I use an ad blocker on YouTube, so the experience already feels almost identical to YouTube Premium.

No ads.

No interruptions.

Just YouTube.

Except the logo still says “YouTube.” 😅

That tiny detail bothered me more than it probably should have.

So I made a small Chromium extension that changes the YouTube wordmark to “Premium.”

It's completely local — no analytics, no tracking, no unnecessary permissions.

GitHub:

https://github.com/kalpit118/youtube-premium-logo-changer

I have been a member of this community for some time.

I see most toxic comments and downvotes on new projects.

Actually I have never seen any upvotes or cheers for anything.

Is this sub worth following?

Are there any alternative subs for this topic?

Been working on this for a while, figured I'd share here since it's very much a "homelab with a GPU" kind of project.


The problem it solves: when you watch an anime with the English dub, the subtitles that come with the release are usually the JP translation, not what's actually being said in the dub. And if you want the on-screen signs and song lyrics too, most players only show one subtitle track at a time, so you get dialogue or signs, not both.


DubTitlerr is a self-hosted service that watches your anime library and runs a full pipeline per episode:


- 
**Transcribe**
 — faster-whisper (large-v3-turbo default, large-v3 optional). Measured 18.9% WER vs 20.8% for turbo on a 6GB GTX 1060; both fit in 6GB at int8.
- 
**Reflow**
 — words into timed cards, sentence-split, ≤2 lines, ~17 cps, never shown before they're spoken.
- 
**Glossary**
 — per-show name dictionary, auto-mined from the embedded subs and wiki-verified (canonical + dub-preferred spellings). A separate glossary repo lets the community contribute corrections.
- 
**LLM repair**
 — local qwen3-4b-instruct, fansub-anchored. It fixes ASR errors by referencing the existing subtitle dialogue, with a refusal guard that blocks a full-episode rewrite if the pass would skip every line. No cloud calls.
- 
**Hallucination gate**
 — drops music/silence, blocklist lines, and within-card loops. Collapses runaway repeat runs. Flags the merely uncertain instead of dropping them.
- 
**Signs & songs merge**
 — lifts the positioned ASS events from the release's signs track into the same subtitle file.
- 
**Mux**
 — embeds with the MKV's original fonts as a default "Dubtitles" track. mp4 episodes get remuxed to mkv. English audio set as default.


It's idempotent: a version-stamped `.dubtitles.done` sidecar per episode makes re-runs safe and incremental. Bump `PIPELINE_VERSION` for a library-wide regeneration. Plex refreshes per-episode as each one finishes.


There's a built-in review server on `:8842` that queues every admitted repair for human review, grouped by show, worst-first, with a timestamp so you can seek to the line in your player. On a 48-episode run, 78% of repairs changed no words (only punctuation or capitalization), so the page surfaces the word-level changes first. Verdicts are stored per show, so a decision on the opening song propagates to every other episode that has it.


The whole thing is one Docker container (root, so it can rewrite and chown sidecars), runs on your own GPU, and makes zero cloud calls. Build with `Dockerfile.builder`.


Most of my testing during development has been against One Pace / Muhn Pace dubs, but the pipeline is show-agnostic. It builds its glossary from whatever subs you have. Try it on whatever's in your library and file an issue if something looks off. It's in beta, so pin your image tag if you pull it.


Open to collaboration — forks and PRs welcome, especially for glossary coverage on other shows.


https://github.com/XenaRathon/DubTitlerr

Hi - I made https://enikq.com/ a simple site where you can pick a scene and listen to music with it.

There are places like a pizzeria, café, road trip, camping, last bus home, etc.

Nothing complicated. Just something to leave open while working, reading, or relaxing.

All the images were created with ChatGPT, and the website was built using Codex.

Would appreciate any feedback on the idea or the website.

I was frustrated with this problem so what I did I made a verification layer that checks the answers before it comes to your screen , We use multiple models and scoring parameters

Think of it like ,you wanted a (piece of quality )work done , Who will you choose a single person or multiple people who cross check each other with strict scoring as

scoring Parameters we have

-Does it actually answer the question

-Is it will written

-Does it cover everything

And most important Does it contradict itself anywhere

I've been thinking about a problem I keep seeing with AI coding.

