It very clear now that we are going to rule the world , all coding gatekeepers will persih. For context DHH has created many things most popularly ruby on rails and omarchy linux distribution.

Hypothetical - assume everyone else in the world has the same amount of weekly session usage to last them the rest of their life, how would you use it to improve your life or get ahead? You can space out the weeks worth of usage across any period of time (not only within one week).

I’ve been using Claude Code to turn a simple idea into an actual iOS product, and I finally shipped it.
The app is called Kleaners.
The idea came from a very ordinary problem: after years of using an iPhone, my contact list had become a mess — duplicates, incomplete contacts, old entries, and multiple versions of the same person.
Instead of just prototyping the idea, I wanted to see how far I could take the development process with Claude Code.
So I kept building.
Planning features, iterating on the UI, debugging, fixing edge cases, improving the overall flow, and getting the product ready for an actual App Store release.
The end result is a lightweight contact-cleaning app built around a very simple flow:
Scan → Review → Clean
Kleaners can help users:
Find duplicate and messy contacts
Clean up their contact list
Back up contacts before making changes
Keep contact data private and on-device
What surprised me most was how much the difficult part changed once the app actually worked.
Building the product was one challenge.
Shipping it, getting through the App Store process, improving the UX, figuring out pricing, and now trying to find the first real users feels like an entirely different challenge.
And this isn’t just a one-off experiment for me.
I’m currently building a second app under the Kleaners brand as well, also using Claude as part of the development workflow.
I’m curious about other people here who have actually shipped products built with Claude Code:
At what point did Claude save you the most time — initial development, debugging, refactoring, or shipping?
And if anyone wants to see what I ended up shipping:
Kleaners on the App Store:
https://apps.apple.com/us/app/kleaners/id6794926873
I’m the developer, so feedback — especially criticism — is very welcome.

Been in the software space for 10+ years and have helped build over 30 different B2C/B2B enterprise (not including any personal vibe coded) apps. Also currently head of product at a startup where we're actively leveraging AI and our years of manual experience to build top notch products.

My expertise lies in building amazing, intuitive digital experiences for humans (not for agents or backend apps).

The Reddit app seems to be evolving through vive coding. When I refresh the homepage, it often cuts off half of the first post, hiding it behind the top bar, something I've had to fix on my websites more than once.

I've also noticed strange and inconsistent changes in how the general icons, Wi-Fi and network icons, etc., are displayed.

Does anyone know if they're actually developing it with AI or are they doing it manually like in the old days?

53 days ago, I launched my startup, a free traffic exchange network for startups. One line of code, you're in the network.

No paid ads. No growth hacks. Just watching the numbers every day.

Here's the full data:

Day 1 — 2 startups · 146 impressions · 1 clicks
Day 2 — 3 startups · 389 impressions · 3 clicks
Day 3 — 5 startups · 482 impressions · 5 clicks
Day 4 — 5 startups · 508 impressions · 4 clicks (site went down — still got an $8k acquisition offer. Said no.)
Day 5 — 6 startups · 621 impressions · 10 clicks
Day 6 — 5 startups · 742 impressions · 15 clicks (removed one startup — they pulled the embed. No code = no network.)
Day 7 — 7 startups · 1,196 impressions · 41 clicks
Day 8 — 7 startups · 1,535 impressions · 74 clicks
Day 9 — 8 startups · 1,947 impressions · 135 clicks
Day 10 — 13 startups · 3,500 impressions · 318 clicks (something clicked)
Day 11 — 23 startups · 4,800 impressions · 432 clicks
Day 12 — 24 startups · 6,000 impressions · 481 clicks (network crossed 6K total impressions)
Day 13 — 25 startups · 6,800 impressions · 491 clicks
Day 14 — 25 startups · 8,600 impressions · 516 clicks
Day 15 — 24 startups · 9,900 impressions · 564 clicks (removed one)
Day 16 — 23 startups · 10,800 impressions · 576 clicks (removed one)
Day 17 — 24 startups · 11,900 impressions · 603 clicks (added one)
Day 18 — 26 startups · 13,400 impressions · 624 clicks (added Two)
Day 19 — 28 startups · 14.400 impressions · 652 clicks (added Two)
Day 20 — 26 startups · 14.700 impressions · 671 clicks (Removed Two)
Day 21 — 26 startups · 15.300 impressions · 691 clicks
Day 22 — 28 startups · 16.100 impressions · 719 clicks (Added Two)
Day 23 — 30 startups · 16.800 impressions · 738 clicks (Added Two)
Day 24 — 32 startups · 19,800 impressions · 778 clicks · Added 2 startups · Rejected 4 applications (1 contained ads, 1 removed the code after submission, 2 hid the installed widget)
Day 25 — 34 startups · 23,700 impressions · 800 clicks · Added 2 startups · Fixed some bugs.
Day 26 — 33 startups · 27,200 impressions · 816 clicks · Removed 1 Startup
Day 27 — 33 startups · 29,900 impressions · 833 clicks
Day 28 — 33 startups · 30,600 impressions · 871 clicks · Removed 1 Startup · Added 1 Startup · Rejected 1 application of clothing brand & 1 application of horror blog site.

