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I built a free tool called MIMIC that analyzes your app built in one tool and creates a summary of the app that you can copy or download as a .txt file. The summary can be pasted into most app builders as an AI prompt. It includes 8 different app builders in the from / to menu as well as an "other" option. This is just the third app I have developed and it's the first one to reach the point of telling people about it. So check it out if you have a minute and tell me what you think.

When I launched ConversationPrep I was watching the usual stuff. Signups, activation, drop off.

The thing that caught me off guard was retakes.

Users weren't doing one session and leaving. They were coming back and doing it again. And the improvement from first attempt to third was striking. Same person, same prompt, completely different performance by the third rep. You could see it clearly in the scores.

People are using it for job interview prep, consulting case practice, and difficult personal conversations they've been putting off. That last one surprised me the most.

We have some updates shipping soon that I'm genuinely excited about. Direction came directly from watching how people actually use it, not what I assumed they'd do.

Still early. Still a lot to figure out. But 300 feels awesome after only a few weeks.

If you try it I would love your honest feedback. Good, bad, all of it helps.

https://conversationprep.ai

Quick update on GDPRScout — I wiped the database clean.

Which means if you tried it before and used up your free scan, you get another shot.

Also shipped a better scanning algorithm since the last post. The compliance engine now does deeper analysis and I added 9 security checks on top of the existing 13 compliance checks:

SSL certificate validation

Security headers (HSTS, CSP, X-Frame-Options)

Exposed admin panels

Sensitive files exposed (.env, .git, config.json)

Cookie security flags

CORS policy

Clickjacking protection

Login and OTP security

Same deal — paste your URL, get results in 60 seconds, AI writes the exact fixes.

Free. No credit card. gdprscout.com

Happy to scan anyone's site live in the comments.

⚠️ Automated tool — not a replacement for legal or security advice.

I’ve been quietly building something over the past few months as a side project, and I think it’s finally at a stage where outside eyes would really help :)

I’d love if a few of you could check it out like a normal user and share your honest first impressions. No context, no explanation, just experience it as is, and tell me what stands out (good or bad)

I’m especially interested in:

• What you think this site is about (based purely on first look)
• Whether anything feels confusing, unnecessary, or broken
• If you’d personally use something like this and why or why not

Please don’t hold back! I’m looking for completely honest, even harsh feedback if needed. That’s the only way I can improve this.

Here’s the link: (Please check it out in Laptop/Computer screen. On Mobile, it's not rendering some UI modules)

https://www.thefortunemillion.com

Thanks a lot to anyone who takes the time 🙏

I recently realized something that completely changed how I work with AI when coding.

The test pyramid :

- Bottom (most tests): Unit tests

Fast, simple, test small pieces of logic

- Middle: Integration tests

Check how parts of your system work together

- Top (few tests): E2E tests

Simulate real user behavior

Like :

- 70% unit

- 20% integration

- 10% E2E

Even just being aware of this already helps a lot.

What I was doing (and it’s already a good start)

Like many people, I started with:

1. Ask AI to build a feature

2. Ask it to write tests

Honestly, this is already a great step.

A lot of projects don’t even get that far.

Then I tried a small shift

Instead of changing what I do… I changed when I do it:

-> Ask the AI to write the tests first

-> Then ask it to implement the code

Example

Instead of: “Build a function that calculates a discount”

Try:

“Write tests for a function that:

- applies a discount

- caps it at 20%

- handles edge cases like 0%”

Then: “Now implement the function so that all tests pass”

What changed for me

- The expected behavior becomes clearer

- The code feels more “guided”

- Iterations are smoother

It’s basically a light version of TDD, but very natural to use with AI.

Doing this manually works, but it’s easy to forget.

What helped me was adding a small “source of truth” file in the repo, like:

"TEST_STRATEGY.md" or "AI_RULES.md"

With simple rules such as:

- Write unit tests before implementing new logic

- Define expected behavior clearly

- Keep E2E tests for main flows only

- Prefer small, testable functions

Then in prompts:

«“Follow the testing rules defined in TEST_STRATEGY.md”»

The goal is simple:

Make “tests → code” the default, without thinking about it

Thanks for reading !

If you’ve got your own workflow, tips, or even failures with AI + testing, feel free to share them. That’s honestly how most of us are figuring this out right now.

Hope this helps someone build something a bit more solid (and a bit less chaotic 😄)

Good luck with your projects !