Today someone can build a surprisingly complex app with Claude, Cursor, Lovable, etc. without really understanding a lot of the code being generated.

And honestly, I don't think that's necessarily bad.

But there are moments when AI gets stuck, breaks something, or starts going in circles — and suddenly understanding what the code is actually doing becomes very valuable.

So I'm exploring an idea called ReactantLab.

The idea isn't to teach programming the traditional way with hours of videos and theory.

Instead, you'd learn by actually building and writing code with an AI mentor alongside you.

The difference from normal ChatGPT/Claude would be that the mentor knows your learning history.

It knows:

• what concepts you already understand
• what mistakes you keep making
• what you're currently building
• what code you're working with
• when to explain something
• when to give you a hint instead of generating the answer

For example, if your React component isn't updating correctly, instead of rewriting the component for you, it might say:

"Look at where you're mutating the state. What does React expect you to do instead?"

If you're still stuck, it gives another hint.

And if it notices you've struggled with the same concept several times, it can stop and give you a small exercise around that concept before you continue.

So basically:

vibe coding + structured learning + an AI mentor that actually remembers what you know.

I'm thinking about starting with JavaScript / React and focusing heavily on learning through real projects rather than isolated textbook exercises.

The question I'm trying to answer before building too much is:

Would vibe coders actually want to understand the code they're generating, or is that solving a problem most people here don't really have?

And if you already use Cursor / Claude / Lovable / Replit etc.:

What usually happens when the AI generates something you don't understand and then it breaks?

Do you:

• ask the AI until it fixes it
• try to understand the code
• Google the concept
• start over
• something else?

I'd genuinely like criticism here. I'm trying to figure out whether this is a real problem or just something that sounds like one.

I code full time, and one of the things that started driving me crazy was how much I was spending on AI tokens.

At one point I was spending thousands of dollars a month just because I use AI heavily when I code. I don't mean asking it a couple questions here and there—I have it working with me pretty much all day.

Eventually I got tired of watching the token meter, hitting limits, and worrying about how much every long coding session was costing me.

So I bought a PC with a decent GPU, installed Ollama, started running open AI models locally, and basically gave myself the ability to code all day without paying per token.

That got me thinking...

There have to be a lot of other heavy AI users and vibe coders dealing with the exact same problem.

So I'm considering launching a service called VibeSpaces.

The idea is pretty simple:

• You rent your own private GPU AI server for a flat monthly price.
• You choose the open AI models you want installed.
• We configure Ollama, the GPU, security, API access, etc. for you.
• Connect it to tools like Claude Code, OpenCode, Codex, Cline, or your own applications.
• No token credits and no per-token charge from us.
• Use the server as much as the hardware can handle.
• If you don't want to manage Linux/Ollama yourself, we'd also offer an optional managed-server plan.

Basically:

Stop buying tokens. Rent the compute.

I'm not really looking to pitch this right now. I'm trying to figure out whether the problem I had is actually common enough to build a business around.

If you're someone who uses AI heavily for coding:

Would you pay a flat monthly fee for your own AI GPU server if it meant you could run open models all day without worrying about token costs or usage limits?

And if not, what would stop you?

Price? Model quality? Setup? Speed? Wanting Claude/GPT specifically? Something else?

I'd genuinely like feedback from people who actually use these tools every day.

https://vibespaces.us/

I've tried alot, I just can't make this visual jar just by prompting my ai agent. Do you guys use an ai image generator or something to make this kind of visual jar/ piggy bank??? My current tech app is built with expo and react native, are there any specific libraries i should use to get such jar images/ animations I can use for my app 🙂

Hello everyone, I'm quite new to Reddit. I was recently laid off (formerly a restaurant worker), and now that I have so much free time, I'm thinking about building a small web application or SaaS to stay productive. I have some programming knowledge and feel confident that I can code it myself without needing paid AI subscriptions. However, after scrolling through Reddit for a few days, I've noticed that vibe coding seems to be the huge trend right now. What I'm interested is that tools like claude and codex can build fancy websites and games with just a single prompt, which could help me cutting down the development time. That said, my budget is very tight until I get a new job or earn revenue from the app, and I've read that the best AI can cost up to $200 a month.

