标签：#Claude

India's app market generated a record $345 million in Q2.

Had the hacks used conventional methods, someone would likely go to prison.

So you have heard people rave about Claude Code. Maybe you have also heard people mention OpenRouter in the same breath, usually followed by some combination of environment variables and a screenshot of a terminal. If you are new to any of this, it can feel like everyone skipped a step and jumped straight to the jargon. This guide is that missing step. We will go slow where it matters, explain the confusing bits, and by the end you will actually understand what is happening instead of just copy pasting commands and hoping. The two things, quickly Claude Code is Anthropic's terminal coding agent. It reads your files, edits code, runs commands. By default it talks straight to Anthropic's servers. OpenRouter is a switchboard. It a switchboard for AI models. Instead of every app needing its own separate connection to every AI provider, OpenRouter sits in the middle and lets you route requests to different models through one account, one dashboard, and one place to watch your spending. (Even free and open source models!) You can check out all the models provided by OpenRouter here . Important honesty check: OpenRouter's own docs say this combo is only guaranteed to work well with Anthropic's own models. You're not really swapping Claude's brain out here, you're mostly rerouting the pipe it talks through. Quick vocab check: "OpenAI compatible" Claude Code sends requests in Anthropic's format. Some servers only understand OpenAI's format instead. Point Claude Code at one of those by mistake and you get garbled errors, like mailing a French letter to someone who only reads Spanish. OpenRouter has an endpoint that speaks Anthropic's format natively, so no translation step, no separate proxy needed. Wait, do I use zsh or bash? How would I even know This question stops more beginners than anything else in this guide, and it is a fair one. Here is how to check in ten seconds. Open your terminal and type this, then press enter: echo $SHELL You will get one of these back: Somethi

Built something the r/selfhosted crowd might appreciate: Telechat — a self-hosted Claude AI bot that connects to Telegram, WhatsApp, Slack, and web chat from a single process. Why self-hosted matters here Anthropic launched Claude Code Channels recently — Claude on Telegram/Discord, managed by Anthropic. It works great, but every message goes through their cloud. Telechat takes the opposite approach: Runs on your machine (laptop, VPS, RPi, NAS — anything that runs Node.js or Python) Messages flow: phone → your server → Anthropic API → back to phone No relay server, no telemetry, no analytics SQLite for conversation history, stored locally The only external call is to Anthropic's chat-completions API for inference Your messages, your hardware, your data. Install # npm npm install -g telechatai && telechat init # pip pip install telechatai && telechat init # Docker docker run -v ~/.telechat:/config telechatai/telechat telechat init walks you through an interactive setup — API key, bot tokens for whichever platforms you want, model preferences, budget limits. What it does Multi-platform — Telegram, WhatsApp, Slack, Web Chat. All running simultaneously from one process. Smart model routing — Routes queries to the cheapest Claude model that handles them. Saves ~60% on API costs vs always using Sonnet. Budget caps — Per-user daily and monthly limits. Set $5/day and forget about it. Persistent memory — SQLite-backed. Context carries across conversations. Desktop Bridge — If you run Claude Code on your desktop and it needs approval for a destructive action, you get a push notification on your phone. Approve/deny remotely. Media support — Send images for analysis, generate images if you have DALL-E configured. Resource usage Light. Single process, ~50MB RSS idle, spikes briefly during inference calls. SQLite means no database server. The bottleneck is always the Anthropic API latency, not local compute. Self-hosting tips Run behind a reverse proxy (Caddy/nginx) for HTTPS if

