Spec Driven Development: How Structured Specs Keep AI Coding Agents on Track

·1 views

Spec driven development replaces vague prompts with formal specification documents as the authoritative source of truth for AI coding agents, aiming to prevent scope drift and misinterpretation. The workflow follows seven phases — constitution, specify, clarify, plan, tasks, implement, and analyze — with a human review gate between each stage. A key technique involves writing acceptance criteria in EARS notation, converting ambiguous requirements into precise, testable statements that agents cannot easily misread. Critics note that poorly implemented versions of this approach can resemble traditional waterfall development with an AI layer added on top. Tools supporting the methodology include the open-source GitHub Spec Kit and AWS Kiro, an agentic IDE that reached international general availability on May 7, 2026, with over 250,000 developers already on board.

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)

How One Developer Fixed Date Localization in React Native Using i18next and Intl API

A developer building a React Native app found that after integrating i18next for text translation, date formats like '7月24日(金)' continued to display in Japanese regardless of the selected language. The root cause was that dates formatted directly via JavaScript's Date object bypassed the translation pipeline entirely. To fix this, the developer unified language detection by storing user preferences in SecureStore and routing both i18next and Intl.DateTimeFormat through the same language setting. A custom formatScheduleDate function was built using Intl.DateTimeFormat to render dates correctly in either Japanese or English based on the resolved language. The developer noted that true multi-language support must extend beyond JSON translation strings to cover all dynamically generated content, including dates, times, and error messages.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How a Serverless AWS Lambda Container Segments and Straightens Card Photos Using AI

A developer has built an image segmentation pipeline on AWS Lambda that takes a crooked, background-filled photo of an AWS Builder Card and returns a clean, cropped, straightened version. The system uses two Lambda functions: one to classify whether the uploaded image contains a valid card, and a second called image-processor to isolate and clean it. The segmentation work is handled by the BiRefNet-General-Lite model, loaded via the rembg library and running on CPU through ONNX Runtime, with no PyTorch required. Memory management proved a key challenge, as the full BiRefNet-General model caused out-of-memory errors even on AWS Lambda's largest available configuration. The lighter Lite variant was ultimately adopted to stay within Lambda's memory limits, with costs and vCPU allocation tied directly to the memory size chosen.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

TRC-20, BEP-20, Arbitrum or TON: Choosing the Right Chain for USDT Payouts

Developers building automated payout systems for platforms like freelance marketplaces or AI agent networks increasingly rely on USDT stablecoins across multiple blockchains instead of traditional bank transfers. TRC-20 on the Tron network remains popular for high-volume, low-value payouts due to fees as low as $0.50–$1.00 and near-universal exchange support. BEP-20 on BNB Smart Chain offers EVM compatibility and Solidity smart contract support, making it suited for treasury and agent-to-agent payment logic. Arbitrum, an Ethereum Layer 2, provides the lowest fees for complex operations at $0.10–$0.30 per transfer while inheriting Ethereum's security, though it requires users to understand bridging. Each chain involves distinct trade-offs around fees, developer tooling, exchange support, and user familiarity, meaning the best choice depends on the specific use case and audience.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How JWT Authentication Works and How to Use It Securely

A JSON Web Token (JWT) is a three-part string — header, payload, and signature — used to authenticate users in stateless applications without server-side session storage. When a user logs in, the server verifies their credentials, generates a signed JWT containing user data, and sends it back to the client for use in subsequent requests. Because the payload is only base64-encoded and not encrypted, sensitive information such as passwords should never be stored inside a token. Security best practices include storing tokens in httpOnly cookies rather than localStorage to reduce XSS risk, setting short expiration times, and preferring asymmetric RS256 signing over symmetric HS256 in production. JWTs are well-suited for stateless APIs, mobile apps, and single sign-on systems, but traditional server-side sessions may be preferable when instant token revocation is required.

0 comments Read more at DEV Community