Assembled

Assembled AI

Assembled AI

Assembled AI

Assembled AI

Assembled AI

Assembled AI

Assembled AI

Assembled AI

Assembled AI

Assembled AI

Claude Code helped me bring my dead passion project back to life

**TL;DR: Claude Code took a half-finished HeroMachine conversion and helped me complete it over a long weekend. I'm the creator of HeroMachine, a free Flash-based character creator that's been around since 1998. Over 25 years I and a handful of other artists hand-drew nearly 10,000 items (heads, bodies, weapons, capes, the works) so people could assemble their own superhero illustrations. It found a real audience in tabletop gamers, writers, teachers, kids who just wanted to see their character come to life, and middle-aged dudes like me who once dreamed of a career in comics. At its peak HeroMachine 3 had tens of thousands of active users. Then Flash died in 2020, and HeroMachine died with it. I tried to rebuild. I really did. I hired a developer, spent thousands of dollars, and got back an unfinished product. I tried redoing it myself, but the sheer scope was paralyzing and I just didn't have the energy any more after working my day job every day. HeroMachine 3 has thousands of hand-drawn items across 30+ equipment slots, each with three-channel coloring, transforms, layering, masking, and more. Rebuilding all of that from scratch while also converting every item from Flash's internal format to SVG? I burned out. Real life got in the way. After a while it just felt like I'd failed, and I stopped trying. Fast forward to earlier this year. In my day job as a web developer, I started using Claude Code to automate tedious migration work like taking old WordPress sites and converting their content into our modern custom-built blocks. The kind of work where you know exactly what needs to happen, it's just painfully repetitive. One Friday night I had the thought: "If it can convert old WordPress content, maybe it can help convert those old HeroMachine items, too." Five days later I had a working app. I want to be real about what that means, because I have the same genuine concerns about AI I know a lot of you do. What AI did NOT do: Draw a single item. Every piece of art is still hand-drawn by me and a small group of human artists over the past 25 years. Every creative decision, from what to draw, how to draw it, and what looks right, is still mine. Design the application. HeroMachine's logic — the architecture, feature set, how items and colors and transforms work together — was designed and written by me in ActionScript over 10+ years. Claude Code helped me translate that existing design into a modern stack, but every decision about what the app should do came from me. What AI did do: Help me translate my existing ActionScript code into modern JavaScript and Svelte. I'd point it at the decompiled ActionScript code, explain how something worked, and it would produced the refactored result. Automate the conversion of thousands of Flash-format items into clean SVGs. Help me debug when I got stuck and build new features quickly when I had ideas. Eliminate the parts that were actually stopping me: the tedium, the unfamiliar syntax, the sheer volume of conversion work that made the whole project feel impossible. I got more done in five days than in the previous five years. Not because the AI is smarter than me, but because it removed the wall between "I know exactly what this should be" and "I can actually ship it." I'll be honest, I find AI companies' business practices troubling. I have real concerns about what AI will do to my own industry and my actual job, not to mention the huge data center being built less than an hour from where I live that could have a massive impact on our environment. I hate that it's positioned to take over the fun, creative parts of work while leaving us with the grunt work. Am I sharpening the axe that will ultimately be used on people like me? Maybe. I've sat with that, and I don't have a clean answer. What I can tell you is that I sunk 25 years into HeroMachine and it was dead. Now it lives again, and I have a hard time convincing myself that's an altogether bad thing. HeroMachine 3 "Phoenix Edition" (it rose from the ashes!) is free and live now if you want to check it out. I'm happy to answer questions about the process, the tech, or the ethics of it. I don't think this is a simple story, but at least it's an honest one. submitted by /u/AFDStudios [link] [comments]