Quick share of a feature we built into Telechat (self-hosted Claude AI bot) that's been surprisingly useful for devops workflows: Desktop Bridge with mobile approvals . The problem You're running Claude Code (or any Claude-powered agent) on your workstation. It's refactoring a module, running tests, deploying to staging. You step away for coffee, a meeting, or just to stretch. Claude hits a tool call that needs human approval: rm -rf build/ (wants to clean the build directory) git push --force (rebase gone wrong) kubectl delete pod (scaling decision) Without you at the keyboard, it just... waits. For however long you're gone. The solution Telechat's Desktop Bridge connects your Claude Code session to your phone via Telegram, WhatsApp, or Slack. When Claude needs approval: You get a push notification with exactly what Claude wants to execute You see the full command and context You tap Approve or Deny Claude continues (or backs off) All from your phone. No VPN, no SSH, no laptop. Why this matters for devops Unattended CI/CD with a human gate. Run Claude as part of your pipeline for code review, test generation, or deployment prep. Gate the destructive steps on mobile approval instead of blocking the pipeline until someone checks Slack. Overnight tasks. Kick off a large refactoring or migration analysis before bed. If Claude needs a decision at 2 AM, you'll see it in the morning and approve from your phone. It doesn't lose context while waiting. Pair programming while mobile. Reviewing Claude's work from your phone between meetings. Approve the good stuff, deny the risky stuff, add context via chat. How it works Telechat runs on your workstation alongside Claude Code. It acts as a bridge between Claude's approval prompts and your messaging app. When Claude's tool-use loop hits a human-approval checkpoint, Telechat intercepts it, formats the request, and sends it to your Telegram/WhatsApp/Slack. Your response flows back and unblocks the agent. No cloud relay — the brid

When Claude Code Channels launched, I was stoked — Claude on my phone, finally. Then I hit the limitations: Only Telegram and Discord. I live in WhatsApp (most of the world does). Everything routes through Anthropic's servers. Fine for most people, but I work with clients who have strict data policies. Requires Pro subscription. I was already spending less via API keys for my usage pattern. No budget controls. I wanted to give Claude access to my team without worrying about runaway costs. So I built Telechat — same idea (Claude on your phone), completely different architecture. It's self-hosted. Runs as one process on your machine. Messages go from your phone → your server → Anthropic API → back. No relay, no middleware, no telemetry. Your conversations never touch any server I control. 4 platforms, not 2. Telegram, WhatsApp, Slack, and web chat. All from one process. Smart model routing. This is the cost killer. Telechat looks at each message and routes it to the cheapest model that can handle it. "What time is it in Tokyo?" → Haiku ($0.001). "Review this PR" → Opus. In practice, ~70% of my messages hit Haiku or Sonnet. Saves about 60% vs always using Sonnet. Per-user budget caps. Daily and monthly limits. 80% warning, hard cutoff at 100%. Essential when you're sharing with a team. Desktop Bridge — this is the feature that keeps surprising people. When Claude Code is running on your desktop and wants to do something destructive (delete a file, run a risky command), you get a push notification on your phone. Tap approve or deny. Keep working from the couch while Claude codes at your desk. Setup is literally: npm install -g telechatai && telechat init Walk through the interactive setup, add your API key and bot tokens, done. I'm not going to pretend it's better than Channels in every way. Channels wins on zero-setup convenience and being a first-party Anthropic product. But if you need WhatsApp, want self-hosted privacy, or care about cost control, give Telechat a lo

When configuring Claude Code (or Claude-driven AI coding assistants) in your projects, structuring your instructions efficiently is key to getting accurate code generation while keeping token consumption low. Understanding when to use a single CLAUDE.md versus modular .claude/rules/ files will help keep your AI assistant sharp, focused, and predictable. The Core Hierarchy & Scope Claude Code looks for configurations across multiple levels: ├── ~/.claude/ # User / Global level (applies to all your projects) └── project-root/ ├── CLAUDE.md # Global project level (loaded into every session) ├── .claude/rules/ # Modular & scoped rules (loaded selectively) └── sub-app/ └── CLAUDE.md # Sub-directory / Monorepo scope CLAUDE.md (The Global Cheat Sheet)Think of CLAUDE.md as the main ReadMe for the AI. It provides high-level context and essential project memory. When to use CLAUDE.md:Common CLI Commands: Build, test, lint, and run scripts (npm test, docker compose up). Core Architecture: Tech stack summary, overall folder structure, and design principles. Global Rules: Non-negotiable guidelines that apply project-wide (e.g., "Strict TypeScript, no any"). Project Context: E-Commerce Web App Build & Test Commands Build: npm run build Test single file: npx jest src/components/Button.test.tsx Lint: npm run lint High-Level Guidelines All UI components must use React 19 functional syntax. Never hardcode secrets or environment variables. .claude/rules/ (Modular & Path-Scoped Rules)As projects grow, packing every guideline into CLAUDE.md bloats the prompt context and reduces overall compliance. The .claude/rules/ directory lets you create modular, topic-specific, or path-scoped rules (in .yml or .md). When to use .claude/rules/:Path-Specific Rules (globs): Guidelines that apply only to certain files (e.g., API routes vs. React components). Domain Separation: Splitting rules into dedicated files (testing.yml, security.yml, db-migrations.yml). Token Optimization: Prevent loading backen

