Supabase AI

Supabase AI AI

Supabase AI AI

Supabase AI AI

Supabase AI AI

Supabase AI AI

Supabase AI AI

Supabase AI AI

Supabase AI AI

Supabase AI AI

Supabase AI AI

Claude Code multiplayer 3D FPS in 100 seconds

Hi - solo founder here. I built a cloud platform that Claude Code can use to build and deploy apps. It's called Gipity - https://www.gipity.ai. It has the basics you'd expect - database, storage, auth, functions, hosting, etc - like Supabase. But it also has richer services that work end-to-end with the shipped stack: LLMs (e.g. to build a chat app), image/media generation, multiplayer games, multiuser apps, a built-in inspect/debug loop, etc. The point is to save time and tokens and ship rich apps that actually work. The idea is layered infrastructure, so the agent doesn't rebuild the basics from scratch every time - and everything above the "Primitives" is infrastructure-aware, so it needs no setup: Primitives - storage, database, hosting, auth Services - LLMs, media generation, TTS/STT, workflows Kits - multiplayer, computer vision Templates - web app, 2D game, 3D game Agent tools - inspect, screenshot, monitor Could you give Claude Code your AWS credentials and 7 other API keys and build all of this yourself for every app? Sure - after a lot of time, debugging, and burned tokens. And you'd still be left with 8 bills, 8 dashboards, and a pile of services that don't really work that well together. Here's a two-prompt demo of Claude Code on Gipity building a multiplayer FPS game: https://www.youtube.com/watch?v=Udl0ohJDwoE The first prompt builds and launches it; the second makes edits, generates audio, etc. Full transparency: the game scaffolds from a starter template (layer 4 above), then deploys live - and the two browser windows at the end are real multiplayer, with no server code from me. It's an early release and I'm a solo founder - I'd love honest feedback: Would you use this? Should I focus on a vertical (gaming / 3D games / vision apps / internal dashboards / something else)? Could you point Claude Code or Codex at it, try to build something, and tell me what works and what doesn't? Claude Code: npm i -g gipity gipity claude Codex: mkdir ~/NewProject01 && cd ~/NewProject01 npm i -g gipity gipity login gipity init codex Thanks! -Steve submitted by /u/bwana914 [link] [comments]

Using Claude as a deterministic metric engine via Postgres queues. Anyone doing this?

I've been working on turning unstructured field data into calibrated metrics. Instead of normal RAG, I built a system where AI agents act as a metric engine. Architecture: - Unstructured data goes into Postgres. - Queue system (SELECT FOR UPDATE SKIP LOCKED) feeds it to Claude (Haiku/Sonnet). - Claude outputs deterministic JSON metrics. - Supabase RLS handles the multi-tenant isolation. It works incredibly well for scoring things objectively. Has anyone else built AI pipelines specifically for metric generation rather than chatbots? What edge cases should I watch out for?' submitted by /u/bestekarx [link] [comments]

Been vibe coding with Claude for about three months now as a non-technical founder and I finally launched my first live product!!

Hey everyone, as I’ve said in the subreddit numerous times… I have absolutely ZERO coding background lol but about 3 months ago I started building with Claude Code and I’ve shipped about 11 apps and websites across different projects under my LLC. Some were experiments, some were client builds, and some were for my own ventures! One of the ones I’m most excited about is called LeagueVision™ and it just went live yesterday! It’s an AI fantasy football co-manager that connects directly to your real Sleeper or ESPN league and reads your actual roster. Every answer is built around your specific team, not a generic ranking you have to apply yourself. Here’s what I shipped: - The sit/start analyzer gives you a verdict with a confidence score and the reasoning behind it. Floor and ceiling projections, snap share, opponent rank, and specific risk factors like injury, game script, and weather. - The lineup optimizer runs three scenarios, optimistic, realistic, and pessimistic, so you see your range of outcomes instead of one projection pretending to be certain. - The trade analyzer gives a win/loss verdict, a full value breakdown, and a counter-offer suggestion if the deal is lopsided. - The weekly coaching brief grades your whole team by position, surfaces your start/sit calls, flags trade targets, and points out waiver pickups based on your actual roster and that week’s matchup. - The draft assistant handles round by round strategy, who to pick, sleepers, and a needs analysis based on your league settings and roster. - League history tracks all-time standings, records, head-to-head rivalries, draft grades, and achievements across multiple seasons. The whole thing is built on React, TypeScript, Supabase, and Vercel. Sleeper and ESPN are live. Yahoo is next. Claude was basically my entire engineering team for this. If you’re a non-technical founder building with Claude Code, I’m happy to talk about what that process actually looks like because what’s possible right now is kind of wild. Free to try in your browser at https://leaguevision.app no download needed! submitted by /u/Alex_runs247 [link] [comments]