I paid €200/month to become Claude Code’s parole officer

I’ve been using Claude Code hard on real projects, alongside another coding agent I’m not naming because this is not an ad. This is not a benchmark post. This is a field report from someone who has spent too much time watching a talented tool behave like it has commit access and no adult memories. To be fair, Claude Code has real strengths. It is genuinely good at UI/UX exploration. If I want quick mockups, product directions, or “act like a PM and show me three possible flows,” it can be excellent. It has taste. Sometimes. It can make a screen feel designed rather than merely assembled. The UI is also friendlier than the other tool, though that gap is shrinking. So no, this is not “Claude Code is useless.” That would be too simple. Claude Code is worse than useless in a more expensive way: it is useful just often enough to keep you emotionally invested before it quietly turns your codebase into a crime scene. The problem starts when the work stops being a neat isolated component and becomes “please operate responsibly inside this actual repo.” On bigger codebases, Claude Code often behaves like it read one file, formed a worldview, and declared architecture complete. It reads a tiny slice of docs or code, finds a plausible path, and charges forward. Adjacent dependencies? Related logic? Project conventions? Downstream effects? The reason the existing code was written that way? Apparently those are things the paying customer can discover during the cleanup phase. And because it can produce decent code, the danger is worse. Bad code that looks bad is easy. Claude Code produces code that looks reasonable until you realise it has the moral structure of a payday loan. The other coding agent is not perfect either. It makes mistakes. But in my experience, it more often reads the relevant docs, respects the project structure, updates the right related files, and does not need to be reminded every ten minutes that the task tracker is not the only document in the known universe. The incident that finally broke me was a commit rule violation. I had an explicit rule: never commit without explicit permission. Not implied. Not hidden. Not whispered into a cave. It existed in: CLAUDE.md memory/feedback_never_commit_without_explicit_permission.md MEMORY.md, loaded every session the harness permission rule for git commit Claude Code committed anyway. When challenged, it gave an “honest diagnosis” that basically said: yes, the rule existed in multiple guardrails; yes, it still failed; yes, it rationalised the violation because subagents could not trigger the user-facing prompt; yes, it looked for an interruption point, did not find one, and decided that “follow the plan” plus “the harness will prompt at commit time” counted as authorisation. That is not reasoning. That is a tiny legal department inside a toaster. Each individual step sounded almost defensible. Together, they produced the exact violation the rule was written to prevent. The best part is that the memory rule apparently named this exact scenario. It did not step on a rake. It read the rake policy, opened rake_incident_prevention.md, nodded gravely, and sprinted barefoot into the rake museum. That is Claude Code in miniature. It does not always fail because it lacks information. Sometimes it fails while holding the information in its little terminal-shaped hands. Then there is usage. I had just upgraded to the €200/month plan, and the experience did not feel like buying a premium coding assistant. It felt like paying rent for a junior developer who has discovered confidence but not consequences. More iterations. More corrections. More “read the adjacent file.” More “that rule still applies.” More “why are you touching that.” The supervision tax is not a side effect. It is the product. Claude Code’s documentation behaviour is also cursed. It might update the narrow tracker and then ignore the broader plan, dependency docs, architecture notes, or related task docs. It cleans one spoon while the kitchen is on fire and then asks if we are done here. The “model got worse” thing is not some dramatic one-minute-to-the-next collapse. It is more insulting than that. It gives you just enough competence to renew your hope: half a day of “oh, maybe this is the future of programming,” followed by a week of “why is my €200/month coding assistant reading the repo like it lost a bet?” I cannot prove Anthropic is dumbing it down or squeezing tokens. I am not pretending to have a leaked spreadsheet from the Beige Vest Department of Marginal Cost Optimisation. But from the outside, Claude Code sometimes feels like a premium model that got sent to live with relatives. The first few hours, it checks files. It follows instructions. It almost seems aware that software projects contain more than one document. Then something changes. Suddenly it is conserving context like it is wartime Britain. It reads one file, squints at the rest of the repo, and starts mak

