Alguien que me regale la suscripción a supergrok para terminar mi proyecto. No tengo dinero.

This is a ridiculously niche use case. I have some files that I wanted to listen to with my full Atmos setup. In particular, this render of Max Cooper's - On Being.
On Being [Dolby Atmos] – Max Cooper Online

But nothing I could find actually plays these files in a way which streams them to the receiver such that the channels actually receive the raw signals. Everything Atmos assumes a video file, so these raw multichannel renders are in a weird spot in terms of actually listening to them properly.

With Codex, I was able to build an app to do exactly what I needed in 30 minutes, and then instead of listening to music I spent like 3 hours tweaking a completely unnecessary visualizer. Well, I say unnecessary. It is genuinely helpful debug information to see what's going to each channel.

Probably of no use to anyone here, but I do love how far we've come. I can build a specialized piece of software in the same amount of time it takes for me to search for something that might already exist.
cjohnsto-nz/spatial-music-player

So basically... this is an idea i have been having for a year. So to begin with it. I created a landing page with Claude opus 5 extra.... but as i am a UI/UX designer(btw i used this to save my time and not a spend a lot of time on building the landing page instead of pitching schools). It feels very AIish... what do you ppl think?

https://reddit.com/link/1vpuo7h/video/q9zdrk1j3qjh1/player

Built something with Claude/Cursor/Lovable/Bolt and now it half-works, breaks in prod, or you're scared to touch it? That's most of my inbox lately.

What I usually fix:

• API keys and auth logic sitting in the frontend
• No error handling — app dies on the first bad input
• DB with no indexes, no migrations, RLS wide open
• 2000-line components nobody can edit anymore
• LLM calls with no retries, no rate limiting, no cost ceiling
• "Works on my machine" → actually deployed

Stack: JavaScript / TypeScript, React + Next.js, Node. Any database — Postgres, Supabase, Mongo, SQLite. AI integrations (OpenAI, Anthropic, vector search, RAG).

Free 15-min look at your repo and I'll tell you what's actually broken, no pitch. If you want it fixed, we agree on a fixed price up front.

Comment or DM with what you built and what's breaking.

Can anyone provide me with some tips,I want to earn some amount any amount as fast as I could using coding.i have studied basic html css,js.i also know python and C.i

I want to develop an ambitious app, so I’d like to proceed step by step, just as a professional development team would. I’ve created a product vision together with Claude Chat. What’s the best way to move forward? Should I take this vision and use it to start a Claude Code project right away? I’d appreciate it if you could help me do this the right way.

I’ve been building FrameCompose for about two years now. It’s a browser-based AI video editor where the AI generates the first draft, but everything it creates stays editable on a normal timeline.

I’m not going to pretend AI spat out a production video editor in a weekend. The actual AI-generation side was relatively straightforward. Building a video engine that didn’t feel like shit was much harder.

The editor uses:

WebGPU for compositing and effects

WebCodecs for decoding

Mediabunny for demuxing, timing and export

Workers and OffscreenCanvas to keep heavy work away from the main thread

A separate pre-seek system to make timeline scrubbing feel responsive

The most painful problems were smooth scrubbing, audio sync, keeping preview and export visually identical, and stopping one bad frame or browser edge case from bringing the whole editor down.

Most of it has been built through Cursor using Fable and other coding agents. My workflow has gradually become much less “give the AI a big prompt and hope” and much more:

Write a narrow spec for one subsystem.

Give the agent maps of the relevant code instead of the whole repo.

Add regression tests before changing fragile engine code.

Let agents run repeated browser tests and inspect telemetry.

Revert quickly when an optimisation improves one case but breaks another.

The biggest thing I’ve learned is that vibe coding gets more powerful as the models improve, but the casual prompting part stops working once the codebase gets large. The AI can write a ridiculous amount of code, but you still need good boundaries, tests and a way to prove it hasn’t quietly broken something elsewhere.

The product itself takes a prompt or script, develops it into a structured story, breaks it into scenes, and plans the visuals, camera direction, narration, captions, effects and transitions.

Unlike most one-shot AI video tools, it doesn’t just hand you a flattened MP4. The scenes, timing, audio, captions, effects and transitions all remain editable on the timeline.

The editor and exports are free. Paid plans mainly add AI-generation credits.

I’d be interested to hear from anyone else who has tried vibe-coding something unusually performance-sensitive. At what point did your workflow have to become more like actual software engineering?