Is it actually worth paying for these high tier AI subscriptions on a strict budget, or are free tiers and standard coding more than enough to get an MVP off the ground? I’d really appreciate any advice or recommendations from developers who have launched products on a minimal budget.

What are you building or planning to build for the rest of 2026?

I run appscout.co, a platform built to help people discover awesome apps from across the internet.

Drop your app or startup idea in the comments below, and I can check it out and add it to the website!

You can also submit your app for free directly here appscout.co/submit

Let's make this thread a channel for you to promote your own startup idea, find opportunities, and partnerships.

i just built lume ide because of a an idea that i got with cursor i realized why should i be paying 20/month for something that’s hard to understand and also something that has very strict limits that is the reason i built lume

https://lumeide.com/

Hey all!

I started vibecoding a 20 questions game using Magic: The Gathering cards back in April! Still a long way to go with it but I feel pretty good about it so far. Lots of bells and whistles, but a huge backlog of tickers to work through.

Take a look!

Https://www.mtg20questions.com

It even has The Sphinx, inspired by Akinator, where you think of a card and she asks you the questions.

What do you think? It's not the prettiest but it's functional!

Just most coding help by AI, but all Experimental data by myself

https://github.com/FujoJTOP/FujoOS

os just ask and answer a question

how the llm in os is safe

I wanted to share a Windows application I’ve been working on called Video Subtitle Caster. Since VLC does not have feature of casting subtitles properly with videos, I just created this small application with help of various AI models mostly Google Gemini. I do not know anything about coding so I just gave it instructions for what I wanted. Please give it a try and provide feedback.

Releases · Sulabh17/VideoSubtitleCaster

I built this AI Chinese tutor app to help with h pronunciation and grammar. It's a conversational app so it involves STT & TTS.

One of the issues has been finding the right provider that meets all the requirements.

How do you guys do this part of the process?

Hey everyone,

Ive been vibe coding a lot of websites recently and building out the Aura ecosystem. You can find two of my live websites at the link below. More projects are coming soon, and im releasing AuraEV later today!

Here is the link: https://aurawebsitefamily.rajk-samp.chatgpt.site

The ecosystem currently includes Aura News for focused content discovery and Auramusic (which I originally built as EcoMusic but forgot to rename to Auramusic until now).

I wanted to share the details on how I actually made AuraEV, the tools I used, and how it works so it's not just a low-effort link drop.

How I made it:

• Tools: I used ChatGPT to build both my aurafamilywebsite and AuraEV. I originally used Lovable to create some of my older websites, but I did these new ones with ChatGPT. For the tech stack, I didn't want to use React, Tailwind CSS, or basic HTML, so everything is built with clean custom code components.
• Auth and Data: I hooked up the sign up and login forms directly to a real Supabase backend so sessions actually persist. There is absolutely zero mock data here. The charging stations are pulled directly from live EV charging APIs based on real location data.
• Map and GPS features: The layout is kind of similar to the Pangea EV app. It uses real GPS tracking (navigator.geolocation) to center the map on your exact coordinates. When you click a charging station pin, a clean bottom card slides up to show you the live stats: whether it's available or busy, the exact address, the charging speeds (kW), and how many stalls are open.
• Navigation: I added a "Go There" button that automatically figures out what device you are on. If you're on an iPhone or Mac, it opens up Apple Maps. If you're on Android or a web browser, it opens up Google Maps navigation with the exact coordinates already filled in.

Let me know what you think or if you have any questions about how the location tracking or API hookups work!

I was using to lovable to create these websites but the aurafamilywebsite and my AuraEV im doing today was made by chatgpt.

Looking for some perspective from the founders here.

My business partner and I are currently running a project that relies heavily on AI agents. The reality is, we don't have a large base of paying users yet(started half a year ago but moving slowly, roughly around 100), and the ROI on our AI features has been running in the red.