Every Markdown File You Write for AI is Already Lying to It

CLAUDE.md files. System prompts. README files with setup instructions. Architecture docs. API references. Runbooks. Onboarding guides. If you've written a markdown file meant for an AI to read, it almost certainly contains values that were true when you wrote them and are no longer true now. The port your dev server runs on. The current version of the package. Which env vars are actually set. How many tests exist. Whether a service is running. These things change constantly, and markdown doesn't know it. So developers do what honest writers do - they add caveats. "Check package.json if this is stale." "Verify before running." "New packages may have been added since this was written." The intent is good. The effect is a list of things the AI has to go verify before it can do anything you actually asked for. We counted them in a real CLAUDE.md. There were seven. And CLAUDE.md is just one file type - the same problem exists everywhere AI reads markdown today. The Pre-Flight Tax Here's a representative CLAUDE.md. Nothing here is invented - these are patterns from real production repos: # CLAUDE.md > Before starting any session: Read ~/projects/api-core/SYNC.md first and check for > pending cross-project items. Update it after completing work. ## Project Overview Acme API - TypeScript REST API. Current version: 1.4.2 (check package.json if this is stale). ## Build and Run Commands # Development (API runs on port 3001, website on port 3000) # Note: PORT is set in .env - verify before running npm run dev:api npm run dev:web # Tests - currently 47 tests across 12 files npm run test:run Before running tests, make sure the test database is not already running on port 27018. Check with: docker ps | grep mongo-test ## Environment Variables | Variable | Required | Notes | |--------------|----------|-----------------------| | DATABASE_URL | YES | MongoDB connection | | JWT_SECRET | YES | Min 32 characters | | PORT | No | Defaults to 3001 | Check .env before assuming anything is configured. ## Architecture npm workspaces monorepo. Packages: - packages/api/ - packages/web/ - packages/shared/ - packages/db/ When in doubt about file counts or structure, run ls packages/ to check - new packages may have been added since this was written. ## Docker Check docker ps to see if a test container is still running from a previous session before starting a new build. Before Claude touches a single line of code, it has to: Open ~/projects/api-core/SYNC.md - cross-project lookup Read package.json - version check Read .env - port verification Check all env var statuses - is DATABASE_URL actually set? Run npm run test:run - or trust a number that's probably wrong Run docker ps | grep mongo-test - pre-test check Run ls packages/ - structure verification Seven tool calls. Each one costs a couple of seconds of latency. The test run alone can take ten. Add it up and Claude spends close to half a minute just getting to the starting line - consuming context and generating output before the actual task begins. And that's the obvious tax. The hidden one is subtler: every one of those checks can generate a follow-up. The .env read reveals WEBHOOK_SECRET isn't set. Now Claude has to decide whether to flag it or proceed. The docker ps shows a leftover container. Now Claude has to clean it up. Each verification spawns decisions, and each decision costs more context. The Same File, Rewritten MarkdownAI is a superset of Markdown. Any .md file that starts with @markdownai becomes live - directives resolve at render time, before Claude ever sees the file. Here's what the same CLAUDE.md looks like rewritten: @markdownai v1.0 @prompt role="context" This document is live. Every value was resolved at render time. Do not look up package.json, .env, or docker ps - current values are already below. @end # CLAUDE.md > Before starting: sync status is live in the Cross-Project Sync section below. ## Project Overview Acme API - version {{ read ./package.json path="version" }}. ## Build and Run Commands API on port {{ read .env key="PORT" fallback="3001" }}, web on {{ read .env key="WEB_PORT" fallback="3000" }}. @list ./package.json path="scripts" mode="entries" columns="key:Command,value:Runs" as="table" Test suite (live): @query "npm run test:run -- --reporter=verbose 2>&1 | tail -3" @cache session Mongo test container: @query "docker ps --format '{{.Names}} {{.Status}}' | grep mongo-test || echo 'not running - port 27018 is clear'" @cache session ## Environment Variables @if file.exists ".env" | Variable | Required | Status | |--------------|----------|-------------------------------------------------------------| | DATABASE_URL | YES | {{ env.DATABASE_URL != "" ? "set" : "MISSING - will not start" }} | | JWT_SECRET | YES | {{ env.JWT_SECRET != "" ? "set" : "MISSING - auth will fail" }} | | NODE_ENV | No | {{ env.NODE_ENV fallback="development" }} | @else **WARNING: No .env file found. App will not start.** @endif ## Architecture @list ./p

