This is a submission for the MLH x DEV Writing Challenge
What I Built
Three Projects, One Goal: Making Everyday Work Smarter with AI
I've been exploring three ideas that address different problems through practical software engineering and AI: LocalLedger AI, CiteWise, and SlothSort.
Each project started with a simple question: How can we use modern technology to make everyday tasks easier, more reliable, and more accessible?
1. LocalLedger AI — Making Financial Records Easier to Manage
LocalLedger AI is a project concept focused on simplifying financial record management through intelligent automation. The goal is to help users organize transactions, understand financial records, and reduce the manual effort involved in maintaining a ledger.
The project explores how AI can assist with categorization, summaries, and more accessible interactions with financial data while keeping accuracy, transparency, and user control central to the experience.
2. CiteWise — Making Research and Citations More Reliable
Research involves more than finding information. Researchers also need to organize sources, verify claims, and ensure that citations accurately support what they write.
CiteWise aims to make research workflows more efficient by helping users manage references, connect claims to supporting sources, and improve the reliability of citation-based writing.
The broader motivation comes from a problem I find particularly important: AI-generated content is only useful when its claims can be checked against credible evidence. I want to explore how software can make that verification process easier rather than asking users to trust generated answers blindly.
3. SlothSort — Let Your Files Organize Themselves
SlothSort addresses a familiar problem: cluttered folders filled with screenshots, documents, receipts, source files, and confusing filenames.
The idea is to build a local-first AI-powered file organizer that can categorize files, suggest meaningful filenames, and identify duplicates. Users should be able to review proposed changes before approving them.
By exploring locally running language models through Ollama, SlothSort aims to make intelligent file organization possible without depending on a cloud-based AI service for its core AI workflow.
What Connects These Projects?
Although the projects address different problems, they share a common direction: building useful software that combines automation with reliability and user control.
- LocalLedger AI explores smarter financial record management.
- CiteWise focuses on evidence, references, and research reliability.
- SlothSort makes file organization more intelligent and less repetitive.
Working on these ideas has encouraged me to think about more than features and interfaces. Good software should address a real problem, communicate what it is doing, handle mistakes responsibly, and give users confidence in the results.
These projects are part of my ongoing exploration of practical AI applications, with an emphasis on building tools that people can understand, evaluate, and improve.
Partner Technologies
The technologies explored across these projects depend on their implementation.
Ollama and local language models: SlothSort is intended to use locally running language models for file categorization and filename suggestions. This provides an opportunity to explore local inference and reduce dependence on external AI services.
AI-assisted research workflows: CiteWise explores ways to improve source management and citation verification. Its implementation should prioritize traceability and verifiable evidence over unsupported AI-generated claims.
Intelligent data organization: LocalLedger AI explores how structured financial records and AI-assisted workflows can make record management easier while preserving user oversight.
Any technologies used specifically as MLH partner technologies will be documented here once their actual integration into an eligible hackathon project is established.
Hackathon Experience
I haven't participated in an MLH hackathon yet. These three projects represent ideas I'm exploring independently, and I hope to develop them further through hands-on building, feedback, and collaboration with other developers.
I'm particularly interested in participating in a future hackathon where I can take one of these ideas from concept to a working prototype, learn from other participants, and share the technical decisions and lessons behind the final result.
For me, the exciting part is not simply adding AI to an application. It's identifying a meaningful problem, choosing the right engineering approach, testing whether the solution genuinely helps, and being honest about its limitations.
I'm looking forward to turning these ideas into polished, useful projects and sharing that journey with the developer community.
Top comments (0)