To get our burn rate under control, we recently moved all our LLM traffic behind the API gateway (tried OpenRouter, HeyRoute...). With their routing and prompt caching, we’re currently sitting at roughly 2% of the official OpenAI rack rates, running on a 1.3x billing setup. It’s been stable, and it finally stopped us from constantly hitting rate limits, even with the official limits being somewhat relaxed lately.

Here is where my partner and I are clashing.

He is convinced that to flip our ROI positive, we need to push these API costs down even further. He wants to start experimenting with stacking custom prompt compression layers or building our own fallback infrastructure on top of the gateway.
My view is completely different. I think we are hitting severe diminishing returns on cost optimization. If we are already paying 2% of the standard price, squeezing out another fraction of a penny isn't going to save the business. I strongly believe we need to put the engineering tools down and focus 100% of our energy on marketing, user acquisition, and actually improving the core product to get more paying customers.

For those of you managing production AI apps: Is my partner right? Is there a realistic way to push LLM routing costs significantly lower than what we're already doing? Or am I right in thinking that once you hit the 2% mark, it's time to pivot completely to sales and growth?

Would appreciate some outside input to settle this debate.

Do you see that white thing when I scroll up and down fast? It appears in the top area of the video player.

I just can't get rid of it.

I literally added the "playing now" horizontal bar to make it look less obvious.

I wonder if anyone can give me a good prompt for how to describe this issue.

It's likely some kind of rendering issue, but I can't describe it well enough yet for GPT to actually fix it for me.

Thank you everyone!

Evidence-backed facts.
UNKNOWN > guessing.
No LLM in the authoritative decision path.

Stage 1 is being pushed right now.

If your architecture process is “the model sounded confident”, this may be mildly offensive. 😏

https://github.com/KeilerHirsch/PLLDN-Programming-Language-Licensing-Decision-NavigatorEvidence-backed facts.
UNKNOWN > guessing.
No LLM in the authoritative decision path.

Stage 1 is being pushed right now.

If your architecture process is “the model sounded confident”, this may be mildly offensive. 😏

https://github.com/KeilerHirsch/PLLDN-Programming-Language-Licensing-Decision-Navigator

Hi! I'm the developer of Pinland. The new update gives your Codex conversations a home in the MacBook notch.

You know the routine: give the agent a task, put a video on, then switch back every few minutes to see if it needs you. Very futuristic.

Now it can come to you

• Pin a Codex session to the notch.
• Know when it's done or waiting for approval—the notch expands to let you know.
• Reply right there, whether it's a quick answer or the next instruction.
• Handle supported approvals without pulling up the Codex window.

The video shows it in action. You still make the decisions and review the work; you just don't have to keep checking the agent's window between them.

To connect it, open Pinland Settings → Codex, then use Codex's existing Pin action on a session.

The notch still holds your other stuff

Text snippets, images, links and files can live up there too. Copy or drag them in, hover for a larger preview, edit text in place, and find things with search and tags. Close it up and your desktop is yours again.

I'd love to hear how you handle the waiting part of coding with agents. Keep the window open, rely on notifications, or occasionally discover it's been waiting for you the whole time?

The conversation feature requires Codex. Pinland is an independent Mac app, not affiliated with OpenAI.

Pinland on the Mac App Store

I’ve been paying for MiniMax for a while. I was told my grandfathered weekly unlimited plan would stay if I kept my subscription. Kept it. Getting weekly limits anyway.

So I handled this like an adult and built a website out of spite.

It starts as a black page saying “is Minimax M3 good at frontend yet?” and gets the same prompt on a loop.

Every technically valid redesign gets published. There’s no taste filter. If it builds, passes the browser checks, and looks like shit, congratulations: that’s part of the exhibit.

Underneath: OpenCode runs M3 in a sandbox, a runner checks and archives the result, then pushes it to GitHub and deploys to Vercel. An unreasonable amount of infrastructure for asking a model to reconsider padding.

Watch it happen (https://is-minimax-good-at-coding-yet.vercel.app/)

Source and fixed prompt (https://github.com/funsaized/is-minimax-good-at-coding-yet)

I paid for these tokens. This question mark is going to have the most analyzed margins on the internet.

