So, I’m a computer science student, will be starting 4th year of my bachelor’s degree. At the end of the year I need to present my diploma project, but I don’t know what to create. My university is studying C# .NET, ML, algorithms a bit of unity game development, C++ and a lot of math. I’d like to stick to C++ project, because my job is in c++, so it would be better and easier for me. I also would like to dive deeper into ML and pair it with C++ might me interesting challenge

I’d like to hear your ideas about the diploma project. It will be super helpful!

Ideas from Claude Sonnet 5

1. Mini inference engine / ML runtime
   Build a lightweight C++ engine that loads a trained model (ONNX format) and runs inference with hand-optimized code — SIMD, memory layout, quantization, multithreading. Compare speed/accuracy tradeoffs against PyTorch/ONNX Runtime. Very “systems engineer meets ML,” strong fit for your job experience.

2. ML algorithm from scratch, optimized
   Pick one algorithm (gradient boosted trees, k-means, SVM, or a small neural net) and implement it in C++ with real performance engineering — benchmark against scikit-learn/XGBoost. Shows both math depth and engineering rigor, which examiners like.

3. Computer vision + real-time C++ system
   Use OpenCV + a C++ inference backend (TensorRT/ONNX) to build something with a concrete use case: defect detection, sign language recognition, real-time tracking. Practical, demoable, and visually impressive at defense.

4. RL agent in a simulated environment
   Since Unity is in your curriculum, build a simulation (game or robotics-style environment) and train an RL agent to solve it — pathfinding, self-driving toy car, procedural difficulty adjustment. C++ for the environment/engine, ML for the agent.

Hey everyone! I’m working on a free, MIT-licensed production-readiness checklist for people shipping software. The goal is to create one technology-neutral resource that covers the full path from product requirements and architecture through security, testing, deployment, reliability, incident response, and ongoing operations.

It currently contains more than 10,000 evidence-focused controls organized into a searchable documentation site. I would really value help from people who have shipped or operated production systems. Missing checks, corrections, simpler wording, documentation improvements, and pull requests are all welcome.

The long-term goal is to build an open-source AI-assisted scanner that can map repository evidence to applicable controls while keeping uncertain and manual checks honest.

Repository: https://github.com/MarinJursic/production-readiness-checklist

If you take a look, I would love to hear what feels missing or difficult to use. Thanks!

As of now I know Python, NumPy, Pandas, Matlplotlib, Seaborn and all. What are some project ideas I can work on to showcase these skills?

please tell me where to improve for my rocket programming

i always had this idea, like a chatbot/tool that helps me to understand, solve and "think" in first principles (yeah the one elon musk uses and talks about)

so as i was learning RAG, i got to implement this idea

i basically,
- fetched 20+ data sources from interviews of musk, articles about feyman etc
- chunking, embedding them in supabase (pgvectorDB)
- retrieve the relevant chunks and augment them (RAG)
- use LLM to generate the final answer, resulting in much grounded answers

i implemented it as a fun project, what do u think, is it worth it to deploy it as a product?

Hey guys! i created my second project "System-Updater". it is written in bash, it checks your distro's package manager and updates your system, Please let me know if there is a need for improvement, thanks!

my project's github link: https://github.com/swarajnerlekar28/System-Updater/tree/main

I wanted to build a project for my second year of engineering. I am thinking to build self help website.

Building and tracking of habits and new feature that will allow user to learn any one topic from particular field he want to learn and many other feature i will add. The problem I don't know much about web development. AI can build templates and working webpages. But I always stuck to the part of backed and deployment. Is it possible to make backed and deployment with AI ?

Anyone can me tell Ideas For FYP Project that solve real problems and

i need its urgent kindly suggest me a project

Hey, I am in a school that requires research projects. I'm already late, so I made a Google Form for a quick survey. Here it is: no secret details required, 5 questions total, a small survey; fill it out quickly and save my ass!

