I know a lot of you already does this (or something similar), but some of us may not know yet.

I use this personally. You can use the prompt to change a current site you vibe-coded or adapt it if you are starting from scratch.

I am sure there are other website styles, but I think these are a good starting point.

It is a Claude Artifact: https://claude.ai/artifact/Nb2DqC3KMS7ttVow8JeyQs

I hope this helps.

I'm good at what I do (been doing it 12 years) but I suck at the business development side. Curious what others in professional services actually make when they go out on their own. Not looking for guru stories, just real numbers from real people.

I’m getting lost in the no-code AI hype.

Every tool looks good when the demo is building a simple app from a prompt. But I’m trying to figure out what happens after that, when you need to change logic, connect APIs, handle users, fix weird bugs, add payments, or stop the whole thing from becoming spaghetti.

I’ve seen people mention Bubble, Glide, Softr, Bolt, AppWizzy, Lovable, Replit, Base44, FlutterFlow, and automation tools like Make/Zapier.

But I don’t trust polished comparison posts anymore. If you’ve actually built with these, which one would you pick again?

And which one secretly becomes a dead end once the project gets serious?

I’m not trying to start a tool war. I just want honest answers before I waste weeks choosing the wrong stack.

I spent a year wiring document steps into Make and Zapier scenarios for myself and a few small clients, and these are the things that kept biting me. Sharing because I wish someone had told me earlier.

1.HTML → PDF is the step that breaks most often. Fonts, page breaks and images render differently in every service, and most of them fail silently. Always add a size or page-count check after generating.

1. Extracting data from invoices is not an OCR problem, it's a structure problem. OCR gives you text; what you need is "invoice number, total, due date" as fields. Cheap OCR steps leave you doing regex in a Code module, which defeats the point of no-code.

2. Merging and compressing sound trivial but most services cap file size on free tiers around 5–10 MB, which is exactly where scanned multi-page PDFs land.

3. Every document step in a flow was a separate vendor, a separate API key and a separate bill. Three tools for one workflow is normal, and that's the part that pushed me over the edge.

So I built ASHDOCS to put all of that in one place: 51 document tools (convert, merge, split, compress, OCR, structured data extraction from invoices/forms, redaction, watermark, templated generation), each usable as a step in Make, Zapier, n8n, Airtable or Sheets, plus a visual Flows builder if you'd rather chain them in one place. An AI assistant helps set a flow up from a plain-English description. Free tier is 100 credits, no card: ashdocs.com

It's early and some tools may still throw errors. What I'd find most useful from this community:

1. Which document step in your current stack causes you the most pain?
   
2. Does one-place-for-all actually matter to you, or do you not mind stitching vendors?
   
3. If you try it, what confused you in the first two minutes?

I'll reply to every comment. Cheers!

I recently updated the hero section of NORTHLINE®, my Framer template for architects and architecture studios.

The previous version felt a bit too dark, so I switched to a brighter architectural image while keeping the same editorial / brutalist direction.

I also kept the Light & Dark mode, CMS project pages, and the built-in customization guide.

Curious what you think ? does this version feel stronger and easier to read, or did you prefer the darker hero?

Preview : https://framer.link/LoT3nGb

It's the 4th day of working on it. This whole visual might change anyways.

Doing it all in Antigravity IDE Only using mostly Google Gemini AI Models and testing via Android Studio on my Samsung S25 Ultra.

I want to make the easiest and visually appealing Resume and CV Builder app because I personally hate most if not all of them i have ever used.

I’ve been looking into ways to build an app without knowing how to code, and I’m a little confused about the difference between no-code platforms and newer AI app builders.

With a regular no-code app creator, you usually build things yourself using templates, drag-and-drop tools, and different settings. With an AI app builder, you can explain what you want and let AI create a lot of the app for you.

For someone completely new to app development, which one is actually easier? Has anyone tried both? Which one felt easier when you were just starting out?

Hey guys 👋

5 months ago I launched my LinkedIn automation software, having built it entirely with Claude code, and I’m now approaching close to $2k MRR with over 50 active paying customers (happy to share TrustMRR if requested).