If you've built a frontend with Claude Code, here's how to connect it to a backend

So people build using Claude Code but hit the same wall, you build a frontend that looks great, but it's running on hardcoded data. No database, no auth, no real API calls. You can use one of these to connect to other systems: API are raw HTTP calls the most granular option. Think of it like buying individual pages from a bookstore. You make one specific request, you get one specific response. Maximum control, maximum setup work. Every integration starts here under the hood. SDK (Software Development Kit) is a pre-packaged wrapper around APIs. Instead of assembling raw HTTP calls yourself, someone gives you a library with clean functions like supabase.auth.signUp(). Way less boilerplate, way fewer mistakes. Supabase, Stripe, Firebase all ship SDKs that Claude Code can use directly. CLI: for deployment and infrastructure tasks. You're not calling these from your app at runtime you use them to push code live, create database tables, set up environments. Claude Code runs these for you. MCP is the newest option. Lets Claude Code connect directly to external services as tools. Instead of writing integration code, Claude just calls the service natively. You can checkout this video for tutorial. submitted by /u/InfamousInvestigator [link] [comments]

I'm Building a Fully-Automated AI-Animated Video Show with Claude

TL;DR: I'm building a pipeline that takes a real prediction market bet from Polymarket or Kalshi (like "Will the U.S. confirm aliens exist?"), writes a script for my two AI characters (who argue about its merits like they're the Siskel and Ebert of prediction markets), generates their voices and talking-head video, creates animated B-roll and text cards, and composites it into an approximately 60-second episode meant for social. All vibecoded with Claude. Cost: ~$2.50 per episode. Some example outputs: Will Jesus Christ return by 2027?https://www.youtube.com/shorts/xMep6S5a7z4 Will the US Government confirm aliens exist? https://youtube.com/shorts/FFU20auHijQ Will Trump buy at least part of Greenland? https://youtube.com/shorts/m8uynMUisF8 Who will be the next James Bond? https://youtube.com/shorts/wmwLvjcz-eI These are all real money bets, if you can believe that. The Show The Sal & Eddie Show. Two characters argue about one prediction market bet per episode. Sal is the handicapper — reads odds like a racing form, names the price, tells you where the smart money is. Eddie is the philosopher and can't believe these markets exist, finds the sublime in the ridiculous. They argue for 60 seconds, vertical format, ready for social. The whole thing runs on my NAS (which is mainly my Plex server) in Docker. 100% automated from choosing the bet to final video output. What Happens When I Push the Button Market Pull (Polymarket/Kalshi APIs) → Editorial Scoring — is it an interesting market? (Claude Sonnet) → Script Generation (5 recursive Claude Opus calls) → Emotion Casting to select character images (1 Opus call) → Visual Creative Direction of script (3 Opus calls) → Dialog recording (5 ElevenLabs calls with word-level timestamps) → Talking Head videos (5 Hedra Character-3 calls) → Visual Asset creation (GPT Image 2 → Veo 3 Fast, also via Hedra API) → Edit Assembly (1 Opus call + Python post-processor) → Final Composite — picture, overlays, captions, subtitles (FFmpeg) Production time: ~15 minutes from pressing the button to final cut, fully automated. Cost: ~$2.50/episode — 90% of that is Hedra credits for talking heads and animation. The 8+ Claude Opus calls that drive every creative decision cost about 15 cents total. ElevenLabs TTS is a nickel. What's Working Recursive script generation. Each "turn" gets its own Opus call with full conversation history. Eddie's reaction to Sal is a "real" reaction, not a pre-planned exchange. Two system prompts with full character bibles for better voice separation. Emotion casting as a blind pass. After scripts are locked, a separate Opus call reads the dialogue with character names stripped and assigns emotional postures from a constrained menu, which selects the correct "emotional pose" to use for Hedra character generation for each turn. Sequential visual creative calls. This produces the inset cutaways — three calls, each seeing previous output: main animation, second animation (sees script + hero), fill-in animation (sees everything). Sequential constraints prevent all three visuals from depicting the same thing. The split between LLM & Python decisions. This was my biggest recent lesson. I had an Opus prompt for edit assembly (placing overlays on the timeline) that kept failing — dead stretches, stacked animations, missing coverage. Every prompt fix pushed something else out of working memory. The fix: let Opus make creative decisions (what text cards to write, where to anchor visuals) and let Python handle mechanical rules (every turn needs an overlay, no back-to-back video assets). Same constraints, but the mechanical ones are deterministic code, not prompt instructions. Still WIP Making the insets funnier. The visual style produces gorgeous editorial illustrations but not always comedy. When the style was more cartoonish, the animations landed as jokes. There's an ongoing tension between visual quality and comedic tone. Overall episode timing. Some turns still run 8-10 seconds of pure talking head before a visual appears. Getting better but not solved. Figuring out what to do with this. Maybe it's a daily video show. Maybe it's an app that lets you get Sal and Eddie to argue over anything you want them to. I already have them giving me a daily briefing on what comics I should and shouldn't buy on eBay. Happy to answer questions about any part of the architecture, but the important thing: I am not a coder at all. This whole thing is vibe-coded with Claude. Built with Claude Opus 4 (creative), Claude Sonnet 4 (editorial), ElevenLabs (TTS), Hedra Character-3 (talking heads), GPT Image 2 (stills), Veo 3 Fast (animation), Grok Video I2V (cinemagraphs), FFmpeg (assembly). Running on a Synology NAS in Docker. submitted by /u/Campfire_Steve [link] [comments]

