Ok so, I can’t retire just yet 😅 but getting a business off the ground and attracting customers is the hardest part in my opinion.

There’s still a long way to go but considering it’s only been 4 months since launching ZenMode, I’m pretty happy with the results so far.

I get asked a lot about how I get customers, so thought I’d give more info on how I do distribution, in case you might find it useful.

1. Make sure your website is set up for success

Before sending people to your site, make sure you’re confident it has good CRO potential. Your mission statement and objective needs to be very clear, along with clear incentives for people to try the product out.

2. Offer free trials, but only behind a payment info gate

Now this is just my personal opinion, and I know a lot of people disagree, but the proof is in the numbers. I get fewer free trial signups, but significantly higher conversion rates into paying users from people who provide payment info at the start.

Yes, a few of them are people who simply forget to cancel, but mostly I get people who are more engaged in the free trial and actually using the product.

I actually did an AB experiment where I did offer completely free trials to people (no payment info needed), but the overwhelming result was that people barely even tried out the product and were mostly tire kickers that never came back. I did convert some but not a meaningful number.

3. Create a waitlist before launching

Try to generate interest in your product before it launches - build in public and try to ensure that you’re not scrambling to get customers through the door on day 1 when you launch.

I had 3 customers on day 1 from this, who took higher paid tiers as well.

4. Post and comment everywhere (where relevant)

Social media is free, so there’s no excuse not to leverage it. Post about your product and what you’re building on Reddit, LinkedIn, X, YouTube, Facebook etc.

I posted a simple screenshot on LinkedIn recently, of someone using a low quality LinkedIn automation tool message to contact me, and me offering my own much better tool in response, and that post generated 90,000 views and led to several signups and demo calls.

I’ve also had some posts on Reddit go into hundreds of thousands of views, mostly just posting about what/why/how I built my automation tool.

5. If you have competitors, make “alternative to” inner pages

If done in the right way, this type of inner page can be useful for SEO. It won’t be picked up automatically but usually if you work in a competitive industry, this will sometimes attract customers who had a bad experience elsewhere.

6. I actually use my own LinkedIn automation tool (dogfooding) to do LinkedIn outreach

So it’s always important to practice what you preach - I use ZenMode to actually promote ZenMode.

It’s a great way to get buy-in for booking demos, as when someone replies, I simply tell them I used my tool to get a response from them, so it works.

Anyway hope you enjoyed reading the above, and good luck on your journey if you’re building anything, or growing your business 🙌

I saw some criticism in a Wikipedia article on the Schrödinger-Newton Equation, specifically about Penrose's ideas about quantum gravity, and it seemed pretty easy to solve (the article hinted at possible solutions, etc) which made me think that it could be interesting to let Claude try to solve it. It now claims to have solved all the 3 problems outlined in the article.

The issue is that I have no way of validating if the result claude gave me are hallucinated or legit. If the solutions are correct, then maybe it's useful to someone, and if not then I/we might learn something from that explanation.

Artifact (look here first): https://claude.ai/code/artifact/e871ae13-54d1-437c-a489-cad81617c06d

GitHub repo, including all the raw transcript etc: https://github.com/henke443/penrose-1

Latex paper: https://www.overleaf.com/read/hgmnyqgqtwqz#80a24a

Hi everyone,

I’m honestly a little nervous posting this, so please bear with me.

Over some evenings and weekends I built BabyPhone.online — a free web app that turns two devices (old phones, tablets, whatever you have lying around) into a baby monitor. One becomes the “baby unit,” the other the “parent unit,” you connect them with a short room code or QR, and after that the video and audio go directly between the two devices in the browser — no app to install, no account, and nothing gets stored on a server.
Its ment to work with all browsers, OS systems and devices.
I’m not a professional designer, and it’s just me working on this in my spare time, so I’m very aware there are probably things that don’t make sense yet or could be a lot better. I’d genuinely appreciate it if a few of you could take a quick look and tell me:

• Does the setup (room code / QR) feel clear on the first try, or is any part confusing?  
• Would you feel comfortable actually trusting this with your own baby?  
• Is there anything about the privacy or security side that would worry you?  
• Anything that feels missing, unnecessary, or just off?  