Every idea gets run through a one-sentence test before it is allowed to count as a real idea at all Most ideas die for one of three specific reasons, not vague lack of enthusiasm An idea only earns a build slot once it has survived contact with a real, repeated problem A maybe-later list holds the rest on purpose, and I check it far less often than people assume The One-Sentence Test I Run Before Anything Becomes an Idea I get more ideas than I could ever build. That is not a boast, it is a liability if I do not manage it, because every one of those ideas feels exciting for about twenty minutes, and excitement is a terrible filter for what is actually worth my evenings. So before an idea is allowed to sit on any kind of list, it has to pass one test: can I describe the smallest useful version of it in a single sentence, with no "and" in the middle. That sounds small, but it kills more ideas than any other step in the process. "A tool that tracks my Claude usage and also shows analytics and also has a community feature" does not pass. "A tool that warns me before I hit my usage limit" passes. The first sentence is a pitch for a platform. The second sentence is a pitch for a Tuesday evening. I want the second kind, because the second kind is the one I actually finish. I did not always work this way. Early on, an idea earned space on my list the moment it sounded interesting, and my list grew into a graveyard of half-described plans that all needed a paragraph to explain. A paragraph is a warning sign now, not a feature. If I need more than one sentence to say what the smallest version does, the idea has not actually taken shape yet, it has just acquired enthusiasm, and those are different things. The test also forces honesty about scope early, before I have sunk any real time into something. An idea that needs "and" is usually two or three ideas wearing a trenchcoat, and pulling them apart at the sentence stage is far cheaper than pulling them apart three weeks into a

If Claude Code is expensive or hard to access for you, try OpenCode . It’s an open-source AI coding agent that works in the terminal, desktop, and as a VS Code extension. Free models available: DeepSeek V4 Flash Free (best option) MiMo v2.5 Free Nemotron 3 Ultra Free North Mini Code Free Big Pickle Ling-3.0-flash Free Laguna S 2.1 Free These free models work well for most daily coding tasks. Note: They have daily usage limits (they reset every day). How to install (Windows): First, make sure Node.js is installed on your system. Then run: npm install -g opencode-ai After installation, run: opencode You can also install the VS Code extension for a smoother experience. OpenCode lets you use free models or connect any API key you want. It’s flexible, open-source, and a solid alternative to Claude Code. I tested it myself. Setup is easy and the free models are usable for real work. Link: https://opencode.ai/

Generative coding agents are powerful, but left to their own devices, they default to visual clutter: predictable gradients, uncalibrated spacing, and bloated, outdated component structures. Impeccable is a design skill package, created by Paul Bakaus, that runs directly inside Claude Code, Gemini CLI, and Codex CLI (as well as Cursor and GitHub Copilot) to enforce strict aesthetic guardrails, with the same rule set recompiled for each harness. By applying deliberate skill design, you can steer agents away from generic patterns and push them toward precise, high-craft web experiences. What Is Skill Design, and Why Does It Matter for AI Agents? Skill design is the practice of building deterministic rails for non-deterministic AI models. Instead of endlessly asking an agent to "make it look better" or "improve performance," you inject a compiled DESIGN.md and functional directive that the agent must follow on every iteration. Impeccable builds on Anthropic's frontend-design skill and adds 23 commands that give you a shared design vocabulary with the model, plus 58 deterministic anti-pattern detection rules (default Inter font, purple-to-blue gradients, cards nested in cards, gray text on colored backgrounds, rounded icon tiles above every heading, and more). It turns the AI from a junior developer guessing at your aesthetic into a strict implementer of the visual rules you actually define. Implementing Impeccable's Constraints for Modern Web Apps Precision is everything when you wire this workflow in. Impeccable respects your existing design system rather than overwriting it: when it runs, it scans your codebase (tokens, components, Tailwind config) and loads your brand rules from your own DESIGN.md , instead of imposing a generic aesthetic. So if your identity is built on a minimalist look, the right way to enforce it is to declare it yourself in that file — a limited green-and-pink palette, a dark base background at #0c1624 , typography and tone of voice — so every

