Supabase

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I began setting up #Supabase for my project this weekend. I'm liking it so far, but MAN they don't make it easy to selfhost, particularly the analytics module. Well at least I got it done in the end and now I have access to great admin tools for my project. #coding #webdev #programming #selfhosting

Connecting AI studio project with database Hi there, I have stunning UI. Everything is perfect. The pain is that I have no backend. All info is just json. Database is absolutely needed, as I need to store and structure data + I will need to enable creating profiles. How can I connect AI studio with proper database (supabase, as an example) that connects well with prompts and the rest of the infrastructure? Is that even possible in terms of then prompting so that database automatically gets included in the logic? Is that even possible? My idea was to push the project through claude code in final stages to clean code and create backend, however I might be able to do it in AI studio before I test orher ways. Will appreciate any comments, tips and help!🙂

Built a wine cellar app because I kept forgetting what bottles I own Hello I collect wine but I’m not organized about it. Bottles end up in different places, I forget what I have, and half the time I’m standing in front of the rack thinking “would this go with dinner?” before just grabbing whatever’s in front. So I built CellarAI to fix it for myself. The main idea: photograph your labels to build your collection, then ask an AI sommelier things like “what should I open with lamb tonight”, and it recommends from what you actually own, not just generic advice. I’ve focused a lot on making it fast to add bottles in bulk, you can photograph a whole case and it’ll recognize the wine. Process way less painful. It also tracks drinking windows so it’ll nudge me if something’s getting past its prime. Latest feature I added is an estimated market-value of all the bottles in the cellar. Took about a weekend to get the first version working, then another few days adding things like logging bottles when you drink them (with notes on what you ate, who you were with, that kind of thing). Stack if anyone’s curious: Lovable for the app (first time using it – worked way better than I expected), Supabase for the backend, GPT-4 Vision for reading labels, Gemini for the sommelier chat. It’s free, works on mobile, and each user gets their own private collection. A few friends are using it now which is fun. Happy to answer questions about how I built it or take feedback on what’s missing. I’m still adding things as I use it. Http://cellar-ai.com Let me know what you think:)

I kept seeing athletes struggle with messy Google Docs, PDFs, and random Instagram pages when trying to show coaches who they are. So I built Sportfoli, a minimal profile builder that creates a clean public page in minutes. You just enter your details → it generates a shareable profile. No templates, no design work, no clutter.<p>Built with Next.js, Tailwind, and Supabase. It’s still early and I’d genuinely appreciate feedback from people who’ve launched small tools before — what to improve, what to cut, what’s missing.<p>Live here: <a href="https:&#x2F;&#x2F;sportfoli.com" rel="nofollow">https:&#x2F;&#x2F;sportfoli.com</a>

I spent 3 months turning a $300/month Replicate nightmare into a $15/month dream – sharing the full boilerplate if anyone actually wants it Hey guys, Quick one – no bullshit, no long story unless you want it. I built a full AI image generator SaaS on Replicate (text-to-image, img2img). In a normal setup, things usually go like this: * Everything crashes at 50–100 concurrent requests * Replicate + storage costs easily hit $300–500/month * No queue system → welcome to timeout hell * No admin panel → you have no idea what’s happening * Even integrating Stripe takes a full week I solved all of that. Here’s the current state: ✓ Full Replicate integration (all models, text-to-image, img2img etc.) ✓ Credit/token system with usage tracking + hard limits ✓ Async queue (BullMQ + Redis) – tested 500+ concurrent jobs, no sweat ✓ Storage optimized (Cloudflare R2 + smart caching) → total cost \~$15/month instead of $300+ ✓ Real-time admin dashboard (live API costs, user usage, job status) ✓ Stripe ready out of the box (one-time credits + subscriptions) ✓ Next.js 15 + Supabase + Vercel – literally one-click deploy ✓ Abuse protection, rate limiting, webhook retry logic… all the annoying shit handled The real value? This took me 3 months of pure pain. Nights spent debugging stuck jobs at 4am, Replicate webhooks randomly failing, costs exploding overnight. Someone could take this, deploy it in an afternoon, and start charging users by evening. Thinking of cleaning it up and releasing it as a proper boilerplate/template. Question is: Would you actually buy this? What would make you go "shut up and take my money"? What's missing? Drop your price guess in the comments , top 3 most upvoted prices get early access + lifetime 50% off when I launch.

thoughts on supabase realtime?

