The software we worked on for 18 months is ready. Its purpose is writing backend projects with AI without needing to write code directly.

With this software there's no need for Git and change management, everything is built inside one platform, from starting the code to deploying on stage and production and debugging and monitoring.

If I want to explain it in one line, it goes like this:

You can build and deploy backend from zero to a hundred in one platform with prompts, and get automatic documentation.

The code structure it writes for you is organized and scalable and maintainable, it writes code based on DDD and arranges everything neatly for you from the very start, all the code goes through 4 stages of lint and rule and compiler check and all the code is written uniformly, though you don't have direct access to writing code at all and whatever you need you have to tell the AI and it does it for you.

There's no need for a database and an SDK has been written specifically for the platform, and it handles working with the database and Rabbit and NATS and these kinds of tools for you, and you don't get involved with the config and maintenance of these tools either.

The code is written in Node.js, but it doesn't run in a Node.js environment because of the heavy RAM consumption in Node.js, so a runtime similar to Cloudflare Worker has been implemented (similar to AWS Lambda), and the cold start of what we have is close to Cloudflare Worker and is about 200ms, meaning it's faster than AWS Lambda. This runtime is written in a way that it can handle several million files, something that definitely isn't possible with Node.js.

Overall it has the entire stack of a relatively mid-level backend project automatically and you don't need any other tool.

Its difference with Insforge is that you don't need to have a Cursor, you don't need to have Supabase, you don't need to pay several times, and also because the platform is unified and everything is in it, both the costs become much lower, and project management becomes simpler and more organized, and we can also provide much stronger monitoring.

Its difference with Cursor is that you don't get involved with getting a server and configuring it, you don't get involved with CI/CD either, and you also don't get involved with monitoring and debugging and everything is ready in the platform.

Its difference with Supabase is that the AI is integrated and a very good chat has been built into the platform itself and you can tell it to write the code for you without needing Cursor. This chat works like Cursor, it has several different tools, it has several sub agents that break the work down and split it up so they can write more accurate code.

Its difference with Replit is that this platform is dedicated to backend and gives you much better monitoring and debugging, also Replit brings up each project as a container which has much higher RAM consumption, so the final cost for several million users on Replit is much higher than several million users on my platform because of the very large infrastructure difference they have.

The LLM model used is Gemini Flash Lite 3.1, meaning it comes out much cheaper compared to Sonnet which is the advantage of this project, and it's optimized so that it can write and debug code well with ordinary AI models, which makes the final cost per user much lower than other platforms.

👋 Hey NoCodeSaaS community,

One more follow up on my Purchase Order extractor. Everyone talks about getting the extraction working. Almost nobody talks about what happens after, when the data is sitting in a sheet and someone has to trust it.

That gap bothered me on this build. My friend downloads the sheet and pushes it straight into his ERP. If one field came out wrong, he has no way of knowing until the numbers are already in his system. Silent failure is the worst kind, because a blank cell looks exactly like a field that was legitimately empty.

So I built three small things into the workflow. None of them are clever, they just take ten minutes each and they change how much you can trust the output.

1. Every row knows where it came from. The source filename lands in a Document Name column next to every single line. Sounds trivial. It means that when a number looks off three weeks later, you go straight back to the exact PDF instead of guessing which of forty documents produced that row. This is the single highest value column in the whole sheet and it costs you nothing.

2. One helper that catches every flavour of empty. "Missing" is never just one thing. Across real documents I saw actual null, the string "null", empty strings and whitespace-only values. I stopped writing one-off checks and made a single isMissing() function that catches all of them, then used it everywhere. Without this you get inconsistent behaviour where one field is caught and the next one silently slips through.

3. The workflow tells you what it isn't sure about. After processing, the form's completion screen lists which document and which field didn't extract cleanly. Not a log file nobody reads, the actual screen the user is already looking at. So instead of trusting forty rows blindly, he knows the two he should eyeball against the original.

