DeadmanDAO Web3 Hacker Network | Iteration 04 - Project Embeddings | Round 18

Project Name

DeadmanDAO Web3 Hacker Network

Project Category

Build & Integrate

Proposal Earmark


Proposal Description

In this iteration we will create GitHub project embeddings. In addition to the commit classifications we generated over the past two iterations, we will analyze project configuration files, import statements in source code, and other cues that give us a more detailed image of projects. We will be applying sequence independent NLP algorithms like Bag-of-words to cluster and classify projects based on their degree and distinctiveness of coupling to third party libraries. We will add this to our commit classifications to create our first project embedding; a vector of scalars that act as a signature of each project’s characteristics.

Additional detail can be found here:

Grant Deliverables

  • Publish a raw dataset containing per-project commit statistics based on Git logs.
  • Provide library usage statistics extracted from repository source files and project config files.
  • Perform a clustering of projects based on the library and commit embeddings, report on findings.
  • Train a Logit classifier based on the library and commit embeddings with several recognizable target classes.
  • Publish a sample dataset of project/project similarity scores above a threshold on Ocean Market.

Project Description

Build a network analytics data pipeline that generates recommendations for matching Web3 hackers to projects. It will pull work history from GitHub, Gitcoin, and other relevant sources to train an ML model. The model will help gig economy Web3 hackers to land independent job placements, without the need for corporate HR departments.

Final Product

Web3 Hacker Network is a data science system incorporating network, regression, clustering, and classification models to increase tech talent mobility in Web3. The final product is a decentralized autonomous opportunity discovery engine that will provide some of the services of hiring managers and HR departments from Web2, but in a fashion that is more aligned with the decentralized and self-service nature of Web3.

Value Add Criteria

Web3 Hacker Network will expose Web3 hackers to Ocean when they are looking for a new project. When they are talking about how they found their new gig, they'll tell other engineers about using Ocean compute-to-data.

Revenue potential is strong. Headhunters in Web2 charge 20 - 25% of first year compensation. Millions of dollars of work happen every month in Web3 and the numbers are skyrocketing. Improving the speed and quality of gig matches will generate a large and sustained revenue stream.

Our founders have experience building large-scale systems for Amazon, Apple, and the Department of Defense. We have worked in data engineering and data science since before those names existed, and have successfully completed our first and second Ocean grant supported iterations of this project. This new iteration is focused on adding more dimensions to an existing data model, as is the standard agile iteration process we have followed many times.

Most importantly, our goal is to improve Web3. We started in Web1, building a new world of free information. Web3 is the renaissance of pro-social Internet projects, after the long dark age of Web2 walled gardens. Contributing to a decentralized information ecosystem in Ocean, while helping engineers to migrate to the new economy, is our mission.

Core Team

Robert Bushman

Matt Enke

Funding Requested

Minimum Funding Requested

Wallet Address

Hi @DeadmanDAO,

Thank you for submitting your proposal for R-18!

I am a Project-Guiding Member and have assigned myself to help you.

I have reviewed your proposal and would like to thank you for your participation inside of the Ocean Ecosystem!

Your project looks promising and I believe it’s aligned with our evaluation criteria of generating positive value towards the Ocean Ecosystem and the W3SL.

The project criteria are:

  1. Usage of Ocean
  2. Viability
  3. Community activeness
  4. Adding value to the community

It may also be more helpful to add more information about the project data sets. Understanding the sample and how ti was obtained may make the value more apparent.

Based on the reasons above, I am in support of your project and proposal. I look forward to continuing providing support and feedback to your project, and you can expect to receive a positive vote from me during the upcoming voting period.

All the best!

-Christian Casazza

Hi Christian,

I appreciate the suggestion - I will work on fleshing out the documentation of our datasets.

Thanks for reviewing the project and offering your guidance!