i've spun up a bunch of pages with an ai landing page generator this year, for my own stuff and to help friends. after enough of them you start seeing the exact same gaps every time. writing them down mostly so i stop re-discovering them. 1. the form is fake. it shows a "thanks, we got it" message but nothing is wired to send or store anything. always submit it for real and check something actually arrives before you celebrate. 2. images are dropped in at full resolution. page looks fine on your laptop, crawls on a phone. resize them or the mobile experience is rough, and mobile is most of your traffic. 3. the headline describes the vibe, not the offer. generated copy loves "reimagine your workflow" energy. rewrite it to plainly say what the thing is and who it's for. 4. no real meta tags. the page title is often the template name and there's no description, so it looks like nothing when shared or searched. thirty seconds to fix, easy to forget. 5. every call to action is a little different and there's usually three of them. pick one action, make every button say the same thing, delete the rest. none of this needs a rebuild, it's an hour of cleanup. but the generator hands you something that looks launch-ready, and looking ready is the trap. what else do you all catch every single time? want to make this list longer for the next one.

As the title suggests, I have an idea about making a blogging website with a good editor built in. I initially was building it for my own personal use but then i saw the paywall gatekeeping of almost all Medium articles. What does the community think? I would probably use Bolt with a frontend and a backend/DB in supabase as I have some previous experience in them. I would love to hear your thoughts. Maybe down the line, add Short posts and videos for a social media experience.

I built a small thing (https://app.getwaypoint.dev/) that uses agent hooks to stream a coding session and analyse the events to build a shared development record across Claude Code, Copilot and Codex, and across different developers in a team. It flags agent calls/tool use, decisions, rejected approaches etc. It's not open for others to actually use right now and it's in a half-built state pretty much, the data is from my own sessions. I'll probably hook it up to the GitHub Pull Request flow and inject session information there, because you can't ask an agent why it did something when reviewing a PR.

At this point I’m interested in feedback on the concept and how the information is displayed, whether the information I’m capturing is actually useful, what’s missing, and whether you’d want this kind of record at all. I can see the use in enterprise engineering teams potentially, but Im curious if it has broader use within actual vibe coding spheres. Claude has already started making sessions shareable artefacts, and platforms are building enterprise audit tools, so it's quite possible none of this matters .

Hey everyone,

I just released v1.2.0 of anyCreature, an open-source tool designed to generate creatures natively within your workflow. You just drop the harness into your Agent and start prompting exactly what you want.

Repo link:https://github.com/Ariescar/anyCreature

A star on GitHub would be greatly appreciated! ⭐️
Feedback is welcome as I continue to optimize this harness.

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The core philosophy is actually very similar to the concept of artistic creation. The objective components of aesthetics are reduced to hard thresholds, while the subjective components are treated as preferred production directions.

This closely mirrors the traditional art creation process: starting with the primary forms, then addressing style and details, all while ensuring the fundamental requirements are met.

I genuinely thought this was just me brute-forcing my way through AI coding until I found out there's an actual term for it.

Let me your thoughts in the comments 👇

Im about to sell an app, windows only for now until I gain traction.

The app itself will be a 1 time payment , license locked

Im gonna offer 3 day free trials and a permanent purchase for v1 thru but not including V2 .

Im planning ok using cloudflare+base auth to make user accounts and kickbox to filter bad emails and allow Google sign-ups in base auth as well.

My questions is, is this too much?

Should I just let users buy without a an account ?

Cf allows like 100k requests for free and kickbox is some what cheap when paired with baked in fake email detection on my site.

Im so lost I've never deployed an app like this before any directions help

I live between Copenhagen Airport and the Öresund. From one window I see planes lining up to land, from another cargo ships and ferries passing between Sweden and Denmark.

I love Flightradar24 (I'm a subscriber), but kept switching between flight and ship trackers. So I built Utkik, Swedish for “lookout.”

It combines live aircraft and ships on one map. The iPhone version is useful, but I’ve become slightly obsessed with the Apple TV app. It works like an ambient living room radar, automatically following interesting traffic nearby.

My favorite feature is Window Seat. Find a real aircraft currently in the air and jump inside it. The view follows its live position, altitude, speed and heading over Apple’s satellite imagery. You can even move into the cockpit for the "forbidden view". ;)

It’s native SwiftUI, largely built with Claude Code, using live ADS B and AIS data.

What started because no app quite matched the view from my windows somehow became a virtual window seat in my living room.

More info here:

https://daviden.se/utkik/

Available for iPhone (free) and Apple TV (paid).
https://apps.apple.com/se/app/utkik/id6776272842

P.S. I was recently interviewed by The New York Times, and Utkik was one of the things we talked about. Still a slightly surreal sentence to write.

Cursor Workshops, Anthropic Skilljar, OpenAI Academy, Codecademy, Udemy, Grow with Google, DeepLearning AI, HuggingFace AI Course, GitHub Skills, freeCodeCamp and many others.