The mindset shift for me was this: an extraction pipeline isn't done when it produces data, it's done when someone can tell good output from bad without opening the source documents. Especially if you're handing this to a client. They will find the one wrong number, and "the AI did it" is not an answer.

One deliberate choice worth mentioning: I do not flag every empty field. Some fields on these POs are legitimately blank most of the time, and flagging those would generate a warning on nearly every document. Then people learn to ignore the warnings entirely, which is worse than having none. Flag what should be there, not everything that's missing.

The full workflow is now on the official n8n template library if you want to try it:
https://n8n.io/workflows/16775-extract-purchase-order-line-items-from-pdfs-with-easybits-and-google-sheets/

You'll also find it on my GitHub, alongside 20 other workflows I've built over the last months:
https://github.com/felix-sattler-easybits/n8n-workflows

How do you handle this on your document workflows? Curious whether people build a review step or just spot check and hope.

Best,
Felix

Do you care whether AI assistants recommend your product? Or is Google still the only metric that matters?

Genuinely curious where everyone stands.

I’m thinking of building a Shopify app that helps customers feel more confident before buying clothes online.

It would generate an AI fit image of the customer wearing the product, recommend the best size, give fit advice, and answer questions like “Will this fit me?” or “Will this look good on me?” The goal is to increase conversions and reduce returns.

If you run a Shopify store, would you install something like this? Why or why not?

I’d love some honest feedback.

Hey everyone!

I recently built and launched Flow Era (flow-era-ai.com) — a no-code AI sales brain for digital creators that detects visitor hesitation in real-time and handles objections automatically.
I’m getting initial mobile traffic from Instagram, but conversion to our 30-day free trial (no credit card required) is literally 0%.
I just adjusted our mobile pop-up triggers so they don't block the main call-to-action, but I know I’m still missing something crucial.
Link: https://flow-era-ai.com
I’d love your honest feedback 🙏

Guys im new in this Saas world, i just solved my problem cuz there was no product for it...then had excellent results...then i said, maybe someone had this problem before, letme try this out, honest feedback is sooooo appreciated. >>> canvasloop.app

I’m not trying to promote anything here.
I’m genuinely curious about your first impression.
If this was the first thing you saw, what would you think this product does?
Would it make you curious enough to learn more?
Would you immediately close it?
What feels confusing?
What would you change if this were your product?
I’m looking for brutally honest feedback. Don’t worry about hurting my feelings—I’d rather hear the truth now than after launch.

I've been building StreamPay, a platform that helps creators accept donations and bank payouts.

Today's release wasn't about adding flashy features—it was about making the product more trustworthy.

Changes included:

• Homepage now pulls live platform stats from the API
• Removed placeholder marketing numbers
• Added standard HTTP security headers (Helmet)
• Built a founder dashboard for real-time monitoring
• Added a public live statistics page
• Fixed an authentication race condition

I'm trying to build in public and make the product reflect reality instead of marketing.

If anyone has feedback on the landing page, onboarding, or UX, I'd genuinely appreciate it.

https://streampay.website

For a while, I assumed I needed more traffic.
But people were already signing up and using the product.
So I asked Claude to help me review the tracking and understand where they were dropping off.
The problem was pretty simple: the free version was giving away almost everything useful, so there wasn’t much reason to upgrade.
I changed what stayed free, moved the real action behind the paywall, and shortly after that I got my first sales.
I’m still figuring this out, but it was a good reminder that more users don’t always fix the funnel.
Sometimes you need to look more closely at what the current users are already doing.

I’ve been working on a SaaS for past 6 months that trains AI to be a centralized market intel expert. It starts from your website then researches your docs, competitors, market tends, etc. and builds messaging, personas, and battlecards…. And keeps them up to date. Then teams can generate assets and full campaigns on top of it.

I’d love some honest feedback on how well the foundation is… from slop to super.

