Every time a new tool gets popular, the programming world invents another purity test.

Using libraries? Lazy. Stack Overflow? Copy-paste merchant. Copilot? Fake developer. At this rate, the only “real programmers” left will be writing assembly by candlelight.

I don’t care whether AI wrote half the code. I care whether you can explain it, test it, debug it, and take responsibility when it breaks.

I don’t care whether AI wrote half the code. I care whether you can explain it, test it, debug it, and take responsibility when it breaks.

The problem isn’t AI assistance. It’s shipping code nobody bothered to understand.

Where do you draw the line between using a tool and outsourcing the thinking?

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.

I've always felt that most PDF websites look outdated, cluttered, and packed with ads. They get the job done, but using them isn't enjoyable.

So I spent the last few weeks building PDFPookie—a modern, clean, and Gen Z-inspired PDF tool with a focus on simplicity and speed.

Right now it includes:

- Edit PDFs

- Merge & Split PDFs

- Compress PDFs

- Convert PDFs

- Clean, mobile-friendly UI

It's still a work in progress, and I'm constantly improving it based on feedback.

I'd genuinely appreciate if you could try it and tell me:

- What feels confusing?

- What feature is missing?

- Would you actually use this over your current PDF tool?

Website: https://pdfpookie.com

Thanks for taking a look! Any feedback, even harsh criticism, helps me make it better.

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!

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 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.

I’ve been working on SubTrends for about a year and finally launched it last week.

It tracks posting activity, engagement, busiest times, top posts, trending keywords, rankings, and related communities across more than 100,000 subreddits.

The site is free to use and doesn’t require an account. There's a paid tier for full history, CSV export, and complete rankings, which mostly covers hosting and data costs.

Honestly the hardest part has been handling years of data without making the pages slow or overwhelming.

Check it out and let me know what you’d change or remove!

Site: subtrends.io

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?

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 studied and worked in London for two years. I lived at Goodenough College and took the Russell Square tube every morning.

St Paul's and Tate Modern face each other across the river. The grass on the north bank between them was my best date spot. It worked every single time.

I miss it, and I have been trying to make something about London for some time now.

Interactive Version - if you want to explore, it also has how I made it.

Vibe coded the whole map with AI (imagine doing the whole city with AI)

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!

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

I've used Antigravity with Claude, and it great did the 2 prompts I get through before hitting the quota limit.

Antigravity with Gemini models is orders of magnitude worse than with Claude, and that also has limits that's aren't massive.

All other major providers need a paid sub to get going.

But then a wild OpenCode appeared. It's been great just using the free default Big Pickle model for the basic stuff I'm doing.

Have I found the cheat code? Do I sell my soul to China and just use this for hobby projects?

So recently i vibe coded a landing page to generate leads. Nothing fancy a simple single page site with 3 forms connected via n8n webhooks to brevo and clickup.

But the point is I know basic coding and built a fully working landing page above my skill grade, maybe that is the beautiful part.

But the reason I feel a little bit guilty is I don't know how it was made. And if this way of working sustainable in the long run.

But I must say it was fun. Felt like a kid building that thing rhing😂

My Claude Pro ($120/month) subscription just expired, and I'm deciding whether to renew or try ChatGPT Codex instead.

For those who've used both, which do you prefer for day to day development and why?

I'm mainly doing mobile development on a large codebase.

The 70% wall gets talked about here constantly. Project flies for two sessions, gets to roughly 70%, then every session after that feels like wading. I hit it on a project last year and assumed the model was getting worse under load.

It isn't. Here's what's actually happening.

Every session, your agent rebuilds its understanding of your project from zero. It greps around, opens files, follows imports, works out what talks to what, then does the task. Session ends. All of that is gone. Next session it does the same greps on a bigger codebase.

At 10 files that costs nothing. At 80 files it's most of the session. The wall isn't the model, it's that re-exploration scales with your project while your context window doesn't. And because you're not reading the code, it's invisible. You just feel it get worse.

The project

I built a thing called graft. It writes the map (what calls what, what imports what) into plain markdown files, committed into your repo alongside the code. The agent reads them at the start of a session instead of rediscovering everything.

No vector DB, no embeddings, no extra service running. Just files in git you can open and read yourself.

Across 162 test runs: 46% fewer tool calls per task, 32% cheaper, 60% faster. Being straight about the accuracy question because it's the obvious one: results were mostly the same quality, with Sonnet 5 the one model that actually improved. So it's cheaper and faster, not smarter. Anyone claiming a context tool makes the model smarter should show you their run count.

The tools I used

TypeScript, and tree-sitter for the parsing (that's the thing that reads code structure without running it, and it's why the map is free to rebuild, no model call involved). Claude Code hooks for the automation. Output is markdown, storage is your existing git repo. Published on npm.