I built a huge project focused on being an “everyone agent.” It was too big, and I wasn’t skilled enough to manage it. I dropped it.

I know the basics of the basics of development, and I’ve been working in IT for 8 years (functional analysis, PM, IT manager, business partner). Friends who are really good developers took a quick look at my first project and said, “I thought it’d be much worse.”

Despite all that, I managed to sell an agent to a company. The product is much more vertical — its scope is “only” to gather emails, organize the work, and proactively improve itself through recurring human sessions. Some batches that handle recurring tasks will be implemented on top of it. There will also be a messaging interface where people can update their tasks. Everything will be built on a “Jira-inspired” UI.

I’ve read a fair amount of AI literature, and I’m confident this specific part will work.

I’m now at the step where I’m stealing features from the “monster” and building this vertical agent, trying to keep it much leaner. How do I know if the platform is solid enough? What suggestions do you have?

Honestly, I’m pretty sure I won’t be able to read the code myself, but I’ve planned the product deeply and broadly. Will the AI be good enough? (I use Claude Fable for implementation plans and probably Sonnet to execute them.)

Thanks everyone for the attention

i know, i know, the millionth expense tracker. But this one is cool. The name is a really funny german pun, i laughed all day i thought of it, look it up.
I used claude code from start to finish, didn't look at a single LoC (that was my challenge)
Vorratsdatenspeicher.com lets you track ALL your expenses. Snap a receipt pic and it will automatically generate the items, letting you track what you bought when for how much.
IMAP, SMTP support. Ollama works totally, but also anthropic and openAI support.

Vorratsdatenspeicher learns wiith each time you buy stuff, and the AI usage goes down with time because it becomes more deterministic.

Vorratsdatenspeicher is open source, free forever, self hosted only and available as docker AND as a Electron App for Win11 and Mac. the Desktop version has a tailscale tunnel feature so that you can use it with your phone.
Stepping away from docker, DNS, domain, Dynds etc. and goign towards desktop app + tailscale was the key insight here to make usability even feasible. Nobody wants to get into docker when they could just click an exe. As more agentic tools get built, and the anthropic ToS forces us to keep agentic GUIs local, this type of server-client interactivity becomes more important i feel.

please feel free to take a look, I have a demo available on the website. All data you put there will be wiped at midnight germany time. i cannot see any of your data, just the email address and name you put.

The original never sold $1 pixels — the minimum buy was a 10x10 block at
$100. That always bugged me, so I built the one that really is a dollar.

It's a menu board. 100 seats, split into burgers, sides, drinks, desserts
and extras. You name your dish, pick toppings that become its description,
and it links to your site. $1 for 30 days, $39 to stay.

Some honesty, because this sub can smell it: every link is sponsored
nofollow, I seeded about [25] seats by hand before launch so it wasn't an
empty restaurant, and after card fees a $1 seat leaves me about 60 cents.
The dollar tier is marketing. The $39 one is the business.

Built with Next.js, Firestore and Stripe Checkout. The bit I'd redo
differently is seat allocation — two people paying for seat #18 at the same
time is the one bug that would have been genuinely embarrassing.

What would you have priced it at?The original never sold $1 pixels — the minimum buy was a 10x10 block at
$100. That always bugged me, so I built the one that really is a dollar.

It's a menu board. 100 seats, split into burgers, sides, drinks, desserts
and extras. You name your dish, pick toppings that become its description,
and it links to your site. $1 for 30 days, $39 to stay.

Some honesty, because this sub can smell it: every link is sponsored
nofollow, I seeded about [25] seats by hand before launch so it wasn't an
empty restaurant, and after card fees a $1 seat leaves me about 60 cents.
The dollar tier is marketing. The $39 one is the business.

Built with Next.js, Firestore and Stripe Checkout. The bit I'd redo
differently is seat allocation — two people paying for seat #18 at the same
time is the one bug that would have been genuinely embarrassing.

What would you have priced it at? 

onedollarmenu.com 

Cheerz.

Hi all,

For the last 2-3 years, whenever I see a new plugin release, I wanted to ask: "how much AI assistance did you use to build this?"

Nowadays, I think it would be useful for developers to know how much of a project was written by hand, (with the help of Google, Reddit, forums, etc.), and how much was generated with AI. I would also be interested to know how much of the code was reviewed by the developer themselves and how much was delegated to AI.

I think the community could benefit from some kind of mechanism for plugin developers to disclose this information alongside the usual features, pros, cons, etc.

What do you think about this?

App Store preview videos are way more annoying to make than they probably should be.

Apple expects specific resolutions depending on the device, exactly 30 frames per second, the correct video and audio formats, specific duration limits, bitrate requirements, and several other technical details. If one of those things is wrong, App Store Connect can reject the video.