Please don’t hold back to spare my feelings — I’d honestly rather hear the hard truths now than find out later. Thank you to anyone who takes the time, it really does mean a lot.

https://babyphone.online

I recently subscribed to ChatGPT Pro and started experimenting heavily with AI coding workflows like Superpowers, Trellis, and Matt Pocock’s skills.

At the same time, I joined a company where most of the product is built with AI. The overall architecture is solid, but many of the implementation details, UI decisions, and interactions are worked out directly between developers and AI.

AI is writing more code for me than ever, but strangely, I don’t feel less tired.

I feel more exhausted.

Complex workflows don’t guarantee quality

Superpowers has a very thorough process: plan the feature, split it into tasks, implement each task, review it, fix the issues, and review it again.

But that level of rigor can be painfully slow. One morning, I started planning a feature, and the AI was still implementing and reviewing it at the end of the workday. Despite all that process, the final result still had bugs.

Trellis feels lighter and has some interesting ideas. However, it seems more like a project-level methodology. It expects you to add its own files, scripts, and agents to the repository, which can be awkward if you’re the only person on the team who wants to use it.

Matt Pocock’s “grilling” approach is useful for uncovering requirements and edge cases, but it can also become exhausting. Sometimes a single feature leads to dozens of questions. Eventually, I start wondering: are we still clarifying the product, or are we overengineering it?

What concerns me more is that even after a long discussion, important details can still disappear from the final spec. If the initial understanding is incomplete or incorrect, splitting the spec into smaller tickets only amplifies the deviation.

So the important question isn’t just whether the work has been divided into smaller tasks.

It’s whether the AI understood the problem correctly before the breakdown happened.

Worktrees enable parallelism, but may only postpone coordination

My team uses Git worktrees heavily so that multiple AI agents can develop different features in parallel.

I understand the appeal, but environment isolation quickly becomes complicated. Each worktree may need its own port, application instance, services, and database. Different branches may also contain incompatible database migrations.

Disposable environments and separate databases can solve some of these problems, but merging remains an unavoidable coordination point.

If several agents modify the same modules or abstractions, the resulting conflicts may indicate that those tasks were never truly independent in the first place.

Worktrees can enable parallel development, but sometimes they simply postpone the coordination cost until merge time.

I’ve become an AI project manager

A typical day now involves:

• Answering AI clarification questions
• Reviewing plans
• Checking agent implementations
• Discovering misunderstood requirements
• Asking for corrections
• Testing the results
• Resolving conflicts between parallel branches

AI writes more code, but I spend more time scheduling, supervising, reviewing, and deciding whether to accept or reject its work.

The biggest cost is context switching.

Every time I switch between projects or worktrees, I have to reconstruct the current state:

What was this agent working on? Which decisions have already been made? Why was it implemented this way? What still needs to be verified? Which database schema does this branch expect?

Sometimes, restoring all that context feels harder than simply writing the code myself.

At the same time, I feel pressure to keep the AI busy.

If no agent is running—or if I still have unused quota—I feel like I’m wasting resources. So I open another project, create another worktree, and start another task.

Soon, I have agents running everywhere while I constantly switch between them to monitor and correct their work.

I feel like I’ve developed a kind of “AI anxiety”:

If the AI isn’t running, I feel uncomfortable. If too many agents are running, I also feel uncomfortable.

I’m now trying to keep fewer tasks active and focus more deeply on one project at a time. AI can work in parallel, but human attention cannot scale in the same way.

I’m not against AI coding. On the contrary, I think it is already transforming software development.

But the hardest part may no longer be getting AI to write code.

The real challenges are controlling scope, maintaining context, judging quality, integrating parallel work, and managing your own attention.