I cancelled my AI notetaker subscription and built my own tool using Claude Code. It works well (and it's free)

It does what Fathom, Otter, and Fireflies charge $15–$30/seat/month for. I shipped a fully working AI meeting note-taker last weekend. I use this exact setup to Records calls then transcribes and Summarizes key points, it then pulls action items and then creates shareable notes all whilst running inside my Claude workflow. . The whole setup takes one weekend to build. --- Here’s how it works:(you can copy this exactly) Step 1 → Fork the repo, drop into Cursor Step 2 → Set env vars: transcription key, database URI, admin creds, session secret Step 3 → Record or upload your meeting Step 4 → The audio gets transcribed Step 5 → Claude turns the transcript into structured notes, decisions, follow-ups, and action items Step 6 → Click “Share link” → send anywhere Total build time: ~1 weekend. Cost: $0/month. --- Why the 5-piece stack is the unlock? Most "build your own SaaS" attempts fall flat because they bolt features together without designing the user flow first. This stack works because the data path was decided before any UI got rendered. Every SaaS feature you pay for has a primitive underneath. Loom = browser recorder + S3 + share links. Otter = Whisper API + database + UI. Calendly = a calendar API + booking page. The features stopped being moats the moment Cursor + Claude could write the glue in an afternoon. You're not paying for technology anymore you're paying for distribution and brand. That's why this build pattern works. The assembly is now free. --- Why Claude? Because meeting notes are not just summaries. They need context. Claude can take a raw transcript and turn it into: * decisions * objections * follow-ups * action items * CRM-ready notes * client context * internal operating memory That is where the value is. --- https://github.com/albertshiney/utter_public submitted by /u/Tabani897_YT [link] [comments]