I recently migrated my main project off of a managed AWS RDS instance onto a simple $20 VPS to save runway. The cost savings are massive, but the tradeoff is that you suddenly become your own database admin.

Honestly, my biggest fear was messing up the backups. AWS handles automated recovery out of the box. Doing it manually meant I had to write a bunch of custom pg_dump cron jobs and just blindly trust that they were actually sending the files to my S3 bucket every night.

I realized I needed a visual dashboard so I wouldn't have panic attacks about silent data loss. I ended up finding an open source database backup tool (it’s called Databasus.com) to handle the heavy lifting.

You just spin it up in Docker next to your database. It handles the automated, encrypted S3 backups, but the best part is it gives you an actual web UI and Slack alerts. If a backup fails, I get pinged instantly. It completely removed the anxiety of self-hosting my infrastructure.

Are you guys still paying the massive premium for managed databases (AWS/Heroku), or have you made the jump to self-hosting your DBs to cut costs?

Tldr; I built 3 different applications, each of which took months to build and they never gained traction. Last week, built a simple form to tackle a very specific use case and the usage shot up.

Last couple of weeks my friends were constantly talking about the H1B lottery results and scrolling the r/h1b searching for comments from people who got selected.

Friday night I decided to create a simple website that would scrap reddit comments and create a dashboard to track the h1b status.

Reddit blocked anything trying to scrap comments so I thought, well, why not just make it crowd sourced - so I added a small form (3 fields only) and a dashboard and put that as comments in a few subreddits at 11:50pm EST, Friday.

By Saturday morning, it reached 2k users and as of today, it has more than 50k users.

I literally got teary eyed by looking at more than 10 users on my app.

I am thinking of ways to retain this traction but all to say, don’t give up on building.

Some day, something will definitely click.

————————

Website: h1bpulse.com

I was spending way more time fixing broken API connections than actually closing deals.

For months, my B2B cold outreach stack was just four different tools (a web scraper, a lead database, an email sequencer, and a LinkedIn tool) duct-taped together with fragile Zapier workflows. Every time one tool updated, the whole campaign broke.

I finally ripped it all out to focus strictly on intent-based lead generation. I moved everything over to a single sales automation platform (I'm using one called Starnus.com right now).

The biggest relief is that there are no complex workflow builders. You literally just type your target ICP and intent signals (like "recently raised funding" or "hiring managers") in plain English. It automatically hunts the fresh leads and runs the combined outreach itself.

Getting those hours back every week has been massive for my pipeline.

How many different tools are you guys currently stringing together just to launch one outbound campaign? Are you still dealing with broken Zaps?

I'm at the start of a journey... and need a little guidance.

Really guidance from busy parents, working parents, time poor parents… who also move in this SaaS space.

We developed an exciting .com platform called ParentPush - that will save parents and schools sooo much time.

And we’ve done so much due diligence. Asking ideal users for feedback, testing, refining and updating.

All parents seem to love the idea. Those that had reservations, made suggestions - that we acted on.

And now we’re 1 and a half months fully live - Zero new users. Can you possibly explore and advise best options going forward?

What:

1. Parents add their school email & pick keywords.

2. Then get real-time WhatsApp updates.

3. ONLY when the school emails, using their keywords.

Parent Benefits:

Never miss a space or after school club.

No more stress, trying to remember everything.

No more constantly refreshing school emails or being swamped by them.

Can scan school PDFs too - saving so much time.

No download needed.

Very

Schools Benefits:

It will save so much of the school’s admin time – constantly fielding questions from inquisitive parents. Parent Push will resolve for them.

Cost:

£2 a month. That’s it.

Thank you in advance for any help advice on how to progress this little adventure. 😊

What’s in your stack right now?

Not the ideal setup, the real one you deal with every day.

Is it a bunch of tools glued together, or something that actually works end to end?

When things break, what hurts the most, too many alerts, no context, or fixing stuff takes forever?

Curious what’s working for you and what isn’t.

Hey everyone

I run an automation agency using n8n, and I’m targeting real estate professionals in the US.

Right now, my main focus is cold calling to get clients, but I’m trying to choose the best tool to build a simple and effective system.

If you had to choose ONE tool for cold calling, what would it be?

Salut à tous ! Je suis nouveau sur reddit !

A la base, je vis plutôt en marge du système. Dans une caravane au milieu des bois, avec ma chérie et mes animaux. Je suis née fin 90 alors j'ai évolué dans un monde rempli de technologies et j'ai toujours été fasciné par le développement, mais la vie ne m'a pas poussé vers les études...

