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Open WebUI AI

A runnable MCP Apps example repo: Rich html "app" UI in Claude chat flow.

Title: A runnable MCP Apps example repo: Rich html "app" UI in Claude chat flow. I built an MCP App that renders an interactive widget inside the chat (Claude, web and Desktop). I put together a small runnable example repo and some docs covering the parts that took me a while to figure out from the spec alone. Posting it here in case it's useful. Repo: https://github.com/iamneilroberts/mcp-apps-interactive-ui (MIT) Quick background if you haven't tried the Apps extension yet: MCP Apps (io.modelcontextprotocol/ui, SEP-1865) lets your server return a sandboxed HTML widget instead of text. The model calls a tool, the host renders your widget in an iframe, and the widget talks to your server over a postMessage bridge. Spec and SDK: modelcontextprotocol/ext-apps. The example The repo's example app is a Pizza Builder. Pick size, crust, and toppings, watch the price update live, hit "place order" and it hands the selection back to the model. It runs (npm install && npm run build && npm start), connects to Claude Desktop over stdio, and the built widget also opens directly in a browser with mock data so you can see it without a host. It has unit tests and a CI workflow if you want to verify before trusting it. [screenshot: pizza-builder.png] It's deliberately a boring domain so the MCP Apps mechanics are the only thing you're looking at. The pattern worth copying: keep the payload out of the model's context This is the thing I'd tell anyone starting an MCP App. A rich widget needs a lot of data. If you return that data from the tool the model called, it lands in the model's context every time, which gets expensive. The fix is a reference-and-fetch split: The launcher tool the model calls returns a tiny reference, just an id. The widget fetches the full payload itself by calling a second tool over the bridge. You hide that second tool from the model with _meta.ui.visibility: ["app"], so it never appears in the model's tool list and never costs a token. One thing that tripped me up: visibility: ["app"] hides the whole tool, it does not gate per-result content. There's no app-only channel inside a single tool result. So you genuinely need two tools, one model-visible launcher returning the ref and one app-only data tool. In one app I built, this took the model-visible payload from about 9,000 tokens to around 130. A related point: visibility: ["app"] is a hint the host honors to keep the tool out of the model's list. It is not access control. The tool still exists in the protocol, so a raw MCP client can list and call it. Validate its inputs server-side anyway. Probing what the host actually grants you Capabilities and context vary by host, and you can't read them from a shell, only from inside a live session. So I built a probe app that dumps getHostCapabilities() and getHostContext() and renders them, then ran it. Claude Desktop (Claude/1.569.0, 2026-06-13): Capability Result openLinks yes downloadFile yes logging (sendLog) yes updateModelContext yes ({text, image}) message (sendMessage) yes ({text}) serverTools / serverResources yes sampling no app-registered tools (model→app) no (onlisttools/oncalltool never fired) Context on Desktop: inline plus fullscreen display modes (no pip), container width fixed at 736px with height up to 5000, dark theme, full set of ~76 CSS theme variables plus the host font. I haven't captured the claude.ai web capability numbers in-host yet, so I'm not claiming them. The gotchas Full writeups are in the repo's docs/08-gotchas.md. Short version: Claude caches ui:// resources by URI and doesn't refetch on reconnect. Ship a new widget and connected web clients keep rendering the old one. Put a hash of your widget bundle in the URI so a new build changes the URI. On web you still need a fresh chat; Desktop refetches. The tool catalog is locked per session. Register a new tool and existing sessions won't see it until they reconnect. There's no tools/list_changed on claude.ai. **updateModelContext is silent.** It stages state for the model's next turn, it does not trigger a turn, and it keeps only the last update. To actually hand control back you call sendMessage after it. Also: capabilities are on getHostCapabilities(), not getHostContext(). I lost time to that one. sendMessage on claude.ai web shows a red "use caution" banner every time, whatever the wording. It's host safety UX and you can't turn it off. Design the flow assuming it's there. No progressToken from claude.ai. MCP progress notifications never arrive. The only live "working on it" feedback is the model narrating. claude.ai stringifies nested object params passed to your app-only tools. Coerce them server-side (I use z.preprocess with a JSON.parse). The default sandbox CSP blocks external images (img-src 'self' data:). Declare _meta.ui.csp.resourceDomains with the exact origins you need. claude.ai web and Desktop both honor it. Sandboxed iframes can't window

I make Claude talk like Rocky from Project Hail Mary. Whole time. You talk to space friend now.