🚀 I built an entire Customer Success Platform using Lovable - from scratch to production Hey everyone! 👋 I wanted to share my experience building a complete **Customer Success Platform** entirely with Lovable. What started as a simple idea turned into a full enterprise-grade SaaS that's now running in production. # What I built: **Core Customer Management** * Complete client lifecycle management with health scores * Behavioral analytics tracking user sessions and engagement patterns * Automated risk detection and alerts **Churn Prevention Suite** * Pre-cancellation workflow with negotiation tracking * Formal cancellation process with digital signatures * Retention playbooks with automated actions **Growth &amp; Revenue** * Upsell and cross-sell opportunity detection * Renewal management dashboard * Referral program with lead tracking Kanban **Engagement Tools** * NPS surveys with automated distribution * Ticket/support management system * WhatsApp integration for notifications * Email campaigns with templates **Playbooks &amp; Automation** * Visual playbook builder with conditional logic * Task management with assignments and deadlines * Automated triggers based on client behavior **AI-Powered Features** * GPT integration for customer insights * Predictive health scoring * Smart recommendations engine **Analytics &amp; Reporting** * Executive dashboards with KPIs * Cohort analysis and trends * Custom report builder # My takeaways: 1. **Break everything into small steps** \- Lovable works best when you're specific and incremental 2. **Supabase integration is powerful** \- Edge functions, RLS policies, real-time subscriptions... it handles serious backend logic 3. **It scales** \- I was skeptical at first, but the codebase grew organically and remained maintainable 4. **Speed is insane** \- Features that would take days/weeks took hours The platform now handles thousands of clients and processes behavioral data in real-time. Never thought I'd build something this complex without a traditional dev team. Happy to answer any questions! 🙌

Community meetups starting this Monday, running all December 🌎 https://preview.redd.it/olnbcrl4rn2g1.png?width=1049&amp;format=png&amp;auto=webp&amp;s=fa205481f6acfd3a0a6e59361a0fa0f8dcd5f743 Join us for swag, food, talks, and surprises! Check out all the [events](https://supabase.com/events)

Lightweight GLB/3MF/STL web viewer for review engineering or 3D print models in color — might help others here Hi everyone, I often need to share 3D models in color with clients or teammates who don’t have 3D program installed, and most of them prefer something simpler than eDrawings or a heavy download. So I put together a small browser-based viewer using three.js + Nuxt + Supabase, and I’m sharing it here in case others find it useful for quick model reviews: 👉 GitHub repo: [https://github.com/CalcsLive/e3d-viewer](https://github.com/CalcsLive/e3d-viewer?utm_source=chatgpt.com) 👉 Live demo (sample model): [https://e3d.calcs.live/viewer/fwt4qufi](https://e3d.calcs.live/viewer/fwt4qufi) **It supports:** * **GLB**, **3MF**, and **STL** * Color preservation for GLB/3MF * Smooth rotate / pan / zoom * Quick engineering views * top / bottom / front / back / left / right * home &amp; fit-to-view * Ortho / perspective toggle * Clean, minimal UI (client-friendly) **My workflow:** I export from SolidWorks → upload → get a link → share with clients. Colored GLB/3MF exports make it much easier for non-technical clients to understand assembly layouts and 3D-print parts. # Two ways you can use this viewer: # 1) Simple use Use my public deployment — no install needed, just open the link: [https://e3d.calcs.live](https://e3d.calcs.live) # 2) Power users / developers Clone the repo and host your own version (it’s lightweight and easy to adapt). # Optional feedback welcome: * Would this help simplify client reviews? * Any features that would make it more useful (measure, section view, part selection)? * Any SolidWorks export tips that would help the workflow? Just sharing in case this helps someone else — happy to answer questions. https://preview.redd.it/bqg198bd1g4g1.png?width=640&amp;format=png&amp;auto=webp&amp;s=009f1a31925982530db85ffbcef0f28eca00dfaa

thoughts on supabase realtime? im building a social consumer app, swiftUI. backend and auth are in supabase mostly, and the app is gonna be heavy on social/realtime features.. think tons of chats, feeds, profiles, bios and overall alot of crud happening async. anyone tried supabase realtime? is it good, worth the price or should i think about migrating to convex? or any other recommendations? appreciate any advice :)

thoughts on supabase realtime?