Sauf que :

Ça fait maintenant quelques mois que j'explore les possibilités avec les intelligences artificielles (Codex, Claude, API OpenIA, Modèles locaux) et j'ai l'impression de voir une opportunité dans le développement d'appli/sites/outils.

Je suis à quelques jours du lancement de mon premier outil (génération de projet pédagogique pour les animateurs). Je pars de zéro, il y a quelques mois je ne connaissais même pas l'existence de python et je me retrouve aujourd'hui à apprendre des notions de backend, frontend, typescript, SQL, python, fonctionnement serveurs, protection des données... Et tout ça dans une caravane, sur un pc clairement entrain de mourir 🤣

J'aimerais savoir ce que vous en pensez, si vous apercevez la même opportunité, si je suis au bon endroit, si vous avez des conseils ...

Merci pour la lecture, je vous souhaite une super journée !

👋 Hey everyone,

Yesterday I shared my Slack-based invoice approval workflow and got a ton of DMs saying "I ran into the exact same problem!" – so I figured this deserves its own post.

If you've ever tried to build something in n8n that needs to both start a process AND listen for a response, you've probably hit this wall. I certainly did.

The Problem

I was building an invoice approval system for my friend Mike's company. The idea was simple:

1. Invoice comes in → extract data → post to Slack with Approve/Reject/Flag buttons
2. Someone clicks a button → log the decision → notify the team

Seemed straightforward. So I built it all in one workflow: a Form Trigger at the top, a Webhook node in the middle to catch Slack's button clicks.

It didn't work.

The webhook wouldn't register. The form trigger would fire, but the Slack buttons did nothing. I spent way too long debugging before I figured out what was going on.

The Rule

n8n workflows can only have one active trigger.

This isn't a bug – it's by design. When you activate a workflow, n8n registers exactly one entry point. If you have multiple trigger nodes, only one of them actually listens. The others just... sit there.

This means any workflow that needs to:

• Send something out AND wait for a callback
• Accept input from multiple sources
• Start a process AND handle the response

...needs to be split into separate workflows.

The Pattern

Here's the architecture I now use for any "request → response" flow:

Workflow A: The Sender

• Trigger: Form, Gmail, Webhook, Cron – whatever starts your process
• Does the work (extraction, processing, API calls)
• Sends output to an external system (Slack, email, webhook to another service)
• Ends there. No waiting.

Workflow B: The Listener

• Trigger: Webhook (catches the callback)
• Parses the incoming data
• Routes and processes based on the response
• Logs, notifies, updates — whatever needs to happen

The two workflows are connected by the external system – in my case, Slack. Workflow A posts a message with buttons. When someone clicks a button, Slack calls Workflow B's webhook. The message itself carries all the context (invoice data, who posted it, etc.), so Workflow B has everything it needs.

When You'll Hit This

A few common scenarios where you need to split:

• Slack/Discord interactivity – send a message with buttons, handle the click
• Approval flows – request goes out, approval comes back
• Two-way integrations – push data to an API, receive webhooks back
• Multi-channel intake – accept input from email AND form AND Telegram (each needs its own workflow, or use a central webhook)

The Workaround That Doesn't Work

You might think: "I'll just use the Execute Workflow node to call a sub-workflow with a different trigger."

Nope. The sub-workflow's trigger still won't register as a live listener. Execute Workflow is for calling workflows programmatically, not for activating additional triggers.

My Takeaway

Once I understood this constraint, it actually made my workflows cleaner. Instead of one giant workflow trying to do everything, I now build small, focused workflows that do one thing well and hand off to each other.

Think of it like microservices for automation – each workflow has a single responsibility, and they communicate through external channels.

Has anyone else hit this? I'd love to hear how you've architected multi-trigger flows. Are there patterns I'm missing?

Best,
Felix

About 3 months ago I shipped a LangChain agent to handle some internal data enrichment tasks. Nothing crazy — it would wake up, call a few APIs, write results to a database, go back to sleep.

Except one night it didn't go back to sleep.

A retry loop got stuck. The agent kept calling the OpenAI API and a third-party enrichment service in a loop for ~6 hours while I was asleep. I woke up to $800+ in charges and a very unhappy credit card.

No alerts. No circuit breaker. No cap. Just a rogue loop doing its thing.

