AgentMash Redefines Remote Pair‑Programming with Secure AI

AgentMash is turning remote pair‑programming on its head. By blending real‑time collaborative editing with a self‑hosted LLM, it lets distributed teams write, test, and ship code without ever sending proprietary snippets to the cloud. Since its debut on Product Hunt, the platform has been referenced thousands of times on Reddit, Hacker News, and Twitter—proof that developers are hungry for a secure, AI‑augmented coding companion that plugs straight into their CI/CD pipelines.

In this guide you’ll get the complete technical stack, step‑by‑step deployment instructions, ready‑to‑run code snippets, cost‑vs‑latency benchmarks, security hardening tips, a hackathon case study, and a CI/CD checklist you can drop into any project today.

┌─────────────────────┐ ┌─────────────────────┐ │ Developer Workstation│ │ CI/CD Runner │ │ (VS Code + AgentMash│ │ (GitHub/GitLab) │ │ Extension) │ │ │ └───────┬───────────────┘ └───────┬─────────────┘ │ │ │ WebSocket (real‑time edit) │ ▼ ▼ ┌─────────────────────┐ ┌─────────────────────┐ │ AgentMash Core │◄────►│ LLM Inference Service│ │ (FastAPI, Redis) │ │ (vLLM / Ollama) │ └───────┬─────────────┘ └───────┬───────────────┘ │ │ │ HTTP/REST (CLI, API) │ ▼ ▼ ┌─────────────────────┐ ┌─────────────────────┐ │ Storage (Postgres) │ │ Artifact Store (S3)│ └─────────────────────┘ └─────────────────────┘ Enter fullscreen mode Exit fullscreen mode FastAPI serves the collaborative editing UI and the duel‑mode engine. Redis handles presence, cursor sync, and short‑lived prompt caches. vLLM (or Ollama for smaller footprints) runs the LLM on GPU or CPU. PostgreSQL stores session history, audit logs, and user preferences.

One‑Click Deployment (Docker Compose) # 1️⃣ Clone the repo git clone https://github.com/agentmash/agentmash.git && cd agentmash # 2️⃣ Create a.env file (example values) cat >.env << EOF POSTGRES_USER=agent POSTGRES_PASSWORD=secret POSTGRES_DB=agentmash LLM_MODEL=mistral-7b-instruct REDIS_PASSWORD=redispass EOF # 3️⃣ Spin up everything docker compose up -d Enter fullscreen mode Exit fullscreen mode The stack is up in ~2 minutes on a machine with 8 CPU cores and a single NVIDIA A10 GPU. The UI is reachable at http://localhost:8080.

#.github/workflows/agentmash.yml name: AI‑Assist Review on: [ push ] jobs: ai-review: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - name: Install AgentMash CLI run: | curl -L https://agentmash.io/cli | sudo bash - name: Run AI code review env: AGENTMASH_ENDPOINT: http://agentmash:8000 AGENTMASH_TOKEN: ${{ secrets.AGENTMASH_TOKEN }} run: | agentmash review \ --repo ${{ github.repository }} \ --ref ${{ github.sha }} \ --output report.json - name: Upload report uses: actions/upload-artifact@v3 with: name: ai-review-report path: report.json Enter fullscreen mode Exit fullscreen mode The same YAML works unchanged on GitLab CI by swapping the uses: lines for the GitLab equivalents.

Running Mistral‑7B on a single A10 costs roughly $30 per million tokens, a fraction of the $120‑plus you’d pay for an equivalent cloud model. Latency stays under 150 ms for typical code‑completion prompts, keeping the editing experience snappy.

Goal: Build a full‑stack TODO app in 48 hours with a team of four developers across three time zones.