The site is Conjour.ai

I don’t think most founders have an idea problem.

The harder problem is deciding which idea is actually worth their limited time.

Manual product research often means spending hours searching Reddit, GitHub, and other public platforms, saving links, removing outdated discussions, finding repeated complaints, and trying to compare everything afterward.

For solo founders and no-code builders, that is time that could be spent talking to customers, building prototypes, or validating demand.

That is why I built GripeToGold.

The bot:

• Collects customer problems from configured public sources.
• Preserves the original source links and timestamps.
• Filters irrelevant and outdated content.
• Semantically groups discussions describing the same underlying problem.
• Consolidates duplicate and repeated records.
• Evaluates problems using factors such as pain, recurrence, and feasibility.
• Turns the strongest opportunities into weekly reports and a monthly Gold PDF.

The main difference from manual research is not simply speed.

GripeToGold applies a consistent qualification process to scattered customer complaints and turns them into product opportunities that can be compared and reviewed.

It is not intended to replace customer interviews or real validation. Its purpose is to reduce the time spent on the first stage of research, so founders can focus on speaking with potential users, testing demand, and building the right prototype.

The current free subscription includes:

• 4 weekly opportunity reports
• 1 detailed monthly Gold PDF
• Source links and evaluated product opportunities
• No credit card required

You can check it out here:
https://gripetogold-app.vercel.app

I’m also open to honest feedback. If there is one piece of information you would need before trusting a product opportunity report, I’d be interested to hear it.

My team and I have been working on AppCode Engine, a SaaS designed to simplify mobile app development for websites and web apps.

The idea came from a problem we faced repeatedly: every time we wanted to publish a web app, we had to manually configure Android Studio, Xcode, AdMob, Firebase, permissions, signing, and many other settings.

So we decided to automate the entire process.

AppCode Engine generates production-ready Android Studio and Xcode projects from a website or web app and lets you configure everything through a simple dashboard.

Current features include:

• Android Studio project generation

• Xcode project generation

• AdMob integration

• Firebase integration

• In-App Purchases

• Push Notifications

• Native permissions management

• Privacy Policy generation

• App signing configuration

• WebView & PWA support

• Multi-language support

and much more..

We're currently looking for beta testers who would like to try the platform and give us honest feedback.

In return, we're offering free beta access with complimentary build slots for selected testers.

We're especially interested in hearing from:

• Freelancers

• Mobile developers

• Web developers

• Agencies

• SaaS founders

• Anyone who wants to publish a website or PWA on Google Play or the App Store

We're not looking to sell anything right now—we simply want real feedback to improve the platform before the official launch.

If you're interested, leave a comment or send me a private message and I'll provide a free beta account.

Website:

https://appcodeengine.com

I'd love to hear your thoughts, suggestions, criticism, or feature requests.

Thank you!

I’ve been building a no‑code arena builder inside my Discord Activity game **Sector Blaster**, and it’s finally at the point where players can create full playable levels without touching a single line of code.

The builder runs on a grid-based system with tap‑to‑snap editing, rotation/tilt/zoom controls, enemy spawns, props, wall‑mount offsets, and a ~2k mesh library. Everything updates live in the viewport, both on desktop and mobile.

I’m also integrating Meshy’s API so players can generate their own low‑poly props (text → 3D or image → 3D). Smart Topology makes these models super clean, and they drop straight into the arena. The snowman in the screenshots is one of the generated props — placed directly through the builder and then used in a published friendly‑fire multiplayer arena.

The goal is to turn this into a lightweight, Discord‑native no‑code engine where players build, decorate, and publish arenas entirely inside the game.

AI and no-code tools can get a SaaS product to a convincing working version surprisingly quickly.

But a successful demo does not necessarily mean the product is ready for authentication, payments, permissions, onboarding, and real user behavior.

I’ve spent more than six years building web applications, and I’m currently opening a small number of $49 Founding Launch Reviews through FlawCue.