Got my first enquiry for my startup/product - an omegle clone, built from scratch in 5 days 5 nights product : https://supameet.in by https://blessl.in I developed it in 5 days 5 nights with 0 users and RS 800 INR / $ 10 USD (Claude Sonnet 4.5) It supports chat, audio, video calls with nearby features. TechStack python, django, channels, sockets, webRTC, redis, postgreSQL, Supabase + storage, HTML + CSS + JS, render I have no idea about anything mentioned above before project. I still have no idea after project. Just vibes.

Finally built a full MVP using “vibecoding” (but with a lot of real engineering work behind it) I know the real dev community is very sensitive to the term “vibecoding.” I’m fully prepared for the criticism, but hold on, this post is probably not what you think. I have a technical background, but I’m not a full-time developer. At work I sit somewhere between a analyst and a technical role, closer to DevOps. So I don’t consider myself “non-technical,” but I’m not a software engineer either. A few weeks ago, I decided to really push the vibecoding workflow, especially given my limits in writing large amounts of code manually. The result is the MVP of a personal product I’m building: "**Zennance**", a platform for technical freelancers to manage clients, projects, hours, budgets, and finances in one place. I want to share the process because I used AI very heavily, but I still had to debug a lot, adjust technical details, tweak architecture decisions, and solve real implementation issues. In the end, I got a working MVP deployed, multilingual, multi-tenant, and pretty usable. If anyone here is experimenting with this approach for real projects, especially with Next.js + Supabase, I honestly think it’s becoming a very viable path. I used Codex, Cursor, and recently Antigravity to leverage Gemini 3 Pro. But it definitely wasn’t just “ask, copy, paste” AI is amazing for UI scaffolding, component structure, and boilerplate — but getting the whole thing actually working still requires a lot of configuration, debugging, and decision-making. I honestly can’t imagine how the full vibecoders are building products with Replit, Lovable, and similar tools without spending a fortune on credits. Here’s the link if you want to take a look and critique: [https://www.zennance.com](https://www.zennance.com)

Finally built a full MVP using “vibecoding” (but with a lot of real engineering work behind it) I know the real dev community is very sensitive to the term “vibecoding.” I’m fully prepared for the criticism, but hold on, this post is probably not what you think. I have a technical background, but I’m not a full-time developer. At work I sit somewhere between a analyst and a technical role, closer to DevOps. So I don’t consider myself “non-technical,” but I’m not a software engineer either. A few weeks ago, I decided to really push the vibecoding workflow, especially given my limits in writing large amounts of code manually. The result is the MVP of a personal product I’m building: "Zennance", a platform for technical freelancers to manage clients, projects, hours, budgets, and finances in one place. I want to share the process because I used AI very heavily, but I still had to debug a lot, adjust technical details, tweak architecture decisions, and solve real implementation issues. In the end, I got a working MVP deployed, multilingual, multi-tenant, and pretty usable. If anyone here is experimenting with this approach for real projects, especially with Next.js + Supabase, I honestly think it’s becoming a very viable path. I used Codex, Cursor, and recently Antigravity to leverage Gemini 3 Pro. But it definitely wasn’t just “ask, copy, paste” AI is amazing for UI scaffolding, component structure, and boilerplate — but getting the whole thing actually working still requires a lot of configuration, debugging, and decision-making. I started to use Lovable, but rapidly moved to other tools. I honestly can’t imagine how the full vibecoders are building products with Replit, Lovable, and similar tools without spending a fortune on credits. Here’s the link if you want to take a look and critique: [https://www.zennance.com](https://www.zennance.com)

