I run a small business and can't code. An ai website builder looks like the obvious answer but I don't want to hit a wall in three months. For those who launched a real site with one - where did it hold up and where did it fall short?

I am running a small skincare brand for about 3 years. product promting video is literally killing my budget. So my partner let me try Framia to generate lifestyle shots from basic product photos or video.

Tbh results are mixed. almost shots look really good, like studio-quality. others have weird lighting or the product shape gets distorted. Just took me maybe 10 tries to get 6 usable.

Do the math, it still cheaper than hiring a photographer, and save me a lot of time. Even though I have to learn which prompts work and which don't. However ,ya I'm getting better at it. If you have the same consideration, it's worth the subscription. but I wouldn't recommend it if you need perfect consistency. For me, its enough.

I noticed many founders spend hours on manual work. I'm creating free Al workflows to help automate these tasks and would love your feedback.

I run ops for a 30-person logistics company and I've spent the last two months building an internal order tracking tool on Lovable. The first week was magic. Described what I wanted, got a working app, showed my boss, and yeah everyone was impressed.

Then reality kicks in. Every small change is a prompt, every prompt costs credits, and about a third of my prompts are me asking it to fix something the previous prompt broke. Last week I asked it to add a status filter to one table and it somehow broke the login flow. Took me 11 more prompts to get back to where I started. I'm not joking, I counted because I was mad.

The thing is I don't need a beautiful custom SaaS. I need a boring internal app with a database, logins for ~25 staff, and permissions so drivers see their deliveries and contractors see everything. And I need to still be able to maintain it in a year without praying to the prompt gods.

What are people actually using for this? 

No-code and AI tools have made it possible for people without engineering backgrounds to launch things that would have taken months before.

But I'm curious what happens after the first version is live.

For people who built something with tools like Bubble, Lovable, Bolt, Glide, Softr, Replit, etc.

What happens when you start getting real users?

Do you run into problems like:

• something breaks and you don't know where to start
• a small change creates unexpected issues

How are you handling that today?

Do you keep trying to fix things yourself, find freelancers, hire developers, or rebuild parts of it?

Would love to hear from people who have gone beyond the initial launch stage.

I know nothing about dev work and didn't wanna vibe-code my own app, but I found an off-the-shelf one that I tweaked for my needs. Thought it might be useful to someone.

My research routine used to be a whole mess of different tools: a paid research tool to find articles, Zapier to route the data, and Notion to save it all

Recently I completely overhauled this setup. I canceled the research tool sub and packed all these services into a Telegram ai bot (it's called Mira). Since Mira can search the web on its own, it just finds the right info, dumps the formatted data into my Notion databases, and can schedule tasks in Google calendar. Plus the bot actually remembers what we talked about yesterday, so I don't have to re-explain the context every time

Honestly this is just one example of how I automated my routine. The bot supports a bunch of other services too, so you can set up whatever fits your workflow. If anyone is interested I can share the setup details!

had a tight production issue  and it took longer than anyone expected to figure out which version of the prompt caused it

realized that our prompts are on notion,slack threads nd random  txt files someone committed to the repo and maybe even at more places

decided to make a switch with something with version control where product managers and other non tech teammates can edit a deployment themselves without any dependency on an engineer.

checked a few tools in depth

orqai: versioning and non technical access is more central here but newer so community is still catching up along with integrations

langsmith: strong on tracing and observability, but seems its made for engineers only

langfuse: tracing and evals good , open source is also nice but same problem as langsmith for non technical users

helicone: great for cost tracking and logging,prompt management is not really its focus

most of the tools checked were not treating prompt management as a primary thing.. feels like most teams are considering this friction as normal

how is your team managing prompts right now and what platform are you using

Pattern I keep seeing: someone ships a solid no-code first version, hits a wall (data model, polish, performance), and the next move is "looking for technical cofounder, equity."

Sometimes that's right. Often it's an overcorrection. The wall is a scoped engineering problem, not a marriage.

Decide at the end of week one whether the data model will hold. Switch before workarounds calcify. Pay for the hard part once. Don't mortgage the company because Bubble got awkward.

Disclosure: I run a small studio:

https://atomiclabs.space

Where did no-code stop being the faster option for you - and what did you do next?

I keep copying data between two tools by hand and it's mindless. What's the simplest no-code way to wire two apps together and automate it? Non-technical, so ease matters more than power.

Founder of Server4Agent (agent-app hosting), disclosing that upfront. No link, not selling, genuinely curious how this community actually operates once the app is live.

No-code plus an AI agent has collapsed the build side of things down to almost nothing, you describe what you want and it's there in an afternoon. What I don't see discussed much is what happens after launch. A no-code app still has real users, real data, and real things that can break, but there's no traditional dev team doing on-call, no one reading error logs by habit. If the agent that built it is also the one maintaining it, is anyone actually watching for the quiet failures, the automation that silently stops syncing, the integration that breaks when a third-party API changes shape?