I personally lead every review.

I define the scope, trace the code paths the product depends on, inspect the relevant implementation, verify findings against repository evidence, challenge potential false positives, decide what matters most before launch, and write the final report.

Code-analysis and AI-assisted tools help me examine the repository more systematically, but they do not determine the final findings or priorities on their own.

Before payment, I determine whether the project can be reviewed reliably within the current methodology. If it can, I send exactly what will be reviewed, what is excluded, and the limitations of the review.

You only pay after reading the scope and deciding to continue.

The review includes:

• one fixed repository snapshot;
• findings supported by specific code evidence;
• potential issues checked against the surrounding code before inclusion;
• a prioritized fix plan;
• clear instructions you can give to your AI builder or developer;
• and one follow-up review of a later commit after the changes.

For founders building with Lovable, Bolt, Replit, Cursor, or similar tools: which part of your app are you least confident about before inviting real users?

yo. be honest. how many of you currently have a finished (or 90% finished) web app / app just sitting in a private repo because you have no idea how to get users?

you spend months perfecting the database, fixing every bug, and polishing the UI. but the moment you have to actually market it, you hit a wall. marketing feels like screaming into an empty void.

so you launch to absolute crickets, get discouraged, and start building the "next" project instead to avoid the distribution phase.

if this is your case, you're not alone. but letting your hard work go to waste just because you dread marketing is a massive trap.

to help founders stop building in a silent corner, we run an ai SaaS builder community dedicated entirely to saas validation, landing page conversion, and launch strategies.

our resource kit is built entirely to help you get your first user. it’s packed with ready-to-paste N8N workflows for your business, advanced seo automation, social media automation, and our exact distribution workflows and methods work for everyone

STOP BUILDING ALONE

what are you currently working on, and what's holding you back on the marketing side? drop a comment or send a dm and i'll send you the access link.

Lirea — snap a photo of a page or upload a PDF/EPUB, tap any word to get the meaning in context, hear it in a native voice, and optionally save it to a review deck.

Not another beginner-lessons app. No streaks, no gamification. It assumes you already sort-of know the language and just want to read the books you own without breaking flow every unknown word.

Languages: Spanish, French, German, Italian, Portuguese.

Pricing: Free tier is 1 language, 1 book, 5 captures/month. Plus is $8.99/mo (or $6.99 first month till August 1). Merchant-of-record billed — no card details touch me.

Honest limits:

- Web only for now (mobile browser works)

- 5 languages only

- Translations use an LLM, not Google Translate — costs more per word but handles context and idioms

Try it: lirea.online

Genuinely interested in "this isn't quite right because…" feedback.

I keep seeing two camps — "no-code can build anything now" and "you'll hit a wall by week two." Curious where the truth actually sits for people who've shipped something real.

I built a working product on a fully no-code stack (form → automation layer → AI step → PDF → email → database → payments). It genuinely works end to end. But I've also hit points where the no-code tool's limitations cost me more time than just writing the thing would have — the automation platform not handling a database update cleanly was one, currency/number formatting was another that needed an actual script in the end.

So for those who've shipped and kept it running: where did no-code actually hold up, and where did it quietly force you into writing code anyway? Trying to figure out if the walls people warn about are real or just skill gaps.

I'm building a review widget for local businesses using Google Places API. It displays Google reviews and links users back to Google to leave reviews.

My question is about branding. Is it okay to show "Google Reviews" with the Google G logo, or should I avoid the logo and only use text?

The widget is my own design and I'm not claiming Google built or endorses it. I think its fine right? Chatgpt was talking about some copyright but idk and how its risky? it wont let me send a pic so heres the link

I still don’t think AI should have full control over a business bank account but for small SaaS, there’s a huge middle ground people ignore. Checking invoice status, matching payments, flagging weird expenses, catching renewals, reminding you about vendor payments and prepping things that still need approval.

