Results for “detection”

Analyze existing code files to generate a project-specific coding standards document, with options for templates, README insertion, and inconsistency detection.

Trains, evaluates, exports, and runs inference for Siamese-network-based optical inspection models to detect manufacturing defects and quality issues in image pairs.

Add light/dark mode, custom branded themes, and runtime theme switching to .NET MAUI apps using AppThemeBinding, ResourceDictionary swapping, and system theme detection APIs.

Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness, providing detailed improvement recommendations with frameworks, testing methodologies, and educational content.

Analyze images using Azure AI Vision SDK: generate captions, tags, detect objects, extract text (OCR), detect people, and suggest smart crops.

Audits test code in any language using the academic testsmells.org 19-smell catalog, producing a severity-ranked report with specific locations and actionable fixes.

Analyze CJA dimensions for cardinality, distribution, trends, anomalies, data quality, comparisons, and forecasting, producing an interactive HTML dashboard or markdown report.

Maps, documents, and onboards into an existing codebase by generating seven structured documents covering stack, structure, architecture, conventions, integrations, testing, and concerns.

Orchestrates a dimensional-analysis pipeline to annotate codebases with unit/dimension comments, discover dimensional vocabulary, and detect arithmetic bugs from unit mismatches or precision loss.

Detect anomalies in time-series data using the Azure AI Anomaly Detector SDK for Java, with support for univariate and multivariate analysis, model training, and inference.

Translate text in real-time, detect languages, transliterate between scripts, and look up dictionary entries using Azure AI Translator SDK for Python.

Analyze a repository to generate recommended Claude Code settings.json permissions for read-only commands, detecting tech stack, build tools, and monorepo structure.

Integrate real-time bidirectional voice conversations with AI assistants using the Azure AI VoiceLive SDK for Java, including WebSocket streaming, turn detection, and voice configuration.

Performs security-focused differential review of code changes (PRs, commits, diffs), adapting analysis depth to codebase size and generating comprehensive markdown reports.

Analyze text and images for harmful content using Azure AI Content Safety SDK for Java. Supports hate, violence, sexual content, and self-harm detection with blocklist management.

Build, deploy, evaluate, debug, and measure latency for a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection and VLM classification.

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Runs a three-layer verification pipeline on AI-generated output: extracts verifiable claims, finds supporting or contradicting sources via web search, and produces a structured verification report with source links for human review.

Scans codebases for security vulnerabilities by reasoning about code context, data flow, and component interactions, covering injection flaws, secrets exposure, authentication issues, and weak cryptography across multiple languages.

Detects fail-open insecure defaults (hardcoded secrets, weak auth, permissive security) that allow apps to run insecurely in production. Use when auditing security, reviewing config management, or analyzing environment variable handling.

Provides file paths to language-specific reference files for polyglot test analysis skills, enabling framework-aware detection of test markers, assertions, and patterns across .NET, Python, TypeScript, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, and C++.

Captures a spatial hierarchy of rendered DOM elements from any webpage via Playwright CLI, returning an LLM-friendly text tree, structured JSON tree, and a node map with CSS selectors and overlay metadata.

Automates the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models, including baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until KPI targets are met.

Detects all languages used on a webpage — both declared (html@lang, hreflang, nested lang=, meta content-language) and actually present in the body text using Google CLD3 WASM. Reconciles the two signal sets and flags mismatches for i18n audits, hreflang validation, and multilingual content verification.