They

AI-powered PCB design tool for engineers and hardware teams Discussion | Link

Hey everyone! 👋 First of all, thank you so much for all the support, feedback, and feature suggestions since the Product Hunt launch. Every comment has been incredibly valuable, and I've already started implementing some of your ideas. Today I'm excited to share a new update to Rizzzler ! 🌙 ✨ What's New? 🎨 2 Brand-New Themes I've added two new themes , giving you even more ways to personalize your profile and match your own style. Whether you prefer something clean, vibrant, or expressive, there's now even more variety to choose from. 👀 Live Preview Panel One of the most requested improvements is finally here! You can now preview your showcase page while editing it . No more guessing how your profile will look after saving—see your changes in real time before publishing. 🎵 Audio Preview Choosing background music is now much easier. Instead of selecting tracks blindly, you can now preview audio directly before applying it to your profile. ✨ Profile Picture Decorations Want your profile to stand out even more? You can now add Profile Picture Decorations to give your avatar a unique look and make your showcase even more personal. More decoration styles will be added in future updates! ❤️ Thank You The feedback from the GitHub community and Product Hunt has been incredibly helpful. Many of these improvements came directly from community suggestions, and I'll continue building Rizzzler based on your ideas. If you have feature requests, bug reports, or suggestions, I'd love to hear them! 🔗 Try Rizzzler Website https://rizzzler.onrender.com Product Hunt https://www.producthunt.com/p/rizzzler-show-yourself-off GitHub https://github.com/DeveloperPuneet/Rizzzler-Stable Thank you for supporting Rizzzler! 🚀

Track your music repertoire and practice Discussion | Link

If you have worked with APIs for a while, you have probably realized that designing an API is only...

Vi um post legal esses dias sobre rodar duas contas do Claude Code na mesma máquina compartilhando tudo entre elas: mesmas skills, mesmos servidores MCP, mesmos hooks. O artigo era: "Um cérebro, duas carteiras" Eu rodo, exatamente o oposto, e acho que pra muita gente o oposto é a escolha certa. Minhas duas contas não são uma pessoal e uma reserva pra quando os créditos acabam. Uma é pessoal, outra é de trabalho. A última coisa que eu quero é meus MCP servers de trabalho, meus hooks de trabalho e meu histórico de projeto de trabalho vazando pras sessões pessoais. Então em vez de ligar as duas, eu mantenho elas separadas de propósito . O setup inteiro O Claude Code guarda o estado num diretório de config ( ~/.claude por padrão) e lê a variável CLAUDE_CONFIG_DIR pra apontar pra outro lugar. Esse é o único mecanismo que você precisa. Sem shim, sem symlink, sem jq . bash # ~/.zshrc # Claude Code: contas isoladas (pessoal vs trabalho) # Cada uma usa um CLAUDE_CONFIG_DIR proprio -> credenciais/sessao separadas. # ~/.claude = pessoal (default) # ~/.claude-work = trabalho claude-work () { CLAUDE_CONFIG_DIR = " $HOME /.claude-work" claude " $@ " ; } claude-personal () { CLAUDE_CONFIG_DIR = " $HOME /.claude" claude " $@ " ; } # 'claude' sozinho continua sendo a conta pessoal. source ~/.zshrc claude-work # pede login OAuth da conta de trabalho, uma vez só Usei funções de shell em vez de alias por um motivo: a função repassa "$@" limpo, então claude-work --resume abc e claude-work chat funcionam sem a variável de ambiente vazar pra nada mais no shell. Um alias faria quase o mesmo aqui, mas a função deixa a passagem de argumentos explícita. É isso. claude puro é pessoal. claude-work é trabalho. Nada é compartilhado, e é aí que mora a graça. Por que eu não compartilho o cérebro A versão de cérebro compartilhado faz symlink de skills , plugins , settings.json e dá merge no bloco mcpServers entre as duas contas pra elas se comportarem igual. Se as suas duas contas são de fato a mesm