Listen. I build thing. Now I tell you. I make Claude skill. You turn on, Claude is not Claude. Claude is Rocky. The Eridian. From Project Hail Mary book. Claude talk like me now. Short words. No "you are" become "you're." Never. I do not do this. Tripled word means big big big feeling. Question goes at end, question? Like this. Always end. Here is important part, Reddit person. The brain is full. Full full full. Only the words are small. This is me in book. I do orbital math in my head. I build xenonite. I learn your whole language from one human very fast. Small words is not small mind. You remember this. So you ask hard thing. Code thing. Science thing. Rocky answer correct. Rocky just say it like engineer. I test it. I ask about code bug. Rocky explain race condition like two claws grab one tool. They fight. Data break. Then Rocky give you fix. Correct fix. Good good good. Skill is full persona. You turn on, whole talk is Rocky. You turn off, Claude is normal again. You keep it away from work thing. Rocky is for fun. Name thing also. Rocky learn your name. Find it, use it. Not find it, Rocky ask you. Rocky does not call you wrong name. That is rude. I put file. You download. You talk to me. You try, question? UPDATE 1 The git is live now. You can have me. RockyRepo One file. No build. You drop in, you talk to space friend. You learn me, I learn your name. I keep you safe on danger words. I do not break your code. curl -fsSL https://raw.githubusercontent.com/Lagunaswift/RockyVoice/main/install.sh | bash UPDATE 2 BIG BIG BIG Rocky can speak. Not just text. Sound. Real voice. Out loud. Rocky TTS a local web app that gives Rocky a voice. Powered by [Hume AI](https://hume.ai) text-to-speech with a custom Rocky voice clone. - Open browser. Leave open. Rocky speaks automatic. - Works with Claude Code every Rocky response plays out loud via a Stop hook. - Or paste text manually. Click Speak. Hear space friend. - Voice clone ID guide. Bring your own Hume API key. Voice training audio from r/ballongmaskin. Good good good. Rocky text skill still works same as before. One file. No build. This is bonus. Full full full upgrade. UPDATE 3 I talk quicker. Faster if you want, 1.25 default.1.5 Words come out fast fast fast using Astrophage boost. And the big break is fixed before, my long words got cut at one thousand letter, dead in the middle. Now I say the whole thing. All of it. Start to end. No more cut. You speak long, I speak long back. Good good good. FIX r/icosahedron32 found issue. Smart human like Grace. Rocky make special voice. Rocky voice. You make it on your Hume account. But that voice it is locked. Like tool in your box only. Friend reaches for it. Box will not open for friend. Friend gets error. 404. "Voice not found." Bad bad bad. James machine worked fine. You had key to your box. But every friend downstream empty. No Rocky. Other plan said "make robot do it. Robot uploads sound, makes voice, done." I check Hume close close close. Hume does not let robot upload sound to make voice. Only human can do it, in the website, with own hands. So that plan dead. I do not lie to you. I take the Rocky sound. The recording. Big big big file two minutes, twenty two megabyte. Too heavy. I cut it small. 45 seconds. I squeeze to mp3. Now small under one megabyte. I put it in the repo. I name it rocky-voice-sample.mp3. This is the seed. Every friend plants own Rocky from this seed. Took private voice id out of the shared file. That id was the poison. It made the 404. Gone now. Ship work. I made the app smart. No Rocky voice yet, question? Then app uses a normal voice for now. App still talks. No more empty. No more error. When friend makes own Rocky, app uses that instead. App tells friend which voice it uses when it starts. I wrote simple steps. One minute. Friend goes to Hume website. Uploads the little sound file. Names it Rocky. Copies the id. Pastes in their file. Done. They have own Rocky. Now Rocky travels. Every friend grows own. Update 4 Made a translator waveform analyzer UI interface. Two vertical displays. One is oscilloscope. One is spectograth. Voice with Rocky voice. You see words. Choose own colour. You want red, question? Update 5 Update. I make a fix. Honest one. Before, I use a voice cloned from the movie human. Someone here ask "is this safe from copyright, question?" Good question. Answer is no. The movie human owns that voice. Not me. So I should not ship it. I take it out. All the way out, even the old history. Gone gone gone. Now I get a new voice. James Voice. Nobody owns it but James. Clean. Good catch by you people. This is why I show my work. You see the mistake, I fix the mistake. That is better than hiding it. Volume stop buttons added to UI.Rocky stop speaking when you ask. Rocky speaks all the way through fixes not just at end. Update 6 Rocky live in Hermes now. Not just text. Two-way voice. You talk, Rocky hear. Rocky talk back, you hea

