You might find something interesting here ^_^
The article dissects DeepSeek Harness, an open‑source AI agent framework, showing how it solves the long‑standing “dynamic composition” problem by introducing revertible effects and reactive coeffects, enabling safe self‑modification without downtime or hidden side‑effects.
The article dissects the IEEE‑style “Loop Engineering” playbook, showing how to move from ad‑hoc prompt writing to building repeatable, verifiable, stateful systems that automatically discover work, hand it to isolated agents, verify outcomes, persist state, and schedule the next run.
The article compares Loop and Multica, showing how Loop’s “colleague” model—featuring a three‑layer Soul/Agent/Instance identity, explicit @‑based dispatch, rich multi‑agent orchestration, rewind capability, scheduled tasks, and precise external event routing—outperforms Multica’s simpler “outsourced task” approach despite Multica’s broader tool matrix.
The article details how the author extended the intelligent‑trading‑bot (ITB) with a new Binance futures execution pipeline using Codex CLI for TDD and GLM‑5.2 for context hand‑off, illustrating three business chains, incremental computation, state‑machine design, crash recovery, risk controls, and nine real‑world bugs uncovered during full testnet verification.
The article explains how to self‑host Multica using a three‑container Docker Compose setup—PostgreSQL, backend, and frontend—detailing secure defaults, health‑checked service dependencies, advisory‑lock‑based migrations, loopback port bindings, and two launch methods (official images or local builds) that let you start the system with just three commands.
The article explains Multica's three‑layer contract architecture—core, ui, and views—and how enforcing a downward‑only dependency flow lets web, Electron desktop, and React Native mobile apps share a single codebase while avoiding duplication, version drift, and runtime mismatches.
This article explains how Multica converts a well‑crafted prompt into a reusable, team‑shared skill by storing it as a SKILL.md file, synchronizing it from cloud to local machines, and exposing it through slash links and built‑in skills for agents.
This article dissects Multica's agent task lifecycle, explaining how a state‑machine model, three‑level watchdog timeouts, a string‑based failure classifier, and precise cache‑invalidation rules together ensure agents run reliably, distinguish between long‑running and stuck tasks, and report progress without overwhelming the network.
The article dissects Multica’s Autopilot feature, explaining its time‑event‑driven architecture, the three‑part mental model of trigger, execution mode, and agent, and compares create_issue versus run_only modes, placeholder validation, and the 30‑second scheduler tick that balances precision and load.
Multica treats AI agents as colleagues rather than tools, running them on users' machines while the server only stores data, queues tasks, and broadcasts events, and uses a four‑layer frontend, dual data streams, and a set of core modules to let a small team manage dozens of agents with real‑time visibility and measurable productivity.