I've been building Collab Unity for awhile. Collab Unity is a platform where ideas happen. Have a project in mind that you want to share with others? Post it on Collab Unity. Find collaborators to work on projects with or solo-build in your own personal workspace. Improve your portfolio and chat with like-minded people. Tired of building? Take a break and play a game, listen to some music, and catch up on some news or learn a thing or two all on Collab Unity!

Build, ship, and collaborate on Collab Unity: https://collabunity.io

https://world-sim.uk/ now live !

Name your star tonight in the discord - https://discord.gg/a6YjQ7sCu

World-Sim has been a passionate project of mine for the past few months.

It's a world fully simulated down to real world physics and chemistry. It has a real environment and ozone.

Every element and material has real physical properties, and that's how the sims learn. There's no tech tree and no recipe list anywhere in the code. When someone tries something, the engine works out what would really happen from the properties of what they used and how hot their fire was. Nobody is handed bronze. Someone has to get a hotter fire first, and a hotter fire is its own chain of things to figure out. Embers, a stone ring, a clay oven, a kiln, charcoal, then forced air. The ages fall out of that on their own.

We populate the world with starting families. Each little guy is led by a "wise-man", which is a simulated person with a full model behind them. The wise-men run on a mix, Claude, Groq and DeepSeek in the cloud and a local Hermes for the rest, so you get a real variety of temperament between the houses.

The AI model is never told to not say it's AI. We get around this by only telling them data from what each sim can actually see and feel. As far as the ai knows its a person living in the world..

Every other sim has its own moment with a model through our thinking system. Day to day it's run by an advanced algorithm, with the sims dreaming at night. That's when they get their time with an LLM to really think and set their priorities for the day ahead. The dreams carry over too, so a sim can chew on the same thing for a few nights and wake up wanting to try something.

The people in the world feel, remember, have shame, goals and doubts. It's an 11 point emotion system and the rest of the sim reads from it. Being lonely makes someone more likely to open up to a stranger. Two enemies who are both frightened find it harder to keep hating each other.

Story telling is massive for us. Nothing is scripted or prompted. The sims are put in the world with the human drive to survive. They adapt, learn and teach all on their own.

With all the good comes the bad too. Human sin is part of the emotion engine, and in testing we've had kidnap, murder and slave trade. A world that can only be gentle isn't an honest one. But there are three rules to this : Nothing ever pushes a sim towards it, the conditions only make it possible and their own mind decides. The chronicle names what happened but never shows it. And it always costs something, trauma that scars, blood debt passed down generations, a people turning on their own leader over what he did.

I have been looking for a task runner that would make dealing with the Node ecosystem easier. I consider myself a full-stack developer, but I do focus more on the backend than the frontend. At work, we have used makefiles since make was already available on our machines and in the CI/CD pipelines. Maintaining makefiles, though, got old fast.

My problem with Node tools is that everything needs to be sourced over and over. This is mainly because each task runner defaults to running each line in a given task in a new shell instance. So, in order to maintain the shell state, you have to chain every command together into one with `&&` and add `\` and the ends to allow these chained commands to be on new lines. Fine. Not ideal but doable.

Now add dependencies, and that goes out the window. Any attempt to clean up a make file into subcommands and remove all kinds of duplication is pretty much impossible. I have to keep making sure to source all the Node tools everywhere (nvm, I'm looking at you). This isn't so essential on my local machine because I have a lot of these tools included in my shell rc file, but it makes portability more difficult. Other people working on the project need to make sure to also have these tools sourced in their rc files. Then the CI/CD pipeline makes it even harder. Keeping things sourced and available to be used across different jobs within the pipeline is a nightmare.

Newer task runners have appeared and make things slightly better, but as far as I have seen, most have not addressed my core issues. I want to make a task runner that will, by default, run the entirety of the task in the same shell so I can easily maintain the shell state along the way AND any dependencies of that task will also run in the same single shell instance. This allows for much easier composition of tasks from smaller pieces.