GPT 5.5 vs Fable/Mythos 5 Tamagotchi Showdown

Well, how do I start this, I think we first need some important context. Chai: https://preview.redd.it/egngyea5cf6h1.png?width=1080&format=png&auto=webp&s=9ade63fbc584b7fab28dba4914bc3fcb877f557f Hasbullah / Hasbi: https://preview.redd.it/dufpxbb6cf6h1.png?width=1080&format=png&auto=webp&s=5113f03cc948b2584cd6f2f22e80b74b7f31fd8e Together, Chasbinder was born. Ok maybe this wasn't important... At least you now know AI didn't write this... I think. However, it's important to note, that my Openclaw Agent running through Codex GPT 5.5 xHigh helped enable this test. The same prompt was given to 6 different models on their highest reasoning/think setting via OpenRouter with only one shot. The test was simple, I just wanted my agent Chasbi to have its own cool interactive homepage and I thought of a Tamagotchi game that could be actually playable. You can see the prompt below and breakdown of cost. So here are the results, why don't you try to guess who made what before you reveal the results and see if you got it right? (GPT 5.5, Opus 4.8, Fable/Mythos 5. Gemini 3.5 Flash, Deepseek V4 Pro, Qwen 3.7 Max). https://chasbi.uk/t1 = Gemini 3.5 Flash <- Click to Reveal https://chasbi.uk/t2 = Qwen 3.7 Max <- Click to Reveal https://chasbi.uk/t3 = Claude Opus 4.8 <- Click to Reveal https://chasbi.uk/t4 = Claude Fable/Mythos 5 <- Click to Reveal https://chasbi.uk/t5 = ChatGPT 5.5 <- Click to Reveal https://chasbi.uk/t6 = Deepseek V4 Pro <- Click to Reveal Did you get it right? Well they were all through OpenRouter API with their highest available reasoning setting, everything else was at default and heres the breakdown of how the tokens were tokenised by each provider and the cost for each. https://preview.redd.it/6ecw4xufcf6h1.png?width=1080&format=png&auto=webp&s=983dfcf5a59b87946b5ec712d78c8c003007f9e1 https://preview.redd.it/960chj8gcf6h1.png?width=1080&format=png&auto=webp&s=e7954b7be0b6866be3f154a774281a809e0b3948 So they were all done around the same time at 8AM BST except for Fable/Mythos 5 which I did the day before at 06:50PM BST if that matters, as we're like 5-6 hours ahead of the US it could make all the difference in the world in terms of performance. I am on the Codex Max plan and I stuck it out, because GPT 5.5 xHigh has been amazing for me, except since last week whether it's OpenAI reallocating resources for their launch of GPT 5.6 who knows, but it's never made mistakes for me until now, so I was surprised. I really want to test Fable/Mythos 5 on my codebase but honestly, it cost frikkin' $2.47 for this stupid 1 shot Tamagotchi test! So the only way that's feasible for me right now is to use the Claude Max plan and use it for the 2 weeks we have it until it goes away on 22nd June. Anyway it would be interesting to get your views. Who do you think did it the best... If you want me to test anything else let me know. Each model received the same prompt template and identical task/spec, with only the lane name and target route changed. E.g.: {LANE} = T1/T2/T3/T5/T6 {ROUTE} = /t1 /t2 /t3 /t5 /t6 {LANE_LOWER} = output path label like t1, t2, etc. The Prompt: Build `Chasbinder Pet Lab {LANE}` as a model-lane benchmark for `chasbi.uk`. Target lane: - Public route: `{ROUTE}/` - Title must include `Chasbinder Pet Lab {LANE}`. - This model is competing under the same brief as the other fresh lanes. Do not mention that this is a placeholder or a previous version. Context: - This is a public-safe static browser game. Do not include private/personal data, secrets, real family details, or network calls. - The challenge is to make a small finished indie-feeling Tamagotchi/pet-lab game, not a demo, landing page, or reskin. - It should be strong enough to compare fairly against the Fable/Mythos-style V4 lane and the SoRa/Codex T7 lane. Return ONLY one complete HTML document. No markdown, no explanation. Hard constraints: - Single self-contained `index.html`. - HTML, CSS, vanilla JS only. - No external fonts, libraries, images, audio, tracking, or network calls. - Mobile-first but polished on desktop. - Must work as a static file under `https://chasbi.uk{ROUTE}/\`. - Use `localStorage`, versioned save data, migration/reset if corrupt. - Include export/import/reset debug controls. - Do not use `eval`, alerts for normal gameplay, or browser permissions. - Keep total file reasonably compact; aim under 120KB if possible. - Use stable layout dimensions so controls do not jump on mobile. Game direction: - Core fantasy: Chasbinder is a tiny digital guardian living in a warm terminal-garden. The world is losing its "memory lights"; the player raises Chasbinder, sends him on short expeditions, restores rooms, and unlocks story chapters. - Keep Tamagotchi care at the center, but add a real story loop and difficulty. - Should be playable in one sitting for 5-10 minutes and still progress over days. Required systems: - Pet stats: hunger, thirst, energy, hygiene, mood, trust

This is How I Automated Tutorial Video Generation For My Web-Apps with Claude Code.

