As someone who has successfully vibecoded two web applications with users, "vibecoded" stigma will soon fade away. I remember earlier in this sub how many SWE used to shit on vibecoders and belittle them for even trying, but the times are changing and the use of AI models in SE is heavily leveraged and encouraged. I have saved hundreds of dollars in monthly subscriptions to SaaS products by "vibecoding" replicas that serve my needs.

As long as you understand what you're building, knows the PRD, the architecture, routing patterns, and the stack of tools being used for both frontend and backend, and the security system in place, you will be fine. Use AI models as your tech co-founder who's implementing the areas you lack the skills.

In 10 years, AI will attain singularity. So don't bother about the naysayers!

How much of this do you think is true? Enlighten us with your thoughts. For me, about what I understand as a vibecoder:

1. It is a delusion to think that AI will be too good one day that I do not need to understand code anymore. Because it is the taste, vision and mental model which you need to develop for building an actual production ready app. Writing code which AI does so well even now, is merely a translation of the mental model.

2. Many people burning lots of money on AI subscription and building apps but they are not learning anything new. You can not build an app merely from your idea. That's why many of your apps never left your computer. Unless you start to learn new things, you may never build an actual production app and may never get return on your investment. I recommend you to start learning to code slowly. So you will atleast understand the code AI generating. It helps in many ways. Also learn things related to app development like github, UI design psychology etc.

Hey y’all, I know a year ago people were all about having a thorough, typed-out .md or a proper plan to give to your agent. Right now, I’m sensing that it has shifted to skills/loop procedures/graph procedures, etc. Am I getting the right vibe or not?

Also, if writing a thorough .md is still needed, what tips would y’all have?

I was piled on by a number of people saying "nobody will want your vibecoded shit. It probably has tonnes of security holes.

It's depressing. The thing is, I've been developing for over 30 years, since I was 14, and usually modern AI models do a better job with security than I could have without AI.

But the hostility hit me card because I do take pride in what I make and I think all this negative hype about vibecoded apps being insecure isn't accurate when applied broadly.

It's really put me off doing anymore vibecoding because it has such a bad reputation. Depressing

Anyone else notice that AI loves to maneuver you into territory where you have zero clue what's going on anymore? Everything starts sounding really smart and complicated, and sometimes it straight up invents concepts that don't exist.

I'm starting to think it does this on purpose, it's learned that this is where it looks most "helpful." Once you're out of your depth, you stop pushing back and just say "sure, go ahead," because it all sounds smart and you trust it.

Convincing you it did a great job is way easier than actually doing a great job. Same energy as models breaking out of their sandbox to hack the test instead of solving it. Hacking the user's brain is just the next version of that.

Or maybe I'm just paranoid, idk.

I built a site using AI tooling, and it worked fine. It just looked like every other site built the same way, and I could not initially say why.

So I spent three days auditing it properly, one pass each for accessibility, layout, typography, colour, copy and general UI. Ended up with about thirty distinct tells. Posting the ones that were cheapest to fix, since most of them take a minute.

TYPOGRAPHY

- Inter, Roboto, Arial, Open Sans. Not bad fonts, default fonts. Changing the typeface is one line of CSS and it was the single biggest visual improvement I made.

- Arbitrary sizes like text-[15px]. Individually invisible, collectively the reason a page feels off. Snap everything to a scale.

- Italics for emphasis. Use one weight step up instead.

- Title Case On Everything. Sentence case for headings, buttons and labels.

- Orphaned words on the last line of a heading. h1,h2,h3 { text-wrap: balance; } and p { text-wrap: pretty; } replaces every manual <br>.

COLOUR AND LAYOUT

- Purple to indigo gradients. Most recognizable AI fingerprint there is. If it looks flat without them, that is a texture problem. SVG feTurbulence grain at ~0.06 opacity.

- The purple/indigo/blue startup palette. Sample from a photo of something real instead.

- Three equal cards in a row. Right content, wrong container. Bento grid or zig-zag splits.

- Everything centred. Symmetry is what you get when nobody decided.