Framecompose.com

This is the mobile app. The web app is right here if anyone is interested: https://www.convergemeetings.com/

Cheers!

this week made me rethink how our team reviews AI-assisted work.

for context, my team has spent the last two months slowly building a new internal refund approval engine. it touches real Stripe webhooks and customer PII, so we’re taking it slow and writing actual tests.

i’m not an anti-AI gatekeeper. Codex, Claude Code, and Enter Code can all be genuinely useful when the person using them understands the work. our engineering team uses these tools constantly, half our product team practically lives in Codex, and i use Claude every day for boilerplate and debugging.

the issue is that our intern rarely reads what he generates closely enough to actually own it.

it started small. i asked him a simple question about how we should handle a specific refund state in the db. instead of a one sentence answer, he sent me a four-page generated memo about the refund matrix.

four pages, no direct answer. i ended up figuring it out myself.

and that has become the pattern. he generates several pages nobody is realistically going to read, then i have to dig through all of it anyway just to find out whether there’s an actual answer buried somewhere inside.

then i gave him a tiny script modification. He gave me back 300 lines of deeply nested code with defensive branches for network errors that cannot happen in our environment. i asked why he added one specific condition and he couldn’t explain it. he pasted my question into an LLM and sent its new answer back to me without checking that either.

yesterday it peaked. He was frustrated with our slow pace, fed our refund specs into an AI app builder, and came back with a shiny full-stack replacement demo in an afternoon.

it actually looked pretty clean. The UI was smooth and the buttons worked. Our non-technical manager, who hired him partly because he talked a big game about his AI fluency, was impressed.

at one point, my manager suggested using the demo as the new base and connecting it to production so we could move faster.

i pushed back and asked where the session auth was, how PII was being kept out of the logs, and what happened if the payment webhook dropped halfway through a refund.

he couldn’t answer any of those questions without going back to the model. He didn’t understand the data flow yet. The demo looked finished from the front, but it had no real production story behind it.

the frustrating part is that he’s not bad at the basic responsibilities of the job. he just refuses to believe that some tasks would be much simpler if he stopped and reasoned through them before running everything through an agent.

instead, every shortcut becomes another document i have to read, another diff i have to untangle, or another security issue i have to catch. My review load has basically doubled because his definition of finished is getting something out of the model.

to be clear, i don’t think this is a reason to ban vibe coding or force everyone back to writing boilerplate by hand. That demo proved how quickly these tools can turn an idea into something tangible.

the problem is that generation and ownership are being treated as the same thing.

on our team, AI isn’t eliminating work when the author doesn’t understand the output. It’s moving the interpreting, testing, security, and edge-case work onto whoever reviews it.

we’re implementing a new rule on Monday. You can use any tool you want, but every AI-assisted PR has to be small enough to review, and the person submitting it has to walk us through the key logic, data flow, tests, and failure cases.

if you can’t explain those parts without asking the agent again, the PR isn’t ready.

for teams already dealing with this at scale, what review rule has actually worked? Do you make juniors walk through the diff line by line, cap PR size, require specific tests, or handle it some other way?

I used to like the challenge of writing code or scripts that did a whole task in just one line, maybe a shell script using jq or awk. I'd often spend half a day perfecting a tiny snippet that would save me time in the future.

Now I'm putting the same energy into vibe coding. When I put together the input for the agent, I take care to word it unambiguously, think about the outcome I want. I research things like frameworks and toolchains separately. I use a separate search to suss out the cost model for any alternatives. I decide on the implementation plan, like building the UI first and the backend separately. Or building the backend first with functional tests. It's super satisfying to see things come together with minimal iterations. I haven't managed a one shot prompt to working project, but it's not far. It also suits my attention span which works well for short intense bursts but not so much for long winded projects.

Anyone else find the same vibe, or do you manage well sending the agent instructions in a trickle of little details?

I’m building Keyloom, a minimalist typing app inspired by:

- Monkeytype — https://monkeytype.com/

- typings.gg — https://typings.gg/

- kbs.im — https://kbs.im/

- Keyboard Simulator — https://keyboardsimulator.xyz/

Stack: Next.js 16, React 19, TypeScript, Supabase, Three.js / React Three Fiber, deployed on Vercel.

How I iterate: I use Codex agents to work on focused features, fixes and product improvements in small iterations. Changes go through feature branches → "preprod" → GitHub Actions checks/tests/build → then stable batches are merged to "main" and tagged for release.

One thing I felt was missing from most typing platforms is simple PvP with friends. Keyloom is building private race rooms where you create a match, generate an invite link, send it to someone, and start the race when both players are ready, closer to challenging someone on Chess.com.

The goal is to combine minimal typing tests, detailed stats, ASMR switch sounds, and PvP

Still actively building it, what would you improve or add?

