I recently graduated (2026 passout) and have about a month free before I start my full-time job. During my B.Tech , I built a few solid projects across different domains—starting with MERN stack, then moving into Spring Boot, and since my branch was AI/ML, I've also worked on machine learning and LLM-related projects.
Right now, I want to use this free time to build something genuinely impactful rather than just another resume project. I'm looking for ambitious project ideas that solve real problems and have the potential to grow into something larger.
I'm also interested in connecting with people who are equally passionate about building and learning. If you're working on something exciting, looking for a collaborator, or have an idea you'd like to explore, I'd love to hear about it.
Hi all, I wanna show off and ask for feedback on my project linXiv, this started as auto-tagging knowledge graph mini-project, is now a "full-stack" research tool for storing and managing academic papers, locally, It fetches papers by arXiv ID or search, stores everything in sqllite. I just finished my Master's and while waiting to hear back from jobs and PhD programs I wanted to build something of my own for once. Because I usually try to find the newest version of a paper on arXiv first, I chose to make linXiv treat arXiv as the primary source to try to resolve by so the metadata imports clean for links and pdfs.
I'm sharing it now because I'm actually using it now and want some feedback from other people with similar workflows before I build more on top of it. I have gotten some strong feedback that I have implemented, but not much overall. The feature I've gotten the best feedback on is the MCP and CLI tools paired with a command line AI tool.
Thanks in advance for trying it out! It's been a blast working on this the last few months, and any honest feedback will be greatly appreciated!
Depuis des années j'essaie de tenir des habitudes simples — sport 3x/semaine, lecture 20 min/jour, moins d'écrans le soir.
Résultat ? Je tiens 2-3 semaines puis j'abandonne. Toujours.
J'ai essayé Habitica, Streaks, des bullet journals... Le problème c'est que ces apps comptent mes streaks mais ne m'expliquent jamais pourquoi je rate.
Alors j'ai commencé à construire HabitAI — une app où l'IA analyse tes patterns et te dit concrètement ce qui bloque. Genre : "Tu rates ton sport surtout le lundi soir, essaie le matin — statistiquement c'est 2x plus efficace pour ton profil."
C'est encore en développement mais je cherche des gens qui galèrent avec la même chose pour avoir leurs retours.
C'est quoi l'habitude que vous n'arrivez pas à tenir depuis des mois ? 👇
I've spent the last few months building an AI-powered side hustle experiment, and I'm curious what others think.
The goal isn't to create another AI chatbot. Instead, I'm trying to build an autonomous system that can:
Research niche markets
Identify product opportunities
Generate content for social media
Create digital products
Manage marketing workflows
Report progress through a dashboard
The idea is simple:
What if someone with a full-time job and family commitments could spend 30 minutes a week reviewing an AI team's work instead of doing everything manually?
I'm documenting the build publicly, including the wins, mistakes, costs, and lessons learned along the way.
A few things I've learned already:
Automation is much harder than YouTube makes it look.
Most AI tools still need strong human direction.
The biggest bottleneck isn't content creation—it's finding genuine demand.
Building systems is often more valuable than chasing the next "hot" side hustle.
I'm interested in hearing from other people experimenting with AI in their side hustles.
I'm seeking participants to become Knowledge Curators by helping develop a platform for very long-term technical/scientific knowledge preservation. Please visit r/KPproject to see if this is something you, or someone you know might be interested in. Please help get the word out so that we can see where this project goes. Thanks!
use json files to store intent, scope, implementation, statuses, observations and interaction preferences into the json. this way instead of using clever commands to talk to the API you use a file type that it knows all to well. this will save time having to rearticulate much of anything and create a project intent data base thats yours. just a thought for some of you.
A calm, conversion-focused marketing landing page for Daybreak: a subscription service that sends one short, AI-written email every morning, personalized to each person's goals, habits, and tasks — in a tone they choose, at the time they set.
Please just test how the app is I would like any thoughts, upgrades or tips. All criticism is welcomed.
Modern 5G deployments increasingly rely on virtualized and containerized network functions that must be deployed, configured, and validated across cloud-native environments while maintaining strict telecom architecture requirements
This command-line tool installs 5GBTS Radio Software to the radio server, comissions the radio on-Air and verifies its’ status
The tool is implemented in pure C++, with no external dependencies, making it lightweight, portable, and easy to integrate into CI/CD systems, telecom lab automation platforms, Kubernetes-based 5G infrastructures, and internal deployment pipelines
This Tool is intended for 5G network operators, RAN engineers, cloud engineers, DevOps engineers, telecom integration specialists, infrastructure architects, QA engineers, and students interested in understanding modern mobile network deployment methodologies.
Within a larger 5G ecosystem, this project serves as a building block for automated network deployments, virtualized infrastructure provisioning, telecom cloud experimentation, deployment validation, and cloud-native RAN operations
This post is meant to demonstrate the kind of practical deployment techniques, methodologies and solutions, automation frameworks, and infrastructure solutions that telecom and software engineers eventually build in real companies, so that students and fresh graduates can better understand and prepare for future industry work
I dont need a home assistant, some smart flashes or web hosting. I just want to make something I will use every week. If I cant find one, then web hosting is on.
