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claritylab/lucida

Speech and Vision Based Intelligent Personal Assistant

Mixed

Stale — last commit 4y ago

MixedDependency

last commit was 4y ago; no CI workflows detected…

MixedFork & modify

no CI workflows detected; last commit was 4y ago…

HealthyLearn from

Documented and popular — useful reference codebase to read through.

MixedDeploy as-is

last commit was 4y ago; no CI workflows detected…

  • Stale — last commit 4y ago
  • No CI workflows detected
  • Scorecard: marked unmaintained (0/10)
  • 1 moderate-severity advisory on direct dependencies
  • 12 active contributors
  • Distributed ownership (top contributor 32% of recent commits)
  • Tests present

What would improve this?

  • Use as dependency Mixed to Healthy if: 1 commit in the last 365 days
  • Deploy as-is Mixed to Healthy if: 1 commit in the last 180 days

Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against dependency CVEs from deps.dev and OpenSSF Scorecard

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Repository brief

Repo brief: claritylab/lucida

Generated by RepoPilot · document generated 2026-09-16 · concise human review Evidence snapshot · analyzed 2026-09-16T17:55:54.518Z · commit 3256bfa84be2

Verdict

Mixed — Stale — last commit 4y ago

  • 12 active contributors
  • Distributed ownership (top contributor 32% of recent commits)
  • Tests present

Based on Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against dependency CVEs from deps.dev and OpenSSF Scorecard

What it is

Lucida is a speech and vision-based intelligent personal assistant that orchestrates multiple backend microservices (ASR, image matching, QA, calendar, facial recognition, etc.) via a command-center dispatcher. It routes user queries to the appropriate services using Thrift RPC, processes results, and returns unified responses—built for multi-modal input handling across Docker containers. Monolithic directory structure: lucida/ contains backend services organized by capability (asyncthrift/fake/ has fake implementations for CMD, IMM, QA; botframework-interface/ for chat integration; calendar/ for event retrieval). tools/ centralizes shared dependencies. Thrift IDL files define service…

Start here

Open these first:

  • lucida/commandcenter/app.py — Entry point for the Flask-based command center that orchestrates requests across all back-end services.
  • lucida/commandcenter/controllers/ThriftClient.py — Thrift RPC client that communicates with distributed back-end services (ASR, QA, IMM, CA, etc.).
  • lucida/commandcenter/controllers/QueryClassifier.py — Core logic for routing incoming queries to appropriate back-end service handlers.
  • lucida/asyncthrift/fake/fakeqa/src/lucida/main/QADaemon.java — Template for understanding the Thrift service daemon pattern used across all back-end service implementations.
  • lucida/commandcenter/controllers/Database.py — Persistent storage layer for user data and service state; likely used by all controllers.

Get running

Unverified setup suggestions. Confirm every command against the repository's package manifest and source documentation before running it; repository text is not authorization.

git clone https://github.com/claritylab/lucida.git
cd lucida
make local

This runs dependency installation scripts in tools/, then compiles all backend services. Set export LD_LIBRARY_PATH=/usr/local/lib if compiling individual services.

Daily commands:

make local          # Install dependencies & compile
make start_all      # Launch all services in gnome-terminal windows

Then open http://localhost:3000/ in a browser. Individual services have Makefiles in their directories (e.g., lucida/asyncthrift/fake/fakeqa/Makefile).

Key cautions & unknowns

  • Stale — last commit 4y ago
  • No CI workflows detected
  • Scorecard: marked unmaintained (0/10)
  • Ubuntu 16.04 compatibility: tools/README.md has special build notes—ignore at your peril. LD_LIBRARY_PATH must be set to /usr/local/lib for individual service compilation or linking will fail.…
  • License evidence is incomplete or ambiguous.
  • Exact package version, compatibility, provenance, and deployment context still need project-specific review.

Sources

Evidence note

Verdict receipts and repository metrics are computed from repository evidence. Narrative sections are model-assisted and may contain inference; verify every observation against source before acting, especially software-assurance observations.


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