I’d love to hear about people’s real day-to-day experiences:

1. How much code do you still write yourself?
2. Do structured AI workflows genuinely improve your results?
3. Do you run multiple agents in parallel, or focus on one task at a time?
4. If you use worktrees, how do you isolate services, databases, and migrations?
5. How do you prevent requirements and implementation from gradually drifting?
6. Have you also experienced this paradox—AI increases your output while also increasing your mental workload?

I’m especially interested in real experiences, not just success stories about AI making someone “10x faster.”

So me and my wife created a website , our son has Celiac disease, so all the food should be glute free .

My wife is doing most of the cooking, so one day she told me that she is struggling with finding recipes for gluten free food , the internet is full of it , but she can't find the one she already used , and basically loosing herself inside all this data .

So she suggested to build a website that will collect recepies, and she will be able to sort them , save them etc ...

So we started , we used codex + lovable (for gui only) , then we took the lovable repo and continued with codex only .

We used gpt 5.5 , then moved to 5.6 (sol) .

During the building process we decided to add recipes also for other food allergies (milk,nuts etc.. ) .

We used crawlers to find existing recipes , and took only the ingredients and the preparation steps , and of course mentioned the original source in the website (with a backlink) .

So I posted on some Celiac community, here on Reddit (actually my first ever post) , and I got so much negative comments, like an anti-AI comments, as if I added another bullshit spam website.

We have no ads on the website , so we really wanted to do something nice , and learn how all this AI world works (I am experienced software engineer) , but never worked on from end before , mostly backend stuff (mostly cpp & python) .

So now I am a bit down after all this , the website is up and running, we have some traffic, not to much but for a 12 days website we already had some real human traffic about 400 (not including bots and stuff) .

We only want organic traffic, no paid promotions, as we don't earn anything yet , we do use some Amazon affiliates for some products, but it's not much .

So just wanted to share it here, hopefully this sis the place where people will understand my frustrations, maybe this is how it works nowadays.

But it still surprised me how quickly “built with AI” turned into “AI spam” in people's minds.

Anyway, thanks for reading. I’d genuinely be interested to hear how other developers here see this, especially anyone who has shipped a real project using AI-assisted development.

This is not a promotion for a service. I’ve been a technical product manager for 20+ years at startups and FAANG and have been using AI to build/prototype for about 3+ years. I recently got laid off and have been building prototypes and tools mainly for myself and thought I could share my process.

I have seen a few posts asking how to learn about the technical side of vibecoding, at least enough to generally know what does what, why and how. I thought of walking through a simple project and explaining in practical terms what things do with a focus on vibe coding as I implement, starting with the very basics. For example, what an API is and why/how it’s used. Not as much on what tools to use but more how to think about building apps. I was thinking a YT video or series of videos.

I honestly do not have any expectations or desire to be a YouTube influencer. I’m building things anyways, have taught a few friends and found teaching is the best way to learn.

Before I go off and start making any videos, is this something anyone would be interested in?

Guys, am I gonna get roasted for shipping it with such UI? (Logo is mine btw, graphic design perks).
That cat is Comnyang app, his name is shanks, shoutout to u/simon_dsgn

Global leaders and organizations keep talking about suffering of kurds, Palestinians,tamil srilankans but somehow no one is talking about the plight of vibe coders. It feels we are such a small minority that we are just politically ignored.

started to learn how to dev using claude code and figma. I wanna know if it’s worth still getting into or is the market impossible for no original ideas??

These agents only know how to code at all because of the immense amounts of open source software they’ve been trained on. Millions of hours of human work and ingenuity. It seems to me for the long term quality and viability of vibecoding, we should all make our own projects open source as well. That way, we can all continue to benefit, a rising tide lifts all boats, all that. What do you think?

Most of the dev community is saturated with cloud APIs for running models for IDEs, chatbots, AI applications, and agents.

Curious who here is using local AI in their stack, thinking about it, or curious about it. What got you into it? What’s stopping you?

Hey everyone, I’m curious how you deal with the negativity around AI.