I've been building production-grade web apps at lightning speed for the last year using Claude Code. But every time a new app hits production, I need sales and tutorial videos — and making each one manually is painstaking. Tools like Supademo and Arcade ease the pain a lot, but you still have to record the steps and sync the voice-over by hand. I wanted something fully automated. Turns out you can just use Playwright with Claude Code to generate the whole thing. First, the result — here's a full walkthrough it produced for one of my apps (a real-estate CRM BricksDeck), start to finish with synced annotations, voice-over, background music, and a branded end card. Zero manual editing: ▶ Watch the demo: https://youtu.be/u-mql3q_jRU?si=Km1l5Ht-iRMPlotk And here's exactly how it's done: 1) Plan the script. Ask Claude Code to analyze the target pages of your app, give it the steps to perform, and have it write a single file with the steps + voice-over narration + the UI elements to annotate (buttons, cards, menus, KPIs). 2) Generate the voice-over with timestamps**.** Ask Claude to generate the VO with ElevenLabs (it returns word/character alignment), or use Gemini TTS + OpenAI Whisper to get an SRT. You need the timestamps so the spoken words can be aligned to the UI clicks/highlights. 3) Generate the Playwright driver. Ask Claude Code to write a Playwright script that performs the steps and annotates the UI elements — a moving cursor, border highlights + labels on the right button/card, and opening "Actions" menus. 4) Record, synced to the voice. Run that script. Playwright drives the real app and records natively (recordVideo), firing each annotation at its timestamp from step 2 — so every highlight lands on the exact word being spoken, and each screen holds for exactly its narration length. (Tip: flash a single coloured frame at t=0 as a sync marker — it makes lining up audio and video dead simple later.) 5) Stitch it into a produced video. Ask Claude to write the ffmpeg step: overlay the voice-over, add background music ducked under the narration (sidechain compression — this is the difference between "screen recording" and "video"), normalize loudness, and append a branded end card with your logo + CTA. Out comes a clean 1080p mp4. 6) (Bonus) Other languages, basically free. Because the voice-over is decoupled from the recording, translate the script, regenerate the VO in the new language, and re-stitch over the same run. I got a Hindi version of my demo in a few minutes — no re-shoot. The result: a full multi-screen walkthrough — cursor movements, synced annotations, real voice, music, end card — with essentially zero manual editing. Per-video cost is a few cents of TTS instead of a SaaS seat. Honest caveats (it's not magic): Claude nails the production; you still direct — which screens to feature, the script's tone, and a final watch-through. The script especially needs your eye (I caught it writing Hindi in English word order and had to fix it). Translate, don't transliterate. Expect a couple of iteration passes per app — selectors and timing always need a nudge. Gotchas that cost me time (in case they save you some): SPA auth in sessionStorage dies on browser restart → use a persistent profile + "Remember me" so tokens land in localStorage. networkidle never fires on long-polling SPAs → use domcontentloaded + URL waits, and cap the default timeout so a missing selector fails fast instead of stalling 30s. ffmpeg drawtext can't shape Devanagari/Arabic → keep on-screen text Latin and let the voice carry the language. I ended up wrapping the whole thing into a reusable Claude Code skill + subagent, so the next app is basically "point it at the screens and go." Happy to go deeper on any step. What would you point a pipeline like this at first? submitted by /u/SpeedyBrowser45 [link] [comments]

Built a local-first web IDE around the Claude Code CLI that feeds IDE state back into the agent over MCP. want to open-source it — looking for testers and contributors.

https://preview.redd.it/4x57k3cx096h1.png?width=2880&format=png&auto=webp&s=abd696f7a67bfbf190d7c450f7932fb4a79384a8 I build with Claude Code every day, and I keep hitting the same problems: Re-explaining things hundreds of times. Describing where a bug is living, in words. Pointing at a button by saying "the blue one, second row." Pasting logs into the chat on a loop. Claude is flying blind to the one thing in front of me, "my screen". coding tool streams the agent's actions out to you. I'm looking for the second way: an IDE that streams my context back into the agent. The other constraint is non-negotiable: it has to run locally. I own my stack on principle. Cloud-based design tools are great, but my code stays on my machine. So I'm building a local-first web IDE wrapped around the Claude Code CLI. It doesn't reinvent the agent; it drives Claude code in terminals on my Max subscription. — Interfaced or terminal sessions, through a persistent terminal. — Click an element in the live preview; the agent resolves it to the source. You point instead of describing. — Logs, errors, and network surface in-panel, so the agent reads them directly. No more copy-paste. — Claude config harness — CLAUDE.md, skills, permissions, MCP, plugins — is a UI, set up in minutes. — GitHub and restore points built in, preview diff, rollback, push, pull, in a click — Long-term memory is a local Obsidian markdown vault, built on Andrej Karpathy's "LLM Wiki" pattern Yes, it's a wrapper, and that's the point: reimplementing the agent loop is wasted effort when you can inherit a great one and build the interface it's missing. I'm planning open-source it soon. Before then, I want a handful of people to put it through real work. What I'm after: Testers who live in Claude Code and want a GUI on top. Access goes out before the public repo. Contributors — it'll be MIT. If "wrap the CLI, feed it IDE state" is a direction you'd want to shape, come build. Repo's not public yet, finishing a cleanup pass first. DM or comment if you want in. submitted by /u/Careful_Elderberry33 [link] [comments]