Finally built a full MVP using “vibecoding” (but with a lot of real engineering work behind it) I know the real dev community is very sensitive to the term “vibecoding.” I’m fully prepared for the criticism, but hold on, this post is probably not what you think. I have a technical background, but I’m not a full-time developer. At work I sit somewhere between a analyst and a technical role, closer to DevOps. So I don’t consider myself “non-technical,” but I’m not a software engineer either. A few weeks ago, I decided to really push the vibecoding workflow, especially given my limits in writing large amounts of code manually. The result is the MVP of a personal product I’m building: "Zennance", a platform for technical freelancers to manage clients, projects, hours, budgets, and finances in one place. I want to share the process because I used AI very heavily, but I still had to debug a lot, adjust technical details, tweak architecture decisions, and solve real implementation issues. In the end, I got a working MVP deployed, multilingual, multi-tenant, and pretty usable. If anyone here is experimenting with this approach for real projects, especially with Next.js + Supabase, I honestly think it’s becoming a very viable path. I used Codex, Cursor, and recently Antigravity to leverage Gemini 3 Pro. But it definitely wasn’t just “ask, copy, paste” AI is amazing for UI scaffolding, component structure, and boilerplate — but getting the whole thing actually working still requires a lot of configuration, debugging, and decision-making. I started to use Lovable, but rapidly moved to other tools. I honestly can’t imagine how the full vibecoders are building products with Replit, Lovable, and similar tools without spending a fortune on credits. Here’s the link if you want to take a look and critique: [https://www.zennance.com](https://www.zennance.com)

Finally built a full MVP using “vibecoding” (but with a lot of real engineering work behind it) I know the real dev community is very sensitive to the term “vibecoding.” I’m fully prepared for the criticism, but hold on, this post is probably not what you think. I have a technical background, but I’m not a full-time developer. At work I sit somewhere between a analyst and a technical role, closer to DevOps. So I don’t consider myself “non-technical,” but I’m not a software engineer either. A few weeks ago, I decided to really push the vibecoding workflow, especially given my limits in writing large amounts of code manually. The result is the MVP of a personal product I’m building: "Zennance", a platform for technical freelancers to manage clients, projects, hours, budgets, and finances in one place. I want to share the process because I used AI very heavily, but I still had to debug a lot, adjust technical details, tweak architecture decisions, and solve real implementation issues. In the end, I got a working MVP deployed, multilingual, multi-tenant, and pretty usable. If anyone here is experimenting with this approach for real projects, especially with Next.js + Supabase, I honestly think it’s becoming a very viable path. I used Codex, Cursor, and recently Antigravity to leverage Gemini 3 Pro. But it definitely wasn’t just “ask, copy, paste” AI is amazing for UI scaffolding, component structure, and boilerplate — but getting the whole thing actually working still requires a lot of configuration, debugging, and decision-making. I started to use Lovable, but rapidly moved to other tools. I honestly can’t imagine how the full vibecoders are building products with Replit, Lovable, and similar tools without spending a fortune on credits. Here’s the link if you want to take a look and critique: [https://www.zennance.com](https://www.zennance.com)

lovable hit $200m arr in 12 months with under $20m spent and i'm trying to figure out if this is the new normal Lovable went from $0 to $200M ARR in basically a year. They hit $100M in June, doubled to $200M by November. With less than $20M in total funding spent. For context: most SaaS companies burn $30M to $50M just to reach $100M ARR. Lovable did it with 5:1 capital efficiency. **What Lovable actually is** AI-powered app builder where you describe what you want in natural language and it generates full stack web apps. Frontend, backend, database, deployment, all of it. Not a no-code builder. More like an AI full stack engineer. Integrates with Supabase, GitHub so you can ship real products not just prototypes. 180,000+ paying subscribers. 2.3 million total users. Started at $20/month, scales to $100/month for premium, custom enterprise deals now hitting multimillion dollars. **The efficiency is kind of insane** $1.7M to $1.9M ARR per employee. Industry benchmark is $275K. They have 45 full time employees. Most unicorns at this stage have 200+. Revenue per employee is 6x to 7x higher than typical SaaS companies. **Why this matters** If Lovable's trajectory becomes normal for AI native dev tools, the entire funding playbook changes. You don't need $50M in VC to hit $100M ARR anymore. You need product market fit and good execution. The CEO said they're adding $8M to $15M in ARR monthly right now. Targeting $250M ARR by end of year, $1B within 12 months. Those numbers used to take 5+ years. **The questions this raises** Is this repeatable or is Lovable a perfect timing outlier? They launched in November 2024 right as vibe coding exploded (even though the term wasn't coined until February 2025). They also pivoted from GPT Engineer (open source, too technical) to Lovable (accessible, monetizable). So it's not like they nailed it first try. Google Trends shows 40% drop in vibe coding search activity after spring 2025 peak. Developers raise concerns about AI hallucinations creating bugs. Entry level dev jobs down 20% since 2022. But the numbers are real. Bloomberg, TechCrunch, Fortune all confirmed $200M ARR. They're raising at $6B+ valuation now. Has anyone here actually built and shipped a real product on Lovable (with paying users or traffic)? How did it hold up past the demo phase?

