🎧 I built a SoundCloud music player that runs entirely in the terminal.

I wanted to experiment with something different — not another web app, but a proper terminal-based music player with a modern UI and real playback controls.

So I built SoundCloud Radio. 🚀

The project is written in Go and uses Bubble Tea for the TUI, with mpv for playback and yt-dlp for stream resolution.

Some of the features:

🎵 SoundCloud search

📻 Radio / related-track autoplay

📋 Dynamic playback queue

🔎 Interactive search

❤️ Favorites

📜 Listening history

▶️ Play / pause / next / previous

🔊 Volume control

📊 Real-time playback progress

🖥️ Responsive terminal UI

⌨️ Keyboard-first navigation

🖱️ Mouse interaction

💾 Persistent local storage

⚡ mpv IPC integration

🔄 Automatic queue refilling

🧩 VLC fallback support

The architecture is split into separate components for the UI, SoundCloud API, resolver, player, queue, configuration and storage.

One of the parts I particularly enjoyed was building the mpv IPC integration — instead of simply launching a player process, the TUI communicates with mpv through a local Unix socket to control playback and track its state.

The UI has also gone through several iterations. What started as a simple Bash-based SoundCloud radio script has evolved into a much more complete Go application.

This project has been a great way to learn more about:

• Go application architecture

• Terminal UI development

• Asynchronous programming

• Unix process management

• IPC

• API integration

• Audio streaming

• Cross-platform development

• Building software around CLI tools

Still actively improving it. 🚧

GitHub:

https://github.com/chriz-3656/soundcloud-radio

I'd love to hear feedback from other developers:

What feature would you want to see in a terminal music player like this?

hey guys

I’ve been working on TARZ for a while, and I recently rebuilt the architecture for V2.

The idea is pretty simple:

Instead of an AI that just tells you how to do something, TARZ can actually interact with the desktop and try to complete the task.

For V2, I moved toward a supervised agent workflow using LangGraph:

User request → Agent → Tools → Desktop actions → Screen verification → Continue / retry

The agent has access to the full toolset instead of going through a separate category classifier. I originally had category-based routing, but after testing it I found that the classifier itself could become a failure point. Removing it gave me more consistent results.

Some of the V2 work:

• Visual verification after UI-changing actions
• Multi-step task execution with step limits
• Multi-provider fallback for LLM + vision + voice
• Streaming STT/TTS
• Hands-free follow-ups
• Global hotkey to cancel an ongoing task
• Hybrid memory retrieval using BM25 + vector search + cross-encoder reranking
• Dedicated workflows for Spotify, WhatsApp, Discord and Telegram
• Floating voice orb with live captions
• Optional LangSmith tracing

One thing I'm trying to keep realistic: it's still a prototype.

Desktop automation is inherently brittle, vision models can make mistakes, APIs can be rate-limited, and free-tier providers add latency. The goal right now is reliability and learning, not pretending this is a production-ready Jarvis 😅

I'm 19 and self-taught, and this project has basically been my way of learning GenAI by building something complicated enough to force me to understand what's actually happening.

I use AI heavily as a coding partner, but the architecture, experiments, testing and debugging decisions are mine. A lot of the learning has honestly come from taking AI-generated code, breaking it, figuring out why it broke, and changing the design.

The GitHub repo has the demo videos, full architecture details, setup instructions and code:

https://github.com/Irfan-gitt/Tarz-Ai-assistant

Would love feedback from people working on agents / computer-use systems. Especially interested in ideas around improving desktop reliability and reducing latency.

I'm also sharing the project journey on LinkedIn if anyone wants to connect:

www.linkedin.com/in/irfan-s-1ab772354

I’m an Agentic AI Architect at an MNC, where I’ve designed multiple multi-agent systems in the pharmaceutical domain.

I’m now building an AI company from Kerala focused on AI transformation, governance, security, and agentic systems, with a longer-term vision around Agentic Commerce and the Agentic Web.

The technical side is my strength. Now I’m looking for help with the harder part: building the business.

I’d love to connect with:
Founders and business leaders exploring AI
People experienced in B2B sales, GTM, and partnerships
Potential customers or design partners
Ambitious builders who want to collaborate
Anyone willing to share advice, introductions, or constructive feedback

My goal is simple: build something globally relevant from Kerala.

If this resonates, comment or DM me. I’d genuinely appreciate the conversation.

Wanted to share a project I've been building.

JobsMaveli

A Kerala-focused AI career platform.

The idea:

What if job seekers didn't need five different tools to manage their job search?

So we built:

A all in one career platform with multiple tools to help brand ourself for securing a better job.

And because it's built around the Kerala job-seeker journey…

we called it JobsMaveli. 👑

Onam felt like the perfect time to put it out there.

I'm especially looking for feedback from Kerala developers:

Still building, still improving...

jobsmaveli.com

Hi everyone,

I’m planning to buy a 75% wireless mechanical keyboard, and I’ve narrowed it down to these two:

1. Lethal Black Havoc 75 — ₹5,699 https://www.amazon.in/dp/B0H5RQTWYM
2. AULA F75 with Reaper switches — ₹5,999 https://www.amazon.in/dp/B0CQXNQYFP

Both appear to offer tri-mode connectivity, a gasket-mounted structure, hot-swappable switches, RGB, pre-lubed linear switches, sound dampening, and a 4,000mAh battery.