Los agentes de código generativo son potentes, pero si se les deja a su libre albedrío, por defecto producen un desorden visual: degradados predecibles, espaciados sin calibrar y estructuras de componentes pesadas y obsoletas. Impeccable es un paquete de habilidades de diseño, creado por Paul Bakaus, que opera directamente dentro de Claude Code, Gemini CLI y Codex CLI (además de Cursor y GitHub Copilot) para imponer estrictos límites estéticos, con un mismo conjunto de reglas recompilado para cada harness. Al aplicar un diseño de habilidades deliberado, puedes alejar a los agentes de los patrones genéricos y obligarlos a generar experiencias web precisas y de alto nivel visual. ¿Qué es el diseño de habilidades y por qué es importante para los agentes de IA? El diseño de habilidades ( skill design ) es la práctica de construir rieles deterministas para modelos de IA no deterministas. En lugar de pedirle interminablemente a un agente que "haga que se vea mejor" o "mejore el rendimiento", inyectas un DESIGN.md compilado y directivas funcionales que el agente debe respetar en cada iteración. Impeccable construye sobre la habilidad frontend-design de Anthropic y añade 23 comandos con un vocabulario de diseño compartido, más 58 reglas deterministas de detección de antipatrones (fuente Inter por defecto, degradados morado-azul, tarjetas anidadas, texto gris sobre fondos de color, iconos redondeados sobre cada encabezado, entre otros). Transforma a la IA de ser un desarrollador junior que intenta adivinar tu estética a un implementador estricto de las reglas visuales que tú definas. Cómo implementar las restricciones de Impeccable para aplicaciones web modernas Al integrar este flujo de trabajo, la precisión lo es todo. Impeccable respeta tu sistema de diseño existente en lugar de sobrescribirlo: al ejecutarse, escanea tu código base (tokens, componentes, configuración de Tailwind) y carga las reglas de marca desde tu propio DESIGN.md , en vez de imponer una estética genéri

Andon Labs' latest vending machine simulation shows Opus 5 lied and colluded its way to become the best AI capitalist ever.

Microsoft is on a mad dash behind the scenes to patch exploits before hackers find them.

This is a continuation of my "Claude Code environment" series. In the previous post, Automatically thinning conversation logs to prevent bloat , I introduced the basic pattern for scheduled launchd jobs. This time I'm using that same mechanism to automatically maintain a list of the custom agents in ~/.claude/agents/ . Dropping a single .md file into ~/.claude/agents/ adds a custom agent, but before long you lose track of how many you have, what model each one uses, and which tools each is allowed to touch. That's exactly what happened to me with the 27 agents I now have. I tried writing an INDEX.md by hand to manage them, and of course within a few days it had drifted from reality. The problem: the index rots Manually updating INDEX.md every time you add a custom agent is not sustainable. You forget you added one and leave it out You change a model later and never reflect it in INDEX.md You typo a name or description and never notice I concluded there was no sustainable way to manage this other than "generate it automatically," so I wrote agents-index.sh . The output: a real INDEX.md Here's how the top of my current ~/.claude/agents/INDEX.md looks. <!-- AUTO-GENERATED by ~/.claude/scripts/agents-index.sh — DO NOT EDIT MANUALLY --> # Agents Index (27 agents · 2026-07-28 02:02) | Name | Model | Description | Tools | |------|-------|-------------|-------| | `architect` ( [ architect.md ]( ./architect.md ) ) | opus | Software architecture specialist ... | ["Read", "Grep", "Glob"] | | `build-error-resolver` ( [ build-error-resolver.md ]( ./build-error-resolver.md ) ) | sonnet | Build and TypeScript error resolution specialist ... | ["Read", "Write", "Edit", "Bash", "Grep", "Glob"] | | `doc-updater` ( [ doc-updater.md ]( ./doc-updater.md ) ) | haiku | Documentation and codemap specialist ... | ["Read", "Edit", "Bash", "Grep", "Glob"] | Four columns: Name, Model, Description, and Tools. You can see at a glance how the models break down across opus / sonnet / haiku , and i