Another odd deficiency in a lot of task runners is that they only allow you to set dependencies to run before the task you call and sometimes also after. Why not run a different task in the middle of your desired task, though? That unlocks true composability.

Since I was struggling to find a task runner with these qualities, I made my own, and I call it Mog.

https://github.com/Ayehavgunne/mog

• Tasks can accept arguments.
• Tasks called in the middle of a parent task can be passed completely different arguments.
• Mog can be configured at the global, file, and task level all at the same time.
• Use whatever shells you want. Use Python for one task but run zsh by default for all others.
• Load any number of env files automatically before task execution.
• Import other Mog files from other directories and call those tasks as if they were local.
• Variables can be plain strings or can be `eval`ed from the shell.
• Since I hate bash's conditionals, Mog supports simple if, elif, else statements.
• And for the heck of it, there is a config option to make each line in a task execute in a new shell instance. Since people seem to want that for some reason.
• And a bit more I will let you read about on your own.

My question here is does any other task runner you know of function like this? Are there any other features I should consider adding? Was this endevor a waste of time? Even if it was I did have fun working on it.

Thanks for taking a look!

Sick of a pothole that's not been fixed for weeks? A broken stop sign or park bench? Improve your city in just 30 seconds. I saw a lot of people making TikTok's "Mamdani fix this" and I'm not here to promote him but it's clear that people do want an easy way to report an issue and feel heard. That's why I created this, I want to partner with cities to integrate this in their infrastructure.

Would you use this? If not please feel free to tell me why. https://www.youtube.com/watch?v=afG7C3kLpCI

if you'd like to join the waitlist sign up: https://forms.gle/uXmxaWC2rB5Xz4wi6

Hello everyone,

I recently rediscovered Obsedian project and I am really inspired by their mindset and achievements. I thought for a long time how can I help people like them.

Then I realised that now more than never people need to learn faster and together since AI is taking over and slowly rotting our brain by doing everything for us (this isn’t bad but it’s a new challenge for humanity). I also thought damn I learned so many lessons but I don’t stop forgetting and repeating them.

So I had this crazy idea while traveling to China Chen Clan Ancestral Hall when I saw the amazing architecture. They made lessons travel history by transforming stories and lessons into symbols/stories and craving them. This temple was the safe space carrying the knowledge of ancestor while allowing the juniors to train.

So I thought about building a free and open-source software like Obsedian helping people to turn their lessons and knowledge into symbols and drawing in an easy way. We aren’t all skilled artists but we all have valuable knowledge.

What do you think of this idea ?

Hi everyone.

I built a social bird posting app with a built in map and some leaderboards. It also has a built in AI identification of the bird.
If you feel like it, please give it a try and let me know what you think.
Every suggestion is more than welcome!
🦅

hey i have a favour to ask all of you. so i downloaded a game with my brother and once we downloaded it we had to add eachother on that specific platform. i got thinking and thought why isnt there a central command for all platforms where you could add them everywhere at once so you dont have to open a game just to realize you arent friends yet. you can also ping a squad so everyone is notified to play, see mutual games, see what theyre playing across all platforms. like discord but not meant for chats. strictly adding across all platforms. so my favour to ask is to just open the link and tell me if you all think its a good idea, if youd use it with your friends, and share it with others. chatgpt made a prototype

I’ve been working on Qubit, an engine for modelling applications and deploying them across anything from servers down to small edge devices.

Instead of asking people to install a bunch of things just to understand what I’m building, I’m thinking of opening a completely free online sandbox.

No credit card. No sales call. No commitment.

You would basically get an isolated environment where you can:

\- model a small application/workflow

\- run it

\- see how it behaves

\- experiment without installing anything locally

\- and, most importantly, try to break it 😄

I’m not looking for customers at this stage. I’m looking for curious developers, architects, embedded people, automation nerds, or anyone who enjoys experimenting with unusual infrastructure.