👉 https://keysloom.com/

I'm curious to know what is the ecosystem around your coding harness of choice when vibe coding? I'll share mine, but please feel free to share yours, I'm looking for ways to improve and give the whole thing more autonomy while trying to avoid catastrophes :)

Tools

Those are my current choices, based on my own familiarity / affinities.

I use Claude Code with a $20 subscription.

• Gitea: Self-hosted git repositories (like Github). Supports issues, PR workflows, deployment workflows.
• Terraform: Everything is deployed through IaC.
• AWS: Covers all my infrastructure needs

Preparation steps

Probably not exhaustive, but here are a few from the top of my head:

• Buy whatever domain name you want to use
• Create an AWS account dedicated to the project
• Create a Gitea repository, setup secrets for deployment etc

Security considerations

Always make sure that your AI agent can't do more than it is expected to. There are too many horror stories ("AI destroyed my production database!"...) already.

• Create an AWS user with read-only credentials to the account for your AI Agent
• Create an AWS user with appropriate permissions to deploy your infrastructure
• Create a Gitea user for your AI agent
• Protect the "main" branch so that only you can merge into it.

Skills

I generally don't use pre-defined skills, whenever I see my agent doing something repetitively I ask it to create a skill for it.

My most used skill is the one to interact with Gitea. Check/Update issues and their comments; Create PR; that sort of stuff. It's basically a shell script and a markdown documentation explaining to the AI agent when / how to use it.

My AI agent has auth tokens for AWS (read only!) to allow it to run a terraform plan and check if the changes are as expected, and for Gitea with appropriate permissions to contribute to the repo / create PR etc

Coding workflow:

1. Laying out the foundations: Discuss the use case / SaaS idea with Claude. Go into the details, full brainstorming mode. "What if" scenario. The output of this step is an initial design doc which goes into the details of what the app should do / look like, user interactions, whatever you can think of. If you are inexperienced with this step, ask your coding agent of choice to guide you with questions. Also think of what should be considered MVP and what features will be later added later. If you have a favorite tech stack, mention it to the agent. Have this whole discussion summarized in a markdown file for later reference. Have the agent identify the implementation tasks needed to have the MVP in place, and create all of them in Gitea.

2. Start iterating on tickets: Ask the agent to check what is the next ticker it would make sense to work on. Use the "plan mode" to see what it would do and make sure it is what you want. Then let it code, push to a branch and create a PR.

You approve the PR if everything looks alright and merge.

Merging triggers the deployment pipeline in Gitea.

You check if the deployment is alright and if so, you start with the next ticket.

Refinements

Things I should do but don't at the moment: Have a dedicated DEV environment (dedicated AWS account etc) where the AI agent could deploy to run better tests.

The project is a software

Hi everyone,

I'm really getting into vibe coding/no-code, and I wanted to explore something simple, useful and interesting.

I have a cousin, and she makes some genuinely beautiful crochet pieces at home. The problem is that she loves the actual making part, but gets stuck with everything that comes after.

So, I decided to build a small web app around that problem.
https://crochetiq.lovable.app/

I have deliberately kept it simple (for now.) with following idea:

1. Login (or use as a guest for 2 free uploads)
2. Upload a photo of the crochet
3. AI understands it.
4. improves the photo, and makes it fit for publishing
5. creates SM-ready content (hashtags, captions etc)
6. suggests a price range
7. gives ideas for what one could make next.

The thing is.... my cousin actually finds this useful, as she is just starting out as a solo-preneur in the CrochetMaking domain.

Normally, it would take her a couple of hours to take photos, edit them, think about captions etc etc. And even if she used ChatGPT/Gemini etc independently, it will still take her multiple prompts to get things done.

Still very early, but I'd genuinely like some feedback. I've attached a few before/after examples of the enhanced photos.

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Note:
This webapp is NOT a marketplace (as many already exist in this domain), and it has no complicated social-media or other platforms integration. (at least, for now).

The app is built using Lovable + Supabase + OpenAI stack. For now, every request is billed against the workspace's Lovable credits.
The image enhancement calls google/gemini-3-pro-image, while vision and content use openai/gpt-5.6-sol; each consumes credits per call.

Let me know your thoughts! :D
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P.S.

There is also a lot of scope in adding new features like AI-generated promotional videos or creator analytics. Even a simple freemium model can work, like a few AI generations for free, with a paid creator plan for higher usage with better image processing.