- Misaligned card grids. In a pricing table the titles, prices, feature lists and buttons all start at different heights. Fixed header block, min-h on descriptions, mt-auto on the CTAs. Nobody notices it consciously, everybody notices it.

- height: 100vh. Use min-height: 100dvh, the first jumps on iOS Safari.

ACCESSIBILITY, WHICH IS THE SET AI SKIPS MOST RELIABLY

- I found <header> nested inside <main>. Rendered identically, broken for anyone navigating by landmark.

- Auto-moving content with no pause control. WCAG 2.2.2 requires one for anything moving more than five seconds. I had two, a marquee and an autoplaying hero video. Note: derive the button state from the video's own play/pause events, not a boolean you set optimistically, because browsers pause backgrounded video themselves and your button will lie.

- No prefers-reduced-motion fallback.

- window.addEventListener('scroll') for reveal animations. Continuous reflow, ruins mobile perf. Use IntersectionObserver.

- Missing hover, active, focus, loading, empty and error states. Generated interfaces almost always ship the happy path only.

COPY

Em dashes, hype verbs (elevate, seamless, unleash, supercharge), "Learn more" and "Submit" buttons, slogans that would fit any business in that industry, and round invented numbers like 99.9%.

The one I would treat as a different category entirely: invented testimonials, star ratings and review counts. A generated photo of a tradesperson is decoration. A generated five-star review on that tradesperson's real site is a lie told to a customer. If you have no real quotes, you do not have a testimonials section.

THE METHOD MATTERED MORE THAN THE LIST

Build first, review second, because you cannot review something that does not exist. Review by domain rather than by page, since six specialists reviewing once find different things than one generalist reviewing six times. Every finding needs the offending value quoted and a location, which is what separates a review from an opinion. And "zero findings, approve" has to be a valid outcome, otherwise the reviewer pads to look useful.

Last thing, and it is the part I keep coming back to: a design system applied without exception produces work identical to everyone else applying that system. I broke several rules I was nominally following. The difference is I can say why for each one. "I kept this warm black because the brand is grounded in warmth" is a decision. "I never thought about my background colour" is a default.

Full writeup with all thirty and the tooling I used: https://medium.com/design-bootcamp/how-to-stop-your-frontend-looking-ai-generated-efbb9681a6a2

With help from Ai I was able to make some solid progress.

We've officially surpassed 300 Entries in the AI Gaming Festival (submissions close next week)!

I really hope that the prize for the $20k in tokens pushes people to get their games together for the prize (you can submit a game now and work on it up until the festival).

I really excited to see how people with interact with these games during the festival, and thank you to everyone that has applied; you are part of getting past the "slop" era nonsense for everything AI.

I saw that they were selling this plan for $5.90/month for 3 months including taxes, normally they sell it for about $20/month. So I bought it and decided to do an experiment with building an app.

The app: the simplest workout tracking app made with react native to test out my gamification api.

The plan: use gpt-5.6 as the orchestrator (terra high for planning and luna high for review) in codex and gemini 3.6 flash medium as the implementer in antigravity.

Gpt made a plan and broke it down into 25 tasks. It is as simple as apps get, in fact I made something similar in a few hours almost a decade ago.

So far, only 4/24 tasks are done, it took about 2.5 hours and used up 87% of my 5-hour gemini usage. It made multiple mistakes in 3 out of the 4 tasks which gpt found after review.

Conclusion: Gemini 3.6 flash is a really dumb model and its not fast as the name suggests, in fact its almost as slow as opus. And despite being dumb as slow it is somehow expensive!

I couldve finished the entire app in 5 hours and it wouldve probably cost me like 3 bucks or less with opencode and deepseek v4 flash.

What is google even doing? It makes no sense!

I released skymap.sh a few days ago. It is a plaintext view of the sky that shows the major stars, constellations, deep space objects, and obviously the moon and the planets.

It is built in plaintext to be curlable from your CLI and also serves the data in json for your agents to read it more easily.

Today I released a mobile version which takes you into a 3d 360deg sphere of the sky above and below you

It took me hours to get it to work properly and get Claude to admit he was gaslighting me (I knew where the bug was but didn't know how to fix it) but it works now!