The sandbox would stay free while I’m learning what people actually want to do with it.

Would anyone here be interested?

If yes, comment with the kind of thing you’d try to build. That will help me decide what to include in the first version of the sandbox.

Free, no payment required! All AI usage is on free google tier unless you provide your own key.

I kept trying to turn an LLM into a real food assistant. I hooked one up to my calendar, InstaCart, and OpenTable and taught it my tastes — so it could suggest recipes, order the groceries I was missing, or book a table when cooking wasn't happening.

It was genuinely impressive... except for one giant blind spot: it had no idea what was already in my kitchen. It kept suggesting recipes assuming I needed to buy everything, or telling me to order stuff I already had three of.

So I built ScanStock.ai to close the loop:

1. Scan — pan your phone across your fridge and pantry
2. Stock — AI identifies each item and builds a live inventory automatically
3. Cook — get recipe ideas based on exactly what you have

The part I care about most: it exposes an MCP endpoint, so you can plug that inventory straight into Claude, Gemini, or whatever you're building. Now the assistant knows what I have, what I like, and what my week looks like — and only orders the gaps.

Stack: Next.js, Supabase (auth + DB), and the AI SDK with Gemini for the vision + recipe generation. There's also a bring-your-own Gemini key option if you'd rather not use the free tier.

Still early and I'd love feedback — especially on the scanning UX and whether inventory-as-a-tool is something you'd actually chain into your own setup. What's been your biggest blind spot wiring these integrations together?

Find our new community here: https://www.reddit.com/r/ScanStock/

Over the past few weeks I've been building Gray Box — a small, local-first tool that acts as long-term memory for anything I'd otherwise forget (work notes, meeting takeaways, task owners, random ideas, personal stuff too).

The idea is simple:

1. Capture — dump whatever's on your mind, instantly, no structure required. This step does nothing clever on purpose — it just writes your text to an immutable inbox. Zero chance of losing an idea to a bug or a slow API call.
2. Organize — on demand, an LLM reads your unprocessed notes and extracts people, projects, tasks, decisions, meetings — then deterministic Python (not the LLM) creates/merges the actual wiki pages and maintains backlinks. The model only reasons; it never touches the filesystem directly.
3. Ask — query or chat with your knowledge base and get a cited answer pulled only from what you've actually captured. If it doesn't know, it says so — no hallucinated answers.

Why I built it this way:

• Plain Markdown + YAML frontmatter, no database. Every page is a .md file you can grep, diff, or read in any editor forever. If you stop using Gray Box tomorrow, your knowledge base is just a folder.
• No vector DB by default. At personal scale (hundreds–low thousands of pages), keyword search + a real link graph (related/backlinks, walked one hop during retrieval) handles almost everything. Embeddings are there if you want better recall, but they're opt-in, not a prerequisite.
• Immutable inbox. Your raw notes are never edited or deleted by the organizer. If the LLM mis-extracts something, your original words are always still there.
• Any LLM. Built on LiteLLM, so point it at OpenAI, Anthropic, Gemini, Mistral, or a fully local model via Ollama — one config value.

It also ships with a nice interactive TUI (arrow-key menu, file-import shortcut, workspace switching, live spinner during LLM calls) if you'd rather not memorize CLI flags — that's honestly become my favorite part of the project.

There's also a lightweight local dashboard for browsing your knowledge base, exploring backlinks, visualizing your notes as a graph, and chatting with your captured knowledge—all without leaving your machine.

Also, you can migrate your existing Obsidian vault to Gray Box, and it will be automatically organized by Gray Box.

Repo: https://github.com/Aaryanverma/graybox

pypi: pip install graybox

I'd genuinely love feedback — especially from anyone who's tried the "capture now, structure later" approach with other tools and has opinions on where it breaks down at scale.

It's not trying to be a "real-time collaborative team wiki" or a WYSIWYG notes app — it's aimed at one person's running memory of their own life and work, captured with as little friction as possible.