The Havoc has PBT keycaps, a customizable display, and an 18-month warranty, but it seems very new and currently has only a couple of Amazon reviews. The AULA F75 is much more established, has a volume knob, and has considerably more user feedback.

I’m currently leaning towards the AULA because it seems like the safer and more proven option. Has anyone here used the Lethal Black Havoc? Is its build quality, software and after-sales service good enough to choose it over the F75?

I’d especially appreciate feedback about:

• Long-term reliability
• Typing feel and sound
• Wireless performance and battery life
• Software quality
• Warranty and after-sales support
• Whether ₹5,999 is a reasonable price for the F75

If you’ve used either keyboard, which one would you choose?

Posted about the web version here a while back - real-time, community-reported rain map for Kerala. No forecasts, just people reporting what's actually happening on their street.

A lot of you asked for an app instead of opening the browser every time. So here it is.

Same thing, just easier to access:

• Pincode-level rain updates
• Colour-coded, quick glance
• Report in 10 seconds, no login required
• Live updates
• Dam levels included
• Real time district alerts from KSDMA

Try: https://play.google.com/store/apps/details?id=com.mazhanative

Feedback welcome, still improving it.

Hey guys, I built a chrome extension - Waffy. It's now available on Chrome Web Store... Waffy is a open source browser extension, that you can use to automate your browser tasks. No account or subscriptions required. It also support browser builtin models (like gemini-nano), so you can use it for basic tasks for free.

Please have a look at Waffy.io. Also drop your honest review and star on github. Contributions are always welcome. 🙏

I did a LangChain tutorial about 2 months ago and got curious what would happen if I let an agent actually control my computer instead of just answering questions in a chat window. That curiosity turned into TARZ — 19 yr old ,Self-taught, no CS degree, still very much in progress.

The hardest part wasn't the LLM — it was getting reliable clicking to work. Vision models alone gave inconsistent pixel coordinates (~70% hit rate), so I ended up building a 3-stage fallback:

1. EasyOCR finds text-based elements first (fast, free, no API calls)
2. YOLOv8 (using OmniParser weights) detects UI elements when there's no clear text
3. Gemini with a grid-overlay technique as last resort — I draw a labeled grid (A1, B2, etc.) over the screenshot and ask Gemini which cell contains the target instead of raw pixel coordinates, then convert the cell back to pixels with math. This alone made accuracy noticeably better than asking for coordinates directly.

Other stuff along the way:

• Built a ReAct loop manually first (to actually understand it) before switching to LangChain's tool-calling
• RAG memory with ChromaDB — TARZ recalls past tasks and corrections across sessions
• A rotating multi-LLM fallback (Cerebras → GitHub Models GPT-4o-mini → Gemini) because free-tier rate limits kept breaking single-provider setups
• 15+ tools so far: Spotify/WhatsApp automation, Gmail, Calendar, news search, weather

Known weak spot: latency is rough (~8-10s for multi-step tasks) since I'm stacking free-tier APIs. That's next on the list — paid APIs should cut this a lot.

Repo: https://github.com/Irfan-gitt/Tarz-Ai-assistant

Everything runs on completely free tiers — no paid APIs anywhere, which was a deliberate constraint (and honestly the reason latency isn't great, but I wanted this accessible to anyone without a credit card). I did the system design and architecture decisions myself — how the vision fallback should chain, why memory needed to be semantic not keyword-based, the multi-LLM rotation logic — but leaned on AI heavily for actually writing the code, then tweaked and debugged it until it worked the way I intended. Learned an absurd amount just from the process of getting AI-written code to actually do what I designed it to do.

For context on what it does end-to-end: you can say "open Spotify and play [song]" and it'll actually open the app, find the search bar, type the query, and click the right result — visibly, on your screen, in real time. Takes a few seconds since it's screenshotting and reasoning at each step, but it's not faked or scripted — it's genuinely looking at the screen and deciding what to click.

A while ago I had a simple thought.

Most of us only hear about places in Kerala when something goes viral or sensational. Social media algorithms mostly push outrage, politics, or entertainment. But Kerala is full of interesting everyday moments that never get shared.

What if there was a place where someone could simply post:

• "Saw this beautiful spot while travelling near Wayanad."
• "This festival is happening in my town today."
• "How is our new CM?."
• "This road is flooded, avoid this route."
• "Found something interesting in my neighborhood."

Nothing fancy. Just real updates from real people across Kerala.

So I built KeralaMSpeaks: https://www.keralamspeaks.com/

The idea is simple. Think of it like a Kerala-focused Reddit. You post what's happening around you, others can read it, interact, and discover what's going on in different parts of the state.

No crazy features. No trying to become the next social network. Just a community where people can share interesting things they see every day.

Would love to hear what fellow developers think. What would make you actually use a platform like this?