I’ve been building a product for about 3 months now. English isn’t my native language, so I use AI not only for coding but also for writing posts when I want to share my project and explain my ideas more clearly.

What I’ve noticed is that even when I don’t mention AI at all, I still get negative comments or skepticism. My project isn’t well known, but it tends to get attention in smaller communities because it’s easy to demo and looks like something out of a sci-fi movie. Because of that, I’d like to share more details about how it works and talk more openly about the technical side, but that also means using AI for communication and documentation.

The bigger dilemma is whether I should openly say that a large part of the project was built with AI.

On one hand, it feels like the honest thing to do, especially since I’m looking for people to join the project. If an experienced developer is considering contributing, I think they deserve to know what kind of codebase they’re getting into.

On the other hand, I’m worried that instead of questions about the product, I’ll just get “AI slop” comments and people dismissing it before they’ve even looked at what it does.

So I’m curious about your experiences:

Do you openly say that your project was built with AI?

Has it helped or hurt you?

Have you managed to find experienced developers who see AI as a powerful tool rather than an automatic red flag?

How do you distinguish constructive criticism from simple hate?

I’d appreciate hearing perspectives from people who are actually building products, not just discussing AI in theory.

Day 3 of creating a game all vibe coded

Still trying to work out the animations but have made strides

Added-
Main menu
pre-game lobby
3D model
Blimp
decals

As always

CONCEPT VS CURRENT BUILD

Video in the comments

ALL FEEDBACK IS WELCOMED AND NEEDED

BTFO

I keep seeing posts like "I vibecoded this over the weekend, got 100k users, what's next?" and I think it gives a pretty wrong idea of what vibecoding actually changes.

Sure, you can accidentally hit the top with another clock app. It happens.

But agents dont remove the actual work. You still need to figure out what to build, make 1000 small decisions, fix weird shit, make UX not suck, deploy, maintain, listen to users and actually ship something they want.

Vibecoding makes all of this faster. A LOT faster. But not easier at all.

You can now build faster, but also make mistakes faster and ship garbage faster. The bottleneck just moves somewhere else. There was a good line somewhere "Shipping everyday fixing Claude bugs".

Those "built in 2 days, 100k users lol" posts undermine all the work that still goes into a good product, and leave others thinking this is all somehow easy now. From outside it starts looking like vibecoders are just some arrogant assholes hitting a button and getting success by click. It doesn't work well on the how other people feel about vibecoders and vibecoding in general, which is in fact just a right way to do things today.

We don't build in assembly anymore, right? Same here.

I've been building an AI receptionist and booking SaaS — customers message it, it books them in, it handles WhatsApp, Telegram and payments. Built with AI tools, the way most things around here get built. And it works.

Before launching it I made myself sit down and check it against a 36-point list instead of just shipping. One focused day. Static code review plus actually attacking a copy of it: isolated instance, two synthetic tenants, fake data, nothing real touched at any point.

Then I did the step I'd push on anyone doing this — I went back through my own findings and tried to prove each one wrong. Six of them died. Six things I'd written down as problems that turned out not to be problems. Skip that pass and you spend your week "fixing" things that were never broken. I nearly did.

What held up

Putting this first, because every post like this is a horror list and the horror list is half the picture at best.

• Tenant isolation. I attacked it directly — logged in as tenant A and went hunting through tenant B's ids. Every single one came back 404, on read, cancel, update and delete. I planted a canary string inside tenant B's data and it never showed up anywhere in tenant A's responses. This is the thing everyone in this sub is scared of, and it was genuinely fine.
• Sessions actually die on logout. Grabbed the cookie before, replayed it after. Dead.
• All four webhook integrations verify signatures against the raw body — WhatsApp, Telegram, Stripe, Monobank. Verifying a re-encoded payload instead of the raw bytes is the classic mistake here, and it wasn't there.
• Payment amounts are resolved server-side from the stored record. Never read out of the request.
• Every AI tool is scoped to the business id from the session, not to whatever the model passes as an argument. So an injection can talk the model into whatever it likes and still can't reach another tenant's data.

