Observed Signal · May 26, 2026 · Technical Case Study · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
AI-Native qrspi Dev Flow for Go Side Projects
A developer describes using an AI-native, structured workflow (qrspi: question → research → structure → plan → implement) to build a production-shaped Go side project from an existing hexagonal backend template. The project is a QR code generator; the author forked a hardened Go template, pruned unused features, documented deviations from an initial spec, and drove each feature through an eight-phase qrspi process that produces committed design artifacts (questions.md, research.md, design.md, plan.md) alongside code. Tooling and patterns used include depguard for package boundaries, sqlc, MinIO for object storage, Redis for a redirect cache, and a write-behind click-count buffer to protect Postgres. The repo and qrspi artifacts are published on GitHub; the post outlines trade-offs, costs (added documentation overhead), benefits (reviewable diffs, architectural drift prevention), and planned follow-ups (metrics slice, deletion endpoints).
Practical example of AI-driven developer workflows and artifact-first agent integration; relevant to teams adopting LLM-assisted engineering but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Author built a QR code generator on GitHub at linkc0829/go-qrcode-generator.
- Development used a feature-first hexagonal Go backend template (linkc0829/go-backend-template) and enforced boundaries with depguard.
- Introduced an eight-phase AI-native workflow called qrspi (question → research → structure → plan → implement) to drive each feature from ticket to PR with committed design artifacts.
- Implementation choices included sqlc over an ORM, MinIO for object storage (no CDN in local setup), Redis for a read-heavy redirect cache, and Postgres for persistence.
- Publication date (page metadata): 2026-05-26.
Connected Companies & Entities
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Related Market Signals & Shifts
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AI Builds Prototypes; Production Remains Hard
The author argues that AI coding tools make it quick and easy to produce working prototypes, but moving projects into reliable production remains difficult due to practical operational gaps such as auth providers, environment variables, database row-level security, webhooks, billing, deployment settings, provider dashboards, monitoring, and version control hygiene. To address this, the author published an open-source tool called VibeRaven that scans a repository and generates a 'mission map' identifying existing components, missing items, provider actions required, and verifications needed before launch. The post includes a GitHub link and a one-line run command (npx -y viberaven). The article was posted on DEV Community by Ohad Krispin on 2026-06-18.
Lore as Code: Compiling a 30‑Chapter Novel with AI
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Spec-driven development for AI coding agents
The article describes a spec-driven development workflow for AI coding agents centered on a short constitution, a WHAT/WHY spec, and a frozen HOW plan. To keep long-running features reviewable and prevent 'context rot' and hallucination across agent sessions, the author adds operational mechanisms: explicit checkpoints (review-sized slices with gates and evidence) and structured handoffs (living documents that orient the next session and record decisions and state). The author also recommends tiering model usage (stronger models for planning/review, lighter models for mechanical coding) and provides a starter-kit repository with templates for constitution, specs, checkpoint trackers, and handoff documents.
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