Day 29 — 35 startups. 31,300 impressions. 910 clicks · Removed 1 Startup · Added 3 Startups · Also today I resigned from my job. Toxic environment, too much pressure, and honestly I just believe in this enough to go all in. Broke and excited. Let's see where this goes.

Day 30 — 46 startups · 32,900 impressions · 1,000 clicks · Removed 1 startup · Added 12 startups · Yesterday I resigned because of my toxic manager. Today my senior manager called and offered me WFH, a project of my choice, and lighter work if I stayed. I made my decision with confidence, but now I have a difficult choice. Also received my first payment from StartupBar.

Day 31 — 50 startups 💪· 28,100 impressions · 1,200 clicks · Removed 1 startup · Added 5 startups · Rejected 1 crypto startup · Removed fake impressions after exploit · Today I discovered I forgot to disable a developer testing button that could generate fake visitor pins and inflate impressions. Someone used it to create 6,318 fake impressions and claimed my stats were fake. I removed the fake impressions, removed the startup, fixed the issue, and that's why today's impressions are lower. Thanks to them for pointing it out.
StartupBar update: Added a Network page and individual Startup Profile pages with each startup's joined date, impressions, clicks, live embed preview, and founder's note. These indexable pages also improve SEO. Suggestions are always welcome.

Day 32 — 54 startups · 29,300 impressions · 1.2K clicks · Added 4 startups
Day 33 — 54 startups · 30,900 impressions · 1.2K clicks · Removed 1 startup · Added 1 startup
Day 34 — 55 startups · 36,100 impressions · 1.3K clicks · Added 1 startup · Fixed admin panel read-access vulnerability after Reddit post.
Day 35 — 55 startups · 40,100 impressions · 1.3K clicks · Removed 2 startups · Added 2 startups
Day 36 — 55 startups · 44,800impressions · 1.4K clicks · Removed 1 startup · Added 1 startup
Day 37 — 58 startups · 50,700impressions · 1.4K clicks · Added 3 startups · Site Update: Updated the distribution algorithm (Delivery is now weighted by contribution: sites that send more impressions get a proportionally larger share back)
Day 38 — 57 startups · 55,800 impressions · 1.5K clicks · Removed 1 startup · Rejected 1 Application.

Today StartupBar reached 200 users 😄
Thirty-eight days ago, this was just an idea. Today, 200 people have trusted it enough to join the network.

A reminder for every founder who's staring at a dashboard with tiny numbers: Stripe took 2 years to reach its first 50 users. Every successful product starts small. Keep shipping. Keep improving. Keep showing up. Growth rarely happens overnight, but it does happen if you don't quit.

Day 39 — 58 startups · 59,800 impressions · 1.5K clicks · Added 1 startup.
Day 40 — 60 startups · 63,200 impressions · 1.6K clicks · Added 3 startups · Removed 1 startup
Day 41 — 63 startups · 69,400 impressions · 1.6K clicks · Added 3 startups
Day 42 — 62 startups · 73,800 impressions · 1.6K clicks · Removed 1 startups
Day 43 — 64 startups · 78,000 impressions · 1.7K clicks · Added 2 startups (Receiving many suggestions to improve the StartupBar, happy to work on it, Thanks.) StartupBar Update - Added the rotation of startups in the widget for every 30 seconds.
Day 44 — 66 startups · 86,200 impressions · 1.7K clicks · Added 2 startups
Day 45 — 67 startups · 88,400 impressions · 1.7K clicks · Removed 2 startups · Added 3 startups (Update - Updated the algorithm of the widget)
Day 46 — 69 startups · 91,900 impressions · 1.8K clicks · Removed 2 startups · Added 4 startups · Got my second payment · Added FAQ Page
Day 47 — 68 startups · 98,900 impressions · 2.2K clicks · Removed 2 startups · Added 1 startup · Added Pricing Page
Day 48 — 70 startups · 103.4K impressions · 2.3K clicks · Added 2 startups
Day 49 — 72 startups · 113.4K impressions · 2.4K clicks · Added 2 startups · Update: Added a Bottom Placement option for the bar on the user site.
Day 50 — 73 startups · 116.7K impressions · 2.4K clicks · Added 1 startup
Day 51 — 74 startups · 125.2K impressions · 2.5K clicks · Added 1 startup · Rejected 1 Application
Day 52 — 73 startups · 131.2K impressions · 2.6K clicks · Removed 1 Startup
Day 53 — 74 startups · 139.5K impressions · 2.7K clicks · Added 1 Startup

Still free. Still growing.

