Project Info
This project did not submit a demo video on Devpost.
Inspiration
Our team is fighting night and day during this recruitment season to internships. As with many others, we have varied interests in the fields that we specialize in, and we spend a lot of time tailoring our Résumes to the specific position we are looking at. We believe that an automatic Résume will immensely simplify this process.
What it does
The program takes two inputs: (1) our CV, a full list of all the projects we've done and (2) The job description of the job posting. The program will then output a new compiled resume that contains rewritten information that is most relevant to the position.
How we built it
We designed this project to utilize the most recent AI technologies. We made use of word2vec to create word embeddings, which we store in a Convex database. Then, using Convex's built-in vector search, we compare the job postings with your list of projects and experiences, and output the 5 most relevant ones. Finally, as a last measure, we run the projects through an LLM to shape them to be a good fit for the job description.
Challenges we ran into
We had a lot of challenges making in handling the difference cases of resumes and parsing the differences in it.
What we learned
We learned all sorts of things from this project. Firstly, the power of vector embeddings and their various use cases with all sorts of media. We also learned a lot regarding the space of ML models out there that we can make use of. Lastly, we learned how to quickly run through documentations of relevant technologies and shape them to our needs.
What's next
We managed to get the nearest work summaries that is associated with the job. Next, we plan to rebuild the resume such that we get pdfs formated nicely using latex
Analysis
View
Metric
- 1
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- JavaScriptIn code
- PythonIn code
- TypeScriptIn code
- OpenAIClaimed
3 of 4 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
27 KB
Source files
18
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
ronaldarifin/calhacks-10
27 files · 49 KB · @ f349ec8
Structure
Application logic
18 files · 67%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- Python41%
- TypeScript29%
- JavaScript17%
- Markdown9%
- XML3%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
my-app/package.json
npm · 1- convex
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
This project’s features have not been analysed yet.
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