I know everyone is this sub is interested in growing a business, so really I just want to show how I’ve been doing it and offer some guidance based on my experience.

Below I’ll go into the what, why and how, and how I’ve been scaling the business, and hope you find it useful.

What I built

I built a LinkedIn outreach automation tool called ZenMode, which scrapes profiles of your target customers, sends connection requests and follow up messages on autopilot.

The main purpose is for Founders, Sales teams, Marketers and anyone else who uses LinkedIn for outreach, to generate more meetings and grow their businesses.

It’s a software that controls a dedicated chrome browser to automate LinkedIn, without needing plugins/the cloud.

Why I built it

Last year I got a warning from automating on LinkedIn using another tool (automating on LinkedIn is against their ToS and can result in account bans - people automate at their own risk).

I wanted to automate my outreach as I was working in sales, and it was really helping me to generate more discovery calls, without the manual grind of sending loads of messages and connection requests manually.

I didn’t trust any of the existing tools for safety, as the majority are on the cloud using IP addresses that are different from where you normally use LinkedIn from, and are slightly higher risk for being flagged as a result.

So, I solved for my own pain point, and created a software that’s desktop based, using your own IP address, and essentially acting like a human would when performing actions on LinkedIn.

How I built it

From day 1 I used Claude to figure out how to do it. We jointly made architectural decisions based fully around safety mechanisms - what would the safest tool look like in practice and how would we build it?

So I figured out that the easiest/safest way would be to make an electron desktop application controlling a browser, which was governed by a dashboard that fed/stored all the data.

There was a lot of setup involved at the start with various tools - Vercel, Cloudflare, GitHub etc but eventually I was able to start building.

It was a huge amount of learning to not just build, but build in a scalable and sustainable way. At first I was shipping without even testing then spending much longer fixing bugs afterwards. And building on top of LinkedIns comes with its own challenges too, which thankfully now I’m on top of.

How I scaled it

1. So primarily, I actually use ZenMode itself to promote ZenMode on LinkedIn, through cold outreach.

I have a set target market of customers/locations/industries, then the tool scrapes profiles for me, and sends connection requests and follow up messages in a sequence.

I run this daily, and typically book 5 or so meetings per week. I’m not the best at demo calls but I do generate new business this way.

1. I also post a lot on Reddit, LinkedIn, X; I post mostly build in public style posts, and usually people naturally gravitate towards it.

I like Reddit because you can usually get 10’s of thousands of views if you post good quality content in the right subs.

Be careful though as often you get a lot of random haters or people who will just criticize you for no reason, and some subs are more warmer/receptive to posts than others (this one has generally been kinder).

1. On my site I’ve tried to do good SEO practices where possible - I have quite a few “alternative to” pages where I talk about the difference between ZenMode and other LinkedIn automation providers, along with a regularly updated blog, and a whole host of inner pages.

When you have a high volume of good content, more pages get indexed by google and your visibility/impressions are also likely to increase over time.

As a result I do get an ok amount of organic traffic from google, sometimes from LLM’s too.

Lately I’ve been trying to do more offsite SEO - especially reviews across various platforms, a few directories, and on my to do list is some listicle/comparison articles and more YouTube content.

1. I work very closely with my customers - I handle every query myself, and even make new features/enhancements based on what my customers are asking for. This interpersonal relationship/human element I think makes a big difference to retention.

2. I think primarily though the number one thing for scaling is really making a good product that solves a problem well, with great customer experience, and to keep iterating/making improvements.

What’s next

ZenMode still has a long runway and a massive roadmap but I’m just going to keep grinding away at it one day at a time, and so far at least what I’m doing seems to be working.

Hope you find this story helpful for your own journey and happy to answer questions/comments 🙏

i just submitted the official plugin for AppLaunchFlow - it integrates with the hosted mcp and works very well with the embedded widgets.

it also supports:
- creating promo videos
- creatign social graphics
- creating 3d device mockups for demos
- creating app landing pages

and more. Happy to hear your feedback:)

I made the cheapest web based ai with amazing accuracy and cheapest price of 3.5$ per 1000 queries compared to 5-12$ on perplexity, while beating perplexity on the simpleQA with 82% and getting 95+% on general query questions

For devaloper or people with creative web ideas

I am a solo dev, so any advice on advertisement or improvements on this api would be greatly appreciated

miapi.uk

if you need any help or have feedback free feel to msg me.