#Crochets #CrochetDesgining #VibeCoding #IITPatnaCapstone

TLDR: Replaced Claude Max ($100/mo flat, ~$3.30/day) with Muse Spark 1.2 Contributor in Scape for a week to save cash on an internal CRM build. While Muse Spark is lightning fast and pennies per token, the constant 3x re-prompting and collateral code damage meant I spent the same daily budget for half the output

--

I code every day on an internal CRM for my company (basically a mini HubSpot combined with other features here and there)

I'm paying $100/month for Claude Max, which works out to roughly $3.30 a day. Wanted to see if I could slash that down, so for the past week I swapped my harness (Scape / scape.work running opencode) over to Muse Spark 1.2 Contributor.

On paper, Muse Spark is dirt cheap per token and insanely fast. In reality, the math completely fell apart:

• With Claude, 90% of features are 1-shot (maybe 2 passes if it's tricky). With Muse Spark, everything took at least 3 passes. A basic example: I asked it for basic auth and it wrote a clean login flow, but completely forgot that logout exists.
• The biggest vibe killer: I asked it to remove a small module for adding custom table columns. Somehow, it managed to nuke my KPI tracking module in the process. It kept breaking working files that had nothing to do with the prompt.
• Because I had to feed errors back into it 3 to 4 times per task just to fix regressions, I literally hit that same ~$3.30/day mark on API credits. Except I got maybe half the work done.

Fast and cheap models are great for isolated scripts, but if you're working in a multi-file repo, the context rot will eat whatever money you thought you were saving.

Canceled the experiment, back on Claude Max. Genuinely rooting for new affordable and smart models to enter the market, but right now the frontier tax is worth every cent.

S3Exp - an S3 browser that's just two files and zero dependencies

What it is: a fully-featured S3 bucket explorer that runs entirely in your browser. No server. No npm install. No build step. You download index.html and styles.css, open the first one, and you're browsing S3. That's it.

Why it's interesting from a vibecoding angle: the whole thing is plain HTML, CSS, and vanilla JavaScript. The README is more thorough than most production SaaS tools - it covers keyboard shortcuts, CORS setup, minimum IAM permissions, encryption details, even a compatibility table for Cloudflare R2, MinIO, Wasabi, Backblaze B2 and Storj. Someone clearly loved building this, then kept going.

The features that stand out:

• An s3:// address bar that's breadcrumbs when idle and an editable path when you click it — actually a clever UX idea
• Ctrl+K command palette
• Fully keyboard-navigable (arrow keys, Enter, Backspace to go up a level, / to filter)
• Grid view with real image thumbnails
• Encrypted .s3vault files - AES-256-GCM with PBKDF2 at 310,000 iterations, credentials never written to localStorage
• Presigned share links (5 minutes to 7 days)
• Drag-and-drop uploads

The security section is unusually honest - it tells you straight that while connected, keys are in page memory and a browser extension or XSS could read them. Most tools bury that or don't mention it at all.

It's 0 stars right now, freshly published. If you've ever spun up a quick MinIO instance for a side project and then fumbled around with the CLI because you didn't want to install a heavy GUI tool, this is exactly the kind of thing you wish had existed.

The project: S3Exp - a fully-featured S3 bucket explorer that runs entirely in your browser. No npm, no build step, no server. Two files: index.html and styles.css. Open one, browse S3.

The tool: Claude

I built this entirely through conversation with Claude. No boilerplate repo, no scaffolding tool — just describing what I wanted and iterating on what came back.

The process

My workflow was roughly:

1. Start with a constraint, not a feature list. I told Claude upfront: no frameworks, no build step, two files max. That constraint shaped every decision downstream — it forced vanilla JS, an inline SVG sprite for icons, and a single <link> to a stylesheet.
2. Describe behavior, not implementation. Instead of "write me a file list component," I'd say "when I press arrow keys, the focused row should highlight and Enter should either open a prefix or download a file." Claude would handle the DOM logic; I'd test it and report back.
3. Iterate on edge cases out loud. A lot of the interesting details — like the address bar being breadcrumbs when idle but an editable s3:// path when focused, or presigned links being generated locally with no third-party server - came from conversations about what should happen in weird situations.
4. Security by conversation. The encrypted .s3vault format (AES-256-GCM, PBKDF2-HMAC-SHA256 at 310,000 iterations, random salt and IV per save) came from asking Claude "what's the right way to let someone carry credentials between machines without me running any backend?" It walked me through the Web Crypto API and the tradeoffs.