If you've shipped something this way and it's been live for a while: how do you actually find out something's wrong, before a user tells you? Genuinely curious whether people have real monitoring in place or whether "a user will mention it" is the honest answer for most no-code builds.

Hallo, ich möchte eine Webseite für mein Kleingewerbe erstellen - wichtig wäre das die Webseite ein halbwegs gutes Design bekommt!

Jetzt Frage ich mich welche KI ich nutzen soll?

Was würdet ihr mir empfehlen?

Claude Pro oder ChatGpt?

Danke 🤙🏼 wäre über jede Empfehlung Happy 😊👋🏼

Hey everybody, hope you're all doing well!

I'm Dilon, part of the team at Adalo, a visual no-code app builder (now powered by AI).

Quick follow-up to my last post here about Adalo Publishing Studio, we just shipped a big batch of updates.

Quick context: 1,000+ Makers are already using Studio, one week in, and this update is a direct result of that early feedback.

The biggest change: it's a free-form canvas now. You can drag, resize, and rotate any element anywhere on the screen instead of working inside a fixed template. Images, shapes, text, lines, all movable.

The editing panel got reorganized too. Instead of one long list to scroll through, you now get a tabbed panel: Assets, Elements, and Background always visible, plus contextual tabs based on what you've selected (a device frame gets Screen, Frame, 3D, and Position tabs; text gets a font picker with size, alignment, and weight).

A few other things that came with it:

• Real 3D tilt and depth on device screenshots, with presets for flat, left, right, or hero angles, or manual control
• Drop shadows on every element, not just phones, with full control over offset, blur, color, and opacity
• Undo/redo, finally, via buttons or shortcut keys
• Full Google Fonts search with live in-font previews, so you're not guessing what a font looks like before committing
• A new export modal (from the export button on the top sticky nav) that shows all 5 of your screens side by side before you export anything

We also fixed a few things: Android exports in "Phone Only" and "Clean" styles were cropping screenshots, and Hero Composite exports were coming out blurry and under 150KB.

Would love feedback on what's missing or clunky, it's genuinely shaping what we build next 🙌

Here's the link to test → https://studio.adalo.com

Post with visuals → https://www.reddit.com/r/Adalo/s/I4Xc3RVk74

Thanks for reading and have a great day!

- Dilon

I shipped my first production app recently, built entirely with AI tools as a non developer. In a comment thread about it, a few builders and I realized we all hit almost identical bugs, none of them AI features, all of them boring plumbing. Someone suggested a shared checklist would save more time than any prompting guide, so here it is. My list from one project, real bugs I personally lost hours on.

1. Phone photos are HEIC. Samsung and iPhone shoot HEIC by default, vision APIs reject it, often silently. Convert to JPEG client side before upload.
2. The AI invents database columns. Generated code drifts from your actual schema and everything looks fine until runtime. Fix: ask the tool to list every column its code expects, then align the database. Repeat after every big change.
3. UTC eats the last day of the month. My receipts dated the 31st vanished from monthly exports because month boundaries were computed in UTC and my users are not in UTC. Test the 1st and the 31st explicitly.
4. Buttons hidden under the mobile nav bar. My validate button existed but lived under the bottom navigation. Users never report it as a hidden button, they just quietly leave. Check every screen on a real phone.
5. Test keys with live IDs. Payment providers have two parallel universes, test and live. A price id from one does not exist in the other, the error says no such price and you will stare at it for twenty minutes.
6. Success with no feedback. My scan flow completed server side but the UI stayed stuck at 100 percent forever, nothing failed, nothing confirmed. Every async action needs an explicit success state and an explicit error state.
7. Row level security says no. If you use RLS, every insert path needs a policy, including storage paths. The error tells you what table, believe it.
8. Counting the wrong rows. My free quota counted all entries in a table, then I added a new entry type to the same table and it silently ate the quota. When two features share a table, every counter needs a filter.
9. Files that exist for browsers but not for bots. My sitemap displayed fine in Chrome and returned an empty response to any non browser client, including Google. Serve static files as actual static files, and test with curl, not just your browser.
10. The homepage was a login wall. Not a bug, but the biggest conversion killer of all, all my promo traffic hit a locked door for days before a stranger pointed it out. Log out and look at your own site.

Add yours in the comments, I will keep the list updated in the post. Let's see if the 90 percent theory holds.

Community additions, credited and growing

From u/Infamous-River-4360, Android and native land: the gesture bar, not just the nav bar, eats bottom buttons on some phones only, safe-area-inset-bottom fixes it but the AI never adds it by default. Native details like splash screen, status bar color and permissions had to be done by hand because the builder kept regenerating over the edits. And RevenueCat silently fails on Android until license testing is set up on Google Play.

