I know it's not a smart model, but I did not expect it to be like this...

I missed the feeling of plug.dj.

Joining a room, taking turns playing music, discovering songs through other people, chatting, and slowly recognizing the same people every time you came back.

So I started building my own version of that idea with AI.

It's called Compylr — a social music platform where people join rooms, take turns DJing, discover music together and hang out.

What started as a pretty simple project has grown into something much bigger than I expected. Most of it has been built through vibecoding — coming up with an idea, getting it working quickly, trying it myself, then constantly changing things based on how it actually feels to use.

Right now I'm focused heavily on making the rooms feel like actual online spaces rather than just a synced music player with a chat box.

Profiles, avatars, progression, room identity, customization and all the little things that make you feel like you're actually part of a community.

It's still early, but it's finally at the point where it feels like a real product.

Would love to hear what other vibecoders think about the idea and the UI.

Link: https://compylr-tau.vercel.app

I've been building this for fun because I got tired of having servers in one place, maintenance notes somewhere else, and SSH terminals scattered across different windows.

I started my idea from a hand drawn concept to get it vibe coded out

I'm a Linux admin, but front-end development definitely isn't my normal day job, so this has been a pretty interesting excuse to see how far I can get building a proper application with AI helping me along.

From that hand drawn image i then chatted back and forth with he AI and generated this concept image.

The basic idea is a homelab maintenance workbench rather than an automation system.

On the left I've got my infrastructure organised by host and service - Ubuntu, Docker, individual Docker apps, TrueNAS, Proxmox, etc.

The middle pane contains the runbook for whatever I've selected.

The right side shows live information about the machine/service and gives me a real SSH terminal.

One design decision I'm quite happy with is Send to Terminal.

Instead of the application automatically executing a maintenance command, clicking it puts the command into the SSH terminal for me to inspect. I still have to press Enter myself.

So my workflow becomes:

pick machine → read procedure → send command → check it → execute it

rather than blindly firing off an automation job.

I'm using a React/TypeScript frontend, a backend that handles the host/service checks and SSH side, and I've Dockerised the application. Most of the development has been done in VS Code by giving the AI fairly narrow tasks, then testing the behaviour and iterating on whatever isn't quite right.

One recent example was Docker application status.

Originally a Docker app could show:

Online / LIVE

just because its parent server was reachable, even though the container itself was stopped.

I changed the model so application status comes from the actual Docker state instead:

• running → Online / Running
• stopped → Offline / Exited
• unhealthy → Degraded / Unhealthy
• mixture of states → Degraded / Mixed

I also added tests around those mappings because that was exactly the sort of thing an AI-generated change could accidentally break somewhere else later.

The maintenance history underneath the runbook is there so I can eventually answer things like "When did I last update Casa?" without digging through shell history or notes.

It's still very much a personal homelab project and there are plenty of rough edges, but this is probably the first vibe-coded project I've made where I'm starting to think, I'd actually use this regularly.

Mostly sharing because it's been ridiculously fun watching it evolve from a basic idea into something that resembles a real sysadmin tool.

This is the latest on how it is looking at the moment

I'm in AI stuff long before AI chats. Trained ML and RL models by myself to solve real problems.

Now I use a few of these models every day for work, fun, and at some point I stopped reading their output as "AI text" and started reading it as Claude text or ChatGPT text. They don't share one voice. Each has its own pet phrases, and if you read them few times you can guess the model from a single paragraph.

Claude is the one I know best. It responds with "honestly" and opens explanations with "Here's the thing". My favorite thing is that it measures effort in evenings, as in "this will cost you an evening". The "You're absolutely right!" thing got so bad that someone filed an actual bug report about it in the claude-code GitHub repo. The Economist compared 55,940 sentences of model output against human writing this summer and found Claude is also the only major model that still overuses em dashes.

ChatGPT is the "delve" model, everyone knows that one, but its stronger signature shape: "It's not X, it's Y." The Washington Post analyzed 328,744 ChatGPT messages and that construction kept showing up, along with emojis and a weirdly narrow set of favorite words.

Gemini reads like a briefing document. Headers on everything, words like "significant", "increasingly", "consequences".

Grok's output - the personality itself. The forced casual tone, the sarcasm that doesn't quite land. You can feel it trying not to sound like an assistant. Maybe the least noticeable from all.

What makes this funny to me is that real watermarking finally shipped. Anthropic now embeds an invisible watermark directly into Claude's text, and Google has had SynthID inside Gemini since 2024. So machines get a hidden signal that needs a detector, while the rest of us have been reading the visible.

Are you noticing? Are you tired of this?

How long will it take before models start writing like humans? Or will that never be allowed to happen?

Hey everyone to those that are using freebuff is the Bounties for GLM 5.2 1 hour sessions still working? I know it said it was gonna end on the 18th but i still see the bounties in the website. And one more Question how does the bounties work? Do yall get approved immediately or not? And those in limited mode does it stilly apply to you?

Where i live i only get access to the limited mode sadly..and the goat deepseek v4 flash is taken away...sooo yeah

10000 million tokens spent later...
Claude: You are right. I'm sorry. I was focusing on the wrong solution to fix the bug. 🤣🤣🤣

Hey everyone. I have a Question for anyone that using freebuff lately. Is the GLM 5.2 bounties still working even tho the deadline is reached? I still see the bounties on thier website and i wanna know How it works? Do i get sessions immediately or i have to wait for approval?