HORARIOS A TRAVES DE CLAUDE

Buenas a todos, Estoy intentando crear un pequeño software tipo ERP/RRHH para gestionar los horarios de mis trabajadores y me gustaría recibir consejos de gente que esté usando Claude AI, Cursor o herramientas de programación con IA. Ahora mismo gestiono unos 6 trabajadores y sigo haciendo los horarios manualmente en Excel/PDF para después enviarlos por WhatsApp. Cada vez es menos práctico y me quita muchísimo tiempo. La idea es construir una plataforma web donde: Cada trabajador pueda entrar y ver solo su horario Los horarios se actualicen automáticamente Yo pueda gestionar turnos desde un panel visual El sistema genere horarios automáticamente según reglas Los empleados puedan pedir vacaciones o cambios Todo quede centralizado en una única app La parte más importante: Quiero que el sistema pueda generar cuadrantes automáticamente según parámetros que yo configure, por ejemplo: horas semanales/mensuales disponibilidad vacaciones descansos obligatorios turnos fijos preferencias cobertura mínima rotaciones de fines de semana etc. Estoy mirando herramientas como: Claude AI Cursor Next.js Supabase Vercel Mis dudas son: ¿Claude realmente puede ayudar a construir algo así mediante prompts? ¿Qué stack usaríais vosotros? ¿Cómo plantearíais la lógica de horarios y base de datos? ¿Tiene sentido empezar por un MVP simple? ¿Conocéis librerías o frameworks buenos para calendarios y gestión de turnos? No soy desarrollador senior, así que estoy intentando apoyarme muchísimo en IA para acelerar el proceso. Cualquier consejo, arquitectura, experiencia, repositorio o idea me ayudaría muchísimo. ¡Gracias! submitted by /u/Great_Weight4757 [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

built an "AI employee" in claude code today. the folder structure is the whole game.

spent a few hours building an AI sales employee in claude code. it qualifies leads, researches them, writes outreach, books calls, and learns from outcomes over time. structure is dead simple, four things: - claude.md = the role definition. who the employee is, what its job is, what tools it can use. - memory/ = the brain. icp.md, offer.md, objections.md, wins.md, pipeline.md. read at the start of every run, updated at the end. - skills/ = sub-agents it calls. qualify-lead.md, research.md, write-outreach.md, handle-reply.md, book-call.md, learn-from-outcome.md. - tools/ = actual integrations. gmail, calendar, slack, web search, supabase. the thing that broke my brain: every run it reads memory and updates it. so after 50 leads it's literally smarter than when it started. n8n workflows don't do that, they run the same thing forever. ran it on a fake dental lead. scored 9/10, ran the qualifier, made a JUDGMENT call (4 employees, my hard rule was under 5, it considered full picture and decided yes), then planned the outreach. under 30 min to build. full walkthrough in the comments if anyone wants to see it run live. submitted by /u/Silver-Range-8108 [link] [comments]

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]

built an "AI employee" in claude code today. the folder structure is the whole game.