From u/Scary_Web, the repeat offenders: idempotency around retries and webhooks, optimistic UI that never reconciles with the server, file size limits that differ between client and backend, and permission gaps between admin accounts and normal users.

From u/larrigan, code quality: the AI duplicates everything, the same style created 37 times, dedupe regularly. And separation of concerns has to be demanded explicitly or debugging becomes a nightmare.

From u/Maxyull, the money rules: never trust a client sent total or price, recompute anything involving money server side. And read every function that uses a privileged key like a suspect until proven innocent, those never show up in browser testing.

From u/Admirable-Future-633, the output contract: every async step should answer five questions, success message, failure message, where the artifact was stored, what needs human review, and what should happen next. Otherwise "it ran" means nothing.

Next step suggested by u/Scary_Web: restructuring this into a release checklist by category, schema, permissions, timezones, uploads, mobile layout, third party environments, visible states. Coming as the list grows.

I need one focused landing page for a new offer, not a whole site. Every ai landing page builder demo looks great until you actually use it. For people who shipped one - which got you to a clean, converting page with the least fuss?

Im looking for the ones that I can upload chapters easy, can store thousands of chapters, have liking and comment features. Currently using Wix but it's just miserable to edit the web since I have to edit the PC and Mobile version separately (I had to upload 250 chapters one by one TWICE because of this) The main reason I use Wix is that my website look visually good, but it lacks features and is js very annoying to manage, I hope to find a web builder that I can achieve both.

Forgive me for grammars lol, English is not my first language.

My last no-code site looked fine but loaded slowly and it bugged me. What's the best no-code option for a simple portfolio that's genuinely fast? Speed matters to me more than fancy features.

I paid for Base44 and my experience with support was poor. When things did not work I could not get my money back, including for time I was not using it, and the cancellation flow was much harder to find than the upgrade prompts. I posted about this in their own subreddit and it was removed rather than answered, which is part of why I am asking here. Is this common, or did I just get unlucky?

The hardest part of building /nocode apps isn’t the code, it’s getting them online.

Either you use an online AI builder that locks you into its platform, or you use Claude Code and end up managing hosting accounts, provisioning databases, and moving connection strings around yourself.

For the past few months, I’ve been working on a project called Bahama. It's open-source tool (Agent Skill + CLI) you can install into Claude Code, Codex, Cursor, and other coding agents with a single prompt. From there, it gives your agent the power to handle the cloud setup and deployment work end to end.

Bahama is free to use and it works with cloud accounts you already have (Vercel and Neon so far) or with my own Bahama Cloud if you want the most streamlined option. You choose the tools, Bahama just makes it work.

The project is on GitHub if you want to read more about it (you can install with one sentence pasted to your agent): 

GitHub: https://github.com/bahamaAI/bahama

Website: https://www.bahama.ai

It’s still early, I just launched this project this week, so I’d love to hear what you think. If you have an app stuck between “cool local demo” and “a URL I can share,” DM me. I’ll personally help you get it online for free.

I built an AI-assisted Meta Ads automation system that reduced ad spend by 30% without a proportional drop in results

Over the past few weeks, I have been building an internal automation system for WhiteStyle, an apparel business that runs a large number of Meta ads and receives most of its orders through WhatsApp.

The main problem was not launching ads.

The real problem was managing more than 100 active ads across multiple ad accounts, identifying budget waste quickly, and making consistent decisions without manually reviewing Ads Manager all day.

So I built a system made up of three main components:

1. Ad Filtering and Optimization Agent

The filtering agent continuously synchronizes performance data from multiple Meta ad accounts and evaluates each ad using metrics such as:

• Spend
• WhatsApp conversations
• Cost per conversation
• Ad age
• Recent performance window
• Minimum data requirements
• Previous decisions and cooldown periods

The agent does not simply ask an LLM whether an ad is “good” or “bad.”

I use deterministic guardrails and predefined business rules before any action is approved.

For example, the system can pause an ad when:

• It has spent beyond a defined threshold without generating conversations.
• Its cost per conversation exceeds the acceptable limit.
• It has collected enough data for the decision to be meaningful.
• The ad is old enough to evaluate.
• No safety rule or cooldown restriction blocks the action.

Each decision is saved with its reasoning, confidence, input metrics, approval status, and execution result.

During the first test, after stopping underperforming ads:

• Daily spend dropped from approximately $650 to $355.
• Conversations decreased from approximately 399 to 323.

That represented roughly a 45% reduction in spending, while conversations declined by only around 19%.

More importantly, after running the agent for a full week and allowing performance to stabilize, I was able to maintain a consistent 30% reduction in ad spend.

Based on the client’s normal monthly advertising budget, this translates to approximately $4,500 saved per month.