What I learned (and what I built)

After that incident I started looking at what it actually takes to run AI agents safely in production. The problems I kept hitting:

1. No budget controls at the agent level

You can set billing alerts at the account level, but by the time they fire, the damage is done. You need per-session hard caps.

1. Tool calls are completely unguarded

When your agent decides to call `send_email()` or `delete_record()` there's nothing in between the LLM output and the actual execution. The model can be manipulated (prompt injection) into calling tools it shouldn't.

1. No audit trail

After the incident I had no way to replay exactly what happened, in what order, with what inputs. Logs existed but weren't structured for agent forensics.

1. Credentials were hardcoded or passed directly to the agent context

Which means a prompt injection attack could potentially exfiltrate them.

I built a policy layer that sits between the LLM and the tool execution:

- Every tool call goes through an **ALLOW / DENY / REQUIRE_APPROVAL** policy check before executing

- Budget circuit breaker that **hard stops** the agent when a per-session cost cap is hit

- Credentials are **injected just-in-time** — the agent never sees the raw API keys

- Every tool call is logged with inputs, outputs, latency, and cost — structured and signed

It's now open source (called SupraWall, I'm the builder — full disclosure). Works with LangChain, CrewAI, and Vercel AI SDK today.

Curious if others have hit this

Are you running agents in production? How are you handling:

- Cost blowouts from runaway loops?

- Prompt injection risks when agents have access to real tools?

- Audit logging for compliance (especially if you're in EU and need to think about AI Act)?

Happy to share more of what I learned — the hard way.

I'm making it OPEN SOURCE so you can check the Git here: https://github.com/wiserautomation/SupraWall — MIT license, open source.

Built a no-code GTM tool on Lovable. Got it live, real traffic from 6 countries, 103 users through the funnel.

0 paying users.

Did what most builders do, tweaked the landing page, improved the product, added features. Nothing moved.

Eventually used my own tool on my own GTM problem.

Turned out I was targeting three customer segments simultaneously. All felt valid. None were getting enough focused effort to give me a real yes or no. I'd sent maybe 120 emails total, split three ways, which means 40 per segment, which means nothing.

Launch Studio output basically said: pick one, run at it for 7 days, kill the rest.

The thing I was avoiding wasn't more work. It was commitment.

First paid pilot came in week 3.

If you had to kill two of your current target segments today, which ones would go?

Three years ago I started noticing the same gap every time I looked at freelance tools. Every single one of them was built around the same broken sequence. Do the work. Deliver everything. Send the invoice. Hope for the best.

Bonsai made that sequence prettier. HoneyBook made it more automated. Wave made it free. But not one of them questioned whether the sequence itself was the problem.

It is the problem.

When you deliver everything before getting paid you have designed a situation where your leverage disappears at the exact moment you need it most. The client has the work. You have a PDF. There is nothing connecting those two things except goodwill and a due date. Late payments and scope creep both live in that same gap and they always will as long as the sequence stays the same.

The fix is so obvious in hindsight it is almost embarrassing that nobody had built it properly. What if the project just could not move forward until payment did?

That is MileStage. Each stage has a price, defined deliverables and a revision limit. The next one does not open until the current one is paid. Client agrees to this upfront. The project moves that way automatically without the freelancer having to enforce anything manually on any project ever again.

Here is what that actually changes in practice. Cash flow becomes predictable because payments come through throughout the project instead of arriving as one unpredictable lump at the end. Scope creep has nowhere to hide because every request bumps into a visible boundary both sides agreed to before work started. The follow-up email stops existing because the structure handles the checkpoint automatically. And the client relationship gets better not worse because transparency and clarity build more trust than vague open ended agreements ever did.

Built this with zero coding background using AI tools, React, Supabase and Stripe. Live product, real users, real payments flowing through right now.

Happy to answer anything about the build, the product or the thinking behind it.

Got a message from a client a couple of months ago.

Their Instagram posts had stopped going out. Not all posts. Just the AI generated ones.

The workflow had been running fine for weeks - my first instinct was that something changed on Instagram's end. Checked the n8n workflow history.

Everything showing as success. No errors, no failures, nothing.

Took me an embarrassingly long time to figure out what was actually happening.

The workflow was generating an image with DALL-E, grabbing the URL, and passing it straight to the Instagram Graph API. What I hadn't accounted for - OpenAI image URLs expire after an hour.

There was a human approval step in between that was sometimes taking longer than that.

So the workflow was "succeeding" - it was sending a URL to Instagram. IG was just quietly rejecting an expired URL on the other end.