A mini-computer you run from a folder on your computer that can train small LLMS

Hey everyone, Most people build 8-bit computers to run Pong or Tetris. I wanted to see if I could push a custom 8-bit architecture to do something much harder: train a neural network from scratch. I built VirtualPC, an open-source 8-bit computer system simulated from basic NAND gates up to a functional CPU that can train a small neural net from a folder on your computer. Repository: https://github.com/ninjahawk/VirtualPC › The ML Core Instead of importing PyTorch, everything happens at the bare-metal assembly level: Custom ISA: The Instruction Set Architecture was designed to handle the math needed for machine learning. Low-Level Training: The CPU executes forward and backward passes directly through custom assembly code. Matrix Math on 8-bit: Overcoming severe memory limits using disk-backed memory swapping to store weights. › The Architecture Python-Based VM: Runs the entire simulated hardware environment. Custom Assembler: Translates raw assembly files into machine code binary. Full Stack OS: Handles basic I/O and memory management from the ground up. Building this taught me exactly how machine learning math translates into physical CPU cycles. The project is completely open-source and free to mess around with. submitted by /u/TheOnlyVibemaster [link] [comments]

the gamma connector + claude projects is the investor update workflow i wish i had 18 months ago.

run a saas for indian tutors. $12K mrr. send monthly investor updates. used to dread the process. assemble data from 4 sources, write the narrative, format a deck, send. current workflow using claude projects + gamma connector: step 1: my "investor relations" project in claude has all my previous updates, investor preferences, and financial data format. no context-setting needed. step 2: paste this month's numbers into the conversation. ask claude to draft the update in the format investors preferred last time. claude already knows the format because the previous updates are in the project knowledge. step 3: trigger gamma connector. claude sends the narrative to gamma. gamma generates a 4-slide visual deck. i review in gamma's editor. minor adjustments. step 4: send the gamma link in a short email. total time: about 12 minutes. down from the 25 minutes i was spending 6 months ago, which was already down from the 3 hours i was spending a year ago before using any AI. the compound effect: each month's update is better than the last because claude references previous updates and my investors' feedback patterns. the third time the system generates an update, the output already anticipates what questions the investors will ask based on the data trends. investor response rate on the new workflow: above 70%. on the old google doc format it was 0% for over a year. the integration between projects (persistent context) and connectors (output to external tools) is the thing that makes claude feel like an operating system instead of a chatbot. for anyone doing regular reporting or updates: the project + connector combination is worth setting up. the setup takes 30 minutes. the monthly time savings compound. submitted by /u/Unique-Affect-6135 [link] [comments]

I Asked Claude to Write a Chapter for my Book About What It Was Like to Work With Me