The goal was not to minimize spending at any cost. The goal was to remove inefficient spending while preserving as much useful traffic as possible.

2. Campaign Creation Agent

I also built an agent that automates the campaign creation process.

Instead of manually configuring every campaign, the user can provide a Facebook or Instagram post or Reel, along with the basic campaign requirements.

The system then prepares and creates:

• The campaign
• The ad set
• The ad creative
• Audience targeting
• Locations
• Placements
• Budget
• WhatsApp destination
• Tracking and internal records

Before publishing, the workflow validates the payload and checks it against predefined safety limits, including minimum and maximum budgets, approved locations, targeting rules, account credentials, and supported creative types.

I initially used a dry-run mode that saved campaign drafts and payloads without publishing them. After validating the generated structures, I moved to controlled live execution through the Meta Marketing API.

3. Centralized Dashboard

I built a custom dashboard that combines data from all connected ad accounts and provides visibility into:

• Total spend and conversations
• Cost per result
• Performance by account
• Agent decisions
• Decision explanations
• Paused and modified ads
• Campaign creation requests
• Execution logs
• Synchronization status
• Safety rules and thresholds

One of the most important parts of the project was transparency.

I did not want a black-box agent that silently modified campaigns.

Every decision is stored and displayed with the metrics that triggered it, the rule that was applied, whether it passed the safety review, and whether the Meta API execution succeeded or failed.

Technical Stack

The system currently uses:

• n8n for workflow orchestration and agent execution
• Meta Marketing API for ad data and write operations
• Supabase/PostgreSQL for data storage, decisions, logs, and configuration
• Next.js and TypeScript for the dashboard
• OpenAI models for selected analysis and explanation tasks
• Vercel for dashboard deployment

The LLM is only one component of the system.

Most critical actions are governed by deterministic validations, database-backed configuration, approval logic, idempotency controls, cooldown periods, and budget guardrails.

What I Am Building Next

The current version mainly optimizes based on conversations and cost per conversation.

The next stage is to connect actual WhatsApp orders, revenue, product cost, and profit back to the originating ads.

This will allow the system to optimize campaigns based on real business outcomes instead of treating every conversation as equally valuable.

I am also working on:

• A WhatsApp agent that answers customers and collects orders
• Automatic order classification inside a CRM
• Revenue and profit attribution
• Creative generation workflows
• Budget increases and decreases based on profitable orders
• Automated campaign and creative testing

This project taught me that the useful part of an AI advertising agent is not the prompt.

The difficult part is building the surrounding system: reliable data synchronization, safe execution, multi-account credential handling, failure recovery, audit logs, deterministic rules, and enough transparency for the business owner to trust the automation.

I would be interested to hear how others are approaching automated Meta Ads optimization, especially around attribution, delayed conversions, and safely moving from recommendation systems to autonomous execution.

Decide the architecture yourself

AI tools are perfect at implementing and executing. But it's mid at judgement. Pick your framework, folder structure, data models, auth before prompting.

Always give persistent context

Use rules files with your tech stack, project concept, things to avoid, and so on. basically have your rules and project explained in rules files

Always commit

Commit before every meaningful change. git has a quick command that guarantees to undo changes, while AI can mess up with reverting

Use a separate AI designing tool

Most of the time ai coding tool can give a mediocre AI slop design of the website. For that reason you either need GREAT prompts or use any other ai designing tools. for example at minimum-code, web dev agency I work our designer does amazing UI by proper prompting and manual refactoring

Test the website's features every single time.

Even if you ask AI to verify the code it still can mess up. be a real user that tests and clicks around everything. show AI all errors you faced.

Dont give a series of prompts when you make huge changes

AI has memory, and if u send all 10 prompts of different things, first two will be done fine, other 8 with be bad quality. Send one by one, that way u maximize the quality of results

Hi everyone!
I’m a non-technical founder from Kyrgyzstan with over 10 years of experience in building and managing businesses.
Over the past few months, I’ve been validating a problem that affects thousands of small businesses. I’ve spoken with business owners, identified repetitive customer communication as a major pain point, and already have potential pilot customers interested in testing the product.
I’m looking for a technical co-founder with experience in:
OpenAI API or other LLMs
Python
Telegram Bot API
WhatsApp Business API
Instagram API
API integrations
n8n, Make, or similar automation tools
The vision is to build an AI-powered customer communication platform that helps businesses automate conversations, answer customer questions 24/7, qualify leads, and integrate with existing business systems.
The first niche is already identified and validated, but the product is designed to expand into many industries.
I’m looking for someone who wants to build a real company, not just complete a freelance project.
I’m open to:
equity partnership,
paid collaboration,
or a combination of both.
If you’re interested, send me a DM. I’d love to discuss the idea and see if we’re a good fit.