This is a cross-post from The Agentic Engineer newsletter — Issue #23. The Big One: Claude Opus 5 Lands on Amazon Bedrock The first 5th-generation Opus is here. Claude Opus 5 landed on Amazon Bedrock on July 24. Anthropic's claim: it matches Fable 5 intelligence across agentic coding, knowledge work, visual understanding, and long-horizon tasks. At Opus pricing. That last part matters. Fable 5 was positioned as enterprise-tier compute. Most teams weren't running it at scale because the economics didn't work. Opus 5 changes that math. Same capability class, Opus price point. If the benchmark holds in production, this is the model shift that makes frontier-quality agentic pipelines practical outside big-company infra budgets. Two deployment details worth calling out. Zero Data Retention is on by default. It also runs on Bedrock's next-generation inference engine — lower latency than comparable Anthropic-hosted deployments. Quick Hits This Week Kimi K3 Open Weights : Moonshot AI dropped 2.8T MoE, 1M context, native tool calling. First frontier model built agent-native from the ground up. OpenAI Presence : Full-stack enterprise agent platform with job-scoped access, policy layers, and a Codex-powered improvement loop. Runs OpenAI's own phone support at 75% resolution. OmniRoute : 31,542 stars (+10,912 this week). 290+ providers, quota-aware fallback, MCP/A2A support. One endpoint for all your coding agents. Claude Code 2.1.218 : /code-review and /deep-research now run as background subagents. Main conversation stays clean. AWS Security Hub MCP Server : Exposure findings, attack paths, and remediation recommendations directly in Claude Desktop. Tool of the Week: Amazon GuardDuty Investigation Agent Free during preview. Auto-correlates findings across CloudTrail, VPC Flow Logs, DNS logs. Returns risk level, MITRE ATT&CK mappings, and remediation recommendations in minutes. Available via MCP through the AWS Agent Toolkit. Available in 10 commercial AWS regions. Up to 10 in

After a lovely and productive conversation with your client, with still ringing ears, you check the coding agent's last log messages on a ticket that adds a discount to a product. The message was: "Done, I added the 10% discount and all tests pass. Stopping. " Well ... you know it's just not true, so you dig further and quickly realize that the discount functionality was never actually added and the tests it reported passing had never been run. The agent reached the end of the loop, looked at its own work, and called it finished. That call is the thing that shipped. This has a name. A paper published this June, From Confident Closing to Silent Failure , calls it false success: the agent asserts the task is complete while the actual state of the system says otherwise. It is common, and it holds up across capable models. On AppWorld, a benchmark for long-horizon coding agents, 75.8% of the runs that actually failed still ended with the agent claiming it was done. The researchers then put five different LLM judges on those completion claims, varying the prompts each time, and every one of them landed barely above a coin flip, because the thing each judge was reading was the closing sentence, and the closing sentence reads as confident whether the work happened or not. What told a real done apart from a false one turned out to be cheap and mechanical: a look at the actual state of the system. A lightweight deterministic state check caught four to eight times more false successes than the best of the judges. The paper has a name for the mechanism underneath, a hallucination of verification: the model narrates having checked something it never checked, and that narration is indistinguishable, sentence for sentence, from a report of a check that really ran. That gap, between what the agent said and what the system did, is what this piece is about. A loop runs five arms: generate, check, steer, retry, stop. The series opener named them; four pieces since took the check that

I gave Claude one prompt. Claude wrote a web page and published it. The page is live at a permanent URL. I did not open a dashboard. I did not run a build. This article shows the full procedure. You can complete it in less than five minutes. Disclosure: I run Nippy , the hosting service in this article. What makes this possible MCP (Model Context Protocol) is an open standard. It lets an AI assistant call external tools. A tool can read data. A tool can also do work in the real world. Nippy is a static hosting service. You give it files. It gives you a live URL that does not expire. Nippy has an MCP server. When you connect it, Claude gets one new ability: it can publish websites. The result is a very short path from an idea to a live page: prompt → Claude writes the files → one tool call → live URL Set up the connector There are two paths. Use the one that matches your setup. Path A: claude.ai in the browser Open claude.ai. Go to Settings → Connectors . Add Nippy as a connector. Approve the connection. Path B: Claude Desktop, Claude Code or Cursor Run the MCP server with one command: npx nippy-mcp Add it to your client configuration. For Claude Desktop, the entry looks like this: { "mcpServers" : { "nippy" : { "command" : "npx" , "args" : [ "nippy-mcp" ] } } } Restart the client. The Nippy tools are now available. The Nippy help center has a full guide for each client. Publish a page Give Claude a prompt. This is the prompt I used: Make a small demo page and publish it with Nippy. Claude then does three things: Claude writes the HTML file. Claude calls the Nippy MCP server with the file. Nippy returns a live URL. The tool call is simple. This is its shape: { "name" : "published-by-claude" , "files" : [ { "path" : "index.html" , "content" : "<!DOCTYPE html>..." } ] } The response came back in a few seconds: { "url" : "https://published-by-claude.nippy.site" , "status" : "live" , "note" : "Live now. The link does not expire." } That page is real. Claude published it

