condi.dev

The system

Five surfaces, one fabric, zero ops burden.

Everything routes through a single Cloudflare Worker backed by D1, KV, and R2. No containers. No uptime monitoring. No infrastructure to babysit.

Terminal CodingMCP

Web PortalsSSE MCP

Telegrambot

Browseringest

Dashboardread

conduit-fabric · api.condi.dev Cloudflare Worker + Durable Object · 25 MCP tools · Cron engine · LLM proxy · Auth

D1tasks, context, widgets

KVOAuth, config, email

R2media, attachments

api.condi.dev - MCP server + REST API

dashboard.condi.dev - widget dashboard

tools.condi.dev - LLM arena

Live surfaces sit behind Cloudflare Access. Happy to walk through a demo.

Capabilities

What it actually does.

LLM orchestration

Route the same prompt to OpenAI, xAI, Gemini, and Claude with provider-specific adapters. Compare responses side by side, pick the best output, switch providers without touching application code. The arena at tools.condi.dev makes this interactive.

// fan out across all four providers
const results = await send_to_llm({
  prompt: "Review this PR for security issues",
  providers: ["anthropic", "openai", "gemini", "xai"],
  reasoning: true
});

// → parallel fetch, per-provider timeout
// → responses returned as they arrive
// → arena UI: compare side-by-side or consolidate

MCP tool server

Around two dozen tools exposed via Model Context Protocol - context ingestion, task tracking, project management, full-text search, cross-model workflows, email, dashboard publishing. Connected to Claude Code and Claude.ai as a live integration, not a demo.

// MCP tool surface
create_task({ title, scope, priority, project, tags })
ingest_context({ kind, content, tags, project })
search_context({ query, filters })
list_projects({ kind, status })
link_items({ from, to, relation })
send_to_llm({ prompt, providers, reasoning })
send_email({ to, subject, markdown })
dashboard_publish({ slot, category, content })
triage_inbox({ limit })

// plus: get_tasks, update_task, get_context, ...

Context capture & retrieval

Content goes in messy - meeting notes, research dumps, raw captures. Conduit tags and distills it into LLM-ready context, chunks it for RAG retrieval, and stores relations as a first-class graph so references don't get lost. Retrieval is FTS5 full-text search across everything, not keyword matching against filenames.

// messy input
await ingest_context({
  title: "standup notes 4/18",
  content: raw_dump,
  tags: ["work", "defense-ux"]
});

// retrieval pipeline
// → LLM-auto-tagged and distilled
// → chunked for RAG retrieval
// → FTS5 full-text search (D1)
// → graph: spawned / blocks / references

Dashboard & automation

A slot-based, cron-driven information dashboard. Morning news briefings, task summaries, evening recaps - all generated by scheduled AI jobs and published to named widget slots. ETag-cached polling keeps it snappy. Time-travel archive lets you scroll back through previous versions.

Morning briefing - cron-generated news digest

Proof of life

This isn't a side project.

Conduit Fabric handles task management, context capture, document intelligence, and multi-provider LLM routing across every working session. The dashboard runs cron jobs at 6am, noon, and 9pm - morning briefing, task triage, evening recap.

The MCP server is connected to Claude Code and Claude.ai right now. Work flows through Conduit.

MCP tools

Scheduled cron jobs

LLM providers

Worker. Zero ops.

About

Steve Biggs

20+ years building defense software, simulation systems, and training platforms. Currently exploring what happens when one engineer treats AI tooling as infrastructure instead of a novelty.

Before Conduit, my AI setup was a pile of disjointed MCP servers - hard to update, capabilities scattered across terminals, apps, and browsers, nothing speaking to anything else. Conduit started as the cleanup. It turned into the system I work through every day: one place to capture context, track tasks, route prompts across providers, and publish back to a dashboard I actually read.

Huntsville, AL · Built on Cloudflare

AI infrastructure I built for myself - and use every day.