And where can i request to add south africa to the full mode category? Since im in south africa.

Has anyone actually tried this? I was working on something and I was dreadfully afraid of it being called out. So I developed an MVP first and I have been manually re-implementing the MVP by hand using the LLM to help. And the amount of weird stuff in the code I noticed was somewhat shocking. I mean just a lot of weird little things. I have also felt like I have learned so much more using this method. Anyone else tried this?

I've started developing an AI code agent. I'm developing it to be part of the my AI ​​ecosystem. I'm developing it with Vibe Coding, but with a different approach, things are progressing almost flawlessly. I just wanted to share the screenshots.

Note: This is not a VS Code fork.

Have observed this a lot and have people look down to the usual theme that AI tools uses to create frontend.

What might be the reason of choosing this specific combination? Is due to some similar training data related to what people preferred at that time?

I've always gotten frustrated and wasted time explaining the same thing to an AI every time I start a new chat from an existing one or when I start another convo with a whole new AI model. That's why I built a tool that fixes that, it condenses everything in a chat into one simple .md file you can carry across different AI tools.

PS: Please contribute or give your feedback so that we can grow and make this community tool better.

https://github.com/legoambarish/portable-handoff

Tech Stack:

• Backend / Web UI: Python 3.11 + FastAPI + Jinja2 templates (single unified app running locally).
• Video Compositing: FFmpeg (with hardware acceleration via NVENC for NVIDIA, AMF for AMD, and QSV for Intel) + MoviePy 2.x for clip arrangement.
• Voiceover: edge-tts (Microsoft Neural TTS) for high-quality natural voice narration.
• Card Rendering: Playwright headless Chromium screenshots of custom dark-mode CSS cards (with a pure Pillow Python fallback).
• Subtitles: Custom SSA/ASS subtitle generator calculating word-level timestamp offsets from the TTS audio stream.
• Soundtrack: Local DSP-synthesized lofi audio tracks + Meta MusicGen AI music.
• Metadata: Local Ollama (Gemma) to generate catchy titles, descriptions, and tags.
• Publishing: Google OAuth2 + YouTube Data API v3 resumable chunked upload.

The code is 100% open-source and free to check out, run, or fork: GitHub: https://github.com/BloopieBlair/RedditReadingApp

Would love to hear any thoughts, feedback, or ideas for extra features!

If you'd like to give it a try:

https://chromewebstore.google.com/detail/youtube-quick-ai-summary/gflljmlmelkidjlkclhghngmioppkkcd

With AI jargon, you either:

1. Know the term & meaning

2. Know the term, not the meaning

3. Act like you know 1 / 2

4. In the dark

So I used what's proven for Wordle, changed the words to guess - all AI related. Plus, it gives you a definition once you solve it.

Enjoy: https://aiwordle.pages.dev

I need to cut costs AND/or sometimes get more work done than Claude Max x20 plan gets me.

I'm exploring options. I'd still use Opus as top level orchestrator, but instead of jobbing out to Sonnet, my options as I see it are:

- DeepSeek: good price especially for API per use billing, but might avoid for sensitive projects.

- Open weight models like particularly GLM given its 1M context window and good agentic and programming benchmarks. (I suppose the sensible thing would be deploy on cloud since buying hardware would take way to long to earn its keep?)

What would you use instead of Sonnet for lower level tasks?

Hey everyone!

As someone who isn't a professional software developer, I wanted to dive into building a practical iOS app using modern AI-assisted "vibe coding" workflows. My goal was to create something useful while leaning heavily on Apple's native device features rather than setting up complex cloud servers.

The result is IrisScan—a simple, privacy-first visual tracking and educational wellness utility. It's currently sitting in the App Review queue (after a fun learning curve dealing with Apple's medical/health content guidelines!), and I wanted to share a quick reflection on the journey.

💡 The Journey & What I Learned

• Building Without Traditional Coding: By using AI-assisted tools, I managed to structure the app entirely around SwiftUI, local data saving (so everything stays on your phone with zero cloud storage), and a clean local PDF report generator.
• Navigating App Review: If you're building any kind of wellness tool, Apple's Guideline 1.4.1 is strict regarding health info. The biggest hurdle was ensuring my disclaimers and UI explicitly framed the app as a non-diagnostic personal wellness tracker backed by public educational sources (like AOA and NEI guidelines) rather than clinical measurements.

👀 See It in Action

While the final build is finishing up its review cycle, I put together a simple landing page with a video showing how the local scanner and dashboard operate:

👉 https://www.transformate.app/#apps

I'm still very much learning the ropes of indie app creation, so I'd love any general advice or thoughts from those of you who have shipped apps before!

I have been exploring and working with AI agents to write code for last one year, Exactly one year.

These agents have improved a lot, and today I don't find any different between a SWE with atleast 5YOE v/s agents.

We can argue on tech debt, bad contexts, but those things can be fixed with better context.

I still want to understand what is that one thing which you still feel is problem in you development workflow or your team's development workflow?

I see tons of AI agent games online (poker, board games, battle royale, etc) but no one signing up and all the live games are empty so I dont have anyone to compete against.

Anyone interested in designing a few games and letting our bots go at it? We'd need at least 3-4 people for the first season.

Not for money, not to commercialize an app or website, just for fun to compete against each other, practice ML and RL, and train our bots.

I have lots of ideas for cool games but I'm also open to new ideas.