Developer shares custom JWT auth setup for Next.js 15 SaaS projects

·1 views

A developer has detailed a custom JWT-based authentication pattern for Next.js 15, opting out of NextAuth in favor of direct token control. The setup uses httpOnly cookies to store signed JWTs, preventing client-side JavaScript from accessing tokens and reducing XSS-based token theft risk. Server actions handle login by verifying credentials with bcrypt and setting a secure session cookie valid for seven days. Next.js middleware intercepts requests to protected routes, redirecting unauthenticated users before any server-side data fetching begins. The approach is intended for SaaS dashboards requiring custom token claims and cookie strategies without third-party abstraction layers.

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)

Terraform and AWS Step Functions Can Automate Dell PowerProtect Backup Deployments

Enterprises deploying Dell PowerProtect backup components — PPDM, DDVE, and DDMC — on AWS often struggle with complex, error-prone manual provisioning across multiple accounts and regions. A Terraform-based framework can automate this process across four layers: infrastructure setup, appliance deployment, post-deployment configuration, and operational monitoring. AWS Step Functions, combined with Lambda, handle sequenced post-deployment tasks such as REST API configuration, SSH-based setup, and credential rotation via Secrets Manager. A notable technical requirement is bidirectional DNS validation, where PPDM checks that forward and reverse DNS records for integrated appliances match before accepting them. Adopting this Infrastructure as Code approach reduces configuration drift, eliminates undocumented manual steps, and enables consistent, repeatable deployments at scale.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

MCP Protocol Offers a Unified Alternative to Custom AI Integrations for SaaS Teams

As AI agents become embedded in everyday software, many development teams are building separate custom integrations for each AI platform — a process that quickly multiplies maintenance costs and complexity. Model Context Protocol (MCP) is a standardized layer designed specifically for AI agents, enabling them to discover and use software capabilities without requiring platform-specific builds. Unlike REST APIs built for human developers, MCP provides structured tool definitions and consistent capability descriptions that AI systems can interpret directly. A single MCP server can serve multiple AI clients — including ChatGPT, Claude, and custom agents — replacing the need for redundant integrations. SaaS companies that already maintain OpenAPI specifications are particularly well-positioned to adopt MCP, as much of the required capability documentation already exists in their existing API specs.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why Engineers Should Use ArgoCD Instead of Terraform to Deploy Into EKS

Many engineering teams use Terraform not just to provision AWS infrastructure but also to manage Kubernetes resources inside EKS clusters, including Helm charts and application manifests. However, this approach blurs the boundary between infrastructure and application layers due to a fundamental lifecycle mismatch. Resources like VPCs and IAM roles change infrequently, while Kubernetes manifests and image tags change constantly, making Terraform an unsuitable release engine for both. Mixing the two can cause Terraform and ArgoCD to conflict over cluster state, force rollbacks to carry unnecessary infrastructure risk, and expose CI pipelines to cluster credentials they should not hold. The recommended model restricts Terraform to AWS and the EKS perimeter while delegating all in-cluster deployments to ArgoCD, keeping the two layers cleanly separated.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer builds lightweight voice preview agent to cut AI content generation costs

A developer has designed a lightweight 'preview agent' to audition AI-generated writing personas before committing to full, costly content generation runs. The agent uses no external tools, relies entirely on inline context, and routes requests to a small local model, keeping each preview to roughly 500 tokens at near-zero cost. Output is schema-enforced, requiring only two to six sample sentences in the target voice, with no storage, no history, and a 90-second timeout to keep the process fast and clean. A simple sample button in the settings UI lets users test a configured identity instantly, without invalidating any existing work. The broader principle the developer highlights is matching the model to the narrowness of the task, arguing that a constrained local model is often more honest and representative than a powerful frontier model given too much freedom.

0 comments Read more at DEV Community