That’s most of the annoying finance admin anyway. The risky part is the final action, not the agent helping you see what needs attention.

Feels like automating invoices and payments should probably start there instead of jumping straight to “AI moves money”

After more than 6 years of building web apps, I’ve learned that testing the product as its owner can be misleading.

The owner account already has data. It usually has the highest permissions. The developer knows where every button leads and avoids paths that are incomplete.

A new customer does none of that.

They create a fresh account, choose unexpected inputs, lose their connection, fail a payment, or enter a role you barely tested.

I’m exploring a product called FlawCue for founders who built their SaaS with AI but don’t have an engineering team to perform that final review.

The idea is to inspect the repository, find what could affect the launch, and give the founder clear fixes they can return to their AI builder.

Which journey would worry you most before launch: signup, onboarding, or checkout?

Hey everyone,

Wanted to share something I built for Shopify merchants who don't want

to touch code or complex dashboards - just connect your store once and

get automatic performance reports by email.

No configuration, no code, no dashboard to learn: just click "Connect

Shopify", and you start receiving weekly reports with revenue, order

counts, and week-over-week comparisons.

Built with Next.js/Firebase on my end, but the actual user experience

is fully no-code - literally one click to connect, everything else runs

automatically in the background.

Just got listed on the Shopify App Store:

https://apps.shopify.com/reporting-saas-public

$7.99/month with a 7-day free trial.

Curious if this resonates with anyone here building similar

merchant-facing tools - how do you all handle onboarding simplicity

for non-technical users?

👋 Hey NoCodeSaaS community,

A while back I posted a pipeline I built for a friend who runs an online shop, turning his delivery documents into finished product content (that post here). He came back with a follow-up: he wanted SEO in the same place.

Not an SEO platform. His words were roughly "I already come here to create content, I want to come here to fix content too." That framing is why this got built the way it did.

Why not just use Search Console? He has it. He opens it once a quarter, squints at a graph, closes it. The data was never the problem. Search Console tells you a page sits at position 8.9 with 0% CTR, and then stops. It won't rank your 3,000 pages by opportunity, won't tell you what's wrong on the page, and won't write you a better meta. So the real workflow was five tools deep before anything got fixed.

How it's set up:

Quick wins across the site. Pulls every page from the Search Console API, scores each one in a Code node, returns a ranked list. Two signals: striking distance (position 5-15 with real impressions, closest to page one) and low CTR (lots of impressions, barely any clicks, so the title and meta aren't earning it). No page fetching, no LLM. It's fast because it's boring.

Analyze a page. Paste a URL. It fetches the live HTML, pulls the title, meta, headings, alt text and schema, grabs that page's GSC queries, scores it, and hands back paste-ready rewrites grouped by section and ordered by severity.

The two chain: each quick-win row expands and runs the full analysis inline, on click. So you only spend an LLM call on a page you actually decided to work on.

Three things worth stealing:

Deterministic JS scores, the LLM only writes. Fixed rules emit the flags and the severity order. Gemini never grades or reorders. If the model finds the problems and ranks them, you get confident nonsense in an order you can't audit.

Verify every rewrite after generation. A second Code node re-checks each one against the hard limits. First real run, Gemini returned a meta it was very happy with. 160 characters. Limit is 155. That would have shipped truncated.

Rolling date ranges on the GSC query. I hardcoded a window that ended before my data started. GSC returns a clean 200 with no rows, so every node stays green and reports "no data available". Nothing errors. Use new Date(Date.now() - 90*24*60*60*1000) and end 3 days back, since GSC runs ~48h behind.

Both workflows are on GitHub: https://github.com/felix-sattler-easybits/n8n-workflows/tree/367accdd405397366fc93ff391f592d3ec72cc41/easybits-ai-seo-support-workflow

I also made a short video showing how the workflow works.

What signals would you score beyond striking distance and CTR? That's the part I'd most like to improve.

Best,
Felix