Related Links

A curated set of external links — a sample implementation, a talk, and write-ups and books by people who have used NexusArchitect

This page collects articles written by people who have actually used NexusArchitect, along with a sample of generated code. Browse these before you install if you want a feel for what the output looks like.

Sample implementation

api-impl: a reference implementation of microservice API patterns

Five API patterns — plain CRUD, 2PC, Saga, TCC, and GraphQL — designed and generated end-to-end with an e-commerce order system as the sample domain. Five microservices built with Java/Spring Boot/ScalarDB

View on GitHub

Event

AI-Driven Modernization with Claude Code, and Tips for Using AI (Japanese)

Held June 23, 2026. A meetup on AI-driven modernization with Nexus Architect, walking through the full process from analyzing an existing codebase to evaluation, redesign, and design review

View on connpass

Series: AI-driven refactoring of a legacy POS system (6 parts, Japanese)

Using Nexus Architect × ScalarDB, this series analyzes, redesigns, implements, and measures the impact of modernizing a legacy POS system carrying real technical debt.

Part 1: The big picture of AI-driven legacy refactoring

Introduces the combined approach of Nexus Architect, ScalarDB, and Compound Engineering, plus the technical debt built into the legacy POS system used as the case study

Read on Zenn

Part 2: From analysis to design review

Runs the automated pipeline against an existing Java monolith, tracing bounded-context redesign, target architecture generation, and a five-lens design review

Read on Zenn

Part 3: Reading the current-state, domain, and evaluation reports

Analyzes the auto-generated current-state and domain reports, and presents an improvement roadmap from a 46% MMI score and 24.5% DDD adoption score

Read on Zenn

Part 4: Reading the ScalarDB design and review results

A redesign into 13 microservices, ScalarDB schema design, Outbox, hybrid Saga, and CQRS — and how the design went from a failing review to passing one

Read on Zenn

Part 5: Carrying the design into implementation with Compound Engineering

Breaks the detailed design into an implementation plan and builds out 13 services incrementally, capturing lessons learned in docs/solutions/ for reuse

Read on Zenn

Part 6: Modernization results and wrap-up

Measures the improvement — 86.8% DDD compliance, 77.4% average MMI — and wraps up with advice for starting a modernization effort in practice

Read on Zenn

Series: AI-driven product development (5 parts, Japanese)

Using an internal web app, “RADAR” (deal and account management), this series follows the project from planning through pairing with AI, MVP validation, production design, and implementation.

Part 1: From spreadsheet to web app

The overall story and repository structure behind migrating deal and account management from Google Sheets to the “RADAR” web app

Read on Zenn

Part 2: Defining requirements with AI

How vision, success metrics, personas, and UI mocks were progressively made concrete through dialogue with AI

Read on Zenn

Part 3: From MVP to production design

Validating hypotheses with a disposable concierge MVP, then the decision to move to a production stack, plus MMI/DDD scoring of the MVP code

Read on Zenn

Part 4: Design review for production readiness

Organizing the domain into five bounded contexts with a modular-monolith design, and cutting critical issues from 7 to 0 through a five-lens review

Read on Zenn

Part 5: Building the platform with Spring Boot × Next.js

Backend, frontend, auth, and audit-log implementation, plus practical AI-driven-development patterns learned from running the product in production

Read on Zenn

Individual articles (Japanese)

Connecting planning, design, issues, and implementation in AI-driven development

How design documents in reports/ are turned into an Epic/Sub-Epic/Issue hierarchy, tying planning through to code review and merge into a single flow

Read on Zenn

Estimating the AI token cost of running nexus-architect

Managing pipeline cost with three mechanisms: upfront estimation, automated measurement during a run, and calibration against actual results

Read on Zenn

Letting AI name a product: putting the naming skill into practice

A worked example of acronym-style product naming with /product:name-product, from building a word bank through candidate generation to trademark/domain checks

Read on Zenn

Books (Japanese)

AI-Driven POS Modernization with Claude Code: Nexus Architect and ScalarDB

The ScalarDB series above, compiled into a book. 23 chapters, free to read, covering analysis and design through implementation and impact measurement

Read for free

Software Development Alongside an AI Agent

The RADAR series above, compiled into a book, from brainstorming with AI through hypothesis validation, design, implementation, and post-launch improvement

Read for free