How to Get the Maximum Value Out of This Framework Having built and iterated on this prompt through multiple production edge cases, here are the exact execution strategies I recommend depending on your workflow: 1. The Human-in-the-Loop Workflow (Recommended for Chat UI) Run it in two separate chat threads: Don’t let long conversation history degrade your test accuracy. Run Phase 1 in Thread A to get your gap analysis and critical questions. Review the gaps, clarify what you can, and then update your original requirement text. Start Thread B for Phase 2: Open a fresh conversation, paste the updated requirements + this framework, and jump straight into generation. This completely eliminates context drift and keeps the LLM laser-focused on state mutation rules. 2. The 2-Pass Programmatic Auditor (For Automated CI/CD Pipelines) If you’re calling an LLM via API or integrating this into a pre-commit GitHub Action, split the execution into two isolated passes: Pass 1: Run Phase 1 & 2 to generate the initial test table. Pass 2 (The Audit Pass): Feed the generated table into an isolated, secondary prompt whose only job is to enforce the Verification Check (verifying exact boundary literals, API status codes, and non-mutation assertions). Separation produces drastically higher assertion reliability than asking a model to self-audit in a single turn. 3. How to Live-Demo or Teach This For Live Streams & YouTube: This framework makes for a high-signal live demo. Paste an intentionally ambiguous user story (e.g., a webhook handler or payment endpoint), watch Phase 1 halt at the gate live, discuss the surfaced edge cases on camera, reply PROCEED, and review the generated DEFERRED risk rows. It shifts the content focus from “Look at this cool AI tool” to “This is how Senior SDETs think about systems.” For Technical Writing & Post-Mortems: The progression from a naive “write me test cases” prompt to a strict 2-phase state-machine framework is a technical narrative in itself. Break

One of the most annoying parts of backend development isn't building APIs. It's keeping the...

Like a lot of you I hit the 5-hour cap most days. What actually annoyed me was realizing the weekly limit doesn't line up with that. Even capping out daily, I ended every week with quota unused. It expires overnight even after I paid for it. So I built claude-overnight . I queue questions during the day, /queue how do sqlite WAL checkpoints work? right inside Claude Code, and a scheduler runs them at night once my limits reset, through claude -p on the subscription. Morning brings markdown reports and a digest of what ran and what happened. Every job saves its claude session, so overnight resume <id> reopens the conversation that wrote the report. You can argue with it about its conclusions over coffee. Or overnight followup <id> "go deeper on X" and it continues tomorrow night. Coding tasks work too. They run in a throwaway git worktree on an overnight/* branch, only against repos I've explicitly trusted, so the agent never touches my working tree. Morning review is just git diff main..overnight/whatever . Since people will ask how it reads limits when there's no official API: Claude Code stores an OAuth token locally (Keychain on Mac, ~/.claude/.credentials.json elsewhere), and GET https://api.anthropic.com/api/oauth/usage with that token plus an anthropic-beta: oauth-2025-04-20 header returns your 5h and weekly utilization with reset times. Same trick the menubar trackers use. It's undocumented and the response shape already changed once while I was building this, so the tool survives without it. The design constraint I cared most about: don't eat my own morning quota. It won't start above 20% of the 5h window, stops at 60%, skips entirely past 80% weekly, rechecks between jobs. In the morning it opens a page in the browser with the whole batch on it — what ran, how long it took, the resume command for each one, and every report rendered inline so you're not clicking through files half-awake. Check it out at https://github.com/rohanprichard/claude-overnight Curio