This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
What I Built
RelayPilot is an AI ops copilot for people who run LLM relay stations — the small, often one-person services that sit between users and model providers, juggling API keys, channel quotas, and bills.
I know this pain firsthand: I run a free public relay station myself. RelayPilot is the night-shift engineer I always wished I had.
It does four things:
- Channel health monitoring — probes every upstream channel on a schedule and tracks latency, error rates, and quota burn, so a dying channel gets caught in minutes, not hours.
- Automatic quarantine — an open-weight model classifies key usage patterns locally; when a key suddenly starts behaving like a stolen one (traffic spikes at 3 AM from a new region), RelayPilot quarantines it automatically and pings the owner.
- Natural-language ops queries — "which channel burned the most tokens yesterday?" or "is the DeepSeek channel healthy right now?" answered in plain language, no dashboard spelunking.
- Telegram alerts — the things that actually need a human get pushed to your phone; everything else stays in the log.
Who would pay for it: anyone running a relay station or reselling API access — indie hackers, small dev-tool teams, campus AI clubs. The pitch is simple: one prevented quota-burn incident pays for a year. I'm launching with a free self-hosted tier (bring your own server) and a $9/month hosted tier that handles monitoring, alerting, and multi-tenant billing. My first user is me.
Demo
62-second terminal demo of the simulated key-leak scenario (SIMULATION_MODE=1 — no real keys were harmed): channel probes → normal traffic → sk-dave's key starts leaking at 03:00 from a new region → WARNING → auto-quarantine + CRITICAL Telegram alert → natural-language queries → daily digest.
Code
https://github.com/2951461586/relaypilot — MIT licensed, repo created Oct 13, 2026, inside the challenge window.
Stack: Python, FastAPI, Ollama (open-weight models, local inference), python-telegram-bot, SQLite. Channel adapters forked from the open-source Veloera/New API gateway.
How I Built It
- Anomaly detection, local by design. A small open-weight model (Qwen3 via Ollama) classifies per-key usage windows — request rate, token profile, time-of-day, geography — into normal/suspicious. It runs entirely on the operator's own machine, with automatic fallback to built-in heuristic rules when Ollama is unreachable. No usage telemetry ever leaves the server, which matters because key logs are the crown jewels of a relay business.
- Deterministic where it counts. The model only classifies; all money-adjacent decisions (quarantine thresholds, quota math, billing) are plain code. An LLM never touches arithmetic it can get wrong.
- Veloera adapters. Instead of writing channel integrations from scratch, I built on the open-source Veloera gateway's channel abstraction, so RelayPilot speaks the same plugin interface as the relay itself — any Veloera-compatible station can drop it in.
- Telegram as the UI. Operators don't want another dashboard; they want their phone to buzz only when something is actually wrong. Alert fatigue was the design enemy: three severity levels, quiet hours, and a daily digest.
- A static landing page ships in the repo (site/) and is headed for relaypilot.tech — the .tech domain I claimed through the Hacktoberfest promo.
Why Does Open Innovation Matter?
Four reasons, all of which are load-bearing for this business — not decoration:
- The data can't leave the building. Key usage logs contain everything an attacker needs. Sending them to a third-party API for "AI monitoring" would be malpractice. An open-weight model running locally means the security tool doesn't become the security hole.
- The unit economics only work at zero marginal cost. Monitoring means classifying thousands of usage windows a day, forever. At paid-API prices, the monitoring bill would exceed the relay's own margin. Local inference makes the $9/month tier actual profit instead of a loss leader.
- Operators need to swap models, not vendors. When a better small classifier drops, I swap a model file — no contract renegotiation, no API migration, no retraining pipeline owned by someone else. The whole product is model-agnostic by construction.
- Trust is the product. Relay operators are trusting RelayPilot with their keys and their users. Every line of the detection logic is inspectable; nothing phones home. You can't sell "we watch your keys" as a closed black box.
My Agent Session
I built this with an AI agent under my direction: I specified the architecture and requirements, the agent wrote the code, and I verified it end-to-end with the simulated leak scenario above. (No DevRelay session link — the build happened in my own environment.)
Prize Categories
- Overall — every valid submission is considered for the overall prize; entering here.
- Best Use of a .tech Domain — relaypilot.tech, claimed through the Hacktoberfest promo, is this project's domain. The landing page goes live there next.
Top comments (1)
Using an open-weight local model via Ollama purely for classification while keeping money-adjacent decisions deterministic is an absolute textbook masterclass in AI Ops architecture. It solves the unpredictable nature of LLMs while keeping infrastructure costs close to zero at the edge.
We’ve been heavily auditing state management and local inference boundaries for our own workflow tools lately. Seeing someone ship a focused, zero-dependency night-shift engineer that falls back gracefully to heuristics is pure validation. Phenomenal project.