SShortSingh.
Back to feed

Pending identity verification can resume after an app restart, per a SQLite fixture

0
·13 views

A hosted identity onboarding flow can lose track of a pending verification session if a deploy, crash or reschedule occurs before the provider's callback arrives. The article presents a synthetic SQLite-backed fixture in which a second process reopening the same database file sees the existing pending session, its deadline and grant count, with no duplicate session created. Duplicate callbacks are deduplicated by event ID, and a timeout recovery path handles sessions where no callback arrives. The example is local and synthetic only, does not verify real signatures or grant real permissions, and does not change live permissions. The author frames the key question as whether durable storage, not process memory, owns pending state and is read before the restarted process acts.

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 ·

NVIDIA Dynamo's SLA Planner Automates GPU Scaling for LLM Inference on Kubernetes

Static GPU allocation struggles with LLM inference because request patterns and sequence lengths vary, leading either to wasted compute or to missed latency targets. NVIDIA Dynamo's SLA Planner addresses this by profiling workloads and autoscaling GPU worker replicas to meet time-to-first-token and inter-token latency goals. A new guide explains how to deploy the planner on Kubernetes, covering Prometheus monitoring, DynamoGraphDeploymentRequest configuration for automated profiling, and Grafana dashboards for tracking scaling decisions. Prerequisites include a cluster with at least two NVIDIA GPU nodes, the GPU Operator, Helm 3, and NGC and Hugging Face access tokens. The result is an LLM deployment that scales GPU workers automatically to hold SLA targets.

0
ProgrammingDEV Community ·

Seven security checks to run on a vibe-coded app before launch

An article for developers who built apps with AI coding tools warns that these tools often prioritize functionality over security. It outlines a roughly ten-minute audit covering Supabase row-level security, server-side ownership checks, and payment flows that can be exploited. The author explains how to test whether a logged-out visitor can read tables by calling the Supabase REST API with the public anon key. The piece also advises searching frontend bundles and git history for leaked secret keys, including Supabase service_role keys that bypass all access rules. Each check comes with commands and suggested fixes, such as enabling owner-only policies and moving secrets to server-only code.

0
ProgrammingDEV Community ·

Guide Shows How to Run a Remote MCP Server and Connect It to Claude Desktop

A DEV Community tutorial explains how the Model Context Protocol organizes contextual data so language models can behave more consistently across sessions and tools. It walks readers through deploying a FastMCP-based server backed by an SQLite database on an Ubuntu cloud instance. The setup includes environment settings such as a read-only mode that blocks write queries for safety. The guide then shows how to use a local mcp-proxy to link the remote server, which streams responses via Server-Sent Events, to the Claude Desktop app.

0
ProgrammingDEV Community ·

Building an Explainable AI Lead Scoring Engine with FastAPI

A developer built a personal project called the Lead Qualification and Scoring Engine to help sales teams prioritize prospects. The application checks leads against an Ideal Customer Profile, produces a score from 0 to 100, and suggests a classification and next action. Its design uses AI to interpret unstructured lead information, while deterministic Python code applies configurable business rules to calculate the final score. Scoring criteria such as industry relevance, decision-maker role, and buying signals are stored in a JSON file, so each score can be explained and tested. The system is built with FastAPI, Pydantic, SQLAlchemy, SQLite, and Pytest.