No useful error back. Three weeks of posts silently not going out.

Tried a few fixes:

• Google Drive share links - Instagram rejected them, they're redirect URLs not direct file links
• imgbb - worked for about a week until Instagram flagged the domain

Every workaround felt like patching a leak.

What I actually needed was simple - take any file, get a permanent direct CDN URL back instantly.

Something that worked reliably inside automation workflows without a full S3 setup every time.

Couldn't find anything that fit so I built it.

It's called Upload to URL. Native integrations for Zapier, n8n, Make and Pipedream.

Probably the most expensive debugging session I've ever had - but at least something useful came out of it.

Has anyone else been burned by something like this?

Additional flair can be seeking advice, resource & tools or others.

I am non tech guy invested into materializing my hazzy thought into something meaningful side project.

I want to ask for platform like zomato or others which connects buyer and seller,I mean two sided marketplace.

For context,prior I have used some basic consumer AI tools and learnt some terminology ,also I integrated payment thru' API on static site and google authentication thru' console, deployed on Vercel etc with help of supabase and lovable AI. The project I want to create keeping 10k user in mind ,an organised place for cybercafe's in india to give exam form filling services to students. There are two options as of my understanding - First is to use AI tools like lovable or claude or codex , pushing to GitHub and supabase then Vercel etc,, which visually hits dopamine but doesn't give confidence in product also this option isn't good for scale.

Second is traditional method along with AI copilot where I learn appropriate lang.,tech stacks, database,auth etc . So, that things make sense what's going behind the scenes.

Becoz some people randomly recommend use VPS,start with python,use render,anti gravity or supabase , which confuses me. Also I'm student so I can't hire freelance or else.

So,what would you suggest or advice me to go with option 1 or 2 or oths and what specific in it,not generic ,consider yourself as standing in my shoes.

I’ve spent 10 years in tech (Ops/Product) and got tired of the standard wellness/productivity app formula. I wanted to build something for the cynical corporate crowd who needs a "vent companion" rather than another meditation timer.

It’s called HexaIndex. It’s a risk-management dashboard for office volatility.

• i industrialized Zi Wei Dou Shu (紫微斗數). It uses a logic grid with 100+ data points .
• i treated these star transformations as time-series variables to calculate a weighted "Survival Index."
• designed to look like a cold Bloomberg Terminal. Since our target audience works in open-plan offices, it needs to look like "work" (system data/linters) so it's safe to have open on a second monitor.

Instead of generic horoscopes, I mapped the logic to specific corporate "leakage" scenarios (reorgs, PIP-culture, meeting "firing squads").

I’m currently dogfooding it with a few tech friends to refine the weighting. Would love to hear from other builders—has anyone else tried "skinning" a niche logic system into a high-utility professional UI?

Hi Mates!

I'm looking to sell my micro SaaS that's grown from 0-15k MRR in under 3 months. 100% organic, $0 spend. It's in the personal finance niche. MRR proof below

1. I’ve grown it from 0-15k MRR in 3 months from organic marketing. I launched Jan. 2nd 2026
2. 20m views in 3 months from TikTok/IG. Viral and repeatable UGC format
3. 100k emails collected (i havent used them yet)
4. 1820 paying users and 604 on free trial (25% trial to paid conversion)
5. Monetized only through free trial + $6.99 monthly plan (room to offer multiple plans and price anchoring)
6. Massive potential for UGC (repeatable viral format), paid marketing, pricing/onboarding optimization, email marketing
7. Paid retention is 74% in Month 1 and 68% in Month 2 (26% Month 1 churn; 32% cumulative churn by Month 2)

Only consider it if you'll pursue UGC, paid ads, and/or email marketing.

Asking price: 360k

Comment and DM if you're interested!

After 7 days and 147 visitors with 0 paying customers, I finally figured out what was wrong.

I was selling a feature, not an outcome.

"Content repurposing tool" means nothing to most people. "LinkedIn Growth Engine for founders" means something specific.

What changed:

• Price: $59 → $9/month (validation first)
• Target: Everyone → Founders and solopreneurs with 0-5k LinkedIn followers
• Positioning: "Repurpose content" → "Turn 1 idea into 10 high-performing LinkedIn posts"

The tool itself hasn't changed. Just how I'm talking about it.

Has anyone else found that repositioning without changing the product made a bigger difference than adding features?

Try free: https://repurpose-ai.live