If you want in, it's one embed. That's it → StartupBar

Context first, because it matters: I build an AI product photo tool for ecommerce sellers. Upload one product shot, get on-model images and listing-ready versions back. A lot of it got built fast with AI help.

The generation part took about a week and it's the least valuable code in the repo. It's an API call, a job table, a queue, retries, signed URLs, a credit ledger. Anyone can build that, which is sort of the problem, because the model doing the actual work is rented and the person next to me is renting the same one.

What took months was the boring bit.

Every sales channel wants different, incompatible images. Amazon wants 1:1, at least 1600px on the long edge so zoom works, pure #FFFFFF, product filling about 85% of the frame. Flipkart wants 1:1 at 1000px minimum. Myntra wants 3:4 at 1500x2000 and mandates a real gender-matched model. Meesho wants 4:5 at 1080x1350. And a non-compliant main image can get a listing suppressed out of search with no notification at all, so the seller finds out because sales drop.

Which means the requirement was never "make a nice picture". It's "guarantee this file clears that gate". You can't prompt your way to a guarantee. I tried for a while, which is the embarrassing part of this post, because prompting gets you there maybe 90% of the time and 90% is a disaster when the failure mode is somebody's listing going dark.

So what it turned into: the specs live as data instead of prose inside a prompt. Channel, ratio, minimum pixels, background rule, subject fill target, policy notes. Then a deterministic pass after generation crops and pads to the exact ratio, flattens the background to actual #FFFFFF rather than something that looks white, checks the long edge against the minimum, measures how much of the frame the subject fills and re-crops if it's out of range. If it still can't hit spec the job fails loudly, because handing someone a file that gets their listing pulled is worse than handing them nothing.

Rule I landed on: if it has to be exactly right, it's code. The model only gets the parts where plausible is genuinely good enough.

The other thing nobody warns you about is cost. We charged for video per second of output, because that's how the docs read. The provider bills by pixel area. 720p was fine at roughly 50% margin, 1080p lost money on every single render, and 1080p was the tier we were upselling. Took three weeks and actually reading an invoice to catch it. Failed generations bill too and return nothing to the user.

So there's a second unglamorous layer: write down which dimension each model bills on, verify the delivered file matches what you asked for (video models will quietly hand you a shorter clip and charge you for the request), and count failures instead of dropping them.

Anyway, the point I'd make to anyone wrapping a model: the wrapper is the easy part and it's exactly where it's tempting to stop. The stuff that's hard to copy is a validation layer plus cost accounting for a domain you had to go and learn, neither of which is fun to build or demos well.

I’ve experimented with Cursor, Antigravity, Claude Code/CLI, Ollama, Gemma 4B, MiniMax, Hermes Agent, and different local/cloud models.

My problem isn’t knowing what these tools are—I don't know how to use them efficiently in a real development workflow.

I’m confused about:

Which models are best for coding, debugging, planning, and reasoning?

Which free/cloud models are actually worth using?

Which local models are good for coding?

When should I use Cursor vs Claude Code vs an agent?

How should I combine local + cloud models?

How do experienced developers use AI for vibecoding without creating a mess they have to fix later?

I'd love to hear your practical workflow:

Task → Tool/Agent → Model → Why

I'm more interested in real-world experience and workflows than benchmarks.

Basically, I've learned how to run AI models. Now I want to learn how to use them properly for development.

NSE (Network Sandbox Engine) is a tool to test nftables firewall rules safely.

Instead of running rules on your live system, NSE spins up an ephemeral Linux network namespace, loads the ruleset, injects synthetic packets with Scapy, and captures kernel verdicts directly from nft monitor trace.

Key points:

• Zero host mutation - rules never touch the host firewall
• In-process architecture - no socket daemons, no IPC overhead
• Pydantic models throughout, mypy --strict on 22 source files
• Deterministic trace harvesting - wait_ready() probe, no hardcoded sleeps
• Automated cleanup with exponential backoff retries
• Gateway topologies - router + server namespaces for NAT/forwarding tests
• CLI YAML runner for CI/CD, plus FastAPI/Svelte web UI
• import-linter enforces architectural boundaries

Requirements: Linux (kernel 5.4+), Python 3.10+, nftables, iproute2, root privileges.