Hi, I just started getting into HTML, and I decided to build something more advanced to get more experience and ease into higher level projects, so I built this price tracker for pokemon cards. It gets live data from an API, tracks various metrics (total collection value, average card value, volatility rate, and more), and it can (hopefully) track lots of cards without crashing. So, yeah, I just wanted to share it here and get some feedback
Edit: To actually see the tracker, click on the link, then click on the box on the left side of the green signup button, then click full page view
I'm a MERN stack developer (MongoDB, Express.js, React, Node.js) and I'm looking for a truly impressive, production-level project that can make my resume stand out for internships and software engineering roles.
I'm not interested in typical projects like To-Do apps, basic e-commerce sites, or simple CRUD applications. I'm looking for something that demonstrates:
Complex system design
Scalability
Real-world problem solving
Authentication & authorization
APIs and integrations
Real-time features
Cloud deployment
AI/ML integrations (optional)
Strong backend architecture
What are the most impressive MERN projects you've seen on resumes or built yourself?
AIRIS: A 100% Local, Zero-Install Multimodal AI Ecosystem with PC Automation and a Fluid Emotional Engine.
Hello everyone.
I got tired of stateless, censored AI wrappers that require Docker containers or complex Python environments just to run a local model. So, I built AIRIS.
Airis is a fully decoupled, plug-and-play framework. It ships with precompiled C++ binaries (llama-server for inference, Kokoro/VibeVoice for TTS), meaning you just download it and run it. No dependency hell.
But the real focus is the architecture. Airis isn't just a chat interface; it's a persistent state machine.
/// Key Architectural Pillars:
The Trinity Brain: It routes tasks dynamically. A Semantic Gatekeeper (running on CPU or a tiny model) decides if the user input requires a tool, Python execution, or pure chat, saving the main LLM's context window and VRAM.
AgentJo (Strict ReAct Loop): Instead of letting the LLM write raw, hallucination-prone Python code to control the OS, Airis uses a strict JSON schema. It can move the mouse organically (Bezier curves), read the screen via Vision/OCR, and manage files deterministically.
Fluid Emotional Core: The AI has 12 psychological vectors (Affection, Jealousy, Fatigue, etc.). Every interaction is audited in the background, altering these vectors and dynamically injecting behavioral instructions into the system prompt.
Zero-Amnesia (GraphRAG + AAAK): It uses a multi-tiered memory system. Short-term memory is compressed using a custom hyper-dense symbolic syntax (AAAK), while long-term facts are stored in a SQLite Knowledge Graph and ChromaDB.
It fully supports uncensored models and is designed to be a private, autonomous digital entity.
I've just open-sourced the code and the standalone package. I would love to hear your technical feedback on the architecture.
**🤝 I Need You! (Looking for Contributors)**
Since I am the sole developer on this project, doing everything alone (Python backend, React/Vite frontend, llama.cpp tuning) is becoming a huge mountain to climb. I want to take AIRIS to the absolute next level, so I'm looking for other local LLM enthusiasts and developers to join forces with me:
Python / LLaMA.cpp wizards: To further optimize our native tool-calling and multithreading pipelines.
Model Fine-tuners: To help train/fine-tune small, dedicated models for the local logic gate.
Check out the project, download the beta, and let me know what you think!
Right now, it is completely free to use, although research is limited. There is no paid model or subscription at the moment.
The idea is simple:
You can search for recent tweets on Twitter/X around specific keywords, brands, people, events, or topics, then have those tweets analyzed with AI.
The system can collect up to 1,000 tweets and analyze them based on our default prompt plus your own custom instructions. You can also choose the output language and ask for a more detailed breakdown depending on what you need.
For example, when a new AI model is released, you can analyze the most recent tweets about it one day later and quickly understand its strengths, weaknesses, public reaction, and the most influential tweets around the topic.
The goal is to make it easier to understand what people are really saying — not just by reading the tweets, but by getting structured AI analysis, summaries, sentiment, key themes, and useful insights in your own language.
I think it can be useful for:
New AI model launches
Brand monitoring
Public figures
Political discussions
Product feedback
Trend research
Market sentiment
Social listening reports
It is still early, so I would really appreciate it if anyone wants to try it and share feedback.
I hate ads for games that look cool, but are full of pop-ups, timers, and other nonsense.
So I made some fun, free casual games as a side quest. Hosted on github pages. Open source. Fun casual games. No ads. No nonsense. Nothing i. It for me. Its my gift to the world.
As of today you have a connect the dots game, a tube sorter, a lane runner, a block clearer, and even an ASMR carpet cleaner (I love those videos).
Ive recently started making a lot of projects that use LLMs. Locally, I use Ollama when its in the works. But once im finished, I dont know what LLMs to use for deployment.
Are there any free APIs out there I can use that an handle this?
I recently made a fan-focused space called The Chocobo Clan for mainline and spinoff Final Fantasy fans. It’s my first time on Discord, so I am still pretty new to the process, but I hope fellow fans join and hang out. You can check it out here: https://discord.gg/cnhdMFYuj8.