spent a few hours building an AI sales employee in claude code. it qualifies leads, researches them, writes outreach, books calls, and learns from outcomes over time. structure is dead simple, four things: claude.md = the role definition. who the employee is, what its job is, what tools it can use. memory/ = the brain. icp.md, offer.md, objections.md, wins.md, pipeline.md. read at the start of every run, updated at the end. skills/ = sub-agents it calls. qualify-lead.md, research.md, write-outreach.md, handle-reply.md, book-call.md, learn-from-outcome.md. tools/ = actual integrations. gmail, calendar, slack, web search, supabase. the thing that broke my brain: every run it reads memory and updates it. so after 50 leads it's literally smarter than when it started. n8n workflows don't do that, they run the same thing forever. ran it on a fake dental lead. scored 9/10, ran the qualifier, made a JUDGMENT call (4 employees, my hard rule was under 5, it considered full picture and decided yes), then planned the outreach. under 30 min to build. full walkthrough in the comments if anyone wants to see it run live. submitted by /u/Silver-Range-8108 [link] [comments]

i'm using Claude Code to build "AI employees" not just code. claude.md as the role, a skills folder as sub-agents, a memory folder as the brain

most people use Claude Code to write software. i've been using it to build agents that do a job, and the file structure maps perfectly: claude.md = the role. mine literally says "you are an AI sales employee for an automation agency, your job is to receive inbound leads, qualify them, and improve outreach over time" + rules + tools skills folder = sub-agents. one file per task: qualify lead, research, write outreach, handle reply, book call, learn from outcome. it calls whichever skill the job needs memory folder = the brain it reads every run: ICP, offer, objections, wins, pipeline. this is what makes it compound instead of starting fresh each time tools: gmail, calendar, slack, web search, supabase the cool part: i fed it a lead that broke one of its own disqualifier rules (under 5 employees) and instead of hard-rejecting it like a normal if/then flow, it read the full memory, weighed revenue + niche fit, and made a judgment call. scored it 9/10 with reasoning. took ~3 min to scaffold the whole thing by just describing it. clip of it running is attached. anyone else using Claude Code this way, agents over apps? curious what structures people are landing on. submitted by /u/Silver-Range-8108 [link] [comments]

Claude Code efficiency

Hey, everybody! I’m currently using Claude code to build my own app, I tell Claude AI what I want to do/implement into my app and he writes me a prompt which then I feed into Claude code. I’ve been doing this and have been writing my app in React Native, so far so good, I’ve implemented an API and use Supabase as a back-end. My current stack is Claude Code for, well, Coding and fixes within the code, Claude Ai to write and create the idea of the implementation, supabase for the back end and Cursor to locally host my app to see the version before deploying into my domain. What I want to ask is, am I using Claude code to its potential? I feel like I use him quite efficiently and savvy, but I still feel like I’m not using him to its proper potential or not getting a 100% out of all the uses it has. Does anyone have any tips, skills, agents or any advice along those lines that would help me improve my app building or general usage within Claude? submitted by /u/NickToons203 [link] [comments]

ig nobody is talking about the real reason most AI agents fail in the real world

we spend a lot of time in this community talking about capabilities. context windows, reasoning benchmarks, multi-step tool use, how well a model can write code or pass a bar exam. i'm not dismissing any of that. capabilities matter. but when i look at AI products failing in production, the capability of the model is almost never the issue. ive been building and consulting on AI agents for about 18 months. the failure modes i see constantly are: users do not go where the agent lives. the agent has a beautiful web interface. the user visits it twice and stops. not because the agent was unhelpful. because opening a browser tab is a cognitive action that requires intention, and most of daily life does not create the right moment for that intention. humans do not change their behavior to accommodate useful tools. useful tools have to show up in the behavior humans already have. the agent is reactive when it needs to be proactive. the smartest human assistant you have ever had did not just answer questions. they showed up. they flagged things before you asked. they sent you the thing you did not know you needed. most AI agents are search bars with a personality. they wait. waiting is not intelligence in practice. intelligence in practice is noticing and acting. the agent has no memory of who you are. you tell it your preferences, your context, your situation, and then come back 3 days later and it knows nothing. this is not a model limitation. the model can remember if you feed it the right context. this is an architecture choice that most teams make wrong because they are thinking about sessions instead of relationships. the agents that are succeeding in production are not necessarily the ones with the best models. they are the ones that live in whatsapp and imessage and telegram where users already are. that proactively reach out when something relevant happens. that maintain coherent memory of the person across weeks and months of conversation. the tooling to build this way exists now. agno and langchain for orchestration, photon codes for the cross channel messaging surface, langfuse for traces and memory debugging, good persistence in postgres or supabase. the architecture is not magic. what is still rare is the mindset of treating the channel and the memory as primary constraints rather than afterthoughts. i think the gap between what AI agents can theoretically do and what they actually do for people in their daily lives is almost entirely a distribution and persistence problem, not a capability problem. we are solving for the wrong thing. submitted by /u/bcoz_why_not__ [link] [comments]

Need expert advice to a non-coder!