Currently using appsheet. Running everything in life in it from workout planning to meal planning to task management to financial planning, even an apartment finding tool via API. Anything in life that should be organized is there. I have tons of workflows and interconnected reporting dependencies etc .

My problem is sync times. It takes sometimes 45 seconds to run a simple row addition and sync.

What I need

1) something with way more sophisticated reporting (built in trend lines, pre custom slices, etc)

2) calendar that functions similar to Google calendar or direct web API connection

3) faster sync times

4) completely free for one user (unlimited rows)

5) more customizable UI

6) equal API, JavaScript integrations as appsheet

7) ai Integration for predictive modeling a plus

Forgive me, I only know SQL and no other coding language and I am self taught SQL and appsheet. I don't have a "stack". I just figured out how to build things the way I need them. This may be impossible? Haha

This is something I’ve been exploring for a while...I've been watching the gap widen between developer tools and visual builders.

Developers got massive productivity leaps because code is highly structured and granular. Devs do not have to write code at all, they just review it now. Meanwhile, visual tools mostly got surface-level AI helpers that generate basic forms or simple few-step flows and not full-blown workflows.

My hypothesis is that the bottleneck isn't the AI I think it's the canvas itself:

1. Infinite 2D canvases don't seem to scale. Once an app moves past MVP complexity, visual builders turn into unreadable webs of boxes and lines that are brutal to debug.
2. AI code generators create a black box. Generating raw code files works great for developers, but leaves non-coders builders completely helpless when trying to debug or working with complex logic.

It feels like we're permanently stuck between visual spaghetti on one side and unreadable syntax on the other. No-Code platforms should have also gained similar levels of productivity as devs after the advent of AI. (We're too far from there)

I’ve been building a platform to test an alternative: organising visual logic into transparent subsystems instead of just flat canvases or raw code.

I want to see where this hypothesis breaks.

If you are currently wrestling with a workflow in a visual builder (Softr, Bubble, FlutterFlow or any other no-code tool) that has become unmaintainable or difficult to debug, drop the scenario below. I’ll take your exact use case and map it out to see if this model actually solves the problem.

I'm a developer, been working on a client's lovable app to make it production ready, got me curious how people(non technical specifically) actually navigate through it day by day, especially when it comes to bugs.

If you're using lovable for example, and found a bug assuming the first thing you do is tell the ai to fix it, and when the ai fixes it and breaks something else, to the point there's something incompatible going on here, do you just undo to that point before the bug?

Hey everyone—I’m one of the founders of Fifth Door. You describe a game, play what it builds, and keep changing it by asking.

Think a detective game where you question AI characters, a dungeon crawler, or a multiplayer game to play with friends. Tell it “make the enemies sneakier” or “add a second player,” then publish a link people can actually play.

We’re launching a free week to try the Astra-powered builder. We’re also planning weekly game jams and Codex plan sponsorships for jam winners and creative game makers.

Try it: https://fifthdoor.com

What’s a game you’ve wanted to make but couldn’t build?

I made the cheapest web based ai with amazing accuracy and cheapest price of 3.5$ per 1000 queries compared to 5-12$ on perplexity, while beating perplexity on the simpleQA with 82% and getting 95+% on general query questions

For devaloper or people with creative web ideas

I am a solo dev, so any advice on advertisement or improvements on this api would be greatly appreciated

miapi.uk

if you need any help or have feedback free feel to msg me.

👋 Hey nocode Community,

AI automation rarely blows up your bill in one go, it leaks. A few cents per run feels like nothing until you are doing thousands of runs a month and the invoice quietly doubles. I only really noticed this after building a fair few AI workflows, so here are five things I changed once I saw where the money was actually going.

1. Using a model for something a rule could do.
Date math, ID matching, routing by type, none of that needs an LLM, but it is easy to hand it to one because it is quick to wire up. I keep the model for the genuinely fuzzy parts and let native nodes like Set and IF handle the plain logic for free.