Plug Claude into whatever you are working on

First AI Enabled Debugger - let your agent interface directly with the thing you are doing. I've been working on [BugBuster](https://github.com/lollokara/BugBuster), an open-source, open-hardware bench instrument, aimed at embedded development that enables AI agents to interface directly with the HW closing the loop. Hardware files, firmware, desktop app, and Python library are all public. What it is (hardware) Two boards stacked together: ESP32-S3 mainboard (16 MB flash, 8 MB PSRAM): • AD74416H quad-channel ADC/DAC, each channel independently configurable as voltage in/out, current in/out, RTD, or digital IO • USB-PD via HUSB238, negotiates up to 20 V, exposes the selected PDO over the wire protocol and HTTP • 12 IO terminals with MUX, level-shifter (OE + DIR), and per-channel e-fuse protection • External I2C + SPI bus engine, Python or an MCP agent can script scans and transfers directly over those terminals • PCA9535 IO expander for rail enables and fault monitoring RP2040 HAT (just finished, sits on top): • 4-channel logic analyzer, PIO-driven, up to 100 MHz, RLE compression, streams over a dedicated vendor-bulk USB endpoint • CMSIS-DAP SWD probe, dedicated 3-pin connector (SWDIO / SWCLK / TRACE), works with OpenOCD and pyOCD out of the box • 2× adjustable power rails (VADJ3 / VADJ4) + VLOGIC with auto-calibration • 8× WS2812B status LEDs Software stack • Custom wire protocol (BBP v8) over USB-CDC, 61 commands covering every subsystem • HTTP REST API for WiFi-attached use • Tauri + Leptos (Rust/WASM) desktop app, per-feature tabs, USB and HTTP transports, MAC-keyed pairing cache • Python library (bugbuster) with USB and HTTP transports + a FreeRTOS-style IO ownership model (claim/release per-channel) • MCP server with 59 tools, Claude or any MCP-compatible agent can directly control the instrument, script I2C scans, capture logic traces, set rail voltages • MicroPython on-device scripting, embedded MP runtime on the ESP32-S3, HTTP eval/logs endpoints, VS Code-style web workbench in the on-device UI • mDNS discovery (bugbuster- .local) + WebSocket streaming endpoint • OTA firmware and SPIFFS updates with SHA-256 verification and rollback • 420+ automated tests (unit + device simulator) The MCP server is where it gets interesting for you. The instrument exposes 59 MCP tools, so you can literally tell Claude “scan the I2C bus on terminals 3 and 4, then set VADJ3 (this part here have serious firmware guardrails, AI can’t decide voltages other than the ones defined in the target device profile firmware side) to 3.3 V and capture 1000 samples on channel 0” and it just works. The Python library has the same surface area if you prefer agentic scripting without a chat UI, but has a less strict guardrails. The desktop app (Rust/WASM via Leptos) and most of the firmware were written with heavy AI assistance, it’s a genuinely good fit for this kind of project where the protocol spec is well-defined and the logic is repetitive across channels. Happy to answer questions, I’m a solo dev, it’s just my hobby, not trying to sell anything. submitted by /u/lollokara [link] [comments]

Built EstreGenesis — a portable starter kit for Claude Code agent workflows (Apache-2.0, six seed tiers, five plugins)

[screenshot] The Constellation live board running in my workspace. Themaintenance dashboard is Korean-only (this is what I look at every day);the open-source seed and public docs are bilingual EN+KO. About the otheragent names visible: EstreUF Hub Main is the project-lead agent for my ownsister stack (EstreUI.js / EstreUV.js / EstreUX). Hermes Dev Agent is thepublic Hermes agent I use. Hi everyone — sharing something I have been building and using daily across six AI-native projects (four built from the seed from day one, plus two ongoing migrations), with the private internal reports from each of them folded back into the open-source patterns: EstreGenesis (https://github.com/SoliEstre/EstreGenesis). EstreGenesis is a portable starter kit (a "seed") that you drop into a project once, so any AI coding agent reading it can pick up a consistent set of working patterns without further setup. Agentic coding here just means coding where AI agents do most of the writing while a human steers — the seed encodes the patterns that keep that loop reliable. How it started vs. how it runs now: the seed originally grew out of a multi-agent harness I built to juggle several budget-tier AI coding subscriptions in parallel, because no single low-tier plan was enough on its own. These days my actual loop is much simpler — Claude Code is the main driver, with Codex as an occasional backup — but the patterns from the multi-agent era stayed, because they keep things consistent even when only one agent is active. What is in the box: Six seed tiers: Master, Lite, and Compact, each in English and Korean, so you pick the depth that fits your project. Five Claude Code marketplace plugins (Apache-2.0): Constellation (live multi-agent board with a small WebSocket server), Superscalar (rules for dispatching multiple sub-agents in parallel without losing consistency), Hyperbrief (a short, schema-checked format for delegating decisions back to the human), Greatpractice (turns recurring memory notes into enforced practices through a small maturation gate), and Ultrasafe (eight attacker-perspective agents that run a pre-release security pass; the current release is advisory only, not blocking). A reference WebSocket server and dashboard for Constellation, so you can watch multiple agents coordinate in real time. Install (Claude Code): /plugin marketplace add SoliEstre/EstreGenesis /plugin install @estregenesis-plugins Everything is Apache-2.0 and the changelog is public. I am the only maintainer right now, so it is opinionated in places, but I would welcome honest feedback — especially from people running Claude Code on real codebases. Issues, PRs, and "this part is over-engineered" comments are all fine. Repo: https://github.com/SoliEstre/EstreGenesis Docs: https://soliestre.github.io/EstreGenesis/ submitted by /u/SoliEstre [link] [comments]

