Over the last few weeks I have been working on (vibecoding) a project called SIR-Engine. It can detect when 2 functions or classes implement the same logic even if they have different names, variable names, and even if they are written in different languages. A java and python function that do the same thing produce the same hash. This week I kind of had the idea to make it a tool that an AI (like claude code) can use on the code it generates. I learned that is called an MCP server. So before creating new functions it checks if the logic as already been used before in your codebase, and if it is then the AI will just reuse it. I also added duplicate class detection this week too using Merkle Tree hashing which I came up with independently! I'm a biomedical engineering student at Ohio State. Started this about two weeks ago. Totally free for any personal or open source projects! Live demo: https://sri-engine-7amwtce7a23k7q34cpnxem.streamlit.app GitHub: https://github.com/lflin00/SIR-ENGINE Happy to answer any questions about how it works!

I am a student and recently admitted to a new program to study intelligent systems engineering. I have been a self learner for the past year just pursuing full stack development and Linux basics, there seems to always be more to know. But, I have been loosely on this AI 2nd brain trail for a while now and now its possible with current models and gaining momentum like in this community. First checked out the Youtube and resonated with this idea of what is the new layer of abstraction, what's worth building and investing into. A little bit ago a document rag processing pipeline seemed to me the best thing you could do and now its all native. In the past few days Gemini now has multimodal rag embeddings too. Structured context and instructions in markdown is the layer where it is both human readable and AI readable. As the models improve you can't take away the need for well structured context, in fact it only makes it more valuable. So what is the best way to do this. The most straightforward thing for me was Obsidian + Claude-Code, Claude-Code runs in my vault root and Obsidian is a great markdown frontend with plugins, notably obsidian CLI and local rest api. First symlink the .claude folder to a folder like CLAUDE so it is not hidden in obsidian. Then the CLAUDE.md tells it how to interact with your vault. Ultimately the native tools Read, Edit, Write, Glob, and Grep are often best but GET /active allows it to see what's open via the rest api. Obsidian CLI has some commands that can allow more capability faster in fewer tokens, but not all are better than the native md editing. My initial strategy is building light commands that instruct it how to use these obsidian commands and also bash to get more deterministic outputs in fewer tokens. Make common tasks repeatable and maybe transition to run it on local ai requiring less inference at some point. However, there are some disadvantages still. It feels like there is not total agreeance between Claude-Code standards and obsidian standards. I gave up on using an Obsidian MCP because there is too many of them and its unclear the tradeoffs between them. Maybe I should be making my own? It also needs deeper integration with an IDE, sandbox dev container environments, a knowledge graph it can traverse not just the obsidian tags and links it has to grep. Another thing is how do I want to interact with it from my phone, a mobile interface focused on different actions and views not just obsidian mobile, although it may be versatile with proper setup.

Hey teams. I'm Maggie, a Product marketing manager in a SaaS environment with an aggressive roadmap. I looked around me five weeks ago and noticed everyone was working more efficiently than I was. Now I'm here. Current objectives: - Token efficiency - Folder setup for training docs - Use cases that naturally fit into my workflow - Documenting my process early for continuity

Hi, I’m Jason — a software engineer working on an enterprise finance platform where AI is increasingly part of our development toolkit. I’m particularly interested in agentic workflows and how teams are integrating AI into real engineering practices. I always enjoy connecting with others in the space, sharing lessons learned, and hearing about different approaches. Feel free to reach out if you’d like to exchange ideas.

Hi everyone, I am glad to join the community of like minded people building and working with AI. I had a quick question.... I am currently on intel 10600K 24gb 2666mhz ram RTX 3060 TI I am going to upgrade to Ryzen 7 9700X x870E motherboard (haven't decided which one yet) 64 GB of ddr5 ram ( just waiting for markets to cool) and I wanted to know what do you all recommend for GPU's to run ? I plan on running dual GPU s but I want to know if single will be better to run a local AI and automation business ? please let me know below based on the poll which GPU would be best suited and if its better to run single or dual GPU. thank you all in advance.