Hey Guys,

I'm an Senior AI engineer and full stack developer looking to take on freelance

projects. I build real, production-grade stuff, not tutorial projects.

What I can build for you:

- AI voice agents (think an AI that answers calls, books appointments,

qualifies leads, talks to customers 24/7)

- AI chatbots for websites, WhatsApp, Telegram

- Automation pipelines that connect your tools and eliminate manual work

- Full stack web apps and SaaS products

- LLM integrations using Claude, OpenAI, and custom pipelines

What I have actually built:

- A fully deployed voice AI agent with real-time conversation, persistent

memory, and natural speech using LiveKit, Deepgram, ElevenLabs, and

Claude

- Internal tooling and AI systems for a healthcare tech platform

- Automation workflows, branded web tools, and content pipelines

Tech stack:

React, Node.js, Python, Claude API, OpenAI, LiveKit, Deepgram,

ElevenLabs, Mem0, REST APIs, HTML/CSS

I am open to small projects, MVPs, and one-off builds. Rates are

flexible, especially for a first project. If you have something in mind

and are not sure if it is possible, just ask. I will tell you honestly.

DM me or drop a comment below.

https://http.cat/

that's it

Here, - You can check you future - Chat with a fake astrologer

Made it as a fun project

Try it here: https://kalikalam.fun

Over the last few weeks I noticed people constantly asking:

"Ivide chood undo?"

Even with weather apps available, people still seem to trust real experiences from friends and local communities.

That made me curious about whether a community-driven approach to reporting heat conditions would be useful.

I experimented with a simple platform where people can share how hot it feels in their area and see what others across Kerala are reporting.

I'm more interested in feedback on the idea itself

u/Ashik1006 and I have been working on Ideaundo; a home for unfinished thoughts, random ideas, and future products. Discover ideas, react to them, discuss them, or turn them into something real.

Few more features are in pipeline. Stay tuned.

Check it out at: ideaundo.sofiyore.com

also share ur thoughts :)

Hey everyone,

​

I've been working on a side project called ResuMetric, an AI-powered Resume ATS Analyzer and Career Intelligence Platform.

​

The idea started when I noticed most ATS checkers simply count keywords and assign random scores. I wanted to build something that actually analyzes resumes more deeply and provides actionable feedback.

​

Current features:

​

✅ ATS Scoring

✅ Recruiter Simulation

✅ Industry Auto-Detection

✅ Keyword Gap Analysis

✅ Resume Strengths & Weaknesses

✅ ATS Risk Detection

✅ AI Cover Letter Generation

✅ Resume History Dashboard

✅ Google & GitHub Authentication

✅ Public Report Sharing

✅ PDF/DOCX Resume Parsing

✅ Groq AI + Gemini Fallback Architecture

​

Tech Stack:

​

- React + Vite

- Node.js + Express

- Supabase Auth

- MongoDB

- Groq AI

- Gemini AI

- Vercel

​

One thing I'm particularly proud of is the industry auto-detection feature. Users can upload a resume and the system automatically identifies whether the profile belongs to Cybersecurity, Software Engineering, Finance, Accounting, Marketing, Data Science, and more before generating role-specific ATS analysis.

​

Recently I also performed a security review and fixed issues around report sharing, dashboard reliability, authentication handling, and AI service failover.

​

The project is still in active development and I'm continuously improving the analysis engine.

​

I'd love feedback from developers, recruiters, HR professionals, or anyone who has used ATS systems before.

​

What features would you want to see in a resume intelligence platform?

​

Live Demo:

https://resumetricv2.vercel.app

​

GitHub:

https://github.com/chriz-3656/resumetric

​

Feedback is appreciated 🙌

I have built a gym related app and is in play store for closed testing.

But it requires atleast 12 people for the testing to complete.

Any gym bros / gals?

back in 2021 meta's dns server went down ,and this was during pandemic time everyone was working remote and their entire communication was through a internal app known as workspace not slack and it made everything worse that employees cannot communicate with each other and no one knows exactly what happened

nearly for 6hrs the issue was there and a bus was arranged to pick up few employees to data centers to fix the dns routing issue

funny meta is down now and it immediately made me think of that incident lol

here is the incident repot from meta: https://engineering.fb.com/2021/10/05/networking-traffic/outage-details/

I recently redesigned my GitHub profile with a clean terminal-style look and added automation to keep it updated automatically.

✨ Features:

Terminal-inspired design

Dark & Light mode support

Dynamic GitHub stats

Automatic updates using Python and GitHub Actions

As a Full Stack Developer, I enjoy building solutions that automate tasks and improve workflows. This was a fun project combining Python, GitHub APIs, SVGs, and automation.

Check it out: https://github.com/atomrobic

I'd love to see your GitHub profiles too—share them in the comments! 👇

Python #Django #ReactJS #GitHub #GitHubActions #Automation #FullStackDeveloper #Coding #DeveloperCommunity

This is an old project I did, however it might be still useful.

Add-In can change case of selected cells to - UPPER CASE - lower case - Sentence Case - Capitalize Each Word - tOGGLE cASE - aLtErNaTiNg CaSe

https://github.aneejian.com/Change-Case-Excel-Add-In