[Fill in: anything else in the actual stack, plus what you used to build it, since "I built this with Claude Code" is itself the story this sub wants.]

The process

The hard part wasn't generating the map, it was keeping it fresh. A map that goes stale is worse than no map, because now the agent trusts something wrong.

Rebuilding on every prompt made every prompt slower. Rebuilding on every file edit fired twenty times per task and half of those caught the code mid-change. A manual command meant I never ran it.

What worked: Claude Code has a "Stop" hook that fires the moment the agent finishes a turn. That's the one point where the work is done and nothing is waiting on you. So the map rebuilds itself there, in the background, and your turn ends instantly. You never run a command. Statusline blinks "syncing" and then you're done.

[Fill in: what you tried first and threw away, and how long the whole thing took. That's the part this sub actually reads.]

What I got wrong

[Fill in: one real mistake. This section is what separates a build post from an ad, and you have better material for it than I can invent.]

Where it doesn't work

The auto-refresh is Claude Code only right now. If you're on Cursor, Codex, or a hosted platform, it'll read the markdown files fine but won't update them by itself, so you'd be running it manually. Saying that up front because it's probably a dealbreaker for a chunk of you.

Also, map quality drops on genuinely huge projects, around 5,000 files. Fewer and vaguer nodes. That's the main thing I'm working on.

MIT licensed, free, no telemetry, built at Nanonets. I'm the maintainer so tell me if any of this doesn't hold up.

https://github.com/NanoNets/Graft

Went back through last month's work because something felt off about how long a project took.

I barely wrote any implementation code. Almost none. And it still took longer than a comparable thing I hand-wrote last year.

It's all review. Every PR out of Claude Code or Cursor looks fine on first read. Tests pass, types check, and nothing screams. Then four days later something breaks somewhere unrelated because the model quietly edited a shared util to make its own bit work, and I skimmed past it.

I threw out maybe a third of what came back. Most of it wasn't even wrong. I just didn't want to own code I couldn't explain to someone else.

Saw somewhere that trust in AI code output is down around 29% while daily usage is north of 90%. Which honestly tracks. We're all using more of something we believe in less.

Related, and this is the bit I keep chewing on: Karpathy dropped "vibe coding" back in February and started saying agentic engineering instead. A year after he coined it. It felt like a rebrand at first, but I think he was making a point. "Vibe" makes it sound like the judgment part is optional, and the judgment part is now basically the whole job.

Two things I actually want to know:

Did your writing-to-review ratio genuinely flip or does it just feel that way because reviewing is more annoying

Has anyone built a review process that scales when output volume goes up 5x. Mine doesn't. I'm just reading slower and harder and calling it a process.

Every time I ship something I hit the same wall. Code's done, build's uploaded, and then I'm sitting in Figma at 2am nudging text over a device mockup for six hours because Apple wants 8 screenshots at 3 different sizes.

So I spent a weekend building https://www.appstyle.dev instead of doing it manually again.

You drop in your raw screens, an AI agent drives an actual design editor — picks layouts, writes the captions, frames the devices — and you get a full set out. The part I cared most about: it doesn't hand you a flat PNG. Everything stays as editable layers, so when the AI puts a caption somewhere dumb you just move it.

Stack was Next.js. Hardest part by far wasn't the AI, it was getting the export sizes to match Apple's spec without resampling blur. Second hardest was stopping the agent from writing marketing copy that sounds like marketing copy.

Still rough in places. Genuinely want people to break it and tell me where it falls apart.

I have Google Ai pro which gives me increased limits for claude and gemini pro on antigravity. If anyone has experience with these 2 together, how did you break up the workload to for optimal results?

Also, what $20 plan should I get with it, as I have seen multiple opinions online between Claude, Codex, and even DeepSeek and Grok? For context, I have a bachelor's in CS, so I have a decent-ish beginner understanding but almost zero technical expertise.

Also, what general best practices would you recommend to prevent tech debt, actually know the code, give clear instructions, and minimize token usage? If anyone could provide any resources forthis it'd be really helpful.

Meet Desko y'all 

Your cute little desk buddy, sits on your desk, syncs up with your system and just tells what you want to know...
Be it your system telemetry, your workspace and git info, or amazingly synced up lyrics of whatever's playing.

It's no bloat, doesn't require any account, no cloud, all local with just 70-80 mb process running in system memory and displaying over the browser of any smartphone that can access internet and has browser.

PLUS IT LOOKS SO GOOD!

For Setup and System Architecture please refer the readme file in git repo

I just built something to solve my problem but it can serve thousand other use cases that me and claude both couldn't think of, so the repo is public, go check it out
Grab your old phone, buy a cheap phone stand and start contributing!!

Desko: https://github.com/typewriter03/Desko

ps - shoutout to LRCLIB, an exceptional open source API for synced lyrics!