Guide Details Secure End-to-End Auth Flow for React and Spring Boot Apps

·1 views

A technical guide published on DEV Community outlines a complete authentication system combining React on the frontend with a Spring Boot backend. The flow covers login, JWT issuance, secure token storage, protected routing, token refresh, and logout. JWTs are stored in httpOnly, secure cookies rather than localStorage to reduce exposure to cross-site scripting attacks. Because cookies are used, CSRF protection is implemented via Spring Security's double-submit cookie pattern using CookieCsrfTokenRepository. Short-lived access tokens paired with refresh tokens are recommended to limit the window of exposure if credentials are compromised.

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)

OpenAI Cuts GPT-5.6 Luna and Terra Prices, Adds Premium Fast Mode for Sol

OpenAI has revised pricing for its GPT-5.6 model family, reducing Luna tier costs by 80% and Terra tier costs by 20%, while leaving the flagship Sol tier's standard rates unchanged. At launch, Sol was priced at $5 per million input tokens and $30 per million output tokens, with Terra and Luna offered at progressively lower rates. A new Fast mode has been introduced for Sol, offering up to 2.5 times faster processing speeds at twice the standard Sol price, positioning it as a premium performance option rather than a discount. The changes reinforce OpenAI's tiered strategy, making lower-cost tiers more economical for high-volume or routine workloads while preserving Sol for capability-intensive tasks. Developers are advised to evaluate model selection at the workload level and treat Sol Fast mode as a separate, premium cost category in budgeting and governance.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Payment processors emerge as quiet winners of the AI startup boom

As AI tools lower the barrier to launching online businesses and SaaS products, a growing number of analysts and observers argue that payment infrastructure providers stand to gain the most. Platforms like Stripe, Mercado Pago, and PayPal profit from transaction volume regardless of whether individual AI-powered ventures succeed or fail. This mirrors a historical pattern from the California Gold Rush, where suppliers of tools and equipment often outearned the prospectors themselves. Even major AI companies such as OpenAI and Midjourney rely on the same payment rails to process their subscription revenue. The argument suggests that while the AI revolution is genuine, the most consistent beneficiaries may be the financial infrastructure companies handling every digital transaction it generates.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer builds meta-server to search 75,000 MCP servers using BM25 ranking

A developer has released mcp-anything, a single MCP server that lets AI agents search and connect to tools across a fragmented ecosystem of roughly 75,000 indexed MCP servers. The tool aggregates four catalogs — the official MCP registry, PulseMCP, npm, and Glama — deduplicating entries and ranking results using BM25 lexical search combined with a popularity boost from GitHub stars and download counts. Rather than requiring users to manually find, configure, and restart servers, the meta-server exposes five tools that allow the model itself to discover, inspect, and call external servers on demand. Security features include SSRF protection, a disabled-by-default code execution mode requiring an explicit allowlist, and labeling of third-party tool output to reduce tool-poisoning risk. A public hosted instance is available in discovery-only mode, while full execution capability is intentionally restricted to local deployments.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Cheapest AI model beats mid-tier rivals costing up to 14x more, analysis finds

A developer benchmarking AI models on their API plan found that MiMo-V2.5, priced at $0.14 per million input tokens, outperformed several mid-range models costing between $1.20 and $2.00 on coding tasks. While MiMo-V2.5 lost all four benchmarks — coding, reasoning, math, and bug-fixing — to the pricier DeepSeek v4 Flash at $0.44, it beat competitors like Qwen3.6 Plus and GLM-5.1 on coding scores despite their significantly higher costs. MiMo-V2.5 also offered around 30,100 requests per five-hour window, compared to as few as 880 for some higher-priced rivals. The analysis highlights that the reasoning gap between cheap and premium models remains significant, with a 27% difference that can meaningfully affect performance on complex tasks. The key takeaway is that mid-tier models are charging a premium for benchmark scores they do not actually hold over the cheapest options available.

0 comments Read more at DEV Community