a5c.ai

Generate visual representations of algorithm execution

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Build medical AI agents for clinical decision support, medical record summarization, diagnostic assistance, and healthcare workflow automation.

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Scaffolds MCP App project structure with correct directory layout, dependencies, entry points, and framework-specific templates. Handles React (useApp hook), Vanilla JS, Vue, Svelte, Preact, and Solid.

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Weaviate vector database setup with GraphQL queries and hybrid search

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Comprehensive verification checklists for MCP Apps. Tests with basic-host reference, validates handler-before-connect, text fallback, resource URI linking, single-file bundling, host styling, CSP, and legacy pattern detection.

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LLM-based zero-shot and few-shot classification for flexible intent detection

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Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.

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Automated Big-O complexity analysis of code and algorithms. Performs static analysis of loop structures, recursive call trees, space complexity estimation, and amortized analysis with detailed derivation documents.

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Integrates MCP App UI with host theming system. Applies host CSS variables, handles onhostcontextchanged, safe area insets, display mode detection, and fullscreen configuration.

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LlamaIndex agent and query engine setup for RAG-powered agents

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Microsoft Semantic Kernel planner and plugin setup for orchestrated AI

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Arize Phoenix observability platform setup for LLM debugging and evaluation

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Query expansion, HyDE, and multi-query generation for improved retrieval

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Redis backend for conversation state persistence and caching

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Batch embedding generation with caching, rate limiting, and multiple provider support

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SetFit few-shot learning for efficient intent classification with minimal data

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Zep memory server integration for long-term conversation memory and user profiling

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Interface with Codeforces API for contest data, problem sets, and submissions

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Qdrant vector database with filtering, payloads, and quantization support

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Select optimal data structure based on operation requirements

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Maintain and match against a library of classic dynamic programming patterns. Provides pattern matching, template code generation, variant detection, and problem-to-pattern mapping for DP problems.

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Assist in designing optimal DP states and transitions

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Prompt injection detection and prevention for secure LLM applications

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PII detection and redaction utilities for privacy-compliant conversational AI

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