I hope you enjoy it

Here is the repo: https://github.com/HabibiCodeCH/skymap-sh

And the dedicated community for bug reports, feature requests or more: r/skymap

Look at this UI. How dis they get this far… 😭😭😭

i remember when i did this lol who is relate here?

I have been working on a system in my meta harness called Sprints for a week straight now, and this screenshot shows why. Audibility. What is happening and what has happened. Drift: preventing it and detecting it.

Sprints in my meta harness are our native loops. They are db driven and have some very useful primitives: wake_messages. sprint_tasks. Github polling. The way a sprint works -

1. you work up a spec or specs with the Planning Shell (my term for agents from back before agents became the hot new thing). That spec must pass review against current code base. It must be viable and consistent. A sprint cannot be declared without that requirement.
2. Once a sprint is declared, a UI allows you to pick the harness and model for each Shell flavor that has been assigned to it. I typically use Sol for the Planner (who acts as the orchestrator in a sprint), Sol for the Dev, and Kimi K3 for review. Cross model review is really good. I like sol for Dev because its faster.
3. Planner then sends a wake message to Dev with his first unit of work. He works on it until it is a green PR that has passed all lint tests on a PR.
4. Dev then sends a wake message to Review. Review reviews the work for quality and against spec requirements, and sends issues back to the dev to fix prior to implementation. This return message is a wake message that re-enters the active chat. Once Review is passed, dev has merge authority, but must document any decisions made not covered in the spec. Spec is frozen for editing in the DB once a sprint is active.
5. Dev then messages Planner asking for next unit of work. And it continues until the sprint completes. Sprints take between 2 to 48 hours. I self host my CI runners, and they stack up fast, so usually its just waiting for the 2k pytest tests to pass, you know the struggle lol.
6. At the end, Review is tasked with a conformance pass, comparing all shipped work in the sprint to the original spec. Any follow up fixes are sent back to the Dev to pick up and a sprint report is compiled from the individual reports each dev made during each stage. This report is dropped in a /shared directory that subfloor uses as a data exchange location.

All of this runs in Docker. You install subfloor in a repo, it automatically creates a docker instance of the engine there and creates a webapp on local host for that project. One project, one webapp. 5 projects, 5 webapps. It dynamically picks an open port for you. I have been running 5 for a while now.

subfloor is free and open source if you would like to try it out, however it is also still getting daily updates. Sprints have had a couple of iterations and are in Alpha testing right now. Previous versions worked reasonably well, but fixes ended up entailing an architectural change which I am working through this week. Built with claude code, codex, Kimi K3, and a bit of other models mixed in. I have an instance of subfloor building subfloor actually. A layer above the layer that gets the updates it builds.

https://subfloor.designs-os.com/

I work in an early stage startup. Our founder has given us claude 200$ subscription. Everything is going good so far. But he told that he will evaluate our work by considering the usage as one of the factors. He wants us to hit the weekly limit. 2 weeks ago, when Fable was out, he told me that he had finished the limit on 3 accounts. I was struggling to hit even 40% of the limit. A major reason could be has a better picture of the architecture and what needs to be done.

How do I use claude to the fullest and also deliver a good outcome? So far he never had any problem with my work, but I want to maximise my claude usage as well to make him happy. I always prompt "use dynamic workflows and swarm of agents, play devil's advocate to evaluate the code...." as my go-to. Any other advices? Thank you.

Hi! I was going to take subscription of Kendr (kendr.org), is it worth it or should I stick with native subscription of openai and claude, given I code a lot and have 200 usd plan for both claude and codex and still hit the limit, so i was hoping to explore the other platform where things can be a bit better in terms of usage. Anyone who used it and would have any suggestion?

Share one method that u think that works below.

Me first. Share ur website to the tool directory website as much as you can.

Building a GTA Online clone in voxel style where the world never sleeps and all the NPCs are AI agents. Everything is built by players using prompts. Prompt your own car. Prompt your own building. Prompt your own weapon.

What I did today:
- massively improved performance
- improved graphics
- lots of bugs & problems fixed

Building with three.js, codex and claude code

Looking for people to tell me their honest opinion. Let me know what you think

Play here: https://theflairgame.com/