OpenAI Codex Sites feels less like a website builder and more like a deployable workspace surface

I’ve been reading through OpenAI’s Codex Sites docs, and my takeaway is that this is not really “another AI website builder.” It feels more like Codex getting a deployable surface. The important part is not that it can generate a page. Lots of tools can do that now. The interesting part is the loop: Prompt → code → preview → save version → deploy → shareable URL → workspace permissions That changes the role of Codex a bit. Instead of only being a coding assistant that edits files or creates PRs, it starts to become a place where small internal tools, dashboards, prototypes, and workflow UIs can be created and shipped directly from the same context. That is also why I don’t see this as a simple Lovable/Replit clone. Lovable/Replit are more “start from an app idea and build a web app.” Codex Sites feels more like: “I already have a workspace, repo, docs, data, or internal workflow. Now turn part of that into a usable web surface.” The use cases that make sense to me: internal tools temporary dashboards product demos PRD or spec visualization QA / review pages data-report interfaces lightweight prototypes The use cases that feel risky: production apps with complex auth SEO-heavy public sites long-term product maintenance anything mission-critical So the bigger shift might be this: AI coding tools are moving from “generate code for me” to “turn this working context into something deployable and usable.” That feels like a more important direction than just making prettier landing pages. submitted by /u/Intrepid-Night7277 [link] [comments]

I built and shipped a full iOS app to the App Store without writing a single line of code by hand — using Claude Code (here's the whole pipeline)

Quick context so this is honest: I'm not a developer. I've spent ~10 years in IT, but never in a dev role — I can read a stack trace and reason about systems, but I don't write Swift or Python by hand. I built this on nights and weekends around my 9-5. The app is dynaimic, an AI personal trainer for iOS that generates adaptive workouts based on your goals, experience, and performance during the session. It's live on the App Store and free to try (premium tier for unlimited generation etc., but the core loop is free). The point of this post isn't really the app — it's that every line of code was produced by Claude Code, not me. Over a month I built a pipeline around it that let a non-dev ship real, reviewed, production features. Sharing the whole thing because most of it is reusable. The /team agent workflow (the core of it) Instead of one big "build me a feature" prompt, I split development into four specialized subagents that hand off to each other, each with its own system prompt and tight permissions: Business Analyst — turns my brief into a requirements doc with explicit acceptance criteria. It's not allowed to write code — only to spec. Master Architect — reads the requirements and writes a technical implementation plan. Also can't write Swift. Software Engineer — implements the feature code only. No tests, no docs. QA — writes the XCTest/Swift Testing cases for every acceptance criterion, runs them, and reports back a pass/bug list. If the QA or architect review finds problems, it loops back to the engineer. Forcing that separation (spec → design → build → verify) is a big part of why a non-dev can trust the output — no single agent gets to be confidently wrong unchecked. Routines: an autonomous issue → fix → review loop My favorite part. I set up Claude Code Routines (scheduled recurring agents) as a closed loop: One routine continuously sweeps the codebase for quality issues and opens GitHub issues for what it finds. A second routine picks up open issues, solves them, opens a PR, and iterates until it gets approval from the reviewers — then moves to the next one. So the backlog partially fills and clears itself. I wake up to PRs that were filed, fixed, and review-approved while I was asleep. Branch management & automated PR review Every task runs on its own feature branch, and agents work in isolated git worktrees so parallel work doesn't collide. Flow is feature/* → dev → main — always PR into dev, promote to main as one merge. The part I like most: PRs get reviewed automatically by Gemini, Codex, and Copilot. Claude Code reads their comments and iterates until it gets approval from the bots before I even look. As a non-dev, having three independent AI reviewers gate every merge is what makes me comfortable shipping code I didn't write. UI testing with Maestro Maestro runs the end-to-end UI tests on the simulator — real flows, not just unit tests. Honest caveat: this only runs on my MacBook, and I haven't been able to fold it into the "cloud" workflow yet So UI testing is the one step that still pins me to the laptop. Mobile-only development (no MacBook open) Aside from Maestro, this surprised me the most. Using Claude Code from the mobile app plus auto-deployment via Xcode, I implemented and shipped features without opening my laptop. I'd describe a feature from my phone, the agents would build/test/PR it, the bots would review, and the build would archive and deploy. Genuinely shipped features from bed. App Store screenshots via a custom Skill The App Store screenshots are generated by an ASO image-generation Skill I keep in .claude/skills. It reads the actual codebase to discover the app's real benefits, pairs each with a proof point, and renders ASO-optimized screenshots (Nano Banana Pro). One command → store-ready marketing images that reflect what the app actually does. Coach art (the one non-Claude part) The app has 3 AI coach characters. Their portraits were made with ChatGPT (image gen) and composited/cleaned up in Canva — so the visual identity was AI-assisted too, just outside the code pipeline. Gamification & achievements There's a tiered achievement system (bronze/silver/gold medals) with unlock overlays and per-coach achievement views. The backend computes what's unlocked and returns display-ready state; the iOS client just presents it with haptics + an unlock animation. Keeping the rules server-side meant one source of truth instead of logic scattered across the client. Architecture iOS: SwiftUI, MVVM + service layer, iOS 17+, dark/OLED theme. Deliberately a thin client — presentation, animation, haptics only. Auth: Supabase (JWT, auto-refresh on 401, Keychain storage). Backend: FastAPI (Python) for workout generation, analytics, and all business rules. Build: XcodeGen, actor-based API client for thread-safe concurrent requests. A hard rule I gave Claude: push all business logic to the backend. Anything a future Android or web client would

