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RepoOnboard
Problem Understanding an unfamiliar codebase is one of the biggest barriers for students, new developers, and contributors joining an existing project. A GitHub repository can contain hundreds of files, multiple directories, configuration files, tests, documentation, and unfamiliar technologies. Simply opening the repository does not explain where to start or which parts matter first. Solution RepoOnboard is a developer onboarding tool that analyzes a public GitHub repository and converts its actual repository structure into a practical, beginner-friendly onboarding guide. A user enters a public GitHub repository URL and selects their role and experience level. The React frontend sends the request to a C backend. The C analysis engine retrieves the repository information and performs deterministic static analysis of the repository tree. It identifies repository statistics, technologies, important files, documentation, configuration files, test files, directory structure, README content, and a bounded selection of source files. The resulting information is returned through a REST API and presented in a developer-focused dashboard. The dashboard separates repository-derived facts from general onboarding guidance so that RepoOnboard does not present assumptions as verified information. It provides an overview of the repository, technology stack, important files, README/setup information, selected source files, codebase structure, an onboarding path, and a suggested first contribution based only on available repository evidence. IBM Bob Integration RepoOnboard also exposes the C repository analysis capability through an MCP server. IBM Bob connects to this local MCP server and can discover and invoke the analyze_github_repository tool. This allows Bob to interact with the same real C analysis engine used by the application rather than relying on a simulated AI response or mock repository data. What makes it different The project combines a lightweight native C analysis engine with a modern web interface and MCP integration. Instead of generating an explanation from assumptions alone, RepoOnboard grounds its onboarding information in measurable repository data such as files, directories, extensions, README content, tests, configuration files, and selected source files. The analysis is bounded to keep the amount of repository content manageable and predictable. Target users RepoOnboard is designed for students, beginner developers, new contributors, and developers joining unfamiliar projects who need a structured starting point before modifying an existing codebase. The MVP requires no user authentication or database and works with public GitHub repositories.
27 Sep 2026
Bifrost
Bifrost — Intelligent Hybrid AI Routing System Bifrost is a token-efficient routing agent built for AMD Developer Hackathon ACT II, Track 1, where the leaderboard rewards the fewest tokens spent above an accuracy gate. Rather than sending every prompt to an expensive remote LLM, Bifrost makes a cost-aware routing decision for each request through a multi-stage pipeline: 1.Zero-Token Router — deterministic prompts (simple math, NER, sentiment) are answered instantly with regex/heuristics, with zero LLM calls at all. 2.Semantic Cache — a thread-safe cache using both exact-match and NumPy cosine-similarity embedding search reuses prior answers, eliminating cost entirely on repeated or near-duplicate queries. 3.Adaptive Hybrid Classifier — for everything else, a routing engine weighs topic heuristics, prompt complexity, each model's historical success rate on that category, and the estimated cost of the remote call to decide: local model (gemma2:2b via Ollama) or remote model (llama-3.1-8b-instant via Fireworks AI)? 4.Two-Stage Quality Verification — a fast local heuristic first screens out weak/empty answers; if local model confidence is borderline, a lightweight remote evaluation gate double-checks for hallucination/correctness before the response is returned — protecting the accuracy floor that gates the whole leaderboard. 5.Observability Engine — structured JSON logs capture every routing decision, latency, complexity score, and token count, so the routing logic is auditable, not a black box. The system is built on FastAPI with asyncio, aiofiles, and asyncio.Lock for race-free concurrent request handling, ships with a Dockerfile/docker-compose for one-command deployment, and includes its own benchmarking suite (benchmark.py) that reports F1 accuracy, average/P95 latency, and remote token cost against an eval dataset — the exact axes the hackathon scores on.
13 Jul 2026