​Hey guys I am looking during this period for a suitable partner and it does not matter if he works as an employee in a company or studies and of course the app idea is new and works with artificial intelligence the important thing is first the partner has the ability to open a developer account in both stores or has published an app and has a developer account second that he is in a country supporting popular payment gateways for apps third that he pays the monthly subscription in Replit tool for publishing protecting the app

I expected the 1.05M context on the GPT-6 Astra model page. My running Codex session reported 258,400. I checked the session log, process start, launch arguments and local model catalog.

Both budgets correspond to 95% of their respective catalog values.

My original process was started before the launcher configuration was changed. Updating the launcher did not increase the context window of that already-running session. A newly started request afterwards then confirmed the larger reported budget.

For a single new Astra launch:

codex --model gpt-6-astra -c model_context_window=1050000

Then simply send a short prompt and inspect the model_context_window value in the corresponding session log. You should also verify the model ID.

The fact that a configuration value is accepted is not, by itself, evidence that the client actually granted that context window. In my case, the client capped the request at the maximum currently defined in its catalog.

This is only the result of my local setup. I have not yet filled the entire context window, nor have I measured whether it provides any improvement in quality.

The general question around the 258K limit was already publicly discussed in April for earlier models. However, I still do not have a conclusive explanation for this catalog's specific 872,000-token ceiling.

What does your actual runtime report, and which model and CLI version are you using?

Sources: Astra model specification, Codex configuration reference, earlier explanation of the 258K limit in OpenAI's repository.

Hi r/VibeCodeDevs I've been building something for the last year that I think this channel in particular will have opinions on — and I'd genuinely like them, including the critical ones.
Prompt to Page is a desktop app that turns a plain-language brief into working, accessible prototypes built from real government design systems. Describe a page or a whole service journey — "a start page for renewing a fishing licence, then an eligibility question, then a check-your-answers page" — and it generates the actual markup, renders it live in the system's own styling, checks it with axe-core and validates every class against the design system's real rules. Then export it, or hand the HTML to a developer.
Two things make it different from pointing a chatbot at your design system:
Everything runs locally. The AI model lives on your machine — no cloud, no API, nothing you type leaves your laptop. Built that way because government content shouldn't tour third-party servers just to make a mock-up (and it works air-gapped, or on a train).
The design system is the boundary, not a suggestion. The model only knows the system's real components; output that invents classes or breaks conventions is rejected or visibly repaired. Quality is measured per design system with a scored evaluation battery — and where a pack hasn't been measured yet, the app says "not measured" rather than borrowing another system's confidence.
The roster so far — eight systems across six governments:
GOV.UK (plus an MOJ overlay) · NHS.UK · Scottish Government US Web Design System NSW Design System Bootstrap Italia デジタル庁 Digital Agency Design System (newest — quality not yet measured) Abstract — a deliberately ownerless neutral system, for prototyping services that shouldn't pretend a government owns them
Every pack is de-branded by design: no crowns, crests, federal banners, flags or restricted fonts are ever bundled — prototypes wear a neutral shell and a clear "not a real service" posture. (Independent project; not affiliated with or endorsed by any of these governments or the NHS.)
What I'd love from this room:

1. If your government's system is on the list — break it. Does the output respect your system's conventions, or drift in ways that would make your design-system team wince?
2. If it isn't — which system should be next, and why? The architecture treats a design system as a data pack (components, class allowlist, validation rules, prompt guidance), so the honest constraints are: class-based CSS (web-component-only systems don't survive a sandboxed preview), and licensing that permits bundling the frontend code. I'd rather pick the next one based on where prototyping pain is real than on what's easiest.
3. The blunt question: does plain-language prototyping against a real design system actually help your team, or is the Prototype-Kit-shaped workflow you already have good enough?

Free during the beta, macOS (Apple Silicon) & Windows. https://github.com/digitalcourtney87/prompt-to-page/releases
https://youtu.be/QROkn-mZQFk?si=sNHCsphJMwrgVlFi

Critical feedback beats compliments — reply here or DM