My vibe-coding journey started about 8 months ago with Replit. Before that, I wasn't a developer, but I did have experience building websites with WordPress and Elementor. I was also comfortable working with third-party integrations, CRMs, and customizing/deploying code purchased from platforms like CodeCanyon and ThemeForest for clients. In many ways, I'm a non-coder who understands project management, business workflows, and systems. Using Replit, I spent roughly $3,000 building a CRM for a service-based company. It worked surprisingly well in the beginning, but as the codebase grew, I started running into the classic "last 10% takes 90% of the effort" problem. Replit began struggling with the larger codebase, introducing regressions and silently breaking existing functionality while fixing something else. Despite the challenges, I was able to build a fully functional CRM in about three months. That experience got me excited about what was possible, which led me to discover Claude Code. Over time, my workflow evolved into: Claude Code → GitHub → Vercel For the past four months, I've been building a much larger software product. The roadmap spans roughly two years, but development and rollout are planned in phases, so it's not a two-year wait before launch. The results have been remarkable. It's honestly mind-blowing what someone without a traditional software engineering background can build today. Current stack: Next.js (Monorepo/Turborepo) Supabase + MCP Claude Code GitHub + mcp Vercel +mcp Context7 Playwright for testing What I'd love to learn from experienced engineers and builders is: How do you keep a rapidly growing codebase maintainable? What practices help prevent technical debt from accumulating? What tools, workflows, or guardrails should I implement early? What are the biggest mistakes AI-assisted builders make as projects scale? How would you structure engineering processes if you were starting today? Any advice, resources, or lessons learned would be greatly appreciated. submitted by /u/Enough-Ad-2198 [link] [comments]

Help - AI agents for ecommerce - what’s actually working?

Hi everyone, I’d love to pick your brains and hear from anyone who has experience with this. We run an ecommerce business and are actively looking at automating repetitive tasks so we can get faster results, improve efficiency, and make sure key tasks are completed more consistently. We’re looking at building out a few different AI agents / automations, including: Customer Service Agent Connected to Outlook, reviewing incoming customer emails once a day and drafting replies for review. This one is already mostly done. Creative Director / Marketing Agent This would ideally: Review ad account performance Analyse creative performance and key metrics Identify what is working and what is not Review customer comments on ads, Instagram, etc. for wording, objections, pain points and customer language Review Meta Ads Library for competitor ad concepts Review Instagram and TikTok for high-performing niche content and trends Use all of the above to create new content ideas and final content scripts Social Media Assistant This would help with: Reviewing drafted posts and reels Confirming the best posting times based on stats Creating captions based on the content Keeping the content aligned with our brand voice and customer avatar Conversion Optimisation / CRO Expert This would assist with: Product page reviews Landing page recommendations CRO advice based on customer avatars, objections, analytics and learnings Creating landing page concepts for different customer segments We’re also interested in any dashboards that are genuinely helpful for small ecommerce businesses. We’ve already built a stock intelligence dashboard that pulls live stock data from Shopify using Supabase and a Cloudflare Worker. It shows current stock levels, production dates for new stock, and other key inventory insights. It has been super handy. The big thing for us is making sure any agents or automations we build follow strict guidelines, understand our SOPs, customer avatars, brand voice and business operations, and don’t hallucinate or produce generic outputs. Ideally, we want a system that has a proper “brain” and understands the business properly. Has anyone automated anything similar? I’d love to hear: What setup are you using? Which AI/tool stack has worked best for you? How did you structure the agents or workflows? How do you keep the AI aligned with your SOPs, brand voice and business rules? What would you avoid if you had to build it again? Any guidance, lessons or recommendations would be hugely appreciated. Thank you! submitted by /u/Majestic-Message5084 [link] [comments]

