Analysis updated 2026-05-18
Read the mission, personas, and MVP documents to understand a proposed AI agent orchestration platform
Review the decision log to see what design choices have already been settled
Cross-check the design proposal from a cold start as an outside reviewer or AI tool
Study the architecture and workflow-engine documents before an implementation begins
| paul-gross/blizzard-discovery | 100/rutgers-pbl-dining-2015 | a15n/a15n_old | |
|---|---|---|---|
| Stars | 0 | — | — |
| Language | HTML | HTML | HTML |
| Last pushed | — | 2015-12-01 | 2016-06-18 |
| Maintenance | — | Dormant | Dormant |
| Setup difficulty | easy | easy | easy |
| Complexity | 1/5 | 1/5 | 1/5 |
| Audience | pm founder | general | general |
Figures from each repo's GitHub metadata at analysis time.
There is no code to run, this repository is a documentation and planning corpus only.
blizzard-discovery is not a working piece of software. It is a written design and planning corpus for a future project called blizzard, which aims to be a platform that runs a fleet of AI coding agents continuously on local machines. The idea behind blizzard is that work would flow in from a backlog, each task would run unattended in its own isolated environment, finished work would be merged automatically or held for a human to approve where that is configured, and a human would only be pulled in when an agent genuinely gets stuck. The repository states plainly that it contains no code. Instead it exists so the design can be written out, refined, and reviewed cold by other AI tools and by human reviewers before any building starts. Every document in the repository is described as a proposal rather than a finished decision. Settled choices are tracked in a decision log, while ideas still being debated are tracked separately as open questions, so a new reader can question the design rather than assume it is already final. The corpus is organized into five kinds of documents. Product documents describe what blizzard is for and who it serves. Design documents describe how the pieces would work, covering things like the workflow engine, how multiple agents would avoid conflicting with each other, and how the system would talk to different coding tools. Research documents record what the authors learned by studying similar existing projects. Decision documents track what has been settled versus what is still open. Implementation documents describe how the eventual build would be organized. The guiding philosophy stated in the README is that the core control logic of the system should be ordinary, predictable code, and that AI models would only be asked to make judgment calls at specific, well defined points rather than running the whole process. The project is planned to use a companion tool called winter for isolating work environments across multiple repositories, along with GitHub issues and pull requests, and Claude Code as the coding tool that actually does the work.
A written design and planning corpus, with no code yet, for a proposed platform that runs a fleet of autonomous coding agents.
Mainly HTML. The stack also includes Markdown, Claude Code, GitHub Issues.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly pm founder.
This repo across BitVibe Labs
Verify against the repo before relying on details.