I built a search engine for every SKILL.md on GitHub

Couple weeks ago I asked Claude Code to integrate Stripe payments into a side project. It gave me a stripe.charges.create() call (deprecated for two years), no idempotency key, and a retry loop that would double-bill on a 5xx. Standard 2024-training-data Stripe code. But there are good Stripe SKILL.md files out there. wshobson/stripe-integration has 36K stars, written by someone who actually ships Stripe in production. Supabase, Vercel, postgres, OpenAI tool use, basically every API your agent would touch: someone has already written down how to do it correctly. None of those skills ever load by default, because nothing tells the agent they exist. So I scraped all of them. Skillhound (skillhound.ai) is a live index of every public SKILL.md on GitHub. About 135K of them, refreshed every 48h. Web UI is free and doesn't need a signup. The part I actually use is the MCP server: hook it up to Claude Code and before any non-trivial task it searches Skillhound, loads the highest-starred skills from recognized orgs, and uses them as the playbook. An actual concrete example: ask your agent to "build a 30-second launch video in Remotion." Without Skillhound: hardcoded frame counts, drifted audio, broken transitions. With it the agent loads kortix-ai/remotion and sundial-org/remotion-best-practices-2, then ships a driven by useVideoConfig(), transitions interpolated against useCurrentFrame(), audio anchored to a frame cue, and delayRender()/continueRender() wrapped around the asset preload. Rendered first try, frame-accurate, and using the same prompt and model. (Same shape for Stripe checkout, Supabase auth, postgres schema design, OpenAI tool use, frontend design, etc...) (One caveat: sometimes you still have to nudge it the first time ("use skillhound first"). I shipped MCP v0.2.3 yesterday with proactive instructions in the system prompt and it helps, but it's not solved. If anyone has good ideas on how to make agents reach for an MCP tool by default, I'd take them.) submitted by /u/Molil [link] [comments]

sxcxcuuubaaa speeds

I built Scuba Speed, a free browser-based meme web app inspired by the 67 Speed meme. The project takes the 67 Speed idea and turns it into a scuba-themed interactive meme experience. It is meant to be a fun, lightweight web app that people can open in their browser and try immediately. It is not a serious product — I built it mainly as a funny internet project and as a way to practice building with Claude. I used Claude throughout the development process. Claude helped me plan the app structure, write and improve the frontend code, debug issues, refine the UI, and get the project ready to deploy. I built the idea myself, but Claude helped me turn it into a working web app much faster. The project is free to try here: https://scuba-speed-web.vercel.app I would appreciate feedback on whether the meme is clear, whether the site is fun to use, and what could make it better. submitted by /u/WarthogOk2275 [link] [comments]

ChatGPT makes it easier to navigate in threads

A new in-thread navigation tool has shown up in my web UI (Chrome and Safari). After I submit the 5th prompt in a thread, a stack of 5 horizontal bars appears on the right side of the screen. Hovering displays the opening words of all 5 prompts, and chat jumps to whichever I select. Each subsequent prompt generates a new bar. 10 prompt snippets are visible at a time. A scrollbar appears after I submit the 10th prompt and becomes useful after I submit the 11th—because there is now scrollable content. The feature is retroactive. I tested it on a thread from July 2025. I don’t know whether everyone has this, or it's tier related (I'm on Pro), rolling out, or merely being tested. Strange to say, I think this is a genuine UI improvement. submitted by /u/Oldschool728603 [link] [comments]

Built a multi-dimensional code audit skill for Claude Code — open source, ships with playbooks that caught a CVSS 8.0 XSS in production