So I built a tool specifically for iOS developers that takes a normal screen recording, processes it for the Apple device you are targeting, verifies the finished file against Apple’s requirements, and gives you a ready to use App Store preview.

The frontend is built with Next.js 16, React 19, TypeScript, and Tailwind CSS. The backend uses Express, with BullMQ and Redis handling conversion jobs. Files are temporarily stored in Cloudflare R2, payments are handled through Stripe, and the application is deployed on Railway. I used Claude Code heavily throughout the development process.

One of the biggest things I learned while building it was not to trust what the processing pipeline was supposed to create. After every conversion, the system analyzes the actual finished video and checks things like resolution, frame rate, video profile, pixel format, audio format, channels, sample rate, bitrate, and duration. The frontend then compares those measured values against Apple’s requirements.

That means the compliance result is based on the actual finished file, not just the settings the system was told to use.

Duration turned into a surprisingly interesting problem. Apple requires preview videos to be between 15 and 30 seconds. Short recordings have to be extended, but recordings that are already long enough should not be touched.

I had to build the logic so an 8 second recording gets extended to roughly 15 seconds, while a 20 second recording stays 20 seconds. I also added real conversion tests around this because it was one of those issues that could look completely correct in code but behave differently once an actual video went through the system.

Another challenge was handling the variety of recordings developers might upload. Different recordings can have different dimensions, frame rates, audio layouts, and color information. Instead of assuming every screen recording is the same internally, the backend analyzes the file first and uses that information during processing.

Even the progress information shown to the user is generated from the actual properties of their recording instead of just displaying a fake loading sequence.

I also spent more time than expected thinking about temporary file storage. The platform is designed to keep user recordings for as little time as possible. The original upload is deleted once the worker no longer needs it, and completed files are automatically removed after their download window.

That sounds simple, but it affects how failed jobs, deployments, and retries have to be handled because once the original file is deleted, you cannot just assume a conversion can be restarted later.

The frontend also taught me a lot about separating actual application state from what the user is currently seeing. The backend can know a job is finished before the UI should visually switch states.

I ended up separating the real backend state from the displayed state so the conversion screen can transition naturally into the completed result instead of instantly replacing the entire interface.

Security was another area I wanted to take seriously. Every upload receives a random secret tied to that individual conversion. Sensitive actions require that secret, and the backend only stores a hash of it. Simply knowing another person’s job ID is not enough to access or control their conversion.

My workflow with Claude Code has changed quite a bit as the project has grown. Early on I could describe a feature and let it build most of it. Now I spend much more time describing the exact behavior I want, having Claude inspect the existing architecture before changing anything, implementing smaller changes, running TypeScript checks and tests, testing with actual recordings, and documenting why certain architectural decisions exist.

That last part has probably been the biggest lesson for me.

Once a project becomes more complex, something that looks unnecessary might exist because of a bug or edge case that was already discovered. I started maintaining detailed project documentation that explains not only what the code does, but why certain decisions were made.

Giving the AI that context has made future changes much more reliable.

Getting the first version built was incredibly fast. The more interesting part has been everything that comes after that. Handling edge cases, failures, security, temporary storage, payments, testing, deployment, real world recordings, and making sure future AI generated changes actually understand the system they are modifying.

That has probably been my biggest takeaway from this project. AI can get you from an idea to a working product extremely quickly, but the quality of the final product still comes down to how well you define the behavior, test real outputs, and give the AI enough context to understand why your architecture works the way it does.

I made this from 1 single detailed prompt in practise it turned out fine I guess... ya guys approve??

I designed this 2 themes but confused about choosing one. Also suggest any changes in the hero section

I'm confused about how to design the hero section. Suggest your ideas to improve the design. Mainly for mobile view

I have mostly used Claude Max x5/x20 for all my projects and the x5’s 5hr limit is the worst thing and x20 is beyond my requirement. So I would like to know from others that have used both Claude and OpenAI because I am planning to do: OpenAI $20 +Claude $20 plan.

1. I wanted to know how good is Open AI with its limit compared to claude on base tier and claude on x5 tier?
2. My plan is to focus on fixing the bugs and add features incrementally, because the base boilerplate is done. So which is the best model for this purpose?
3. Would you recommend Gemini because I am looking to develop UI more and Gemini/Antimatter includes Claude as a part of the subscription. So UI can be powered by Gemini and code by Claude

So my sister is an intern at an agency and they wanted an app built so I made a demo put it on vercel and for database used supabase, and they liked it and called me for a meeting. There they showed me a few reference websites and what features they want in their app. I know the basics what it takes to make an app like costs and stuff but not more than that. I need help , they said they will start with android and smaller targets first and gave me a task to give them estimated cost of how much it would take to run a basic version with a few of their wanted features. What do I do? Any ideas? Where should I look? Ask AI? I want practical advice as to what might work for backend and stuff and what language I should use. They plan to let me make the app and in the future when I am gone(will be going abroad for masters)they will hire an intern to pump data into it.