SShortSingh.
Back to feed

Developer Refines iOS Fretboard App Using AI Pair Programming and Swift Testing

0
·1 views

A developer shared their Week 16 progress log detailing advances on a personal iOS fretboard practice application built with SwiftUI. Working alongside OpenAI's Codex, they defined and implemented the completion logic for a Note Practice mode, including progress display, feedback timers, fret-range controls, and automatic question transitions. When Codex generated unit tests using the older XCTest framework, the developer revised them to use Apple's newer Swift Testing framework, updating project documentation to preserve the policy for future AI sessions. Key technical work included implementing NotePracticeCalculator, introducing FretCellVisualState for explicit UI state representation, and resolving a Swift Concurrency warning by marking FretPosition as nonisolated. The developer also made incremental progress on TryHackMe's AI Security Learning Path alongside the app work.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Event-Driven Architecture: Key Patterns, Benefits, and Common Pitfalls

Event-Driven Architecture (EDA) is a software design pattern that enables loosely coupled, highly scalable systems by having components communicate through asynchronous events rather than direct calls. Core benefits include easier scalability, component flexibility, and a complete audit trail when events are stored as an immutable sequence. Developers are advised to separate read and write models, design events to be immutable, and implement robust error handling with retry mechanisms. Common mistakes include over-engineering solutions, introducing unnecessary complexity, and misusing events for synchronous communication. In cloud-native environments, EDA pairs well with services like AWS Lambda and Google Cloud Functions to handle real-time event spikes at scale.

0
ProgrammingDEV Community ·

Why Structured Workflows Often Outperform Autonomous AI Agents in Practice

A developer and AI builder argues that structured workflows deserve more attention than autonomous agents, which have become the default recommendation across the AI community. While agents offer flexibility, they introduce compounding complexity — more prompts, APIs, failure points, and harder-to-trace errors — that many projects do not actually require. Predictable, step-by-step workflows are easier to test, debug, monitor, and scale over time, since each component holds a clearly defined responsibility. The author also emphasizes that integration with external systems like GitHub, databases, and APIs often delivers greater business value than sophisticated but isolated agent architectures. The core advice is to first ask what the simplest workflow is that solves a problem, rather than defaulting to an autonomous agent from the outset.

0
ProgrammingDEV Community ·

Engineers Design Fail-Closed WORM Architecture for Multi-Agent AI Coordination

A development team has published details of a 10-tuple canonical envelope architecture designed to ensure reliable state management across asynchronous, multi-agent AI systems. The approach enforces five strict invariants, including transactional ingestion boundaries using PostgreSQL ACID transactions and cryptographic HMAC witness seals on all inter-agent messages. A fail-closed default principle means any unverified or unwitnessed claim is automatically placed on hold, preventing unauthorized state mutations. Chaos testing across 82 continuous integration cycles reportedly achieved a 100% pass rate for single-effect-per-event enforcement with zero duplicate state transitions. The team recommends that system architects avoid unauthenticated webhooks, isolate secrets outside cloud workspaces, and use autonomous cleanup agents to manage expired claims.