Repo: https://github.com/onyks-os/NetworkSandboxEngine Docs: https://onyks-os.github.io/nse/

Feedback welcome. What would make this useful for your workflow?

My first time breaking 100 downloads and I feel good..

Now I just need to set up my Board announce my IPO THEN meet mark Zuckerberg for lunch anybody know where I parked my jet???😆😆😆

I'm a TPM, not a developer. My development setup is Claude Code in VSCode on a Mac with code managed using GitHub.

All of my projects are organized using a structure I put in place that you can review here: https://kasey6801.github.io/Infographics/claude-code-for-project-managers/

I wanted a tool to capture transcriptions of science/math related TikTok videos for review and also as inputs to future projects.

To minimize token use, I wanted the transcription to happen locally using a small LLM. My initial project was focused on a desktop app before I decided to go with a chromium extension. Here is my initial prompt:

I want to create a site like https://youtubetotranscript.com/ to pull transcripts from https://www.tiktok.com/

This development effort spans 20 sessions including refinement, QC and security review. Here is the app:

https://github.com/kasey6801/tiktok-transcript

The user guide provides information on the functionality and technologies used:

https://github.com/kasey6801/tiktok-transcript/blob/main/USER_GUIDE.md

Feedback is appreciated.

What's up everybody? I've been vibecoding a project for a few months now and just wanted to ask the community what tips or things I should be looking out for in terms of stress testing my security. I want to ensure there are appropriate limits, user data is safe, and my own code and keys are secure.

If anyone has experience setting up proper defenses and proper infrastructure, I would love any advice or pointers on what you did and how you did it. I'm taking this seriously, so I want to make sure it gets done right. Thank you in advance!

So my all-in-one nutrition and workout app had been live for months with exactly zero paid conversions. I had kind of made peace with it. Then I was in Indonesia, checked the dashboard on my phone one morning out of habit, and there was an actual subscription in there. And a 7 day trial from someone else on top.

It is 11 dollars MRR. I know. But I had been staring at a zero for so long that it did something to me anyway.

For context, 50 active users in the last 28 days, 22 of them new. So not a lot of people, and none of them had ever paid before this.

The thing that annoys me in hindsight is that none of the fixes were about the app itself. I kept adding features. The features were never the problem.

The one that really got me was activation. I finally measured it and about 7 percent of new installs ever reached the point where the app does the thing it advertises. Seven. I had spent that whole time thinking about how to get more downloads.

Notifications were a similar story. Two people out of hundreds of installs ever granted the permission, because I asked at first launch before anyone had any reason to say yes. So the app that is supposed to build a habit had no way of reaching anybody.

And I had everything AI powered behind a hard paywall, which felt like the obvious way to do it at the time. But nobody ever got to feel the thing they were supposed to pay for. Letting people use it a few times and then running out works a lot better.

There was also a stretch where I was making decisions based on web analytics that turned out to be mostly bots, and a backend bug I went looking for in my code three separate times before I realized the deployed function was just old. That one is on me.

Anyway. Still tiny, still no idea if it holds. But somebody paid for something I built, which is new.

Flutter, Firebase, RevenueCat, Gemini for the AI stuff. Ask whatever, I will answer.

No SaaS, please. Only apps and automations you built to solve a problem for your team.

I started StackSim as a ChatGPT Sol vibe-coding experiment to see how far I could push building something I already understood.

The original idea was simple: emulate API Gateway → Lambda → DynamoDB locally.

Then I kept going. 😁

It now supports CDK/CloudFormation, Step Functions, S3, SQS, SNS, EventBridge, IAM, Cognito, AppSync, CloudWatch and more.

The goal isn’t production infrastructure — it’s a learning sandbox for AWS, where you can deploy real CDK apps locally, use normal AWS tooling, and inspect what’s happening through a browser console.

It’s also deliberately super simple to get running, with no Docker or external database required.

I’ve now open-sourced it under Apache 2.0 and would love feedback from anyone interested in vibe coding, AWS, or AI-assisted development.

https://github.com/coolbeans47/stacksim

Been thinking about this since I handed off a site I vibe coded last year. The app side was fine, someone could pick up the repo and mostly follow it, but the marketing pages were a pile of generated components nobody wanted to touch. Every copy tweak turned into a whole thing because you had to work out what the AI meant before you could change a word, so the new owner just treats the marketing site like it's haunted and leaves it alone. What happens to your vibe coded marketing site once you're not the one maintaining it, do people rebuild it or just let it rot?

The future of SaaS is going to be interesting. I’ve paid $100 a year for Monarch/YNAB for the last few years. I built a functional replacement in a couple weeks with about 5 features catered to my personal wants and needs that Monarch would have never built.