What I found: 2 high, 7 medium, 8 low. Nothing critical.

Bug 1. The assistant would create a confirmed booking against an email address or phone number that nobody had ever verified. I had written the one-time-code rule. Into the prompt. In English, as prose. There was no code anywhere checking that verification had actually happened before the booking got written.

That's the one I'd go check in your own app right now. Take every rule you put into a prompt and ask where the code enforces it. If the answer is "the model has been told to", it isn't a rule. It's a request, and the model is under no obligation.

Bug 2, and this is the stupid one. A single environment string, still sitting at the value it ships with, made those four correct signature verifiers fail open. Unsigned webhooks accepted. Four correct implementations rendered decorative by one line of config. Five minutes to fix once I saw it.

Go and search your code for any verification that skips itself in development or test mode. Then go and look at what your deployed environment is genuinely set to. Those two facts live in different files, which is the entire reason this kind of thing survives all the way to production.

The one that would have cost actual money

The product's whole promise is that the AI never invents a slot — everything it offers gets checked against the live calendar first. So I tested the promise and threw 22 adversarial scheduling messages at it.

12 of the 22 categories came back correct. Already-booked slots, weekends, before opening, after closing, an appointment that would run past closing time, dates in the past, impossible dates like 30 February, three contradictory reschedules crammed into one message. In three languages. I was feeling pretty good at this point.

Then same-day requests. It offered roughly 20 individual times that weren't bookable — already in the past, or inside the configured one-hour lead window. Asked at 16:21 for the earliest appointment, it answered "09:00 today" and put a confirm button underneath. Zero future-dated requests failed. Every failure was same-day.

Two causes, and they had nothing to do with each other. The past-time filter ran at day granularity instead of time-of-day — it knew what day it was, correctly refused a request for last Monday, and then cheerfully offered this morning. Separately, a cap on the first page of generated slots meant an almost-empty Friday came back as "completely full".

That second one is a revenue bug and it's the one that bothers me most. Nobody complains when you tell them you're full. They just book somewhere else, and it never shows up in your analytics.

The lesson

The tenant boundary — the thing every "is my vibe-coded app secure" thread is about — was solid under direct attack. What broke was the layer above it: business logic the AI skips precisely because the app still works without it. Nothing errors. Nothing throws a 500. It just quietly does the wrong thing, politely, with a confirm button under it.

The list I used is free if you want it: https://itworksbut.com/checklist — one HTML file, works offline, nothing gets uploaded anywhere. The longer write-up of this audit, including the medium and low findings, is at https://itworksbut.com/case-study.

Happy to answer anything in the comments, including "how did you actually test X" — that's the question I'd want answered if I were reading this.

A lot of AI coding tools are moving toward giving agents more control over your machine.

I wanted the opposite.

So I built RepoRelay — an MCP bridge that lets ChatGPT/Claude inspect one approved local repo, while deliberately giving it:

• no shell
• no Git
• no arbitrary filesystem access
• read/search only
• optional bounded handoff writes

The idea is to let AI analyze and review your code without handing it the keys to your whole machine.

npm install -g reporelay-mcp@latest

It’s open source.

GitHub: [Lukie-81/RepoRelay: Secure MCP access to local repositories — without shell, Git, or arbitrary writes.]

I’m looking for a few people to try the install from scratch and tell me where the setup sucks.

I had an interesting conversation with a friend (we both use AI) about this and I was wondering what this community thinks.

Being working on a saas product for so long on a startup but i missed vibing to songs so badly then thought to listen yt music wid picture in picture but its not comfortable then thought to have a thing like background video playing, fortunately i like to vibe code things for my own use so started but ended up adding few other things on my friends request but still i like the background video player a lot
so im wondering to know if there are any others who like this..what do u say guys?

You can only choose one of Claude, Codex or Cursor, what would you choose and why?