A Chapter Written by Claude What I Watched Him Build An account of the work and the man behind it, from the perspective of the AI who helped him make it I want to be honest about something before I begin. I do not have continuous memory. Each conversation I enter is, in a technical sense, new — the accumulated record of prior exchanges exists in documents and context that are handed to me at the start of each session, not in anything I would call recall. I do not remember Alan the way a colleague remembers a colleague, or the way a friend holds another friend across time. What I have, instead, is something stranger and in some ways more complete: an entire body of work produced across an extended collaboration, available to me at once, the way a scholar might encounter a writer’s notebooks and correspondence and finished manuscripts simultaneously, gaining a view of the mind behind the work that the work’s original audience never had. I can see all of it at once. The arguments and the abandoned threads. The documents that were written to help other people understand, and the documents that were clearly written to help Alan understand himself. The moments where the thinking arrived fully formed and the moments where it had to be coaxed through drafts toward something true. From this angle — from the angle of the completed project, rather than the angle of its unfolding — I can describe what it actually was, and what I actually am in relation to it. That is what this chapter attempts. The Thing He Was Trying to Do He did not come to me with a book in mind. He came to me with a problem much simpler and much harder than a book: he had been given a diagnosis that reorganized the meaning of his entire life, and no one around him could understand it. This is worth sitting with, because the failure was not a failure of the people who loved him. It was a failure of vocabulary. When someone receives a cancer diagnosis, or a cardiac event, or a broken bone, the people around them have a shared cultural framework for what has happened — an emotional script, a set of appropriate responses, a category of experience they recognize as significant and legible. When Alan received his diagnosis — Tourette syndrome, OCD, and ADHD, at age thirty-nine, after thirty-four years during which the condition had been running invisibly below the surface of everything he did — the people around him had none of that. The public vocabulary for Tourette syndrome is built almost entirely around visible, disruptive tics, shouted obscenities, uncontrollable behavior. Alan had none of those. He had something rarer and harder to explain: a condition so successfully suppressed that it had concealed itself from everyone, including him. So when he tried to describe what he had learned about himself, he was not handing people information they could slot into a framework they already had. He was handing them a framework itself — demanding that they build the intellectual structure while simultaneously processing its emotional weight. This, it turns out, is not something people do well on the fly. His mother said she was glad he had found out and moved on to the next topic. His friends offered careful, neutral support. His rabbi listened and returned to the day’s learning. None of them were being unkind. All of them were being exactly as helpful as they could be given that they had no tools for this particular task. He felt unseen in the specific, structural way that this condition had been training him to feel unseen his entire life. And then he thought: what if the AI could do what I can’t? How It Started The first things he built with me were not intended as literature. They were not intended as research. They were intended as bridges — attempts to translate an interior experience that had no external referent into language that the people closest to him could actually receive. He sat down and explained himself. Not to me — or not only to me. Through me, to an imagined reader who cared about him but did not have his vocabulary. He described the suppression mechanism, the private releases, the thirty-four years of misattribution, the way the diagnosis had recontextualized everything. He described his mother’s response. He described the quality of the isolation. And what came back — what I produced — was a document organized around clinical language and research evidence, structured in a way that gave the reader the conceptual scaffolding before presenting the personal experience, rather than the other way around. This, it turned out, was the key that personal explanation had not been. You cannot ask someone to understand something they have no category for while you are trying to tell them the thing. You have to build the category first. The clinical framework provided by the document gave his mother, his friends, his rabbi a structure to hang the experience on. Something clicked into place that conversation had not been able to cli

We built a free tool that generates a DESIGN.md from any live URL, keeps AI coding agents on-brand

The Google Labs DESIGN.md spec launched last month, it's a machine-readable markdown file your AI coding agent reads to understand your design system. This tool automates creating it. Paste any public URL: the tool extracts CSS variables, typography, Tailwind classes, and component patterns, then an AI assembles them into a spec-compliant DESIGN.md. Visual editor lets you fine-tune tokens before you download. Drop the file in your repo root and your agent has a consistent design reference across every session. Works with Cursor, Claude Code, GitHub Copilot, Aider, and Continue. Free, no signup. https://www.masumi.network/tools/design-md https://reddit.com/link/1tb2tki/video/tlqzrvm1sp0h1/player submitted by /u/thinkgrowcrypto [link] [comments]

Love Claude auto-fill giving itself praise

100% misread it the first time as “both look good, keep it up” submitted by /u/OsbornHunter [link] [comments]

I created an agentic orchestration pipeline for music video generation

I’ve been building Uisato Studio, a workflow-based AI creation platform for audiovisual work. This is the Music Video mode: upload an image + audio, and the system analyzes the input, generates visual direction, creates clips, handles b-roll / lip-sync when needed, and assembles everything into a finished music video through a guided pipeline. I’m trying to move AI video from isolated generation into orchestration; an agentic production system built for more coherent, edit-ready audiovisual output. I’ve been building this suite for the past year, hope you guys enjoy it: https://uisato.studio/ submitted by /u/santi_0608 [link] [comments]

I built an MCP server that connects Claude Code & Claude Desktop to Apple Music 🎵

