Using Git with coding agents shows how to use Git as the authoritative context, audit trail, and control plane for coding agents. Treating Git as the agent control plane makes sessions reproducible, provides an undoable audit trail, and folds documentation and collaboration into agent workflows (Principles 03 & 13).

The Bug That Shipped documents how coding agents commonly miss deployment-level failures and trigger thundering‑herd problems when left untested. Outcome engineers must embed explicit testing, throttling, and runtime safety gates before agents reach production to avoid systemic failures (Principle 14).

4 tips for building better AI agents that your business can trust distills measurement, collaboration, experimentation, and human oversight as core rules for trustworthy enterprise agents. Operationalize those rules to create continuous evaluation and human-in-the-loop feedback loops so agents become reliable infrastructure rather than one-off experiments (Principle 16).

Tinybox — offline AI device, 120B parameters ships affordable on‑prem machines that can run and train large models locally. On‑prem LLMs shift your architecture: lower latency, tighter data control, and different failure modes to design for — build your island and rethink deployment and compliance boundaries (Principle 07).

Show HN: Atomic – Self-hosted, semantically-connected personal knowledge base turns markdown notes into a self-hosted, AI-augmented knowledge graph with search, canvas, synthesis, and chat. Using a lightweight, auditable personal graph as an agent memory or context layer improves retrieval quality, traceability, and composability for downstream agents (Principle 11).