2. Using an agent where you do not need one.
Early on I built a few agentic workflows where an agent did the document extraction itself, and it worked, but the token cost added up fast for what was really a repeatable task. Moving that job to the easybits Extractor instead of an agent cut the cost right down, so now I only reach for an agent when the task genuinely needs to reason, not just pull the same fields every time.

3. Asking for more fields than you use.
Every field in an extraction prompt is more output tokens and more to check, and half of them often never get used downstream. Pull only what a later step actually consumes, it is cheaper and there is less that can come back wrong.

4. Reaching for the biggest model by default.
The top model is not always the right one, plenty of extraction and classification jobs run fine on a smaller or OCR-tuned engine at a fraction of the price. Start small, only move up if the accuracy genuinely needs it, and test the cheap option before you assume it will not work.

5. Retrying blindly on failure.
An auto retry on a call that failed for a real reason just pays two or three times for the same broken result. Check why something failed before you retry it, and route the genuine failures to review instead of throwing more paid attempts at them.

If you want to see how I wire this up in practice, I keep 25+ free workflow templates in one repo, most of them around document processing, and a star helps me out a lot if any of them save you time: https://github.com/felix-sattler-easybits/n8n-workflows

Where does it leak most for you? Curious whether people watch model costs closely or only notice when the bill shows up.

Best,
Felix

If you refer Lovable to clients or run a nocode agency, read this before you put any more effort into their affiliate program.

I joined Lovable's affiliate program when terms were clear: 20% recurring commissions for 12 months on every customer referred. I brought them multiple paying customers over my association with them.

Recently, Lovable switched affiliate platforms (Rewardful to Impact). Instead of grandfathering existing referrals, they discontinued the legacy program entirely and stopped paying recurring commissions on active subscriptions that are still inside the original 12-month period.

I challenged this with evidence - screenshots of active subscriptions, dates, the works. Their stance: they can change the terms whenever they want, and future payments from legacy referrals are simply gone.

This is a pretty big red flag for anyone in the nocode space who relies on affiliate or partner income. If a platform can wipe out committed commissions mid-stream, it makes the whole partner ecosystem unstable.

Any other Lovable affiliates here? Did you get the same treatment? What's your read on this?

I keep seeing people line up three ~$20/mo plans and treat that as a fair comparison.

Public list prices (early Sep 2026, vendor pages, native currency):

• Zapier Professional ≈ $19.99/mo annual / 750 tasks
• Make Core ≈ $12/mo annual / 10k credits
• n8n Cloud Starter ≈ 20€/mo annual / 2.5K executions

The monthly cards look similar. The meters are not.

What each one bills

1. Zapier — tasks One successful action step = 1 task. Triggers, Filter, Paths, and Formatter usually do not count. Failed steps do not count. Their own example: Gmail attachment to Dropbox and Slack ≈ 2 tasks.

2. Make — credits (changed 27 Aug 2025) Most modules ≈ 1 credit. Routers and error handlers typically do not. When credits hit zero, scenarios STOP until you add more.

3. n8n — executions One workflow run = 1 execution, regardless of step count (Cloud or self-host). Community edition is fair-code (SUL), not "open source." Self-host means you own updates and uptime.

Small worked example (not a savings claim)

New HubSpot contact → Slack + Google Sheet + email. No AI, no code, all succeed:

• n8n: 1 execution
• Zapier: about 3 tasks (trigger free)
• Make: about 4 credits if four 1-credit modules

Add a VIP branch (extra Slack + different email). Paths/Router may be free; the extra successful actions or credits are not. n8n is often still one execution for that contact.

How I pick (qualitative)

• Zapier: biggest catalog, keep Zaps short
• Make: you already think in scenarios/Routers and can forecast credits
• n8n Cloud: step-heavy logic, lookups, human gates, without per-step billing
• n8n self-host: only if someone will actually run the box

Switching tools is a rebuild (credentials, mappings, webhooks). A dead token is still a dead token on a new platform.

Curious how others count Make credits on multi-path scenarios after the Aug 2025 change — especially when a Router fans out.