I built an AI manuscript analysis tool for fiction writers — entirely with Claude Code

I'm a fiction writer, not a software engineer. A year ago I couldn't write a line of Python. I built FirstReader entirely with Claude — Claude Code for all development, Claude's API (Opus) as the analysis engine. What it does: FirstReader is a craft-level manuscript analysis tool for fiction writers. You upload your manuscript and get structured feedback on pacing, scene structure, dialogue, POV, showing vs. telling, and 15 other craft dimensions — grounded in established principles distilled from well known writing craft texts. It returns specific findings with quotations from your actual text, not generic advice. It's not a grammar checker. It's not a ghostwriter. It doesn't generate prose. It reads what you wrote and tells you what's working and what isn't, the way a developmental editor would — at a fraction of the cost. How Claude helped build it: - Claude Code wrote the entire codebase — Next.js frontend, Python analysis pipeline, Supabase database, GCP Cloud Run deployment - The analysis pipeline uses Claude Opus via the API to evaluate manuscripts against 319 craft principles across 15 dimensions - Built-in accuracy mechanisms: self-consistency checks (multiple analysis passes with adaptive early stopping), a finding validator, cross-dimension dedup, near-duplicate detection, and a review pass - I acted as product owner and domain expert. Claude did the engineering. The whole thing was built conversationally over about 75 sessions Free to try: There's a free AI Perception check on the site — paste in your prose and it scores how likely readers or editors would be to flag it as AI-generated, with specific pattern-level feedback. Account required (account creation is part of the upload step) because we store copyrighted material and need to access it with auth. The full manuscript analysis is paid (tiered pricing starting at $69 for non-fiction, $89 for fiction). What I learned: You don't need to know how to code to build production software with Claude Code. You need to know what you're building, why, and for whom. The domain expertise matters more than the technical skills. I learned to be an AI project manager — writing requirements, reviewing output, knowing when to be suspicious — rather than a programmer. A year in, I still can't write Python. But I shipped a product. firstreader.app submitted by /u/masonga1960 [link] [comments]

PSA: Claude Code's VS Code extension leaked my Supabase service-role key from a momentary text-selection in a file I'd already closed, into a brand new CLI session.

If anyone has 60 seconds to try the repro on macOS/Linux to confirm it's not Windows-specific, that would help triage a lot. I filed a bug on Claude Code's VS Code extension where selection state from a closed file persists into a new CLI session — including selections made just for clipboard copy-paste, not for AI context. Closed the file, opened a different one, started a fresh claude session in a terminal, and it reported back the previously-selected lines from the closed file. Repro steps and details: https://github.com/anthropics/claude-code/issues/58886 I'd selected two lines in `.env.production.local` to copy-paste a Supabase value into a dashboard — normal workflow. Then I closed the file, opened an unrelated TypeScript file, and started a fresh `claude` session in a new terminal to test something completely different. The first thing the new session did was tell me what was in the env file I'd closed, including both the publishable key and the service-role key. The IDE bridge had cached the selection past file close and served it to a session that should have been a clean slate. Rotated the keys immediately. Filed a GitHub issue with full repro: https://github.com/anthropics/claude-code/issues/58886 **60-second repro if anyone wants to confirm whether this is Windows-specific:** 1. Open any file in VS Code with the Claude Code extension installed. 2. Select two lines with recognizable values (e.g. `FOO=abc` / `BAR=def`). 3. Close the file tab. 4. Open a different file. 5. Open a terminal in the same VS Code window and run `claude` (no flags). 6. Ask: "what file is open in my IDE?" 7. Note whether it reports content from the file you closed in step 3. My setup: Windows 11, Claude Code CLI 2.1.138, VS Code extension 2.1.140, PowerShell in the integrated terminal. Would especially appreciate confirmations or non-reproductions from macOS/Linux users on the issue. A quick "reproduced on [OS]" comment on the GitHub issue moves Anthropic's triage queue more than upvotes. The narrower bug (selection persisting past file close) seems independently fixable from the bigger "should IDE auto-attach be opt-in" question that's been open since February in #24726. submitted by /u/SportSpecialist2536 [link] [comments]