Shared World - Explore. Capture. Connect. (LENS DROP) Hey everyone! 👋 Excited to share a passion project I've been working on for Spectacles: Shared World! It turns your city into a massive, multiplayer canvas for Augmented Reality stories. Here's what it does: * Capture &amp; Anchor: Run around and capture images of your surroundings using the Spectacles. The app instantly anchors them to their exact GPS coordinates in the real world. * Leave Messages: You can attach personalized messages, thoughts, or metadata to these anchored images. * Explore: Other users can then use an in-lens map (built using the Outdoor Navigation Kit) to find and walk up to the exact physical spot where a story was left. When they arrive, the AR story materializes right there! I wanted to find out what is possible with the new Supabase backend. * Storage: Images are stored securely in Supabase Storage Buckets. * Data Structure: I created a table in Supabase to connect the image file, the precise GPS coordinates, and all the associated metadata (like a personal message). This is how the AR experience is tied seamlessly to the real world. I'm keen to hear what you think and what features you'd like to see next! Let me know if you have any questions about the development process. Happy exploring! Try the Lens here: https://www.spectacles.com/lens/94faf2690d7144ef8a823fb7f38da60b?type=SNAPCODE&amp;metadata=01 https://reddit.com/link/1p9y9pp/video/zxjtg3jx494g1/player

Supabase + Expo SDK 54 + React Native 0.81: TextDecoder polyfill never loads before Supabase code Body: I've been stuck for 4 days on a TypeError: Cannot read property 'decode' of undefined error when trying to use Supabase with Expo SDK 54 and React Native 0.81.5 in a monorepo setup. Environment: * Expo SDK 54 * React Native 0.81.5 * u/supabase/supabase-js 2.86.0 * Monorepo with npm workspaces **The Problem:** Supabase requires `TextDecoder` which doesn't exist in React Native/Hermes. No matter where I put the polyfill, Supabase's code executes before it loads. **What I've tried:** 1. Polyfills in `index.js` entry point using `require()` before `expo-router/entry` 2. Multiple polyfill packages: `text-encoding`, `fast-text-encoding`, u/bacons`/text-decoder` 3. Installing on `global`, `globalThis`, and both 4. Mocking u/supabase`/realtime-js` in Metro resolver (the main TextDecoder user) 5. Metro's `getPolyfills` serializer option (breaks with Expo's Metro config) 6. Disabling/enabling `unstable_enablePackageExports` 7. Downgrading to Expo SDK 52 **Current index.js:** const { TextEncoder, TextDecoder } = require('text-encoding'); global.TextEncoder = TextEncoder; global.TextDecoder = TextDecoder; globalThis.TextEncoder = TextEncoder; globalThis.TextDecoder = TextDecoder; require('react-native-get-random-values'); global.Buffer = global.Buffer || require('buffer').Buffer; require('expo-router/entry'); **Error:** [runtime not ready]: TypeError: Cannot read property 'decode' of undefined [runtime not ready]: Invariant Violation: "main" has not been registered The polyfills are definitely in my entry point, but Metro's module evaluation order seems to run Supabase's code before the entry point executes. **Questions:** 1. Has anyone successfully integrated Supabase JS client with Expo SDK 54? 2. Is there a way to force Metro to evaluate polyfills before any other modules? 3. Should I just use Supabase's REST API directly with fetch instead of the JS client? Any help appreciated - I'm at my wit's end here.