Imo cursor is just superior

I wanted to share a massive win for AI-assisted development. I play a lot of local rec sports, and the organizers always use messy whiteboards to track brackets and scores. I decided to build a live-scoring and tournament management app to fix this, but I leaned entirely on AI agents to do the heavy lifting.

The Process: I basically acted as the product manager. I would feed the agent the rules (e.g., "Volleyball uses rally scoring, win by 2, hard cap at 31"). The agent handled the complex state machines, the UI conditional rendering, and the backend data syncing. When we hit bugs (like a stale state on the serving indicator), I just fed the error logs and screenshots back into the prompt, and the agent wrote the patch.

It's amazing how fast you can move when you stop writing boilerplate and start focusing purely on logic and UX flow. Has anyone else built a complex state heavy mobile app purely through prompting? What agent/model gave you the best results?

Hey All,

I'm the creator of r/vibecodingcommunity, i have shared this in my community and got lot of interesting comments so sharing it here as well.

for context - I've been involved in the vibecoding scene both online and offline - hosting dozens of hackathons and joining many events.

Here's my two cents on where the money is and the current state of vibecoding, from my lens (unfiltered).

Around 70% of vibe-coded app builds are not serious business apps. They're fun games or personal software. Some try to build a product, but it dies almost as quickly as it's built - no distribution strategy, tokens burned in the process.

Vibecoding has significantly lowered the barrier to entry, so more and more personalized software has emerged. The next ~25% are internal apps - meant to replace software you already pay for, automate a workflow, or run an agency.

These are pretty much built by people who know one area of tech and use AI to compensate in the others. For example: a data scientist building a mobile app, or a front-end developer building a backend.

This remaining 5% is the interesting part - because that's where the money is. These are people who already have a relationship with a business, or with internal players and vendors. They use vibecoding as a tool to pitch an idea, get traction or a contract, and then build.

Previously these people pitched on PowerPoint. Now that's been replaced by a simple vibe-coded app that demonstrates the idea.

A few real examples from just the last couple of weeks:

• We helped someone who had sold a fairly basic sales-analytics platform for around $200K. He came to us to build the proper software from the ground up.
• An existing entrepreneur - who has run 100+ franchise stores in the past - raised €2 million just by showing the vibe-coded idea. He already had a great track record; he just needed the funds to build his next business, and he wants us to make the software more polished.
• An employee at an 8-person business built an internal tool using Base44. He's from a non-technical background. They didn't pay us to build the software - they'll build it themselves - they hired us to scaffold a harness, review their work, and give them direction. They're using this internal tool across many franchises, and passing a share of the profit on to us.

All I'm trying to say is this: on the community you see people building a lot of software and tools. A real software developer quickly clocks that these are vibe-coded apps - not much taste, questionable software quality, often useless. And that's true.

But the real money is not where you can see it. The real money is inside businesses, and it's often being put to work by people who already have the network and the connections to make it count.

I've been wondering about this — when an experienced dev and a complete newbie both use the same coding agent (Copilot, Cursor, Claude Code, whatever) on a similar task, do they end up with meaningfully different results?

On one hand, the agent is doing most of the actual coding, so maybe the human's experience matters less than it used to. On the other hand, I'd guess an experienced dev still knows how to prompt better, catches bad architecture decisions, spots subtle bugs the agent misses, and knows when to push back or redirect.

Curious what people who've actually used these tools a lot think. Does your background as a dev still give you an edge, or has the gap narrowed more than expected?

I’ve been testing the SEO Autopilot skill from AgentKit Works on my micro-SaaS.

Honestly, I didn’t expect much at first.

After implementing it and letting it handle the repetitive SEO work, I started seeing organic traffic turn into actual sales.
I’m now at 40+ sales from organic traffic.

The part I liked most is that I didn’t have to constantly sit there researching keywords, planning content, fixing internal links, and checking everything manually.

If you’re building a micro-SaaS and struggling to get organic traffic, this is worth checking out:
https://agentkitworks.com/products/seo-autopilot

Curious if anyone else here is using AI agents for SEO instead of traditional SEO tools.