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The locked, opinionated stack behind the Novus ecosystem and how it is built — code-as-content architecture, type-safe content, edge rendering, and the AI-assisted engineering workflow that ships it solo.

15 articles

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All15The stack6AI-assisted engineering9

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  1. How we standardized on Claude Code and Claude to build our apps

    Field notes · Jun 7, 2026

    How we standardized on Claude Code to build our apps

    Our path through AI coding tools and LLMs, and why an agentic flow won out for a small business.

    Novus Stream Solutions (hub)

  2. Reliability layers: device detection, integrity checks, canonical queue, result guards, and honest errors

    Field guide · Jun 7, 2026

    Reliability hardening: device lifecycle, model integrity, and honest failures

    An engineering note on the unglamorous guarantees that make a tool trustworthy — and why honest failures beat silent wrong answers.

    Stack & engineering

  3. A bug reported in one tool traced to a shared pattern present across the whole suite, fixed everywhere at once

    Field guide · Jun 6, 2026

    Audit every tool, not just the broken one: the "all-tools" doctrine behind our refactors

    Why a bug reported in one place is almost always a pattern that lives in several, and the discipline of fixing the pattern everywhere at once.

    Engineering

  4. Parallel investigation agents mapping different facets of a problem before any implementation begins

    Field guide · Jun 5, 2026

    Running a multi-agent research sprint before touching code

    How to use parallel agents to understand a problem completely before changing anything, and why that front-loaded research pays for itself on large work.

    Engineering

  5. A large refactor decomposed into self-contained units backed by durable artifacts rather than held in working memory

    Field guide · Jun 5, 2026

    Managing the context window on a large refactor: what broke and how we fixed our sessions

    A workflow lesson, not model marketing: how to structure a large refactor so it survives the limits of what can be held in working context at once.

    Engineering

  6. A human approver directing and reviewing AI agent execution across a build pipeline

    Field guide · Jun 4, 2026

    The approver model: running a build pipeline where AI writes and you review

    The operating model for one person directing AI execution: what the human keeps, what the agent takes, and how to keep the review meaningful.

    Engineering

  7. A map of autonomous agent zones and mandatory human-review checkpoints divided by reversibility and blast radius

    Field guide · Jun 4, 2026

    Guardrails and human review: where we let the agent run and where we don't

    A practical map of where AI agents operate autonomously and where a human checkpoint is non-negotiable, and the reasoning behind each boundary.

    Engineering

  8. A three-stage workflow from prompt expansion through planning to coding, with the planning step highlighted

    Field guide · Jun 3, 2026

    Our three-mode workflow: prompt expansion → planning → coding (and why we never skip the middle)

    Why the planning pass between understanding a request and writing code is the step that determines whether AI-assisted development produces something maintainable.

    Engineering

  9. A model registry being checked against the Hugging Face API, with unverified entries flagged and replaced

    Field guide · Jun 1, 2026

    Shipping a model registry you can trust: auditing against Hugging Face

    An engineering note on auditing an AI model registry for honesty — verified IDs, real sizes and licenses, and classical baselines labelled as such.

    Stack & engineering

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