Projects

Long multi-stage plans turn you into a babysitter: pick the next stage, spawn an agent session, watch for stalls, check the work, repeat until midnight. Conductor mechanises that loop — one session at a time, resumable at any point, watchable from a phone.

The core design decision is the trust model: an agent's report of its own work is never evidence. After every session Conductor independently re-runs the gate battery and reads the real exit codes, checks git for new commits, and diffs the progress tracker. A checkpoint counts only when all three agree. Red gates spawn a fix session with the actual failing output embedded in the prompt.

Under the hood it's built to be killed: the full run state is snapshotted atomically on every transition and owed work is recorded as data, so you can kill the process anywhere — Ctrl+C, reboot, power cut — and `conductor run` picks up where it left off, resuming the interrupted agent session. Sessions resolve to one of eight outcomes rather than pass/fail, a watchdog catches stalls and token-budget exhaustion with clean handoffs, and a cheap second-model advisor is consulted only at genuine dead ends. A live TUI dashboard runs in-process, with a separate Go companion app attaching over SSE.

Coding agents re-explore a codebase from cold at the start of every session — grep, open, follow, forget. And the naive alternative, a call graph, dead-ends at exactly the places modern .NET hides its wiring: MediatR handlers, DI registrations, message-bus consumers. Nothing calls anything; everything is connected by convention.

DevContext analyses a solution once into a typed code graph and answers two questions: the Map (what is here — architecture style, topology, entry points) and the Trace (how things connect — an endpoint through handlers, events, consumers, entities). Edges are built by joining detections rather than following calls, and each one carries provenance (file:line), a resolution kind, and a confidence. A priority ladder makes semantic edges outrank raw syntactic calls, so the trace follows meaning, not text.

It has three faces over the same graph: a CLI, a desktop app with an explore workbench and a context studio that assembles LLM-ready context packs against a token budget, and an MCP server so agents can query the graph instead of grepping.

Algorithmic trading under prop-firm rules is an unforgiving environment: breach the daily drawdown once and the account is gone. You cannot learn those lessons against a live broker, and a backtester that's a separate implementation of the strategy proves nothing about the code that actually trades.

So the engine is built around a pure reducer kernel: (state, market event) → (new state, intents). No I/O, no clock, no randomness inside — every side effect lives at the edges. Backtesting is not a parallel code path; it is the same reducer fed recorded events, which means a backtest exercises the exact logic that runs live, and any trading day can be replayed deterministically to inspect why a decision happened.

Strategy output passes through a risk governor before it becomes an order — position sizing, exposure caps, and prop-firm constraints (daily and total drawdown) modelled as first-class domain objects with the power of veto. Around the kernel: multi-symbol, multi-timeframe indicators, an event log in SQLite, a cTrader adapter, a web dashboard, and a simulation test tier that runs credential-free.