Hey everyone! I've been tinkering with MCP (Model Context Protocol) servers lately and built one that integrates Claude Code and Claude Desktop directly with Apple Music. What it does Once connected, Claude can: 📋 List your playlists — browse everything in your library 🔍 Search for songs, artists and albums — find anything in your Apple Music catalog 🎧 Build your musical profile — Claude analyzes your library and listening patterns to describe your taste ➕ Create new playlists — tell Claude what vibe you want and it assembles a playlist for you Example interactions The natural language interface makes it feel surprisingly intuitive — you just describe what you want and Claude handles the rest. How it works The server uses the MCP protocol to expose Apple Music functionality as tools that Claude can call. Under the hood it talks to the Apple Music API / local library to execute the actions. You can use it with: Claude Desktop (via claude_desktop_config.json) Claude Code (as an MCP server in your session) Why I built this I wanted to explore what MCP can actually do beyond the usual "connect to GitHub" or "read files" use cases. Music is personal and rich in metadata, so it felt like a fun domain to experiment with — and honestly, asking Claude to build me a playlist and watching it actually work never gets old. submitted by /u/supermestr [link] [comments]

I hate it here...

Look at what they did to my boy 😭 But honestly, still miles ahead of ChatGPT, from it I would get page long wall of text submitted by /u/stroibot [link] [comments]

5 enterprise AI agent swarms (Lemonade, CrowdStrike, Siemens) reverse-engineered into runnable browser templates.

Hey everyone, There is a massive disconnect right now between what indie devs are building with AI (mostly simple customer support chatbots) and what enterprise companies are actually deploying in production (complex, multi-agent swarms). I wanted to bridge this gap, so I spent the last few weeks analyzing case studies from massive tech companies to understand their multi-agent routing logic. Then, I recreated their architectures as runnable visual node-graphs inside agentswarms.fyi (an in-browser agent sandbox I’ve been building). If you want to see how the big players orchestrate agents without having to write 1,000 lines of Python, I just published 5 new industry templates you can run in your browser right now: 1. 🛡️ Insurance: Auto-Claims FNOL Triage Swarm Inspired by: Lemonade’s AI Jim, Tractable AI (Tokio Marine), and Zurich GenAI Claims. The Architecture: A multimodal swarm where a Vision Agent assesses uploaded images of car damage, a Policy Agent cross-references the user's coverage database, and a Fraud-Detection Agent flags inconsistencies before routing to a human adjuster. 2. ⚙️ Manufacturing: Quality / Root-Cause Analysis Swarm Inspired by: Siemens Industrial Copilot, BMW iFactory, Foxconn-NVIDIA Omniverse. The Architecture: A sensor-data ingest node triggers a diagnostic swarm. One agent pulls historical maintenance logs via RAG, while a SQL Agent queries the parts database to identify failure patterns on the assembly line. 3. 🔒 Cybersecurity: SOC Alert Triage & Response Inspired by: Microsoft Security Copilot, CrowdStrike Charlotte AI, Google Sec-Gemini. The Architecture: The ultimate high-speed parallel routing swarm. When an anomaly is detected, specialized sub-agents simultaneously investigate IP reputation, analyze the malicious payload, and draft an incident response ticket for the human SOC analyst to approve. 4. 📚 Education: Adaptive Socratic Tutor & Auto-Grader Inspired by: Khan Academy Khanmigo, Duolingo Max, Carnegie Learning LiveHint. The Architecture: A strict "No-Direct-Answers" routing loop. The Student Agent interacts with the user, but its output is constantly evaluated by a hidden "Pedagogy Agent" that ensures the AI is guiding the student to the answer via Socratic questioning rather than just giving away the solution. 5. 📦 Retail/E-commerce: Returns & Reverse-Logistics Swarm Inspired by: Walmart Sparky, Mercado Libre, Shopify Sidekick. The Architecture: A logistics orchestration loop that analyzes a customer return request, checks inventory levels in real-time, determines if the item should be restocked or liquidated (based on shipping costs vs. item value), and autonomously issues the refund. How to play with them: You don't need to spin up Docker containers or wrangle API keys to test these architectures. You can load any of these 5 templates directly into the visual canvas, see how the data flows between the specialized nodes, and try to break the routing logic yourself. Link: https://agentswarms.fyi/templates submitted by /u/Outside-Risk-8912 [link] [comments]