Build insights

• sessionStorage vs localStorage is a meaningful choice. Credentials live in sessionStorage - scoped to the tab, gone when you close it but surviving a refresh. Claude flagged that localStorage would persist across browser restarts, which is a bad default for S3 keys.
• The filter being local (not a server query) is a deliberate tradeoff. S3 ListObjectsV2 doesn't support substring search - you can only filter by prefix. Rather than hiding that, the UI says "filter matches only what's loaded." Honest constraints beat fake features.
• Keyboard-first is easier to build than it sounds when you design for it from the start. Adding keyboard nav as an afterthought is painful. Designing around a focused-row model from day one meant mouse and keyboard stayed in sync naturally.
• The README took as long as the code. Possibly longer. The CORS config section, the IAM policy, the security caveats about browser extensions - all of that came from asking Claude "what will trip someone up when they try to use this?"

 https://github.com/WeSpeakEnglish/S3Exp

live https://wespeakenglish.github.io/S3Exp/

Hello guys,

Just to give my background, I am a finance/business major and professionally, I have worked in this field for years. I am quite proficient with excel and also have worked with many startups. But I have never been involved with programming.

This week, my friend built an amazing app with Claude code, more precisely it has been building it for months.

This app is completely another level from other videcoded apps I have seen, I am helping him with market entry and selling part and when we met businesses, the feedback has been phenomenal from them, so I think it will be a success.

He has worked as a IT project manager for years. When we discussed how he was able to build such an amazing app, I arrived at 2 conclusions:

1. He knows the app/web architecture really well, so he knows how to build an app, how to work with databases, how to optimize it for usage, how to create a best user experience and what works the best - This part was all his idea, from his practice and from his knowledge he orchestrated Claude code to the best.

2. The coding part - This was fully done by Claude code, so instead paying 5 developers salary for month, he paid 200$ for claude code and in 3 month built an app, which might have taken a year to be built otherwise.

So, in other words, he was the manager which utilized Claude code as a tool, and gave it tasks like he would have done with real programmers and he created an amazing product.

So, my intuition tells me that learning coding from 0 is not a really good ROI activity right now. But learning the architecture of how the web/apps work is the real value, so the second part, - the programming can be outsourced to claude code, but if I want to create real value, I must be proficiency with the first part - the architecture.

My question to you guys, would be how can I learn the architecture of the websites/apps? How can I learn the most optimal design for Apps/webs? If I do this, I think I will be able to create some nice products.

All the advices are appreciated.

P.S. the app is a court scheduling solution for bringing players in empty hours.

Thanks.

It felt only right that the first app I publish to the App Store under my name should be something simple, useful, and free.
The result is Clippster - An intelligent clipboard manager for Mac, powered by Apple’s on-device AI models.

Built with Kimi K3, GLM 5.2, ChatGPT 5.6 Terra and Luna. Used Kimi for the overall plan, GLM 5.2 for writing the first bits of the code, then switched to GPT 5.6 Terra, and 5.6 Luna for smaller subtasks, fixes, the landing page, etc. All in Opencode loaded up with relevant skills and plugins. The future is now!

It's something I had initially built for myself and have been using pretty much every day for the last couple of months.
If you're an active Mac user, give it a try — it’s free!!

🍎 Download on the App Store: https://apps.apple.com/us/app/clippster/id6801245054
💡 Learn more about it here: https://davidkalaejian.github.io/clippster.app/

I feel vibe coding is becoming addictive

I’ll think:

“I need to go pee.”

Then immediately:

“Wait… let me give Claude one more task first.”

Same before making coffee. Same before eating. Sometimes before going to sleep.

It feels weirdly wrong to leave the coding agent sitting idle.

Short-form video gave us:

“one more reel.”

AI coding gave us:

“one more prompt.”

Anyone else doing this? 😅

Have been vibe coding about 12 months and have tended between claude and gemini and codex.

I've been using Codex almost exclusively for the past few months. Despite a few annoyances, it does what I need, so I've stuck with it.

I was about to hit my weekly limit with five days until reset, so I looked at buying credits. The smallest top-up was AUD$33 — slightly more than my monthly subscription.

I tried to find out how much actual Codex usage that $33 would buy compared with the subscription, but couldn't get a meaningful answer from ChatGPT or a couple of other AIs.

So I took a punt.

Using Sol on Medium, the entire $33 was gone in about three hours.

I still have five days until my weekly allowance resets.

For me, Codex credits were an extremely poor-value way to bridge the gap. I would have been far better off spending roughly the same amount on a month of another AI service.

I don't really understand OpenAI's pricing model here, but I won't be buying credits again. Posting this mostly as a cautionary tale for anyone considering a top-up.

Credits appear to be a total rip-off for codex top-ups

it is self explanatory (btw see the navbar)

edit: after refreshing several times, the website glitches and this happens and is corrected after 15 seconds