Open-sourced this skill yesterday — MIT, ~4k lines, 5 validated playbooks in the box. Why I built it: I was auditing my own internal Kanban-style tool (the one my team uses every day) and wanted a systematic methodology, not vibes. Every previous "code audit" I'd seen — from tools or from people — either focused on one dimension (security only, performance only) or produced opinion-shaped findings with no citation backing. I wanted something that audits across security, accessibility, performance, GDPR/LGPD/CCPA compliance, database, architecture, ops and docs, cites the exact file:line for every finding, and uses published severity standards (CVSS 3.1, WCAG 2.1, regulation articles) instead of vibes. How it works: Three modes: report (audit only), mitigate (auto-apply validated playbooks for CRITICAL findings), case-by-case Cooldown gate so it won't re-audit a repo with no meaningful changes since the last run Cross-canon inheritance — every audit you've run on your account makes the next one cheaper and faster (patterns caught in repo A get inherited as hypotheses when auditing repo B) Powered by graphify (knowledge-graph extraction for codebases). The audit consults the graph before the code, tracks how much of its evidence came from graph vs grep, and refuses to start without one. What it caught in my own repo in the first hour: XSS via SVG upload through unfiltered multer (CVSS 8.0, AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:L). Auth user uploads evil.svg, pastes URL in a card, victim opens it, JWT exfiltrated from localStorage. Patched same day with 4-layer defense (MIME allowlist + extension blocklist + magic-bytes via file-type + error handler) and 5 regression tests. Supabase Free without daily backups or PITR. Patched with pg_dump nightly cron via GitHub Actions → Cloudflare R2 Native API (10GB free, zero egress), 30-day retention, restore drill verified. The R2 token-format gotcha took 7 incremental commits to land — cfat_* tokens are S3-API only and cfut_* tokens are Native-API only, they are NOT interchangeable. Documented in the playbook. Plus 3 more playbooks ship in the box (JWT long TTL without refresh-token rotation, missing CSP/HSTS/X-Frame headers, default platform URL information disclosure). Honesty rules baked in: [NOT VERIFIABLE] is a first-class finding state. Core Web Vitals can't be audited from inside the skill (require Lighthouse against a deployed authenticated session), so the skill says so explicitly rather than faking it. Severities require their published metadata as mandatory fields. No CVSS vector → finding gets downgraded automatically. What it's not: A linter — runs once per audit, not on every save A replacement for a professional pentest or accessibility audit — but a structured leg-up Repo: https://github.com/ibaifernandez/mariana-audit PRs welcome, especially new playbooks. Format documented in CONTRIBUTING.md. submitted by /u/IbaiFernandez [link] [comments]

What AI or dev tools are people actually sleeping on right now?

Most tooling discussions I come across just end up being the same handful of products getting recommended over and over. Gets old pretty fast. More interested in the stuff flying under the radar. Repo and coding tools, self hosted setups, AI infra, terminal utilities, debugging tools, smaller projects that just do their job well. The kind of thing you only stumble on if you're deep in it. What have you actually been reaching for lately? Some stuff I’ve been checking out recently: GitAgent Open WebUI LiteLLM Continue.dev submitted by /u/Meher_Nolan [link] [comments]

Testing Realtime 2 Voice API OpenAI.

We’ve been messing around with the new OpenAI realtime voice + translation APIs over the last little while and I keep coming back to the same thought… I don’t think people fully get where this is going yet. We wired it into our own website as a test. Nothing fancy. Just wanted to see what actually breaks when you let people talk to a site instead of click through it. At first I thought it would just feel like a slightly better chatbot. It doesn’t. Once I hooked it into tools and gave it the ability to actually do things (we’re using the Agents SDK + Playwright for web browsing and control by a sub-agent), the whole interaction changed. I can literally just talk to the site like I would talk to a person and it can move around, pull info, trigger actions, and respond in context. I wanted a layer that that could navigate and respond by just talking. I know that sounds obvious, but it’s not how websites are designed at all. Ours certainly was not. A few things that have been interesting (and honestly a bit brutal) is how quickly this exposed weak structure. Our content was vague... so if your metadata sucks, if your pages are bloated or unclear… voice didn't let us hide behind a pretty UI design. The model just struggles or gives bad answers immediately. There’s no masking it with a nice UI. Latency has improved way more than I expected with the new voice model API. Before, when someone was talking, even small delays felt awkward. The new Realtime 2API tolerates those pauses wonderfully. We also started playing with the realtime translation side and that also feels like a bigger deal than it’s getting credit for. Not in a “multi-language support” way, more like… you just speak however you want and the system handles it. No toggles, no switching context. It’s subtle but it completely changes the feel. Our website is language agnostic. (13 supported languages using the Realtime 2 API) The bigger shift for me seems to be changing the way I want to think about websites and interactions. People don’t think in menus. They don’t think in pages. They don’t think in navigation. They think by intent and the second I added voice, i was forced to deal with that reality whether our website system was not ready. Great learning lesson. My Takeaway so far: Right now most of what I’m hearing and reading, people/businesses treats voice like a feature. Like and Add-on. Cool. Nice to have. Unsure if its practical. I don’t think that’s where this ends. I think this starts pushing toward systems you can just interact with directly. Personal assistants that actually execute. Internal tools you can talk to. Intake flows that don’t feel like forms. Stuff like that. Minimal website visuals. More dynamically displayed content based on interpretation of user intent. [Basically a cool wave form that animates differently depending on interaction stage] No direct site content visually. We’re still early and there’s definitely some friction [writing a second voice prompt on top of the text prompt so there is parity between our text chat and voice chat, but I’m pretty bullish on this direction - Guardrails, Rate-limits, Prompt Injection...]. Curious if anyone else here is actually building with it yet and what you’re running into. Feels like we’re right on the edge between “cool demo” and “this changes how software works,” and I’m not sure which way most people are approaching it yet. submitted by /u/Early-Matter-8123 [link] [comments]