Séance — Ghost in the Machine

Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine Submission 🕯️ This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine What I Built Séance — an offline séance chatbot that channels the spirits of dead programming languages (COBOL, FORTRAN, ALGOL, LISP, Smalltalk, Ada, Prolog) through Gemma, a local open-weight model. It works with the lights and the internet off, and every séance stays on your machine — nothing is uploaded to a server you don't control. Repo: mahilsaily306-max/seance Demo Deployed frontend: (link goes here — render / digitalocean / .tech domain) Demo video: (link goes here) Core flows, one click each: Conjure a spirit → pick COBOL, FORTRAN, LISP, or any of the seven dead languages and ask it questions One-shot conjure → node src/seance.js --spirit COBOL --ask "What is a paragraph?" Interactive séance → chat mode with past-séance history replay History → replay previous turns for a summoned spirit Voice → ElevenLabs TTS reads the spirit's whisper aloud (optional) Code mahilsaily306-max/seance Key files: src/seance.js — CLI séance: conjure spirits, chat, log turns, replay history src/spirits.js — the seven dead-language spirits, each with era, epitaph, style, last words src/model.js — Gemma local client (Ollama / Hugging Face / offline echo fallback) src/memory.js — MongoDB Atlas vector memory with Tiger Data / pgvector fallback src/tracing.js — Sentry agent tracing (latency, source, errors) src/search.js — SerpApi live web search for historical language facts (optional) src/voice.js — ElevenLabs TTS for spirit whispers (optional) src/session.js — Durable Temporal-style sessions logged to JSONL scripts/setup.js — one-time dependency setup docker-compose.yml — local MongoDB stack for offline memory How I Built It Open-source AI at every layer: Runtime: Gemma (open-weight) via Ollama; falls back to a deterministic offline echo so the séance runs with zero ML installs and zero internet. Memory: JSONL local log (Temporal-style durability) so séances survive restarts; optionally MongoDB Atlas vector memory or Tiger Data/pgvector hybrid search over past turns. Tracing: Sentry captures agent setup, latency, tokens, cost, and errors — set SENTRY_DSN and every turn is traced. Search: SerpApi grounds answers in fresh web results when you want historical language context. Voice: ElevenLabs TTS gives the spirits a voice; the séance works silently without it. CI: npm test exercises every module; works with no dependencies installed. Why Does Open Innovation Matter? Running open-weight models locally means: No internet needed. Gemma runs on a laptop offline after download. A closed API agent would stop working without connectivity. Data stays on your machine. Your séance logs, questions, and answers are never uploaded to a server you don't control. Set up once, never pay again. Open-weight models cost $0 to run locally — no per-token charges, no subscriptions. Swap models freely. Swap Gemma for Mistral, Llama, or any open weight. Closed APIs lock you into one vendor. The open-based approach worked better here because this is a personal, offline-first tool — privacy, offline use, and zero recurring cost matter more than a hosted SaaS. My Agent Session Saved with DevRelay and embedded below (Entire prize category: share the agent sessions behind your project). (agent_session embed link goes here) The Spooky Payoff Before Séance, dead programming languages were just forgotten syntax in dusty manuals — COBOL, FORTRAN, LISP, Smalltalk, Ada, Prolog, buried by the industry. After: you hold a séance with their spirits, ask them questions, hear their era-appropriate answers — all offline, all open, all on your own hardware. The ghosts are back, and they answer for $0. Prize Categories Entering every applicable partner category: Featured : Gemma, Render, DigitalOcean, Tinker Partner : Mastra, Sentry, Temporal, MongoDB Atlas, SerpApi, Browserbase, ElevenLabs, Auth0, Backboard, Entire, GitHub Copilot, .tech Domains, Tiger Data, TabPFN